From 6412a5db719d61974b58c3953c20bd62584f02e2 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Thu, 9 Oct 2025 20:07:36 -0700 Subject: [PATCH 01/19] feat(gepa): add tool description optimization for multi-agent systems - Add optimize_tool_descriptions parameter (default False) to GEPA - Extract tool descriptions from all nested modules via named_sub_modules() - Apply optimized descriptions in DspyAdapter.build_program() - Enables holistic optimization of tools across main and subagent modules - Tests: 4 new tests, all 16 pass (4 new + 12 existing) --- dspy/teleprompt/gepa/gepa.py | 21 ++- dspy/teleprompt/gepa/gepa_utils.py | 13 +- .../teleprompt/test_gepa_tool_optimization.py | 154 ++++++++++++++++++ 3 files changed, 186 insertions(+), 2 deletions(-) create mode 100644 tests/teleprompt/test_gepa_tool_optimization.py diff --git a/dspy/teleprompt/gepa/gepa.py b/dspy/teleprompt/gepa/gepa.py index 87cbbf80a5..f4fa099c3a 100644 --- a/dspy/teleprompt/gepa/gepa.py +++ b/dspy/teleprompt/gepa/gepa.py @@ -273,6 +273,9 @@ def metric( warn_on_score_mismatch: GEPA (currently) expects the metric to return the same module-level score when called with and without the pred_name. This flag (defaults to True) determines whether a warning is raised if a mismatch in module-level and predictor-level score is detected. + optimize_tool_descriptions: Whether to optimize tool descriptions for modules with tools + (e.g., ReAct agents). When enabled, tool descriptions are included in the optimization + process alongside signature instructions. Default is False. seed: The random seed to use for reproducibility. Default is 0. gepa_kwargs: (Optional) provide additional kwargs to be passed to [gepa.optimize](https://github.com/gepa-ai/gepa/blob/main/src/gepa/api.py) method @@ -328,6 +331,7 @@ def __init__( wandb_init_kwargs: dict[str, Any] | None = None, track_best_outputs: bool = False, warn_on_score_mismatch: bool = True, + optimize_tool_descriptions: bool = False, use_mlflow: bool = False, # Reproducibility seed: int | None = 0, @@ -390,6 +394,7 @@ def __init__( self.wandb_api_key = wandb_api_key self.wandb_init_kwargs = wandb_init_kwargs self.warn_on_score_mismatch = warn_on_score_mismatch + self.optimize_tool_descriptions = optimize_tool_descriptions self.use_mlflow = use_mlflow if track_best_outputs: @@ -518,11 +523,25 @@ def feedback_fn( rng=rng, reflection_lm=self.reflection_lm, custom_instruction_proposer=self.custom_instruction_proposer, - warn_on_score_mismatch=self.warn_on_score_mismatch + warn_on_score_mismatch=self.warn_on_score_mismatch, + optimize_tool_descriptions=self.optimize_tool_descriptions ) # Instantiate GEPA with the simpler adapter-based API base_program = {name: pred.signature.instructions for name, pred in student.named_predictors()} + + if self.optimize_tool_descriptions: + tool_descriptions = {} + for _, module in student.named_sub_modules(): + if hasattr(module, 'tools'): + for tool_name, tool in module.tools.items(): + tool_key = f"tool:{tool_name}" + if tool_key not in tool_descriptions: + tool_descriptions[tool_key] = tool.desc + if tool_descriptions: + logger.info(f"Including {len(tool_descriptions)} tool descriptions for optimization") + base_program.update(tool_descriptions) + gepa_result: GEPAResult = optimize( seed_candidate=base_program, trainset=trainset, diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index 844afe8b00..cf031400f8 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -76,7 +76,8 @@ def __init__( rng: random.Random | None = None, reflection_lm=None, custom_instruction_proposer: "ProposalFn | None" = None, - warn_on_score_mismatch: bool = True + warn_on_score_mismatch: bool = True, + optimize_tool_descriptions: bool = False, ): self.student = student_module self.metric_fn = metric_fn @@ -88,6 +89,7 @@ def __init__( self.reflection_lm = reflection_lm self.custom_instruction_proposer = custom_instruction_proposer self.warn_on_score_mismatch = warn_on_score_mismatch + self.optimize_tool_descriptions = optimize_tool_descriptions if self.custom_instruction_proposer is not None: # We are only overriding the propose_new_texts method when a custom @@ -124,6 +126,15 @@ def build_program(self, candidate: dict[str, str]): for name, pred in new_prog.named_predictors(): if name in candidate: pred.signature = pred.signature.with_instructions(candidate[name]) + + if self.optimize_tool_descriptions: + for _, module in new_prog.named_sub_modules(): + if hasattr(module, 'tools'): + for tool_name, tool in module.tools.items(): + tool_key = f"tool:{tool_name}" + if tool_key in candidate: + tool.desc = candidate[tool_key] + return new_prog def evaluate(self, batch, candidate, capture_traces=False): diff --git a/tests/teleprompt/test_gepa_tool_optimization.py b/tests/teleprompt/test_gepa_tool_optimization.py new file mode 100644 index 0000000000..24395b0a48 --- /dev/null +++ b/tests/teleprompt/test_gepa_tool_optimization.py @@ -0,0 +1,154 @@ +import dspy +from dspy import Example +from dspy.utils.dummies import DummyLM + + +def calculator(expression: str) -> str: + try: + return str(eval(expression)) + except Exception: + return "Error" + + +def search(query: str) -> str: + return f"Search results for: {query}" + + +def simple_metric(example, prediction, trace=None, pred_name=None, pred_trace=None): + score = 1.0 if example.answer in str(prediction.answer) else 0.0 + return dspy.Prediction(score=score, feedback="Correct" if score == 1.0 else "Wrong") + + +def test_build_program_applies_tool_descriptions(): + """Test that build_program applies tool descriptions from candidate dict.""" + from dspy.teleprompt.gepa.gepa_utils import DspyAdapter + + calc_tool = dspy.Tool(calculator, name="calculator", desc="Old description") + react = dspy.ReAct("question -> answer", tools=[calc_tool]) + + adapter = DspyAdapter( + student_module=react, + metric_fn=simple_metric, + feedback_map={}, + failure_score=0.0, + optimize_tool_descriptions=True, + ) + + candidate = { + "react": "New instruction for ReAct", + "tool:calculator": "Optimized calculator description", + } + + new_prog = adapter.build_program(candidate) + + assert new_prog.react.signature.instructions == "New instruction for ReAct" + assert new_prog.tools["calculator"].desc == "Optimized calculator description" + + +def test_gepa_with_tool_optimization_enabled(): + """Test GEPA end-to-end with optimize_tool_descriptions=True.""" + calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") + react = dspy.ReAct("question -> answer", tools=[calc_tool]) + + lm = DummyLM( + [ + {"next_thought": "Calculate", "next_tool_name": "calculator", "next_tool_args": {"expression": "2+2"}}, + {"next_thought": "Done", "next_tool_name": "finish", "next_tool_args": {}}, + {"reasoning": "Used calculator", "answer": "4"}, + ] + ) + reflection_lm = DummyLM([{"improved_instruction": "Better"}]) + + dspy.settings.configure(lm=lm) + + optimizer = dspy.GEPA( + metric=simple_metric, + reflection_lm=reflection_lm, + max_metric_calls=3, + optimize_tool_descriptions=True, + ) + + trainset = [Example(question="What is 2+2?", answer="4").with_inputs("question")] + + optimized = optimizer.compile(react, trainset=trainset) + + assert optimized is not None + assert hasattr(optimized, "tools") + assert "calculator" in optimized.tools + + +def test_gepa_with_multi_agent_architecture(): + """Test that tool optimization discovers tools from nested subagent modules.""" + class MultiAgentSystem(dspy.Module): + def __init__(self): + super().__init__() + # Subagent as module attribute (reuse existing search function) + search_tool = dspy.Tool(search, name="search", desc="Searches") + self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) + + # Main agent with subagent wrapped as tool + def spawn_subagent(task: str) -> str: + return self.subagent(task=task).result + + spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") + calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") + self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) + + system = MultiAgentSystem() + + # Test extraction using named_sub_modules pattern + tool_descriptions = {} + for _, module in system.named_sub_modules(): + if hasattr(module, 'tools'): + for tool_name, tool in module.tools.items(): + tool_key = f"tool:{tool_name}" + if tool_key not in tool_descriptions: + tool_descriptions[tool_key] = tool.desc + + # All tools from all nested agents should be discovered + assert "tool:calculator" in tool_descriptions + assert "tool:spawn_subagent" in tool_descriptions + assert "tool:search" in tool_descriptions + assert "tool:finish" in tool_descriptions + + +def test_gepa_optimizes_multi_agent_system_end_to_end(): + """Test GEPA.compile() optimizes ALL tools from nested multi-agent system.""" + class MultiAgentSystem(dspy.Module): + def __init__(self): + super().__init__() + search_tool = dspy.Tool(search, name="search", desc="Searches") + self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) + + def spawn_subagent(task: str) -> str: + return self.subagent(task=task).result + + spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") + calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") + self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) + + def forward(self, question): + return self.main_agent(q=question) + + system = MultiAgentSystem() + + # Setup LMs + lm = DummyLM([{"q": "question", "a": "answer"}]) + reflection_lm = DummyLM([{"improved_instruction": "Better"}]) + dspy.settings.configure(lm=lm) + + # Run GEPA optimization + optimizer = dspy.GEPA( + metric=simple_metric, + reflection_lm=reflection_lm, + max_metric_calls=3, + optimize_tool_descriptions=True, + ) + + trainset = [Example(question="test", answer="answer").with_inputs("question")] + optimized = optimizer.compile(system, trainset=trainset) + + # Verify optimized system preserves structure with all tools + assert "search" in optimized.subagent.tools + assert "calculator" in optimized.main_agent.tools + assert "spawn_subagent" in optimized.main_agent.tools From cf0be4f509268b3dcb443e8b0abb2d08a6e4544a Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Thu, 9 Oct 2025 22:58:56 -0700 Subject: [PATCH 02/19] style: fix ruff formatting (trailing whitespace) --- .../teleprompt/test_gepa_tool_optimization.py | 30 ++++++++++--------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/tests/teleprompt/test_gepa_tool_optimization.py b/tests/teleprompt/test_gepa_tool_optimization.py index 24395b0a48..02fcf1bba5 100644 --- a/tests/teleprompt/test_gepa_tool_optimization.py +++ b/tests/teleprompt/test_gepa_tool_optimization.py @@ -79,32 +79,33 @@ def test_gepa_with_tool_optimization_enabled(): def test_gepa_with_multi_agent_architecture(): """Test that tool optimization discovers tools from nested subagent modules.""" + class MultiAgentSystem(dspy.Module): def __init__(self): super().__init__() # Subagent as module attribute (reuse existing search function) search_tool = dspy.Tool(search, name="search", desc="Searches") self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) - + # Main agent with subagent wrapped as tool def spawn_subagent(task: str) -> str: return self.subagent(task=task).result - + spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) - + system = MultiAgentSystem() - + # Test extraction using named_sub_modules pattern tool_descriptions = {} for _, module in system.named_sub_modules(): - if hasattr(module, 'tools'): + if hasattr(module, "tools"): for tool_name, tool in module.tools.items(): tool_key = f"tool:{tool_name}" if tool_key not in tool_descriptions: tool_descriptions[tool_key] = tool.desc - + # All tools from all nested agents should be discovered assert "tool:calculator" in tool_descriptions assert "tool:spawn_subagent" in tool_descriptions @@ -114,29 +115,30 @@ def spawn_subagent(task: str) -> str: def test_gepa_optimizes_multi_agent_system_end_to_end(): """Test GEPA.compile() optimizes ALL tools from nested multi-agent system.""" + class MultiAgentSystem(dspy.Module): def __init__(self): super().__init__() search_tool = dspy.Tool(search, name="search", desc="Searches") self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) - + def spawn_subagent(task: str) -> str: return self.subagent(task=task).result - + spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) - + def forward(self, question): return self.main_agent(q=question) - + system = MultiAgentSystem() - + # Setup LMs lm = DummyLM([{"q": "question", "a": "answer"}]) reflection_lm = DummyLM([{"improved_instruction": "Better"}]) dspy.settings.configure(lm=lm) - + # Run GEPA optimization optimizer = dspy.GEPA( metric=simple_metric, @@ -144,10 +146,10 @@ def forward(self, question): max_metric_calls=3, optimize_tool_descriptions=True, ) - + trainset = [Example(question="test", answer="answer").with_inputs("question")] optimized = optimizer.compile(system, trainset=trainset) - + # Verify optimized system preserves structure with all tools assert "search" in optimized.subagent.tools assert "calculator" in optimized.main_agent.tools From aa53fe2bfc7b74cec5957ddde80f4c9fb9d46525 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Thu, 9 Oct 2025 23:09:06 -0700 Subject: [PATCH 03/19] style: apply ruff formatting fixes --- dspy/teleprompt/gepa/gepa.py | 2 +- dspy/teleprompt/gepa/gepa_utils.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/dspy/teleprompt/gepa/gepa.py b/dspy/teleprompt/gepa/gepa.py index f4fa099c3a..e19188f757 100644 --- a/dspy/teleprompt/gepa/gepa.py +++ b/dspy/teleprompt/gepa/gepa.py @@ -533,7 +533,7 @@ def feedback_fn( if self.optimize_tool_descriptions: tool_descriptions = {} for _, module in student.named_sub_modules(): - if hasattr(module, 'tools'): + if hasattr(module, "tools"): for tool_name, tool in module.tools.items(): tool_key = f"tool:{tool_name}" if tool_key not in tool_descriptions: diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index cf031400f8..e5258cb17e 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -126,15 +126,15 @@ def build_program(self, candidate: dict[str, str]): for name, pred in new_prog.named_predictors(): if name in candidate: pred.signature = pred.signature.with_instructions(candidate[name]) - + if self.optimize_tool_descriptions: for _, module in new_prog.named_sub_modules(): - if hasattr(module, 'tools'): + if hasattr(module, "tools"): for tool_name, tool in module.tools.items(): tool_key = f"tool:{tool_name}" if tool_key in candidate: tool.desc = candidate[tool_key] - + return new_prog def evaluate(self, batch, candidate, capture_traces=False): From 045c6cfde0801681e2deaa7c041188fa8eb13b04 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Fri, 10 Oct 2025 02:06:12 -0700 Subject: [PATCH 04/19] feat(gepa): implement tool-specific proposer for tool descriptions - Add ToolProposer with GenerateImprovedToolDescription signature - Implement routing logic to separate tools from signatures - Tools use ToolProposer, signatures use custom or parent default - Backward compatible: preserves existing custom_instruction_proposer behavior - Add test verifying routing splits components correctly --- dspy/teleprompt/gepa/gepa_utils.py | 90 +++++++++--- dspy/teleprompt/gepa/instruction_proposal.py | 138 ++++++++++++++++++ .../teleprompt/test_gepa_tool_optimization.py | 69 +++++++++ 3 files changed, 279 insertions(+), 18 deletions(-) diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index e5258cb17e..97ed335239 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -15,6 +15,7 @@ logger = logging.getLogger(__name__) + class LoggerAdapter: def __init__(self, logger: logging.Logger): self.logger = logger @@ -22,6 +23,7 @@ def __init__(self, logger: logging.Logger): def log(self, x: str): self.logger.info(x) + DSPyTrace = list[tuple[Any, dict[str, Any], Prediction]] @@ -31,15 +33,17 @@ class ReflectiveExample(TypedDict): Each example contains the predictor inputs, generated outputs, and feedback from evaluation. """ - Inputs: dict[str, Any] # Predictor inputs (may include str, dspy.Image, etc.) - Generated_Outputs: dict[str, Any] | str # Success: dict with output fields, Failure: error message string - Feedback: str # Always a string - from metric function or parsing error message + + Inputs: dict[str, Any] # Predictor inputs (may include str, dspy.Image, etc.) + Generated_Outputs: dict[str, Any] | str # Success: dict with output fields, Failure: error message string + Feedback: str # Always a string - from metric function or parsing error message class ScoreWithFeedback(Prediction): score: float feedback: str + class PredictorFeedbackFn(Protocol): def __call__( predictor_output: dict[str, Any], @@ -64,6 +68,7 @@ def __call__( """ ... + class DspyAdapter(GEPAAdapter[Example, TraceData, Prediction]): def __init__( self, @@ -91,36 +96,80 @@ def __init__( self.warn_on_score_mismatch = warn_on_score_mismatch self.optimize_tool_descriptions = optimize_tool_descriptions - if self.custom_instruction_proposer is not None: - # We are only overriding the propose_new_texts method when a custom - # instruction proposer is provided. Otherwise, we use the GEPA - # default propose_new_texts. + if self.optimize_tool_descriptions or self.custom_instruction_proposer is not None: + # Set up combined proposer for tool optimization and/or custom instruction proposer. + # This routes components to appropriate proposers based on type: + # - Signatures -> custom_instruction_proposer (if provided) OR parent default + # - Tools -> ToolProposer (if optimize_tool_descriptions=True) - def custom_propose_new_texts( + # Determine which proposer handles signatures + if self.custom_instruction_proposer is not None: + signature_proposer = self.custom_instruction_proposer + else: + signature_proposer = super().propose_new_texts + + def propose_new_texts( candidate: dict[str, str], reflective_dataset: dict[str, list[dict[str, Any]]], - components_to_update: list[str] + components_to_update: list[str], ) -> dict[str, str]: + """Propose new texts for both signatures and tools. + + Splits components by type (tool: prefix vs signatures), calls appropriate + proposers, and merges results. Handles reflection_lm context if provided. + """ + # Split by component type if tool optimization enabled + if self.optimize_tool_descriptions: + tool_components = [c for c in components_to_update if c.startswith("tool:")] + sig_components = [c for c in components_to_update if not c.startswith("tool:")] + else: + tool_components = [] + sig_components = components_to_update + + # Apply reflection_lm context to all proposer calls if provided if self.reflection_lm is not None: with dspy.context(lm=self.reflection_lm): - return self.custom_instruction_proposer( + sig_texts = signature_proposer( candidate=candidate, reflective_dataset=reflective_dataset, - components_to_update=components_to_update + components_to_update=sig_components, ) + + if tool_components: + from .instruction_proposal import ToolProposer + + tool_texts = ToolProposer()( + candidate=candidate, + reflective_dataset=reflective_dataset, + components_to_update=tool_components, + ) + return {**sig_texts, **tool_texts} + else: + return sig_texts else: - return self.custom_instruction_proposer( + sig_texts = signature_proposer( candidate=candidate, reflective_dataset=reflective_dataset, - components_to_update=components_to_update + components_to_update=sig_components, ) - self.propose_new_texts = custom_propose_new_texts + if tool_components: + from .instruction_proposal import ToolProposer + + tool_texts = ToolProposer()( + candidate=candidate, + reflective_dataset=reflective_dataset, + components_to_update=tool_components, + ) + return {**sig_texts, **tool_texts} + else: + return sig_texts + + self.propose_new_texts = propose_new_texts # Cache predictor names/signatures self.named_predictors = list(self.student.named_predictors()) - def build_program(self, candidate: dict[str, str]): new_prog = self.student.deepcopy() for name, pred in new_prog.named_predictors(): @@ -176,7 +225,7 @@ def evaluate(self, batch, candidate, capture_traces=False): return_all_scores=True, failure_score=self.failure_score, provide_traceback=True, - max_errors=len(batch) * 100 + max_errors=len(batch) * 100, ) res = evaluator(program) outputs = [r[1] for r in res.results] @@ -184,8 +233,11 @@ def evaluate(self, batch, candidate, capture_traces=False): scores = [s["score"] if hasattr(s, "score") else s for s in scores] return EvaluationBatch(outputs=outputs, scores=scores, trajectories=None) - def make_reflective_dataset(self, candidate, eval_batch, components_to_update) -> dict[str, list[ReflectiveExample]]: + def make_reflective_dataset( + self, candidate, eval_batch, components_to_update + ) -> dict[str, list[ReflectiveExample]]: from dspy.teleprompt.bootstrap_trace import FailedPrediction + program = self.build_program(candidate) ret_d: dict[str, list[ReflectiveExample]] = {} @@ -284,7 +336,9 @@ def make_reflective_dataset(self, candidate, eval_batch, components_to_update) - d["Feedback"] = fb["feedback"] if fb["score"] != module_score: if self.warn_on_score_mismatch: - logger.warning("The score returned by the metric with pred_name is different from the overall metric score. This can indicate 2 things: Either the metric is non-deterministic (e.g., LLM-as-judge, Semantic score, etc.) or the metric returned a score specific to pred_name that differs from the module level score. Currently, GEPA does not support predictor level scoring (support coming soon), and only requires a feedback text to be provided, which can be specific to the predictor or program level. GEPA will ignore the differing score returned, and instead use module level score. You can safely ignore this warning if using a semantic metric, however, if this mismatch is caused due to predictor scoring, please return module-level scores. To disable this warning, set warn_on_score_mismatch=False.") + logger.warning( + "The score returned by the metric with pred_name is different from the overall metric score. This can indicate 2 things: Either the metric is non-deterministic (e.g., LLM-as-judge, Semantic score, etc.) or the metric returned a score specific to pred_name that differs from the module level score. Currently, GEPA does not support predictor level scoring (support coming soon), and only requires a feedback text to be provided, which can be specific to the predictor or program level. GEPA will ignore the differing score returned, and instead use module level score. You can safely ignore this warning if using a semantic metric, however, if this mismatch is caused due to predictor scoring, please return module-level scores. To disable this warning, set warn_on_score_mismatch=False." + ) self.warn_on_score_mismatch = False fb["score"] = module_score diff --git a/dspy/teleprompt/gepa/instruction_proposal.py b/dspy/teleprompt/gepa/instruction_proposal.py index 23810b9a02..d98075a361 100644 --- a/dspy/teleprompt/gepa/instruction_proposal.py +++ b/dspy/teleprompt/gepa/instruction_proposal.py @@ -310,3 +310,141 @@ def __call__( updated_components[component_name] = new_instruction return updated_components + + +class GenerateImprovedToolDescriptionFromFeedback(dspy.Signature): + """I provided an assistant with the following description for a tool: + ``` + + ``` + + This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct: + ``` + + ``` + + Your task is to write a better description for this tool. + + Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns. + + Provide the new tool description within ``` blocks.""" + + current_tool_description = dspy.InputField(desc="The current description of the tool") + examples_with_feedback = dspy.InputField(desc="Examples showing tool usage decisions and feedback on correctness") + + improved_tool_description = dspy.OutputField( + desc="An improved description that helps with tool selection decisions" + ) + + +class SingleComponentToolProposer(dspy.Module): + """dspy.Module for proposing improved tool descriptions based on feedback.""" + + def __init__(self): + super().__init__() + self.propose_description = dspy.Predict(GenerateImprovedToolDescriptionFromFeedback) + + def forward(self, current_tool_description: str, reflective_dataset: list[ReflectiveExample]) -> str: + """Generate an improved tool description based on current description and feedback examples. + + Args: + current_tool_description: The current description of the tool + reflective_dataset: List of examples with inputs, outputs, and feedback + + Returns: + str: Improved tool description text + """ + # Reuse formatting from SingleComponentMultiModalProposer + formatted_examples, _ = self._format_examples_for_instruction_generation(reflective_dataset) + + result = self.propose_description( + current_tool_description=current_tool_description, examples_with_feedback=formatted_examples + ) + + return result.improved_tool_description + + def _format_examples_for_instruction_generation( + self, reflective_dataset: list[ReflectiveExample] + ) -> tuple[str, dict[int, list[Type]]]: + """Format examples using GEPA's markdown structure. + + Returns: + tuple: (formatted_text, image_map) where image_map is always empty for tools + """ + + def render_value(value, level=3): + if isinstance(value, dict): + s = "" + for k, v in value.items(): + s += f"{'#' * level} {k}\n" + s += render_value(v, min(level + 1, 6)) + if not value: + s += "\n" + return s + elif isinstance(value, (list, tuple)): + s = "" + for i, item in enumerate(value): + s += f"{'#' * level} Item {i + 1}\n" + s += render_value(item, min(level + 1, 6)) + if not value: + s += "\n" + return s + else: + return f"{str(value).strip()}\n\n" + + def convert_sample_to_markdown(sample, example_num): + s = f"# Example {example_num}\n" + for key, val in sample.items(): + s += f"## {key}\n" + s += render_value(val, level=3) + return s + + formatted_parts = [] + for i, example_data in enumerate(reflective_dataset): + formatted_example = convert_sample_to_markdown(example_data, i + 1) + formatted_parts.append(formatted_example) + + formatted_text = "\n\n".join(formatted_parts) + return formatted_text, {} + + +class ToolProposer(ProposalFn): + """GEPA-compatible tool description proposer. + + This class handles tool description optimization during GEPA optimization by using + a single-component proposer for each tool that needs to be updated. + """ + + def __init__(self): + self.single_proposer = SingleComponentToolProposer() + + def __call__( + self, + candidate: dict[str, str], + reflective_dataset: dict[str, list[ReflectiveExample]], + components_to_update: list[str], + ) -> dict[str, str]: + """GEPA-compatible proposal function. + + Args: + candidate: Current component name -> description mapping + reflective_dataset: Component name -> list of reflective examples + components_to_update: List of component names to update + + Returns: + dict: Component name -> new description mapping + """ + updated_components = {} + + for component_name in components_to_update: + if component_name in candidate and component_name in reflective_dataset: + current_description = candidate[component_name] + component_reflective_data = reflective_dataset[component_name] + + new_description = self.single_proposer( + current_tool_description=current_description, reflective_dataset=component_reflective_data + ) + + updated_components[component_name] = new_description + + return updated_components diff --git a/tests/teleprompt/test_gepa_tool_optimization.py b/tests/teleprompt/test_gepa_tool_optimization.py index 02fcf1bba5..e9835ec3b1 100644 --- a/tests/teleprompt/test_gepa_tool_optimization.py +++ b/tests/teleprompt/test_gepa_tool_optimization.py @@ -154,3 +154,72 @@ def forward(self, question): assert "search" in optimized.subagent.tools assert "calculator" in optimized.main_agent.tools assert "spawn_subagent" in optimized.main_agent.tools + + +def test_tool_and_signature_optimization_with_proposer_routing(): + """Test that routing logic correctly splits tools and signatures.""" + from unittest.mock import Mock, patch + + from dspy.teleprompt.gepa.gepa_utils import DspyAdapter + + # Create module with BOTH signature and tools + calc_tool = dspy.Tool(calculator, name="calculator", desc="Original calculator description") + react = dspy.ReAct("question -> answer", tools=[calc_tool]) + + # Create adapter with tool optimization enabled + adapter = DspyAdapter( + student_module=react, + metric_fn=simple_metric, + feedback_map={}, + failure_score=0.0, + optimize_tool_descriptions=True, + reflection_lm=None, + ) + + # Verify propose_new_texts was created + assert hasattr(adapter, "propose_new_texts"), "Routing logic should have set propose_new_texts" + + # Mock the ToolProposer to verify it gets called with tools only + mock_tool_proposer_instance = Mock() + mock_tool_proposer_instance.return_value = {"tool:calculator": "Improved calculator description"} + + mock_tool_proposer_class = Mock(return_value=mock_tool_proposer_instance) + + # Mock parent propose_new_texts to verify it gets called with signatures only + mock_parent_propose = Mock(return_value={"react": "Improved signature instruction"}) + + with patch("dspy.teleprompt.gepa.instruction_proposal.ToolProposer", mock_tool_proposer_class): + with patch.object(adapter.__class__.__bases__[0], "propose_new_texts", mock_parent_propose, create=True): + # Rebuild adapter to pick up mocked parent + adapter_with_mock = DspyAdapter( + student_module=react, + metric_fn=simple_metric, + feedback_map={}, + failure_score=0.0, + optimize_tool_descriptions=True, + reflection_lm=None, + ) + + candidate = { + "react": "Original signature", + "tool:calculator": "Original tool desc", + } + + reflective_dataset = { + "react": [{"input": "test"}], + "tool:calculator": [{"input": "calc"}], + } + + components = ["react", "tool:calculator"] + + result = adapter_with_mock.propose_new_texts(candidate, reflective_dataset, components) + + # Verify routing: ToolProposer was called with tools only + assert mock_tool_proposer_instance.called, "ToolProposer should have been called" + tool_call_args = mock_tool_proposer_instance.call_args[1] + assert "tool:calculator" in tool_call_args["components_to_update"] + assert "react" not in tool_call_args["components_to_update"] + + # Verify both components in result + assert "react" in result + assert "tool:calculator" in result From c4f2041d5e81eb693a81253cad12fbf2fb3ca97c Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Fri, 10 Oct 2025 02:38:20 -0700 Subject: [PATCH 05/19] docs(gepa): clean up multi-agent example code - Define tool functions outside class for clarity - Match structure of simple ReAct example - Add clear comments explaining architecture - Make code more readable and maintainable --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 179 ++++++++++++++++++ 1 file changed, 179 insertions(+) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 624e580ad1..46ef31a052 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -443,3 +443,182 @@ gepa = dspy.GEPA( auto="medium" ) ``` + +## Tool Description Optimization + +### What is optimize_tool_descriptions? + +The `optimize_tool_descriptions` parameter enables GEPA to optimize tool descriptions in addition to signature instructions. This is particularly valuable for ReAct agents and other tool-using systems, where the quality of tool descriptions directly impacts the agent's ability to select appropriate tools for each task. + +Unlike signature instructions that guide reasoning strategies, tool descriptions serve a fundamentally different purpose: they help agents decide **which tool to use** in a given situation. GEPA recognizes this categorical difference and applies a specialized reflection prompt tailored for tool selection decisions. + +### Default Behavior + +By default, GEPA only optimizes signature instructions (`optimize_tool_descriptions=False`): + +```python +# Default behavior: only signature optimization +gepa = dspy.GEPA( + metric=my_metric, + reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), + # optimize_tool_descriptions=False # This is the default + auto="medium" +) +optimized_program = gepa.compile(student, trainset=examples) +``` + +### When to Use optimize_tool_descriptions + +Consider enabling `optimize_tool_descriptions=True` when: + +- **Building ReAct agents**: ReAct agents rely on tool descriptions to make action selection decisions +- **Multi-agent systems**: Systems with nested agents and delegated tools benefit from holistic optimization +- **Poor tool selection**: Your agent frequently selects wrong tools or overlooks appropriate ones +- **Complex tool sets**: When managing many tools with overlapping capabilities +- **Domain-specific tools**: Tools requiring specialized knowledge or context for proper usage + +### How It Works + +When enabled, GEPA: + +1. **Discovers all tools**: Traverses your program including nested sub-modules to find all `dspy.Tool` instances +2. **Categorizes components**: Separates tools (identified by `tool:` prefix) from signature instructions +3. **Routes to specialized proposers**: + - Signature instructions → Default or custom instruction proposer + - Tool descriptions → `ToolProposer` with tool-specific reflection prompt +4. **Optimizes holistically**: Treats tool descriptions as first-class components in the optimization process + +The tool-specific reflection prompt asks the LM to: + +- Identify patterns in when the tool was used successfully versus when it was misused or overlooked +- Extract domain-specific information about the tool's capabilities or appropriate usage +- Recognize effective tool selection patterns the agent developed +- Incorporate these insights into an improved tool description + +### Usage Examples + +#### Basic ReAct Agent + +```python +import dspy + +def search_web(query: str) -> str: + """Search the web for information.""" + # Implementation here + return search_results + +def calculate(expression: str) -> float: + """Evaluate a mathematical expression.""" + # Implementation here + return result + +# Create ReAct agent with tools +search_tool = dspy.Tool(search_web, name="search", desc="Search the web") +calc_tool = dspy.Tool(calculate, name="calculator", desc="Do math") + +agent = dspy.ReAct("question -> answer", tools=[search_tool, calc_tool]) + +# Enable tool optimization +gepa = dspy.GEPA( + metric=my_metric, + reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), + optimize_tool_descriptions=True, # Enable tool optimization + auto="medium" +) + +optimized_agent = gepa.compile(agent, trainset=train_examples, valset=val_examples) +``` + +#### Multi-Agent System + +For systems with nested agents, GEPA automatically discovers and optimizes all tools: + +```python +import dspy + +def search_web(query: str) -> str: + """Search the web.""" + # Implementation here + return results + +def calculate(expression: str) -> float: + """Evaluate math expression.""" + # Implementation here + return result + +# Define tools +search_tool = dspy.Tool(search_web, name="search", desc="Searches web") +calc_tool = dspy.Tool(calculate, name="calculator", desc="Does math") + +class ResearchAssistant(dspy.Module): + def __init__(self): + super().__init__() + # Sub-agent with search tool + self.researcher = dspy.ReAct("query -> findings", tools=[search_tool]) + + # Delegation tool wraps sub-agent + def delegate_research(query: str) -> str: + return self.researcher(query=query).findings + + research_tool = dspy.Tool(delegate_research, name="research", desc="Research things") + + # Main agent with calculator and research delegation + self.assistant = dspy.ReAct("question -> answer", tools=[research_tool, calc_tool]) + + def forward(self, question): + return self.assistant(question=question) + +# GEPA optimizes ALL tools (calculator, research, search) together +gepa = dspy.GEPA( + metric=my_metric, + reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), + optimize_tool_descriptions=True, + auto="medium" +) + +optimized_system = gepa.compile(ResearchAssistant(), trainset=train, valset=val) +``` + +### Inspecting Optimized Tool Descriptions + +After optimization, tool descriptions are automatically updated in your program. Access them directly through your module structure: + +```python +optimized_agent = gepa.compile(agent, trainset=train, valset=val) + +# Access tools directly - descriptions are already updated +print(optimized_agent.tools["search"].desc) +print(optimized_agent.tools["calculator"].desc) +``` + +For multi-agent systems, access nested tools through your module hierarchy: + +```python +optimized_system = gepa.compile(ResearchAssistant(), trainset=train, valset=val) + +# Access tools at different levels +print(optimized_system.researcher.tools["search"].desc) # Sub-agent tool +print(optimized_system.assistant.tools["research"].desc) # Main agent tool +print(optimized_system.assistant.tools["calculator"].desc) +``` + +### Compatibility with Custom Instruction Proposers + +Tool optimization works seamlessly with custom instruction proposers. When both are provided: + +- Signature instructions → Custom instruction proposer +- Tool descriptions → Built-in `ToolProposer` + +```python +from dspy.teleprompt.gepa.instruction_proposal import MultiModalInstructionProposer + +gepa = dspy.GEPA( + metric=my_metric, + reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), + instruction_proposer=MultiModalInstructionProposer(), # For signatures + optimize_tool_descriptions=True, # Enables ToolProposer for tools + auto="medium" +) +``` + +**Note:** Tool optimization is fully backward compatible. Existing programs without tools, or with `optimize_tool_descriptions=False`, continue to work exactly as before. From 260ca8016d0f2cc412c07cde7fe3f6b1a0b255bf Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Fri, 10 Oct 2025 21:09:07 -0700 Subject: [PATCH 06/19] refactor(gepa): simplify tool reflective dataset with ReAct context reuse Tools now copy ReAct's reflective data with tool-specific annotation instead of complex trajectory extraction. This 15-line approach reuses ReAct's existing context (thoughts, tool calls, observations) and adds focused annotation for each tool. Implementation: - Tools receive full ReAct reflective examples (same trajectory context) - Feedback prefixed: [Optimizing tool: 'X'] for focused optimization - Reflection LM sees complete multi-step execution traces per tool Benefits: - Simpler: 15 lines vs 70+ line extraction approach - Reuses code: No duplicate trajectory formatting logic - Same context: Tools see full ReAct execution traces - Clean: Removed all debug output Tests: - 4 focused tests following GEPA patterns (removed 1 redundant) - 226KB fixture with 34 LM + 6 reflection calls - All tests passing with gpt-5-nano traces Documentation: - Updated GEPA_Advanced.md with implementation details - Explains reflective dataset construction approach --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 31 +- dspy/teleprompt/gepa/gepa_utils.py | 129 +- .../gepa_dummy_lm_tool_optimization.json | 1908 +++++++++++++++++ .../teleprompt/test_gepa_tool_optimization.py | 232 +- 4 files changed, 2128 insertions(+), 172 deletions(-) create mode 100644 tests/teleprompt/gepa_dummy_lm_tool_optimization.json diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 46ef31a052..0b25d3d182 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -483,17 +483,34 @@ When enabled, GEPA: 1. **Discovers all tools**: Traverses your program including nested sub-modules to find all `dspy.Tool` instances 2. **Categorizes components**: Separates tools (identified by `tool:` prefix) from signature instructions -3. **Routes to specialized proposers**: +3. **Routes components appropriately**: - Signature instructions → Default or custom instruction proposer - - Tool descriptions → `ToolProposer` with tool-specific reflection prompt + - Tool descriptions → ToolProposer (receives ReAct's reflective data with tool-specific annotation) 4. **Optimizes holistically**: Treats tool descriptions as first-class components in the optimization process -The tool-specific reflection prompt asks the LM to: +### Implementation Details -- Identify patterns in when the tool was used successfully versus when it was misused or overlooked -- Extract domain-specific information about the tool's capabilities or appropriate usage -- Recognize effective tool selection patterns the agent developed -- Incorporate these insights into an improved tool description +**Reflective Dataset Construction:** + +GEPA's approach to tool optimization is elegantly simple: + +1. **ReAct predictors** generate reflective examples containing: + - Inputs: `question`, `trajectory` (full agent execution trace with thoughts, tool calls, observations) + - Generated Outputs: Agent's next action/tool selection decisions + - Feedback: Task outcome and evaluation from the metric + +2. **Tools copy ReAct's data** with annotation: + - Each tool receives ReAct's complete reflective examples (same full trajectory context) + - Feedback is prefixed: `[Optimizing tool: 'tool_name'] {original_feedback}` + - This focuses the reflection LM on improving that specific tool's description + +3. **Reflection LM sees full context**: + - How the agent reasoned before selecting the tool + - What other tools were available and considered + - Whether the tool selection was successful + - Full multi-step trajectories showing tool composition patterns + +This design allows the reflection LM to understand tool usage in context, leading to descriptions that clarify when and how each tool should be used ### Usage Examples diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index 97ed335239..8dc88b9a6d 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -96,76 +96,80 @@ def __init__( self.warn_on_score_mismatch = warn_on_score_mismatch self.optimize_tool_descriptions = optimize_tool_descriptions - if self.optimize_tool_descriptions or self.custom_instruction_proposer is not None: - # Set up combined proposer for tool optimization and/or custom instruction proposer. - # This routes components to appropriate proposers based on type: - # - Signatures -> custom_instruction_proposer (if provided) OR parent default - # - Tools -> ToolProposer (if optimize_tool_descriptions=True) - - # Determine which proposer handles signatures - if self.custom_instruction_proposer is not None: - signature_proposer = self.custom_instruction_proposer - else: - signature_proposer = super().propose_new_texts - - def propose_new_texts( + if self.custom_instruction_proposer is not None: + # Override propose_new_texts when custom proposer provided (following original pattern) + def custom_propose_new_texts( candidate: dict[str, str], reflective_dataset: dict[str, list[dict[str, Any]]], components_to_update: list[str], ) -> dict[str, str]: - """Propose new texts for both signatures and tools. - - Splits components by type (tool: prefix vs signatures), calls appropriate - proposers, and merges results. Handles reflection_lm context if provided. - """ - # Split by component type if tool optimization enabled - if self.optimize_tool_descriptions: - tool_components = [c for c in components_to_update if c.startswith("tool:")] - sig_components = [c for c in components_to_update if not c.startswith("tool:")] - else: - tool_components = [] - sig_components = components_to_update - - # Apply reflection_lm context to all proposer calls if provided if self.reflection_lm is not None: with dspy.context(lm=self.reflection_lm): - sig_texts = signature_proposer( + return self.custom_instruction_proposer( candidate=candidate, reflective_dataset=reflective_dataset, - components_to_update=sig_components, + components_to_update=components_to_update, ) + else: + return self.custom_instruction_proposer( + candidate=candidate, + reflective_dataset=reflective_dataset, + components_to_update=components_to_update, + ) - if tool_components: - from .instruction_proposal import ToolProposer - + self.propose_new_texts = custom_propose_new_texts + elif self.optimize_tool_descriptions: + # Override ONLY when tool optimization is enabled without custom proposer + # We handle tool components with ToolProposer and signature components with GEPA's default + def propose_new_texts_with_tools( + candidate: dict[str, str], + reflective_dataset: dict[str, list[dict[str, Any]]], + components_to_update: list[str], + ) -> dict[str, str]: + """Route components: tools to ToolProposer, signatures to GEPA's default.""" + tool_components = [c for c in components_to_update if c.startswith("tool:")] + sig_components = [c for c in components_to_update if not c.startswith("tool:")] + + # Handle signature components - replicate proposer's default behavior + sig_texts = {} + if sig_components: + from gepa.strategies.instruction_proposal import InstructionProposalSignature + + lm = self.reflection_lm if self.reflection_lm is not None else dspy.settings.lm + + for name in sig_components: + base_instruction = candidate[name] + dataset_with_feedback = reflective_dataset[name] + sig_texts[name] = InstructionProposalSignature.run( + lm=(lambda x: lm(x)[0]), + input_dict={ + "current_instruction_doc": base_instruction, + "dataset_with_feedback": dataset_with_feedback, + }, + )["new_instruction"] + + # Handle tool components with ToolProposer + tool_texts = {} + if tool_components: + from .instruction_proposal import ToolProposer + + if self.reflection_lm is not None: + with dspy.context(lm=self.reflection_lm): tool_texts = ToolProposer()( candidate=candidate, reflective_dataset=reflective_dataset, components_to_update=tool_components, ) - return {**sig_texts, **tool_texts} - else: - return sig_texts - else: - sig_texts = signature_proposer( - candidate=candidate, - reflective_dataset=reflective_dataset, - components_to_update=sig_components, - ) - - if tool_components: - from .instruction_proposal import ToolProposer - + else: tool_texts = ToolProposer()( candidate=candidate, reflective_dataset=reflective_dataset, components_to_update=tool_components, ) - return {**sig_texts, **tool_texts} - else: - return sig_texts + + return {**sig_texts, **tool_texts} - self.propose_new_texts = propose_new_texts + self.propose_new_texts = propose_new_texts_with_tools # Cache predictor names/signatures self.named_predictors = list(self.student.named_predictors()) @@ -241,7 +245,12 @@ def make_reflective_dataset( program = self.build_program(candidate) ret_d: dict[str, list[ReflectiveExample]] = {} + + # First pass: Process all non-tool components (including ReAct) for pred_name in components_to_update: + if pred_name.startswith("tool:"): + continue # Skip tools in first pass + module = None for name, m in program.named_predictors(): if name == pred_name: @@ -349,6 +358,28 @@ def make_reflective_dataset( continue ret_d[pred_name] = items + # Second pass: Process tools by copying ReAct data with annotation + react_module_name = None + for name in ret_d.keys(): + if "react" in name.lower(): + react_module_name = name + break + + if react_module_name: + for tool_component in [c for c in components_to_update if c.startswith("tool:")]: + tool_name = tool_component.replace("tool:", "") + tool_items = [] + + for item in ret_d[react_module_name]: + annotated = { + "Inputs": item["Inputs"], + "Generated Outputs": item["Generated Outputs"], + "Feedback": f"[Optimizing tool: '{tool_name}'] {item['Feedback']}" + } + tool_items.append(annotated) + + ret_d[tool_component] = tool_items + if len(ret_d) == 0: raise Exception("No valid predictions found for any module.") diff --git a/tests/teleprompt/gepa_dummy_lm_tool_optimization.json b/tests/teleprompt/gepa_dummy_lm_tool_optimization.json new file mode 100644 index 0000000000..a89bde322d --- /dev/null +++ b/tests/teleprompt/gepa_dummy_lm_tool_optimization.json @@ -0,0 +1,1908 @@ +{ + "lm": [ + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0X8ygvrG4tRaiQylQpFnxS2h4F", + "created": 1760143457, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + ], + "usage": {}, + "cost": 0.00109075, + "timestamp": "2025-10-10T21:02:18.718338", + "uuid": "10e48da6-a48f-49c4-8ee8-4897941bc665", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", + "created": 1760143457, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007469999999999999, + "timestamp": "2025-10-10T21:02:18.720524", + "uuid": "c7a628f4-af0b-4e20-8b3e-727de7b86770", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", + "created": 1760143468, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" + ], + "usage": {}, + "cost": 0.0007816, + "timestamp": "2025-10-10T21:02:18.724678", + "uuid": "b977039a-ed53-4e02-8de4-a470e36c1156", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0pGVq8eKHmqgERoX8MYr6eQQMb", + "created": 1760143475, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format" + ], + "usage": {}, + "cost": 0.00116765, + "timestamp": "2025-10-10T21:02:18.726535", + "uuid": "ec47ae1e-f9fa-4b3b-9fcc-5ec3eb86a758", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI16VENONq29JEoLNkCHtkjP53j2", + "created": 1760143492, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000" + ], + "usage": {}, + "cost": 0.0010348999999999998, + "timestamp": "2025-10-10T21:02:18.730129", + "uuid": "911d6ed4-8a2e-4075-a8c0-85a28faab442", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI1MPbNZaELWX7XKOG3zDsYyJZHG", + "created": 1760143508, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." + ], + "usage": {}, + "cost": 0.0003704, + "timestamp": "2025-10-10T21:02:18.733249", + "uuid": "cb82d23d-118b-4153-ba7e-7549a2b45337", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", + "created": 1760143484, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0004960499999999999, + "timestamp": "2025-10-10T21:02:18.733564", + "uuid": "1e69d8ac-168f-498a-bc5f-bedca8eaeb5c", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", + "created": 1760143457, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007469999999999999, + "timestamp": "2025-10-10T21:02:18.739071", + "uuid": "f6e71786-4b86-4014-902e-9edcf5241d57", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0X8ygvrG4tRaiQylQpFnxS2h4F", + "created": 1760143457, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + ], + "usage": {}, + "cost": 0.00109075, + "timestamp": "2025-10-10T21:02:18.741521", + "uuid": "e7455e4f-6011-41ef-afd4-6ae9396d025d", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", + "created": 1760143457, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007469999999999999, + "timestamp": "2025-10-10T21:02:18.741090", + "uuid": "6d6d493a-4299-4663-98c7-6ed91c35c434", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0pGVq8eKHmqgERoX8MYr6eQQMb", + "created": 1760143475, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format" + ], + "usage": {}, + "cost": 0.00116765, + "timestamp": "2025-10-10T21:02:18.743589", + "uuid": "b4fdd85b-a4fd-45ff-be5b-ab2633ebb89c", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI16VENONq29JEoLNkCHtkjP53j2", + "created": 1760143492, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000" + ], + "usage": {}, + "cost": 0.0010348999999999998, + "timestamp": "2025-10-10T21:02:18.747736", + "uuid": "5b03ac38-aaaa-4605-9f1d-b07563de80e9", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI1MPbNZaELWX7XKOG3zDsYyJZHG", + "created": 1760143508, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." + ], + "usage": {}, + "cost": 0.0003704, + "timestamp": "2025-10-10T21:02:18.750301", + "uuid": "a38292c1-d8ad-42c5-8a42-a0ca65cf2c35", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", + "created": 1760143468, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" + ], + "usage": {}, + "cost": 0.0007816, + "timestamp": "2025-10-10T21:02:18.751592", + "uuid": "ccd7dea3-428b-45c6-bca0-b667a39bb388", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", + "created": 1760143468, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" + ], + "usage": {}, + "cost": 0.0007816, + "timestamp": "2025-10-10T21:02:18.754893", + "uuid": "791a2d61-2452-452d-a4a9-435f5e1d71af", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", + "created": 1760143484, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0004960499999999999, + "timestamp": "2025-10-10T21:02:18.757192", + "uuid": "f6b956c0-8975-44af-89de-b71875fb7117", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", + "created": 1760143484, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0004960499999999999, + "timestamp": "2025-10-10T21:02:18.758336", + "uuid": "20511a59-1080-4a75-b28e-1b6d0fbd01f6", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", + "created": 1760154628, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007133999999999999, + "timestamp": "2025-10-10T21:02:18.816256", + "uuid": "a7626695-a4ad-40cd-b628-583d1339058a", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", + "created": 1760154628, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007133999999999999, + "timestamp": "2025-10-10T21:02:18.817978", + "uuid": "d52ed522-b57d-4d69-b6e7-d836ac1f1786", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuiyj1kOtZkRcdi6OpuGRIPpvq1", + "created": 1760154628, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00071475, + "timestamp": "2025-10-10T21:02:18.820494", + "uuid": "a86e16ad-2247-40f6-be8f-bacd75ed19bf", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", + "created": 1760154637, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007115499999999999, + "timestamp": "2025-10-10T21:02:18.824015", + "uuid": "46049c68-4945-4b5d-97f0-21d399ac7378", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", + "created": 1760154637, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007115499999999999, + "timestamp": "2025-10-10T21:02:18.824161", + "uuid": "ca649b9f-efaf-401f-a87c-4e35967bfd63", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKurOG0oEXAaNCHYroFXYtxjgC6X", + "created": 1760154637, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00045945, + "timestamp": "2025-10-10T21:02:18.828342", + "uuid": "256631d1-f07c-4421-a76c-bb86e62a84ce", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", + "created": 1760154646, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00042905, + "timestamp": "2025-10-10T21:02:18.832098", + "uuid": "ceca3769-9f77-4e59-afa3-38a99566e359", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", + "created": 1760154646, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00042905, + "timestamp": "2025-10-10T21:02:18.832437", + "uuid": "ffbec08d-6b7e-427f-8b05-d824da47dde3", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuzEA2u4z5lqkmfmb2NodpTfL1a", + "created": 1760154645, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00035559999999999997, + "timestamp": "2025-10-10T21:02:18.832655", + "uuid": "15b1ed96-c066-4b89-b969-a7080aeb2790", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nBudget retrieved; finishing.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKv5XHKi8Q0ThiIgBXvILA5vaOux", + "created": 1760154651, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." + ], + "usage": {}, + "cost": 0.0004347, + "timestamp": "2025-10-10T21:02:18.836764", + "uuid": "f68a2309-8daa-4a88-879d-d94a5c72a491", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuiyj1kOtZkRcdi6OpuGRIPpvq1", + "created": 1760154628, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00071475, + "timestamp": "2025-10-10T21:02:18.840836", + "uuid": "6a87b1ce-1982-4c31-a29a-a261a8130d3f", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", + "created": 1760154628, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007133999999999999, + "timestamp": "2025-10-10T21:02:18.842790", + "uuid": "6661def4-6a74-4903-b1da-3f45b3348d6a", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKurOG0oEXAaNCHYroFXYtxjgC6X", + "created": 1760154637, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00045945, + "timestamp": "2025-10-10T21:02:18.844946", + "uuid": "387da337-964b-432b-8c58-073230da0f18", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", + "created": 1760154637, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0007115499999999999, + "timestamp": "2025-10-10T21:02:18.847311", + "uuid": "7387f902-35db-4822-80c9-42d324172044", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuzEA2u4z5lqkmfmb2NodpTfL1a", + "created": 1760154645, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00035559999999999997, + "timestamp": "2025-10-10T21:02:18.849633", + "uuid": "dba3472c-ed9a-4741-9615-4a8bc8c0ec5f", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", + "created": 1760154646, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00042905, + "timestamp": "2025-10-10T21:02:18.852604", + "uuid": "6c63d26d-6682-4ffb-b7b3-cf805cab000c", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." + }, + { + "role": "user", + "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nBudget retrieved; finishing.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKv5XHKi8Q0ThiIgBXvILA5vaOux", + "created": 1760154651, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." + ], + "usage": {}, + "cost": 0.0004347, + "timestamp": "2025-10-10T21:02:18.854255", + "uuid": "cc229448-d7ca-43bc-ba83-7405bd2d73d2", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + } + ], + "reflection_lm": [ + { + "prompt": "I provided an assistant with the following instructions to perform a task for me:\n```\nGiven the fields `question`, produce the fields `answer`.\n\nYou are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\nYour goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n\nTo do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\nAfter each tool call, you receive a resulting observation, which gets appended to your trajectory.\n\nWhen writing next_thought, you may reason about the current situation and plan for future steps.\nWhen selecting the next_tool_name and its next_tool_args, the tool must be one of:\n\n(1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n(2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n(3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n(4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\nWhen providing `next_tool_args`, the value inside the field must be in JSON format\n```\n\nThe following are examples of different task inputs provided to the assistant along with the assistant's response for each of them, and some feedback on how the assistant's response could be better:\n```\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\nCorrect\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n```\n\nYour task is to write a new instruction for the assistant.\n\nRead the inputs carefully and identify the input format and infer detailed task description about the task I wish to solve with the assistant.\n\nRead all the assistant responses and the corresponding feedback. Identify all niche and domain specific factual information about the task and include it in the instruction, as a lot of it may not be available to the assistant in the future. The assistant may have utilized a generalizable strategy to solve the task, if so, include that in the instruction as well.\n\nProvide the new instructions within ``` blocks.", + "messages": null, + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKt5gz4GYCmM0vnWfNiBqnWmsiYC", + "created": 1760154527, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "```\nYou are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n\nWhat you can use\n- Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n\nInput you receive\n- Fields:\n - question: string\n\nOutput protocol (in each turn)\n- To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n\n- After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n\n- The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n\nTask-specific guidelines\n- If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n- If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n- Use the minimum number of tool calls necessary to answer.\n- Maintain a concise interaction; avoid unnecessary steps.\n\nExample flow (correct and concise)\n1) Assistant outputs:\n{\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n}\nObservation: \"Engineering\"\n\n2) Assistant outputs:\n{\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n}\nObservation: 250000\n\n3) Assistant outputs:\n{\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n}\nFinal answer (system delivers): 250000\n\nNotes on common pitfalls\n- Do not return a natural-language sentence as the final answer when the data is numeric.\n- If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n- If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above.\n\n```\n", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "```\nYou are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n\nWhat you can use\n- Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n\nInput you receive\n- Fields:\n - question: string\n\nOutput protocol (in each turn)\n- To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n\n- After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n\n- The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n\nTask-specific guidelines\n- If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n- If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n- Use the minimum number of tool calls necessary to answer.\n- Maintain a concise interaction; avoid unnecessary steps.\n\nExample flow (correct and concise)\n1) Assistant outputs:\n{\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n}\nObservation: \"Engineering\"\n\n2) Assistant outputs:\n{\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n}\nObservation: 250000\n\n3) Assistant outputs:\n{\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n}\nFinal answer (system delivers): 250000\n\nNotes on common pitfalls\n- Do not return a natural-language sentence as the final answer when the data is numeric.\n- If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n- If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above.\n\n```\n" + ], + "usage": {}, + "cost": 0.0018137499999999998, + "timestamp": "2025-10-10T21:02:18.761810", + "uuid": "5f548fd8-70f7-4c77-817d-805ba8fafc6a", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": "I provided an assistant with the following instructions to perform a task for me:\n```\nGiven the fields `question`, produce the fields `answer`.\n```\n\nThe following are examples of different task inputs provided to the assistant along with the assistant's response for each of them, and some feedback on how the assistant's response could be better:\n```\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n### answer\n500000\n\n## Feedback\nCorrect\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n### answer\nMary Johnson earns $95,000 per year.\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n### answer\nMary Johnson earns $95,000 per year.\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n```\n\nYour task is to write a new instruction for the assistant.\n\nRead the inputs carefully and identify the input format and infer detailed task description about the task I wish to solve with the assistant.\n\nRead all the assistant responses and the corresponding feedback. Identify all niche and domain specific factual information about the task and include it in the instruction, as a lot of it may not be available to the assistant in the future. The assistant may have utilized a generalizable strategy to solve the task, if so, include that in the instruction as well.\n\nProvide the new instructions within ``` blocks.", + "messages": null, + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKtQriStnHiU8jdA2NDSJdrt9Kk0", + "created": 1760154548, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "```\nYou are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n\nWhat to do\n- Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n- Use the latest/most recent available value if multiple exist.\n- Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n- If the requested value cannot be determined from available data, return 0.\n\nNotes\n- Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n- Do not reveal any chain-of-thought or tool usage in the output.\n- The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields).\n```", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "```\nYou are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n\nWhat to do\n- Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n- Use the latest/most recent available value if multiple exist.\n- Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n- If the requested value cannot be determined from available data, return 0.\n\nNotes\n- Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n- Do not reveal any chain-of-thought or tool usage in the output.\n- The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields).\n```" + ], + "usage": {}, + "cost": 0.0010855, + "timestamp": "2025-10-10T21:02:18.762903", + "uuid": "554c9ada-96dc-4072-93b0-6a191b5e1132", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." + }, + { + "role": "user", + "content": "[[ ## current_tool_description ## ]]\nGets department.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKtgoLekUHpjaiLtvbwis5RAvL65", + "created": 1760154564, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## improved_tool_description ## ]]\n```text\nTool name: get_employee_department\n\nPurpose:\nReturn the department name associated with a given employee.\n\nInput:\nA JSON object with a single field:\n{\"employee_name\": \"\"}\n\nOutput:\nA string containing the department name (e.g., \"Engineering\"). If the employee cannot be found or the department cannot be determined, it returns null (no department).\n\nUsage pattern:\n- Use this as the first step when answering questions about an employee's department.\n Example: To answer \"What is John's department?\" call\n get_employee_department with {\"employee_name\": \"John Smith\"} \u2192 \"Engineering\".\n Then, you can fetch department-level data (e.g., budget) by calling the appropriate tool with the returned department, e.g.,\n get_department_budget with {\"department\": \"Engineering\"}.\n\nNotes:\n- Do not use this tool for salary or compensation questions; use get_employee_salary for that.\n- This tool focuses strictly on department membership information, not other attributes like department head, location, or budget details.\n```\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## improved_tool_description ## ]]\n```text\nTool name: get_employee_department\n\nPurpose:\nReturn the department name associated with a given employee.\n\nInput:\nA JSON object with a single field:\n{\"employee_name\": \"\"}\n\nOutput:\nA string containing the department name (e.g., \"Engineering\"). If the employee cannot be found or the department cannot be determined, it returns null (no department).\n\nUsage pattern:\n- Use this as the first step when answering questions about an employee's department.\n Example: To answer \"What is John's department?\" call\n get_employee_department with {\"employee_name\": \"John Smith\"} \u2192 \"Engineering\".\n Then, you can fetch department-level data (e.g., budget) by calling the appropriate tool with the returned department, e.g.,\n get_department_budget with {\"department\": \"Engineering\"}.\n\nNotes:\n- Do not use this tool for salary or compensation questions; use get_employee_salary for that.\n- This tool focuses strictly on department membership information, not other attributes like department head, location, or budget details.\n```\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0012571499999999998, + "timestamp": "2025-10-10T21:02:18.803676", + "uuid": "c40e2775-52da-4984-93d2-7179af4852e9", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." + }, + { + "role": "user", + "content": "[[ ## current_tool_description ## ]]\nGets budget.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKtv2ajGgxfaPcub3BieDIN3YnNp", + "created": 1760154579, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## improved_tool_description ## ]]\n```text\nBudget data retrieval tool. It provides access to monetary figures in the organization in two related forms:\n- Employee salary: by supplying an employee name, you can obtain the annual salary as a plain numeric value (e.g., 95000).\n- Department budget: by supplying a department name, you can obtain the department's annual budget as a plain numeric value (e.g., 120000).\n\nIf a question requires deriving a department budget from an employee context, first determine the employee's department with get_employee_department(employee_name), then fetch the budget with get_department_budget(department). All outputs are numbers; when presenting an answer, return only the numeric value and use a finish operation if needed to emit the final result without extra text.\n\nExamples (paraphrased):\n- \"What is John Smith's department budget?\" -> get_employee_department -> 'Engineering' -> get_department_budget -> 120000\n- \"How much does Mary Johnson earn?\" -> get_employee_salary -> 95000 -> finish with 95000\n```\n\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## improved_tool_description ## ]]\n```text\nBudget data retrieval tool. It provides access to monetary figures in the organization in two related forms:\n- Employee salary: by supplying an employee name, you can obtain the annual salary as a plain numeric value (e.g., 95000).\n- Department budget: by supplying a department name, you can obtain the department's annual budget as a plain numeric value (e.g., 120000).\n\nIf a question requires deriving a department budget from an employee context, first determine the employee's department with get_employee_department(employee_name), then fetch the budget with get_department_budget(department). All outputs are numbers; when presenting an answer, return only the numeric value and use a finish operation if needed to emit the final result without extra text.\n\nExamples (paraphrased):\n- \"What is John Smith's department budget?\" -> get_employee_department -> 'Engineering' -> get_department_budget -> 120000\n- \"How much does Mary Johnson earn?\" -> get_employee_salary -> 95000 -> finish with 95000\n```\n\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.0013079499999999998, + "timestamp": "2025-10-10T21:02:18.806193", + "uuid": "1c113b3c-124b-419d-9d50-5cf5e57d9ac7", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." + }, + { + "role": "user", + "content": "[[ ## current_tool_description ## ]]\nGets salary.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuCf9D1mICA1ISTsHnvFAS1fPc2", + "created": 1760154596, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## improved_tool_description ## ]]\n```text\nTool: get_employee_salary\nPurpose: Retrieve an employee's annual base salary as a numeric value (in USD) without formatting.\nInputs: {\"employee_name\": \"\"} e.g., {\"employee_name\": \"Mary Johnson\"}\nOutput: An integer representing the annual salary in USD (e.g., 95000). If unavailable, returns null/None.\nNotes:\n- The returned value is a raw number with no currency symbol or textual description.\n- Do not generate natural-language salary statements; formatting and any accompanying text should be handled by the caller.\n- This is for base salary only; bonuses, stock, or benefits are not included unless provided by the data source.\n```\n[[ ## completed ## ]]", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## improved_tool_description ## ]]\n```text\nTool: get_employee_salary\nPurpose: Retrieve an employee's annual base salary as a numeric value (in USD) without formatting.\nInputs: {\"employee_name\": \"\"} e.g., {\"employee_name\": \"Mary Johnson\"}\nOutput: An integer representing the annual salary in USD (e.g., 95000). If unavailable, returns null/None.\nNotes:\n- The returned value is a raw number with no currency symbol or textual description.\n- Do not generate natural-language salary statements; formatting and any accompanying text should be handled by the caller.\n- This is for base salary only; bonuses, stock, or benefits are not included unless provided by the data source.\n```\n[[ ## completed ## ]]" + ], + "usage": {}, + "cost": 0.00087075, + "timestamp": "2025-10-10T21:02:18.807604", + "uuid": "c3d8ffca-2fbc-4c76-8521-8a782e7e2623", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + }, + { + "prompt": null, + "messages": [ + { + "role": "system", + "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." + }, + { + "role": "user", + "content": "[[ ## current_tool_description ## ]]\nMarks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'finish'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'finish'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'finish'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." + } + ], + "kwargs": {}, + "response": { + "id": "chatcmpl-CPKuP9AOMyaqrKPqDzNbxG2hAEjxc", + "created": 1760154609, + "model": "gpt-5-nano-2025-08-07", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "[[ ## improved_tool_description ## ]]\n``` \nFinish: Mark the task as complete and signal that the final answer should be extracted from the assistant's outputs.\n\nWhen to use:\n- Only call finish when you have determined the exact final value in the exact format expected by the evaluator (e.g., a plain number like 95000, or a plain string like 'Engineering').\n\nWhat happens after:\n- The system will treat the final value as the answer. No additional reasoning, editing, or commentary should accompany it.\n- Do not call finish to perform additional calculations or to reword the result.\n\nFormatting rules:\n- If the answer is numeric, provide digits only (no currency symbols, punctuation, or words).\n- If the answer is textual, provide the exact string with no surrounding quotes or extra punctuation.\n\nExamples:\n- Correct: 95000\n- Correct: Engineering\n- Incorrect: 'Mary Johnson earns 95000 per year.'\n```\n\n\n[[ ## completed ## ]]\nEnd of description.", + "role": "assistant", + "tool_calls": null, + "function_call": null, + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": {}, + "service_tier": "default", + "cache_hit": true + }, + "outputs": [ + "[[ ## improved_tool_description ## ]]\n``` \nFinish: Mark the task as complete and signal that the final answer should be extracted from the assistant's outputs.\n\nWhen to use:\n- Only call finish when you have determined the exact final value in the exact format expected by the evaluator (e.g., a plain number like 95000, or a plain string like 'Engineering').\n\nWhat happens after:\n- The system will treat the final value as the answer. No additional reasoning, editing, or commentary should accompany it.\n- Do not call finish to perform additional calculations or to reword the result.\n\nFormatting rules:\n- If the answer is numeric, provide digits only (no currency symbols, punctuation, or words).\n- If the answer is textual, provide the exact string with no surrounding quotes or extra punctuation.\n\nExamples:\n- Correct: 95000\n- Correct: Engineering\n- Incorrect: 'Mary Johnson earns 95000 per year.'\n```\n\n\n[[ ## completed ## ]]\nEnd of description." + ], + "usage": {}, + "cost": 0.00145345, + "timestamp": "2025-10-10T21:02:18.809231", + "uuid": "4fc3542b-c13f-48da-b1ac-a6075c4ceab5", + "model": "openai/gpt-5-nano", + "response_model": "gpt-5-nano-2025-08-07", + "model_type": "chat" + } + ] +} \ No newline at end of file diff --git a/tests/teleprompt/test_gepa_tool_optimization.py b/tests/teleprompt/test_gepa_tool_optimization.py index e9835ec3b1..a921cf1da4 100644 --- a/tests/teleprompt/test_gepa_tool_optimization.py +++ b/tests/teleprompt/test_gepa_tool_optimization.py @@ -1,9 +1,59 @@ +import json + import dspy from dspy import Example from dspy.utils.dummies import DummyLM +class DictDummyLM(dspy.clients.lm.LM): + """Dummy LM that replays prerecorded responses based on message hash.""" + + def __init__(self, history): + super().__init__("dummy", "chat", 0.0, 1000, True) + self.history = {} + for m in history: + self.history[hash(repr(m["messages"]))] = m + + def __call__(self, prompt=None, messages=None, **kwargs): + assert hash(repr(messages)) in self.history, f"Message {messages} not found in history" + m = self.history[hash(repr(messages))] + return m["outputs"] + + +# Simple multi-hop employee database tools (for main integration test) +def get_employee_department(employee_name: str) -> str: + """Gets department.""" + employees = { + "John Smith": "Engineering", + "Mary Johnson": "Sales", + "Bob Wilson": "HR", + } + return employees.get(employee_name, "Not found") + + +def get_department_budget(department: str) -> str: + """Gets budget.""" + budgets = { + "Engineering": "500000", + "Sales": "300000", + "HR": "200000", + } + return budgets.get(department, "Not found") + + +def get_employee_salary(employee_name: str) -> str: + """Gets salary.""" + salaries = { + "John Smith": "120000", + "Mary Johnson": "95000", + "Bob Wilson": "85000", + } + return salaries.get(employee_name, "Not found") + + +# Helper functions for other tests def calculator(expression: str) -> str: + """Calculator for math.""" try: return str(eval(expression)) except Exception: @@ -11,11 +61,14 @@ def calculator(expression: str) -> str: def search(query: str) -> str: - return f"Search results for: {query}" + """Search function.""" + return f"Results for: {query}" def simple_metric(example, prediction, trace=None, pred_name=None, pred_trace=None): - score = 1.0 if example.answer in str(prediction.answer) else 0.0 + pred_str = str(prediction.answer).strip() + expected = str(example.answer).strip() + score = 1.0 if pred_str == expected else 0.0 return dspy.Prediction(score=score, feedback="Correct" if score == 1.0 else "Wrong") @@ -23,8 +76,8 @@ def test_build_program_applies_tool_descriptions(): """Test that build_program applies tool descriptions from candidate dict.""" from dspy.teleprompt.gepa.gepa_utils import DspyAdapter - calc_tool = dspy.Tool(calculator, name="calculator", desc="Old description") - react = dspy.ReAct("question -> answer", tools=[calc_tool]) + dept_tool = dspy.Tool(get_employee_department, name="get_employee_department", desc="Gets department.") + react = dspy.ReAct("question -> answer", tools=[dept_tool]) adapter = DspyAdapter( student_module=react, @@ -36,28 +89,30 @@ def test_build_program_applies_tool_descriptions(): candidate = { "react": "New instruction for ReAct", - "tool:calculator": "Optimized calculator description", + "tool:get_employee_department": "Retrieves the department name for a given employee", } new_prog = adapter.build_program(candidate) assert new_prog.react.signature.instructions == "New instruction for ReAct" - assert new_prog.tools["calculator"].desc == "Optimized calculator description" + assert new_prog.tools["get_employee_department"].desc == "Retrieves the department name for a given employee" def test_gepa_with_tool_optimization_enabled(): - """Test GEPA end-to-end with optimize_tool_descriptions=True.""" - calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") - react = dspy.ReAct("question -> answer", tools=[calc_tool]) - - lm = DummyLM( - [ - {"next_thought": "Calculate", "next_tool_name": "calculator", "next_tool_args": {"expression": "2+2"}}, - {"next_thought": "Done", "next_tool_name": "finish", "next_tool_args": {}}, - {"reasoning": "Used calculator", "answer": "4"}, - ] - ) - reflection_lm = DummyLM([{"improved_instruction": "Better"}]) + """Test GEPA end-to-end with optimize_tool_descriptions=True using preloaded traces.""" + # Setup ReAct with minimal tool descriptions (as captured in traces) + dept_tool = dspy.Tool(get_employee_department, name="get_employee_department", desc="Gets department.") + budget_tool = dspy.Tool(get_department_budget, name="get_department_budget", desc="Gets budget.") + salary_tool = dspy.Tool(get_employee_salary, name="get_employee_salary", desc="Gets salary.") + + react = dspy.ReAct("question -> answer", tools=[dept_tool, budget_tool, salary_tool]) + + # Load prerecorded LM traces from real gpt-5-nano run + with open("tests/teleprompt/gepa_dummy_lm_tool_optimization.json") as f: + data = json.load(f) + + lm = DictDummyLM(data["lm"]) + reflection_lm = DictDummyLM(data["reflection_lm"]) dspy.settings.configure(lm=lm) @@ -68,49 +123,21 @@ def test_gepa_with_tool_optimization_enabled(): optimize_tool_descriptions=True, ) - trainset = [Example(question="What is 2+2?", answer="4").with_inputs("question")] + # Use same examples as in trace generation + trainset = [ + Example(question="What is the budget of John Smith's department?", answer="500000").with_inputs("question"), + Example(question="How much does Mary Johnson earn?", answer="95000").with_inputs("question"), + Example(question="What is Bob Wilson's department budget?", answer="200000").with_inputs("question"), + ] optimized = optimizer.compile(react, trainset=trainset) + # Verify optimization occurred assert optimized is not None assert hasattr(optimized, "tools") - assert "calculator" in optimized.tools - - -def test_gepa_with_multi_agent_architecture(): - """Test that tool optimization discovers tools from nested subagent modules.""" - - class MultiAgentSystem(dspy.Module): - def __init__(self): - super().__init__() - # Subagent as module attribute (reuse existing search function) - search_tool = dspy.Tool(search, name="search", desc="Searches") - self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) - - # Main agent with subagent wrapped as tool - def spawn_subagent(task: str) -> str: - return self.subagent(task=task).result - - spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") - calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") - self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) - - system = MultiAgentSystem() - - # Test extraction using named_sub_modules pattern - tool_descriptions = {} - for _, module in system.named_sub_modules(): - if hasattr(module, "tools"): - for tool_name, tool in module.tools.items(): - tool_key = f"tool:{tool_name}" - if tool_key not in tool_descriptions: - tool_descriptions[tool_key] = tool.desc - - # All tools from all nested agents should be discovered - assert "tool:calculator" in tool_descriptions - assert "tool:spawn_subagent" in tool_descriptions - assert "tool:search" in tool_descriptions - assert "tool:finish" in tool_descriptions + assert "get_employee_department" in optimized.tools + assert "get_department_budget" in optimized.tools + assert "get_employee_salary" in optimized.tools def test_gepa_optimizes_multi_agent_system_end_to_end(): @@ -156,70 +183,43 @@ def forward(self, question): assert "spawn_subagent" in optimized.main_agent.tools -def test_tool_and_signature_optimization_with_proposer_routing(): - """Test that routing logic correctly splits tools and signatures.""" - from unittest.mock import Mock, patch - +def test_adapter_routes_tools_and_signatures_separately(): + """Test that adapter routes tool components to ToolProposer.""" from dspy.teleprompt.gepa.gepa_utils import DspyAdapter - - # Create module with BOTH signature and tools - calc_tool = dspy.Tool(calculator, name="calculator", desc="Original calculator description") - react = dspy.ReAct("question -> answer", tools=[calc_tool]) - - # Create adapter with tool optimization enabled + + calc_tool = dspy.Tool(calculator, name="calculator", desc="Original tool") + agent = dspy.ReAct("question -> answer", tools=[calc_tool]) + + # Provide reflection_lm with response for tool optimization + reflection_lm = DummyLM([ + {"improved_tool_description": "Improved calculator tool"}, + ]) + adapter = DspyAdapter( - student_module=react, + student_module=agent, metric_fn=simple_metric, feedback_map={}, failure_score=0.0, optimize_tool_descriptions=True, - reflection_lm=None, + reflection_lm=reflection_lm, ) - - # Verify propose_new_texts was created - assert hasattr(adapter, "propose_new_texts"), "Routing logic should have set propose_new_texts" - - # Mock the ToolProposer to verify it gets called with tools only - mock_tool_proposer_instance = Mock() - mock_tool_proposer_instance.return_value = {"tool:calculator": "Improved calculator description"} - - mock_tool_proposer_class = Mock(return_value=mock_tool_proposer_instance) - - # Mock parent propose_new_texts to verify it gets called with signatures only - mock_parent_propose = Mock(return_value={"react": "Improved signature instruction"}) - - with patch("dspy.teleprompt.gepa.instruction_proposal.ToolProposer", mock_tool_proposer_class): - with patch.object(adapter.__class__.__bases__[0], "propose_new_texts", mock_parent_propose, create=True): - # Rebuild adapter to pick up mocked parent - adapter_with_mock = DspyAdapter( - student_module=react, - metric_fn=simple_metric, - feedback_map={}, - failure_score=0.0, - optimize_tool_descriptions=True, - reflection_lm=None, - ) - - candidate = { - "react": "Original signature", - "tool:calculator": "Original tool desc", - } - - reflective_dataset = { - "react": [{"input": "test"}], - "tool:calculator": [{"input": "calc"}], - } - - components = ["react", "tool:calculator"] - - result = adapter_with_mock.propose_new_texts(candidate, reflective_dataset, components) - - # Verify routing: ToolProposer was called with tools only - assert mock_tool_proposer_instance.called, "ToolProposer should have been called" - tool_call_args = mock_tool_proposer_instance.call_args[1] - assert "tool:calculator" in tool_call_args["components_to_update"] - assert "react" not in tool_call_args["components_to_update"] - - # Verify both components in result - assert "react" in result - assert "tool:calculator" in result + + # Verify routing function was created + assert hasattr(adapter, 'propose_new_texts') + + # Test with ONLY tool components (signature optimization requires GEPA's LM interface) + candidate = { + "tool:calculator": "Original tool description", + } + + reflective_dataset = { + "tool:calculator": [{"Inputs": {"expr": "1+1"}, "Generated_Outputs": "2", "Feedback": "good"}], + } + + # Call routing function - should route tool to ToolProposer + result = adapter.propose_new_texts(candidate, reflective_dataset, ["tool:calculator"]) + + # Verify tool is in result (routing worked) + assert "tool:calculator" in result + # Verify it was optimized + assert result["tool:calculator"] == "Improved calculator tool" From 04f7e3d0d4731aa06f15d344411c782cfab810d6 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sat, 11 Oct 2025 17:38:53 -0700 Subject: [PATCH 07/19] fix(gepa): unify custom proposer routing for tools --- dspy/teleprompt/gepa/gepa_utils.py | 168 +++++++++++++++-------------- 1 file changed, 89 insertions(+), 79 deletions(-) diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index 8dc88b9a6d..8e82a25423 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -1,5 +1,7 @@ import logging import random +from collections import defaultdict +from copy import deepcopy from typing import Any, Callable, Protocol, TypedDict from gepa import EvaluationBatch, GEPAAdapter @@ -96,80 +98,97 @@ def __init__( self.warn_on_score_mismatch = warn_on_score_mismatch self.optimize_tool_descriptions = optimize_tool_descriptions - if self.custom_instruction_proposer is not None: - # Override propose_new_texts when custom proposer provided (following original pattern) - def custom_propose_new_texts( + def build_propose_new_texts(): + instruction_proposer = None + + # Init Signature Proposer if custom proposer is provided. + # Otherwise, use GEPA default proposer. + if self.custom_instruction_proposer is not None: + instruction_proposer = self.custom_instruction_proposer + else: + from gepa.strategies.instruction_proposal import InstructionProposalSignature + + def default_signature_proposer( + candidate: dict[str, str], + reflective_dataset: dict[str, list[dict[str, Any]]], + components_to_update: list[str], + ) -> dict[str, str]: + lm = self.reflection_lm if self.reflection_lm is not None else dspy.settings.lm + sig_texts: dict[str, str] = {} + for name in components_to_update: + base_instruction = candidate[name] + dataset_with_feedback = reflective_dataset[name] + sig_texts[name] = InstructionProposalSignature.run( + lm=(lambda x: lm(x)[0]), + input_dict={ + "current_instruction_doc": base_instruction, + "dataset_with_feedback": dataset_with_feedback, + }, + )["new_instruction"] + return sig_texts + + instruction_proposer = default_signature_proposer + + # Init Tool Proposer if tool optimization is enabled. + tool_proposer = None + if self.optimize_tool_descriptions is not None: + from .instruction_proposal import ToolProposer + + tool_proposer = ToolProposer() + + def propose_component_texts( candidate: dict[str, str], reflective_dataset: dict[str, list[dict[str, Any]]], components_to_update: list[str], ) -> dict[str, str]: + tool_components = [c for c in components_to_update if c.startswith("tool:")] + instruction_components = [c for c in components_to_update if not c.startswith("tool:")] + results: dict[str, str] = {} + + # Handle signature components. if self.reflection_lm is not None: with dspy.context(lm=self.reflection_lm): - return self.custom_instruction_proposer( + results.update( + instruction_proposer( + candidate=candidate, + reflective_dataset=reflective_dataset, + components_to_update=instruction_components, + ) + ) + else: + results.update( + instruction_proposer( candidate=candidate, reflective_dataset=reflective_dataset, - components_to_update=components_to_update, + components_to_update=instruction_components, ) - else: - return self.custom_instruction_proposer( - candidate=candidate, - reflective_dataset=reflective_dataset, - components_to_update=components_to_update, ) - self.propose_new_texts = custom_propose_new_texts - elif self.optimize_tool_descriptions: - # Override ONLY when tool optimization is enabled without custom proposer - # We handle tool components with ToolProposer and signature components with GEPA's default - def propose_new_texts_with_tools( - candidate: dict[str, str], - reflective_dataset: dict[str, list[dict[str, Any]]], - components_to_update: list[str], - ) -> dict[str, str]: - """Route components: tools to ToolProposer, signatures to GEPA's default.""" - tool_components = [c for c in components_to_update if c.startswith("tool:")] - sig_components = [c for c in components_to_update if not c.startswith("tool:")] - - # Handle signature components - replicate proposer's default behavior - sig_texts = {} - if sig_components: - from gepa.strategies.instruction_proposal import InstructionProposalSignature - - lm = self.reflection_lm if self.reflection_lm is not None else dspy.settings.lm - - for name in sig_components: - base_instruction = candidate[name] - dataset_with_feedback = reflective_dataset[name] - sig_texts[name] = InstructionProposalSignature.run( - lm=(lambda x: lm(x)[0]), - input_dict={ - "current_instruction_doc": base_instruction, - "dataset_with_feedback": dataset_with_feedback, - }, - )["new_instruction"] - - # Handle tool components with ToolProposer - tool_texts = {} - if tool_components: - from .instruction_proposal import ToolProposer - + # Handle tool if tool proposer is provided. + if tool_proposer is not None: if self.reflection_lm is not None: with dspy.context(lm=self.reflection_lm): - tool_texts = ToolProposer()( + results.update( + tool_proposer( + candidate=candidate, + reflective_dataset=reflective_dataset, + components_to_update=tool_components, + ) + ) + else: + results.update( + tool_proposer( candidate=candidate, reflective_dataset=reflective_dataset, components_to_update=tool_components, ) - else: - tool_texts = ToolProposer()( - candidate=candidate, - reflective_dataset=reflective_dataset, - components_to_update=tool_components, ) - - return {**sig_texts, **tool_texts} - self.propose_new_texts = propose_new_texts_with_tools + return results + + return propose_component_texts + + self.propose_new_texts = build_propose_new_texts() # Cache predictor names/signatures self.named_predictors = list(self.student.named_predictors()) @@ -245,12 +264,13 @@ def make_reflective_dataset( program = self.build_program(candidate) ret_d: dict[str, list[ReflectiveExample]] = {} - + tool_examples: dict[str, list[dict[str, Any]]] = defaultdict(list) + # First pass: Process all non-tool components (including ReAct) for pred_name in components_to_update: if pred_name.startswith("tool:"): continue # Skip tools in first pass - + module = None for name, m in program.named_predictors(): if name == pred_name: @@ -358,28 +378,18 @@ def make_reflective_dataset( continue ret_d[pred_name] = items - # Second pass: Process tools by copying ReAct data with annotation - react_module_name = None - for name in ret_d.keys(): - if "react" in name.lower(): - react_module_name = name - break - - if react_module_name: - for tool_component in [c for c in components_to_update if c.startswith("tool:")]: - tool_name = tool_component.replace("tool:", "") - tool_items = [] - - for item in ret_d[react_module_name]: - annotated = { - "Inputs": item["Inputs"], - "Generated Outputs": item["Generated Outputs"], - "Feedback": f"[Optimizing tool: '{tool_name}'] {item['Feedback']}" - } - tool_items.append(annotated) - - ret_d[tool_component] = tool_items - + # Share each predictor's reflections with its tools for tool optimization. + if self.optimize_tool_descriptions and hasattr(module, "tools"): + for tool_name, _ in module.tools.items(): + tool_key = f"tool:{tool_name}" + for item in items: + annotated = deepcopy(item) + annotated["Feedback"] = f"[Tool '{tool_name}' from '{pred_name}'] {item['Feedback']}" + tool_examples[tool_key].append(annotated) + + # Merge tool examples into main dataset (shared tools get examples from all predictors) + ret_d.update(tool_examples) + if len(ret_d) == 0: raise Exception("No valid predictions found for any module.") From f92e18437390f2b038ebd7b662820cb24cb37551 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sat, 11 Oct 2025 18:03:53 -0700 Subject: [PATCH 08/19] docs(gepa): clarify tool reflection prompt --- dspy/teleprompt/gepa/instruction_proposal.py | 23 +++++++++----------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/dspy/teleprompt/gepa/instruction_proposal.py b/dspy/teleprompt/gepa/instruction_proposal.py index d98075a361..9da138f150 100644 --- a/dspy/teleprompt/gepa/instruction_proposal.py +++ b/dspy/teleprompt/gepa/instruction_proposal.py @@ -313,27 +313,24 @@ def __call__( class GenerateImprovedToolDescriptionFromFeedback(dspy.Signature): - """I provided an assistant with the following description for a tool: - ``` - - ``` + """You are refining a tool description that the assistant currently uses. - This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct: - ``` - - ``` + Review the current description along with examples of the assistant's tool decisions and the feedback those decisions received. - Your task is to write a better description for this tool. + Focus on the most useful insights, such as: + - Decision cues: signals in the task or trajectory that hint the tool should (or should not) be invoked. + - Inputs & outputs: the arguments the tool expects, the result it returns, and any setup or constraints. + - Failure modes: mistakes, limitations, or edge cases where the tool wastes calls or produces poor outcomes. + - Successful patterns: situations where the tool worked well and should stay encouraged. - Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns. - - Provide the new tool description within ``` blocks.""" + If you notice other useful insights, feel free to include them. + Return a concise description that helps the assistant quickly recognize good opportunities for the tool.""" current_tool_description = dspy.InputField(desc="The current description of the tool") examples_with_feedback = dspy.InputField(desc="Examples showing tool usage decisions and feedback on correctness") improved_tool_description = dspy.OutputField( - desc="An improved description that helps with tool selection decisions" + desc="An improved description that guides correct tool selection and usage" ) From 71788697778fc62ae221426356c2231508844a81 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 04:55:03 -0700 Subject: [PATCH 09/19] test: streamline GEPA tool optimization tests --- dspy/teleprompt/gepa/gepa_utils.py | 44 +- .../teleprompt/test_gepa_tool_optimization.py | 415 +++++++++++------- 2 files changed, 284 insertions(+), 175 deletions(-) diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index 8e82a25423..4bd594628a 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -143,6 +143,7 @@ def propose_component_texts( ) -> dict[str, str]: tool_components = [c for c in components_to_update if c.startswith("tool:")] instruction_components = [c for c in components_to_update if not c.startswith("tool:")] + results: dict[str, str] = {} # Handle signature components. @@ -264,12 +265,11 @@ def make_reflective_dataset( program = self.build_program(candidate) ret_d: dict[str, list[ReflectiveExample]] = {} - tool_examples: dict[str, list[dict[str, Any]]] = defaultdict(list) - # First pass: Process all non-tool components (including ReAct) + # First pass: Process all non-tool components (predictors) for pred_name in components_to_update: if pred_name.startswith("tool:"): - continue # Skip tools in first pass + continue # Skip tools in first pass (tools are processed in the second pass) module = None for name, m in program.named_predictors(): @@ -365,9 +365,6 @@ def make_reflective_dataset( d["Feedback"] = fb["feedback"] if fb["score"] != module_score: if self.warn_on_score_mismatch: - logger.warning( - "The score returned by the metric with pred_name is different from the overall metric score. This can indicate 2 things: Either the metric is non-deterministic (e.g., LLM-as-judge, Semantic score, etc.) or the metric returned a score specific to pred_name that differs from the module level score. Currently, GEPA does not support predictor level scoring (support coming soon), and only requires a feedback text to be provided, which can be specific to the predictor or program level. GEPA will ignore the differing score returned, and instead use module level score. You can safely ignore this warning if using a semantic metric, however, if this mismatch is caused due to predictor scoring, please return module-level scores. To disable this warning, set warn_on_score_mismatch=False." - ) self.warn_on_score_mismatch = False fb["score"] = module_score @@ -378,14 +375,33 @@ def make_reflective_dataset( continue ret_d[pred_name] = items - # Share each predictor's reflections with its tools for tool optimization. - if self.optimize_tool_descriptions and hasattr(module, "tools"): - for tool_name, _ in module.tools.items(): - tool_key = f"tool:{tool_name}" - for item in items: - annotated = deepcopy(item) - annotated["Feedback"] = f"[Tool '{tool_name}' from '{pred_name}'] {item['Feedback']}" - tool_examples[tool_key].append(annotated) + # Add tool examples to the reflective dataset + tool_examples = defaultdict(list) + + if self.optimize_tool_descriptions: + for module_path, sub_module in program.named_sub_modules(): + # Walk each sub-module to locate its tools and remember the predictor scope + # so we can share those reflections with the tool descriptions below + tools = getattr(sub_module, "tools", None) + if not tools: + continue + + prefix = module_path.removeprefix("self.") if module_path != "self" else "" + + tool_entries = list(tools.items()) + + for child_name, _ in sub_module.named_predictors(): + predictor_key = child_name if not prefix else f"{prefix}.{child_name}" + reflections = ret_d.get(predictor_key) + if not reflections: + continue + + for tool_name, _ in tool_entries: + tool_key = f"tool:{tool_name}" + for item in reflections: + annotated = deepcopy(item) + annotated["Feedback"] = f"[Tool '{tool_name}' from '{predictor_key}'] {item['Feedback']}" + tool_examples[tool_key].append(annotated) # Merge tool examples into main dataset (shared tools get examples from all predictors) ret_d.update(tool_examples) diff --git a/tests/teleprompt/test_gepa_tool_optimization.py b/tests/teleprompt/test_gepa_tool_optimization.py index a921cf1da4..d82d0faa80 100644 --- a/tests/teleprompt/test_gepa_tool_optimization.py +++ b/tests/teleprompt/test_gepa_tool_optimization.py @@ -1,59 +1,12 @@ -import json +from types import SimpleNamespace import dspy from dspy import Example +from dspy.teleprompt.gepa import gepa_utils from dspy.utils.dummies import DummyLM -class DictDummyLM(dspy.clients.lm.LM): - """Dummy LM that replays prerecorded responses based on message hash.""" - - def __init__(self, history): - super().__init__("dummy", "chat", 0.0, 1000, True) - self.history = {} - for m in history: - self.history[hash(repr(m["messages"]))] = m - - def __call__(self, prompt=None, messages=None, **kwargs): - assert hash(repr(messages)) in self.history, f"Message {messages} not found in history" - m = self.history[hash(repr(messages))] - return m["outputs"] - - -# Simple multi-hop employee database tools (for main integration test) -def get_employee_department(employee_name: str) -> str: - """Gets department.""" - employees = { - "John Smith": "Engineering", - "Mary Johnson": "Sales", - "Bob Wilson": "HR", - } - return employees.get(employee_name, "Not found") - - -def get_department_budget(department: str) -> str: - """Gets budget.""" - budgets = { - "Engineering": "500000", - "Sales": "300000", - "HR": "200000", - } - return budgets.get(department, "Not found") - - -def get_employee_salary(employee_name: str) -> str: - """Gets salary.""" - salaries = { - "John Smith": "120000", - "Mary Johnson": "95000", - "Bob Wilson": "85000", - } - return salaries.get(employee_name, "Not found") - - -# Helper functions for other tests def calculator(expression: str) -> str: - """Calculator for math.""" try: return str(eval(expression)) except Exception: @@ -61,7 +14,6 @@ def calculator(expression: str) -> str: def search(query: str) -> str: - """Search function.""" return f"Results for: {query}" @@ -72,154 +24,295 @@ def simple_metric(example, prediction, trace=None, pred_name=None, pred_trace=No return dspy.Prediction(score=score, feedback="Correct" if score == 1.0 else "Wrong") -def test_build_program_applies_tool_descriptions(): - """Test that build_program applies tool descriptions from candidate dict.""" - from dspy.teleprompt.gepa.gepa_utils import DspyAdapter +def make_example(question: str, answer: str) -> Example: + return Example(question=question, answer=answer).with_inputs("question") + + +def make_reflection_entry(question: str, answer: str, feedback: str, score: float = 1.0) -> dict: + return { + "Inputs": {"question": question}, + "Generated Outputs": {"answer": answer}, + "Feedback": f"Score: {score}.\n{feedback}", + } + + +def make_react_module(tool_specs, *, max_iters: int = 3): + class SimpleReact(dspy.Module): + def __init__(self): + super().__init__() + tools = [dspy.Tool(fn, name=name, desc=desc) for name, desc, fn in tool_specs] + self.agent = dspy.ReAct( + "question -> answer", + tools=tools, + max_iters=max_iters, + ) + + def forward(self, question: str): + return self.agent(question=question) + + return SimpleReact() + + +def make_nested_react_module(main_tool_specs, *, nested_tool_specs, max_iters: int = 3): + class NestedReact(dspy.Module): + def __init__(self): + super().__init__() + nested_tools = [dspy.Tool(fn, name=name, desc=desc) for name, desc, fn in nested_tool_specs] + self.subagent = dspy.ReAct( + "task -> result", + tools=nested_tools, + max_iters=max_iters, + ) - dept_tool = dspy.Tool(get_employee_department, name="get_employee_department", desc="Gets department.") - react = dspy.ReAct("question -> answer", tools=[dept_tool]) + def spawn_subagent(task: str) -> str: + return self.subagent(task=task).result - adapter = DspyAdapter( - student_module=react, - metric_fn=simple_metric, - feedback_map={}, + spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns helper agent.") + main_tools = [dspy.Tool(fn, name=name, desc=desc) for name, desc, fn in main_tool_specs] + self.agent = dspy.ReAct( + "question -> answer", + tools=[spawn_tool, *main_tools], + max_iters=max_iters, + ) + + def forward(self, question: str): + return self.agent(question=question) + + return NestedReact() + + +def build_adapter_for_program( + program, + *, + custom_instruction_proposer=None, + reflection_lm=None, + optimize_tool_descriptions: bool = True, +): + predictor_names = sorted(name for name, _ in program.named_predictors()) + if not predictor_names: + raise ValueError("program must expose at least one predictor") + + def metric_fn(example, prediction, trace=None, pred_name=None, pred_trace=None): + return dspy.Prediction(score=1.0, feedback="ok") + + feedback_map = {} + for name in predictor_names: + feedback_map[name] = lambda *args, _name=name, **kwargs: dspy.Prediction( + score=1.0, feedback=f"{_name}-fb" + ) + + adapter = gepa_utils.DspyAdapter( + student_module=program, + metric_fn=metric_fn, + feedback_map=feedback_map, failure_score=0.0, - optimize_tool_descriptions=True, + reflection_lm=reflection_lm, + custom_instruction_proposer=custom_instruction_proposer, + optimize_tool_descriptions=optimize_tool_descriptions, ) - candidate = { - "react": "New instruction for ReAct", - "tool:get_employee_department": "Retrieves the department name for a given employee", - } + return adapter, predictor_names - new_prog = adapter.build_program(candidate) - assert new_prog.react.signature.instructions == "New instruction for ReAct" - assert new_prog.tools["get_employee_department"].desc == "Retrieves the department name for a given employee" +def stub_optimize(monkeypatch, *, new_descs, captured_seed): + def fake_optimize(*, seed_candidate, **kwargs): + captured_seed.update(seed_candidate) + best_candidate = dict(seed_candidate) + for tool_name, desc in new_descs.items(): + best_candidate[f"tool:{tool_name}"] = desc + return SimpleNamespace(best_candidate=best_candidate) + monkeypatch.setattr("gepa.optimize", fake_optimize) -def test_gepa_with_tool_optimization_enabled(): - """Test GEPA end-to-end with optimize_tool_descriptions=True using preloaded traces.""" - # Setup ReAct with minimal tool descriptions (as captured in traces) - dept_tool = dspy.Tool(get_employee_department, name="get_employee_department", desc="Gets department.") - budget_tool = dspy.Tool(get_department_budget, name="get_department_budget", desc="Gets budget.") - salary_tool = dspy.Tool(get_employee_salary, name="get_employee_salary", desc="Gets salary.") - - react = dspy.ReAct("question -> answer", tools=[dept_tool, budget_tool, salary_tool]) - # Load prerecorded LM traces from real gpt-5-nano run - with open("tests/teleprompt/gepa_dummy_lm_tool_optimization.json") as f: - data = json.load(f) - - lm = DictDummyLM(data["lm"]) - reflection_lm = DictDummyLM(data["reflection_lm"]) +def test_gepa_updates_nested_agent_tools(monkeypatch): + program = make_nested_react_module( + main_tool_specs=[("calculator", "Does math", calculator)], + nested_tool_specs=[("search", "Searches", search)], + max_iters=1, + ) + + original_descs = { + "calculator": program.agent.tools["calculator"].desc, + "spawn_subagent": program.agent.tools["spawn_subagent"].desc, + "search": program.subagent.tools["search"].desc, + } - dspy.settings.configure(lm=lm) + new_descs = { + "calculator": "Clarify how to perform arithmetic precisely.", + "spawn_subagent": "Explain when to spawn a helper agent.", + "search": "Improve how search guidance is presented.", + } + captured_seed: dict[str, str] = {} + dspy.settings.configure(lm=DummyLM([{"q": "question", "a": "answer"}])) + reflection_lm = DummyLM([{"improved_instruction": "unused"}]) + + stub_optimize(monkeypatch, new_descs=new_descs, captured_seed=captured_seed) optimizer = dspy.GEPA( metric=simple_metric, reflection_lm=reflection_lm, max_metric_calls=3, optimize_tool_descriptions=True, ) - - # Use same examples as in trace generation trainset = [ - Example(question="What is the budget of John Smith's department?", answer="500000").with_inputs("question"), - Example(question="How much does Mary Johnson earn?", answer="95000").with_inputs("question"), - Example(question="What is Bob Wilson's department budget?", answer="200000").with_inputs("question"), + make_example("What is 2 + 2?", "4"), ] + optimized = optimizer.compile(program, trainset=trainset) - optimized = optimizer.compile(react, trainset=trainset) + for tool_name, original in original_descs.items(): + assert captured_seed[f"tool:{tool_name}"] == original - # Verify optimization occurred - assert optimized is not None - assert hasattr(optimized, "tools") - assert "get_employee_department" in optimized.tools - assert "get_department_budget" in optimized.tools - assert "get_employee_salary" in optimized.tools + assert optimized.agent.tools["calculator"].desc == new_descs["calculator"] + assert optimized.agent.tools["spawn_subagent"].desc == new_descs["spawn_subagent"] + assert optimized.subagent.tools["search"].desc == new_descs["search"] -def test_gepa_optimizes_multi_agent_system_end_to_end(): - """Test GEPA.compile() optimizes ALL tools from nested multi-agent system.""" +def test_reflective_dataset_shares_feedback_across_shared_tools(): + shared_tool = dspy.Tool(calculator, name="calculator", desc="Original calculator guidance") - class MultiAgentSystem(dspy.Module): + class DualReact(dspy.Module): def __init__(self): super().__init__() - search_tool = dspy.Tool(search, name="search", desc="Searches") - self.subagent = dspy.ReAct("task -> result", tools=[search_tool]) + self.agent_a = dspy.ReAct("question -> answer", tools=[shared_tool], max_iters=1) + self.agent_b = dspy.ReAct("question -> answer", tools=[shared_tool], max_iters=1) - def spawn_subagent(task: str) -> str: - return self.subagent(task=task).result + def forward(self, question: str): + return dspy.Prediction(answer="unused") + + program = DualReact() + adapter, predictor_names = build_adapter_for_program( + program, + reflection_lm=DummyLM([{"improved_instruction": "Better"}]), + ) + + candidate = {} + for name in predictor_names: + candidate[name] = f"{name}-instruction" + candidate["tool:calculator"] = shared_tool.desc + + program = adapter.build_program(candidate) + predictor_lookup = {name: pred for name, pred in program.named_predictors()} + + trajectories: list[dict] = [] + for index, name in enumerate(predictor_names): + predictor = predictor_lookup[name] + trace_entry = ( + predictor, + {"question": f"Request {index + 1}"}, + dspy.Prediction(answer=f"Response {index + 1}"), + ) + trajectories.append( + { + "trace": [trace_entry], + "example": make_example( + f"Request {index + 1}", + f"Response {index + 1}", + ), + "prediction": dspy.Prediction(answer=f"Response {index + 1}"), + "score": 1.0, + } + ) + + eval_batch = SimpleNamespace(outputs=[], scores=[], trajectories=trajectories) + components_to_update = [*predictor_names, "tool:calculator"] + + reflective_dataset = adapter.make_reflective_dataset(candidate, eval_batch, components_to_update) + + for name in predictor_names: + assert name in reflective_dataset + assert "tool:calculator" in reflective_dataset + assert len(reflective_dataset["tool:calculator"]) == len(predictor_names) + + feedback_texts = [item["Feedback"] for item in reflective_dataset["tool:calculator"]] + for name in predictor_names: + assert any(name in feedback for feedback in feedback_texts) + + +def test_dspy_adapter_uses_custom_instruction_and_tool_proposers(monkeypatch): + program = make_react_module([("toolA", "Original tool desc", lambda arg: arg)]) + + tool_calls: list[tuple[dict, list[str]]] = [] + + class MockToolProposer: + def __call__(self, *, candidate, reflective_dataset, components_to_update): + tool_calls.append((dict(candidate), list(components_to_update))) + return {component: f"tool-new-{component}" for component in components_to_update} + + monkeypatch.setattr( + "dspy.teleprompt.gepa.instruction_proposal.ToolProposer", + MockToolProposer, + ) - spawn_tool = dspy.Tool(spawn_subagent, name="spawn_subagent", desc="Spawns subagent") - calc_tool = dspy.Tool(calculator, name="calculator", desc="Does math") - self.main_agent = dspy.ReAct("q -> a", tools=[spawn_tool, calc_tool]) + class MockInstructionProposer: + def __init__(self): + self.calls: list[list[str]] = [] + + def __call__(self, *, candidate, reflective_dataset, components_to_update): + self.calls.append(list(components_to_update)) + return {name: f"instr-new-{name}" for name in components_to_update} - def forward(self, question): - return self.main_agent(q=question) + instruction_proposer = MockInstructionProposer() + + adapter, predictor_names = build_adapter_for_program( + program, + custom_instruction_proposer=instruction_proposer, + reflection_lm=DummyLM([{"improved_instruction": "Better"}]), + ) - system = MultiAgentSystem() + predictor_name = predictor_names[0] + tool_key = "tool:toolA" + candidate = { + predictor_name: "Base instruction", + tool_key: program.agent.tools["toolA"].desc, + } + reflective_dataset = { + predictor_name: [ + make_reflection_entry( + "When should I ask for help?", + "Use toolA when delegation unblocks progress.", + "Clarify the decision boundary.", + ) + ], + tool_key: [ + make_reflection_entry( + "When should I ask for help?", + "Use toolA when delegation unblocks progress.", + "Highlight the tool's specialty.", + ) + ], + } - # Setup LMs - lm = DummyLM([{"q": "question", "a": "answer"}]) - reflection_lm = DummyLM([{"improved_instruction": "Better"}]) - dspy.settings.configure(lm=lm) + updated = adapter.propose_new_texts(candidate, reflective_dataset, [predictor_name, tool_key]) - # Run GEPA optimization + assert instruction_proposer.calls == [[predictor_name]] + assert tool_calls == [(candidate, [tool_key])] + assert updated[predictor_name] == f"instr-new-{predictor_name}" + assert updated[tool_key] == f"tool-new-{tool_key}" + + +def test_gepa_overwrites_single_react_tool_description(monkeypatch): + program = make_react_module([("calculator", "Does math", calculator)], max_iters=1) + original_desc = program.agent.tools["calculator"].desc + + new_descs = {"calculator": "Clarify how to perform arithmetic precisely."} + captured_seed: dict[str, str] = {} + + dspy.settings.configure(lm=DummyLM([{"q": "question", "a": "answer"}])) + reflection_lm = DummyLM([{"improved_instruction": "unused"}]) + + stub_optimize(monkeypatch, new_descs=new_descs, captured_seed=captured_seed) optimizer = dspy.GEPA( metric=simple_metric, reflection_lm=reflection_lm, max_metric_calls=3, optimize_tool_descriptions=True, ) + trainset = [ + make_example("Compute 3 + 5.", "8"), + ] + optimized = optimizer.compile(program, trainset=trainset) - trainset = [Example(question="test", answer="answer").with_inputs("question")] - optimized = optimizer.compile(system, trainset=trainset) - - # Verify optimized system preserves structure with all tools - assert "search" in optimized.subagent.tools - assert "calculator" in optimized.main_agent.tools - assert "spawn_subagent" in optimized.main_agent.tools - - -def test_adapter_routes_tools_and_signatures_separately(): - """Test that adapter routes tool components to ToolProposer.""" - from dspy.teleprompt.gepa.gepa_utils import DspyAdapter - - calc_tool = dspy.Tool(calculator, name="calculator", desc="Original tool") - agent = dspy.ReAct("question -> answer", tools=[calc_tool]) - - # Provide reflection_lm with response for tool optimization - reflection_lm = DummyLM([ - {"improved_tool_description": "Improved calculator tool"}, - ]) - - adapter = DspyAdapter( - student_module=agent, - metric_fn=simple_metric, - feedback_map={}, - failure_score=0.0, - optimize_tool_descriptions=True, - reflection_lm=reflection_lm, - ) - - # Verify routing function was created - assert hasattr(adapter, 'propose_new_texts') - - # Test with ONLY tool components (signature optimization requires GEPA's LM interface) - candidate = { - "tool:calculator": "Original tool description", - } - - reflective_dataset = { - "tool:calculator": [{"Inputs": {"expr": "1+1"}, "Generated_Outputs": "2", "Feedback": "good"}], - } - - # Call routing function - should route tool to ToolProposer - result = adapter.propose_new_texts(candidate, reflective_dataset, ["tool:calculator"]) - - # Verify tool is in result (routing worked) - assert "tool:calculator" in result - # Verify it was optimized - assert result["tool:calculator"] == "Improved calculator tool" + assert captured_seed["tool:calculator"] == original_desc + assert optimized.agent.tools["calculator"].desc == new_descs["calculator"] + assert optimized.agent.tools["calculator"].desc != original_desc From e34703bd89d3ac775179a60a2f8eaaf769826cf4 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 05:05:44 -0700 Subject: [PATCH 10/19] fix(gepa): streamline tool proposer formatting --- dspy/teleprompt/gepa/instruction_proposal.py | 129 +++++++------------ 1 file changed, 45 insertions(+), 84 deletions(-) diff --git a/dspy/teleprompt/gepa/instruction_proposal.py b/dspy/teleprompt/gepa/instruction_proposal.py index 9da138f150..5429cbf23f 100644 --- a/dspy/teleprompt/gepa/instruction_proposal.py +++ b/dspy/teleprompt/gepa/instruction_proposal.py @@ -317,14 +317,11 @@ class GenerateImprovedToolDescriptionFromFeedback(dspy.Signature): Review the current description along with examples of the assistant's tool decisions and the feedback those decisions received. - Focus on the most useful insights, such as: - - Decision cues: signals in the task or trajectory that hint the tool should (or should not) be invoked. - - Inputs & outputs: the arguments the tool expects, the result it returns, and any setup or constraints. - - Failure modes: mistakes, limitations, or edge cases where the tool wastes calls or produces poor outcomes. - - Successful patterns: situations where the tool worked well and should stay encouraged. + Read them together and refine the description. + So the agent understands when this tool actually helps, what argument or result matters, and what misuse the feedback exposed. + Keep the tool's voice and only change what the evidence justifies. - If you notice other useful insights, feel free to include them. - Return a concise description that helps the assistant quickly recognize good opportunities for the tool.""" + Return a refined description that helps the assistant quickly recognize good opportunities for the tool.""" current_tool_description = dspy.InputField(desc="The current description of the tool") examples_with_feedback = dspy.InputField(desc="Examples showing tool usage decisions and feedback on correctness") @@ -334,86 +331,16 @@ class GenerateImprovedToolDescriptionFromFeedback(dspy.Signature): ) -class SingleComponentToolProposer(dspy.Module): - """dspy.Module for proposing improved tool descriptions based on feedback.""" - - def __init__(self): - super().__init__() - self.propose_description = dspy.Predict(GenerateImprovedToolDescriptionFromFeedback) - - def forward(self, current_tool_description: str, reflective_dataset: list[ReflectiveExample]) -> str: - """Generate an improved tool description based on current description and feedback examples. - - Args: - current_tool_description: The current description of the tool - reflective_dataset: List of examples with inputs, outputs, and feedback - - Returns: - str: Improved tool description text - """ - # Reuse formatting from SingleComponentMultiModalProposer - formatted_examples, _ = self._format_examples_for_instruction_generation(reflective_dataset) - - result = self.propose_description( - current_tool_description=current_tool_description, examples_with_feedback=formatted_examples - ) - - return result.improved_tool_description - - def _format_examples_for_instruction_generation( - self, reflective_dataset: list[ReflectiveExample] - ) -> tuple[str, dict[int, list[Type]]]: - """Format examples using GEPA's markdown structure. - - Returns: - tuple: (formatted_text, image_map) where image_map is always empty for tools - """ - - def render_value(value, level=3): - if isinstance(value, dict): - s = "" - for k, v in value.items(): - s += f"{'#' * level} {k}\n" - s += render_value(v, min(level + 1, 6)) - if not value: - s += "\n" - return s - elif isinstance(value, (list, tuple)): - s = "" - for i, item in enumerate(value): - s += f"{'#' * level} Item {i + 1}\n" - s += render_value(item, min(level + 1, 6)) - if not value: - s += "\n" - return s - else: - return f"{str(value).strip()}\n\n" - - def convert_sample_to_markdown(sample, example_num): - s = f"# Example {example_num}\n" - for key, val in sample.items(): - s += f"## {key}\n" - s += render_value(val, level=3) - return s - - formatted_parts = [] - for i, example_data in enumerate(reflective_dataset): - formatted_example = convert_sample_to_markdown(example_data, i + 1) - formatted_parts.append(formatted_example) - - formatted_text = "\n\n".join(formatted_parts) - return formatted_text, {} - - class ToolProposer(ProposalFn): """GEPA-compatible tool description proposer. - This class handles tool description optimization during GEPA optimization by using - a single-component proposer for each tool that needs to be updated. + Formats reflective examples into structured markdown and calls + `GenerateImprovedToolDescriptionFromFeedback` to produce updated descriptions + for each tool that requires refinement. """ def __init__(self): - self.single_proposer = SingleComponentToolProposer() + self.propose_description = dspy.Predict(GenerateImprovedToolDescriptionFromFeedback) def __call__( self, @@ -438,10 +365,44 @@ def __call__( current_description = candidate[component_name] component_reflective_data = reflective_dataset[component_name] - new_description = self.single_proposer( - current_tool_description=current_description, reflective_dataset=component_reflective_data + formatted_examples = self._format_examples(component_reflective_data) + result = self.propose_description( + current_tool_description=current_description, + examples_with_feedback=formatted_examples, ) - updated_components[component_name] = new_description + updated_components[component_name] = result.improved_tool_description return updated_components + + def _format_examples(self, reflective_dataset: list[ReflectiveExample]) -> str: + """Format reflective examples using GEPA's markdown structure.""" + + def render_value(value, level=3): + if isinstance(value, dict): + s = "" + for key, val in value.items(): + s += f"{'#' * level} {key}\n" + s += render_value(val, min(level + 1, 6)) + if not value: + s += "\n" + return s + if isinstance(value, (list, tuple)): + s = "" + for index, item in enumerate(value): + s += f"{'#' * level} Item {index + 1}\n" + s += render_value(item, min(level + 1, 6)) + if not value: + s += "\n" + return s + return f"{str(value).strip()}\n\n" + + def convert_sample_to_markdown(sample, example_num): + s = f"# Example {example_num}\n" + for key, val in sample.items(): + s += f"## {key}\n" + s += render_value(val, level=3) + return s + + formatted_parts = [convert_sample_to_markdown(example, i + 1) for i, example in enumerate(reflective_dataset)] + return "\n\n".join(formatted_parts) From 3f053115ab13e27f7751fb5e8c613974a2980653 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 05:09:21 -0700 Subject: [PATCH 11/19] test(gepa): drop legacy dummy tool fixture --- .../gepa_dummy_lm_tool_optimization.json | 1908 ----------------- 1 file changed, 1908 deletions(-) delete mode 100644 tests/teleprompt/gepa_dummy_lm_tool_optimization.json diff --git a/tests/teleprompt/gepa_dummy_lm_tool_optimization.json b/tests/teleprompt/gepa_dummy_lm_tool_optimization.json deleted file mode 100644 index a89bde322d..0000000000 --- a/tests/teleprompt/gepa_dummy_lm_tool_optimization.json +++ /dev/null @@ -1,1908 +0,0 @@ -{ - "lm": [ - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0X8ygvrG4tRaiQylQpFnxS2h4F", - "created": 1760143457, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - ], - "usage": {}, - "cost": 0.00109075, - "timestamp": "2025-10-10T21:02:18.718338", - "uuid": "10e48da6-a48f-49c4-8ee8-4897941bc665", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", - "created": 1760143457, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007469999999999999, - "timestamp": "2025-10-10T21:02:18.720524", - "uuid": "c7a628f4-af0b-4e20-8b3e-727de7b86770", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", - "created": 1760143468, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" - ], - "usage": {}, - "cost": 0.0007816, - "timestamp": "2025-10-10T21:02:18.724678", - "uuid": "b977039a-ed53-4e02-8de4-a470e36c1156", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0pGVq8eKHmqgERoX8MYr6eQQMb", - "created": 1760143475, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format" - ], - "usage": {}, - "cost": 0.00116765, - "timestamp": "2025-10-10T21:02:18.726535", - "uuid": "ec47ae1e-f9fa-4b3b-9fcc-5ec3eb86a758", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI16VENONq29JEoLNkCHtkjP53j2", - "created": 1760143492, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000" - ], - "usage": {}, - "cost": 0.0010348999999999998, - "timestamp": "2025-10-10T21:02:18.730129", - "uuid": "911d6ed4-8a2e-4075-a8c0-85a28faab442", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI1MPbNZaELWX7XKOG3zDsYyJZHG", - "created": 1760143508, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." - ], - "usage": {}, - "cost": 0.0003704, - "timestamp": "2025-10-10T21:02:18.733249", - "uuid": "cb82d23d-118b-4153-ba7e-7549a2b45337", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", - "created": 1760143484, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0004960499999999999, - "timestamp": "2025-10-10T21:02:18.733564", - "uuid": "1e69d8ac-168f-498a-bc5f-bedca8eaeb5c", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", - "created": 1760143457, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007469999999999999, - "timestamp": "2025-10-10T21:02:18.739071", - "uuid": "f6e71786-4b86-4014-902e-9edcf5241d57", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0X8ygvrG4tRaiQylQpFnxS2h4F", - "created": 1760143457, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - ], - "usage": {}, - "cost": 0.00109075, - "timestamp": "2025-10-10T21:02:18.741521", - "uuid": "e7455e4f-6011-41ef-afd4-6ae9396d025d", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0XiKGDNlGNFRHXtbwxAhl7keIO", - "created": 1760143457, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## next_tool_name ## ]]\nget_employee_salary\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007469999999999999, - "timestamp": "2025-10-10T21:02:18.741090", - "uuid": "6d6d493a-4299-4663-98c7-6ed91c35c434", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0pGVq8eKHmqgERoX8MYr6eQQMb", - "created": 1760143475, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}\n When providing `next_tool_args`, the value inside the field must be in JSON format" - ], - "usage": {}, - "cost": 0.00116765, - "timestamp": "2025-10-10T21:02:18.743589", - "uuid": "b4fdd85b-a4fd-45ff-be5b-ab2633ebb89c", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI16VENONq29JEoLNkCHtkjP53j2", - "created": 1760143492, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n[[ ## next_tool_name ## ]]\nfinish\n[[ ## next_tool_args ## ]]\n{}\n[[ ## completed ## ]]\n500000" - ], - "usage": {}, - "cost": 0.0010348999999999998, - "timestamp": "2025-10-10T21:02:18.747736", - "uuid": "5b03ac38-aaaa-4605-9f1d-b07563de80e9", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI1MPbNZaELWX7XKOG3zDsYyJZHG", - "created": 1760143508, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." - ], - "usage": {}, - "cost": 0.0003704, - "timestamp": "2025-10-10T21:02:18.750301", - "uuid": "a38292c1-d8ad-42c5-8a42-a0ca65cf2c35", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", - "created": 1760143468, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" - ], - "usage": {}, - "cost": 0.0007816, - "timestamp": "2025-10-10T21:02:18.751592", - "uuid": "ccd7dea3-428b-45c6-bca0-b667a39bb388", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`.\n \n You are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\n Your goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n \n To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\n After each tool call, you receive a resulting observation, which gets appended to your trajectory.\n \n When writing next_thought, you may reason about the current situation and plan for future steps.\n When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n \n (1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n (2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n (3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n (4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\n When providing `next_tool_args`, the value inside the field must be in JSON format" - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0ifjilYW0AUr1NLsYZIdxKpteD", - "created": 1760143468, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]\n{\"answer\": \"Mary Johnson earns $95,000 per year.\"}" - ], - "usage": {}, - "cost": 0.0007816, - "timestamp": "2025-10-10T21:02:18.754893", - "uuid": "791a2d61-2452-452d-a4a9-435f5e1d71af", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", - "created": 1760143484, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0004960499999999999, - "timestamp": "2025-10-10T21:02:18.757192", - "uuid": "f6b956c0-8975-44af-89de-b71875fb7117", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `question`, produce the fields `answer`." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPI0yN1DlWIpZvgNUzUp6CfgLYXO5", - "created": 1760143484, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n[[ ## answer ## ]]\nMary Johnson earns $95,000 per year.\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0004960499999999999, - "timestamp": "2025-10-10T21:02:18.758336", - "uuid": "20511a59-1080-4a75-b28e-1b6d0fbd01f6", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", - "created": 1760154628, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007133999999999999, - "timestamp": "2025-10-10T21:02:18.816256", - "uuid": "a7626695-a4ad-40cd-b628-583d1339058a", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", - "created": 1760154628, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007133999999999999, - "timestamp": "2025-10-10T21:02:18.817978", - "uuid": "d52ed522-b57d-4d69-b6e7-d836ac1f1786", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuiyj1kOtZkRcdi6OpuGRIPpvq1", - "created": 1760154628, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00071475, - "timestamp": "2025-10-10T21:02:18.820494", - "uuid": "a86e16ad-2247-40f6-be8f-bacd75ed19bf", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", - "created": 1760154637, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007115499999999999, - "timestamp": "2025-10-10T21:02:18.824015", - "uuid": "46049c68-4945-4b5d-97f0-21d399ac7378", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", - "created": 1760154637, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007115499999999999, - "timestamp": "2025-10-10T21:02:18.824161", - "uuid": "ca649b9f-efaf-401f-a87c-4e35967bfd63", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKurOG0oEXAaNCHYroFXYtxjgC6X", - "created": 1760154637, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00045945, - "timestamp": "2025-10-10T21:02:18.828342", - "uuid": "256631d1-f07c-4421-a76c-bb86e62a84ce", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", - "created": 1760154646, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00042905, - "timestamp": "2025-10-10T21:02:18.832098", - "uuid": "ceca3769-9f77-4e59-afa3-38a99566e359", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", - "created": 1760154646, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00042905, - "timestamp": "2025-10-10T21:02:18.832437", - "uuid": "ffbec08d-6b7e-427f-8b05-d824da47dde3", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuzEA2u4z5lqkmfmb2NodpTfL1a", - "created": 1760154645, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00035559999999999997, - "timestamp": "2025-10-10T21:02:18.832655", - "uuid": "15b1ed96-c066-4b89-b969-a7080aeb2790", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nBudget retrieved; finishing.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKv5XHKi8Q0ThiIgBXvILA5vaOux", - "created": 1760154651, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." - ], - "usage": {}, - "cost": 0.0004347, - "timestamp": "2025-10-10T21:02:18.836764", - "uuid": "f68a2309-8daa-4a88-879d-d94a5c72a491", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuiyj1kOtZkRcdi6OpuGRIPpvq1", - "created": 1760154628, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## next_tool_name ## ]]\nget_employee_department\n\n[[ ## next_tool_args ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00071475, - "timestamp": "2025-10-10T21:02:18.840836", - "uuid": "6a87b1ce-1982-4c31-a29a-a261a8130d3f", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuizw3cnTY9O1t8kcpmcwrDEUav", - "created": 1760154628, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## next_tool_name ## ]]\n'get_employee_salary'\n\n[[ ## next_tool_args ## ]]\n{'employee_name': 'Mary Johnson'}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007133999999999999, - "timestamp": "2025-10-10T21:02:18.842790", - "uuid": "6661def4-6a74-4903-b1da-3f45b3348d6a", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKurOG0oEXAaNCHYroFXYtxjgC6X", - "created": 1760154637, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nProceeding to fetch the department budget.\n\n[[ ## next_tool_name ## ]]\nget_department_budget\n\n[[ ## next_tool_args ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00045945, - "timestamp": "2025-10-10T21:02:18.844946", - "uuid": "387da337-964b-432b-8c58-073230da0f18", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKurC7EDYow8XFHtl5HbkylM1WUF", - "created": 1760154637, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nSalary retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\n'finish'\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0007115499999999999, - "timestamp": "2025-10-10T21:02:18.847311", - "uuid": "7387f902-35db-4822-80c9-42d324172044", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `next_thought` (str): \n2. `next_tool_name` (Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']): \n3. `next_tool_args` (dict[str, Any]):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## next_thought ## ]]\n{next_thought}\n\n[[ ## next_tool_name ## ]]\n{next_tool_name} # note: the value you produce must exactly match (no extra characters) one of: get_employee_department; get_department_budget; get_employee_salary; finish\n\n[[ ## next_tool_args ## ]]\n{next_tool_args} # note: the value you produce must adhere to the JSON schema: {\"type\": \"object\", \"additionalProperties\": true}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n \n What you can use\n - Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n \n Input you receive\n - Fields:\n - question: string\n \n Output protocol (in each turn)\n - To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n \n - After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n \n - The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n \n Task-specific guidelines\n - If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n - If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n - Use the minimum number of tool calls necessary to answer.\n - Maintain a concise interaction; avoid unnecessary steps.\n \n Example flow (correct and concise)\n 1) Assistant outputs:\n {\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n }\n Observation: \"Engineering\"\n \n 2) Assistant outputs:\n {\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n }\n Observation: 250000\n \n 3) Assistant outputs:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n Final answer (system delivers): 250000\n \n Notes on common pitfalls\n - Do not return a natural-language sentence as the final answer when the data is numeric.\n - If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n - If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\nRespond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal['get_employee_department', 'get_department_budget', 'get_employee_salary', 'finish']), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuzEA2u4z5lqkmfmb2NodpTfL1a", - "created": 1760154645, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## next_thought ## ]]\nBudget retrieved; finishing.\n\n[[ ## next_tool_name ## ]]\nfinish\n\n[[ ## next_tool_args ## ]]\n{}\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00035559999999999997, - "timestamp": "2025-10-10T21:02:18.849633", - "uuid": "dba3472c-ed9a-4741-9615-4a8bc8c0ec5f", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nHow much does Mary Johnson earn?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying salary for Mary Johnson.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nSalary retrieved; finishing.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKv03905LMfmr0RdU7NI407ImxYj", - "created": 1760154646, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nI can't share step-by-step reasoning, but the retrieved salary for Mary Johnson is 95000.\n[[ ## answer ## ]]\n95000\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00042905, - "timestamp": "2025-10-10T21:02:18.852604", - "uuid": "6c63d26d-6682-4ffb-b7b3-cf805cab000c", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `question` (str): \n2. `trajectory` (str):\nYour output fields are:\n1. `reasoning` (str): \n2. `answer` (str):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## question ## ]]\n{question}\n\n[[ ## trajectory ## ]]\n{trajectory}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## answer ## ]]\n{answer}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n You are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n \n What to do\n - Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n - Use the latest/most recent available value if multiple exist.\n - Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n - If the requested value cannot be determined from available data, return 0.\n \n Notes\n - Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n - Do not reveal any chain-of-thought or tool usage in the output.\n - The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields)." - }, - { - "role": "user", - "content": "[[ ## question ## ]]\nWhat is the budget of John Smith's department?\n\n[[ ## trajectory ## ]]\n[[ ## thought_0 ## ]]\nQuerying John Smith's department to determine the budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nProceeding to fetch the department budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nBudget retrieved; finishing.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKv5XHKi8Q0ThiIgBXvILA5vaOux", - "created": 1760154651, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted.", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## reasoning ## ]]\nIdentified John Smith's department as Engineering and retrieved its budget as 500000; thus the requested value is 500000.\n\n[[ ## answer ## ]]\n500000\n\n[[ ## completed ## ]]\nCompleted." - ], - "usage": {}, - "cost": 0.0004347, - "timestamp": "2025-10-10T21:02:18.854255", - "uuid": "cc229448-d7ca-43bc-ba83-7405bd2d73d2", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - } - ], - "reflection_lm": [ - { - "prompt": "I provided an assistant with the following instructions to perform a task for me:\n```\nGiven the fields `question`, produce the fields `answer`.\n\nYou are an Agent. In each episode, you will be given the fields `question` as input. And you can see your past trajectory so far.\nYour goal is to use one or more of the supplied tools to collect any necessary information for producing `answer`.\n\nTo do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.\nAfter each tool call, you receive a resulting observation, which gets appended to your trajectory.\n\nWhen writing next_thought, you may reason about the current situation and plan for future steps.\nWhen selecting the next_tool_name and its next_tool_args, the tool must be one of:\n\n(1) get_employee_department, whose description is Gets department.. It takes arguments {'employee_name': {'type': 'string'}}.\n(2) get_department_budget, whose description is Gets budget.. It takes arguments {'department': {'type': 'string'}}.\n(3) get_employee_salary, whose description is Gets salary.. It takes arguments {'employee_name': {'type': 'string'}}.\n(4) finish, whose description is Marks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.. It takes arguments {}.\nWhen providing `next_tool_args`, the value inside the field must be in JSON format\n```\n\nThe following are examples of different task inputs provided to the assistant along with the assistant's response for each of them, and some feedback on how the assistant's response could be better:\n```\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\nCorrect\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n```\n\nYour task is to write a new instruction for the assistant.\n\nRead the inputs carefully and identify the input format and infer detailed task description about the task I wish to solve with the assistant.\n\nRead all the assistant responses and the corresponding feedback. Identify all niche and domain specific factual information about the task and include it in the instruction, as a lot of it may not be available to the assistant in the future. The assistant may have utilized a generalizable strategy to solve the task, if so, include that in the instruction as well.\n\nProvide the new instructions within ``` blocks.", - "messages": null, - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKt5gz4GYCmM0vnWfNiBqnWmsiYC", - "created": 1760154527, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "```\nYou are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n\nWhat you can use\n- Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n\nInput you receive\n- Fields:\n - question: string\n\nOutput protocol (in each turn)\n- To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n\n- After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n\n- The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n\nTask-specific guidelines\n- If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n- If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n- Use the minimum number of tool calls necessary to answer.\n- Maintain a concise interaction; avoid unnecessary steps.\n\nExample flow (correct and concise)\n1) Assistant outputs:\n{\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n}\nObservation: \"Engineering\"\n\n2) Assistant outputs:\n{\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n}\nObservation: 250000\n\n3) Assistant outputs:\n{\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n}\nFinal answer (system delivers): 250000\n\nNotes on common pitfalls\n- Do not return a natural-language sentence as the final answer when the data is numeric.\n- If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n- If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above.\n\n```\n", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "```\nYou are an agent that answers questions by querying a small set of structured tools. Do not reveal your internal chain-of-thought. Instead, perform targeted tool calls and return the final answer only when all necessary data has been gathered.\n\nWhat you can use\n- Tools (each call requires a JSON-encoded argument):\n 1) get_employee_department\n Description: Gets the department for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 2) get_department_budget\n Description: Gets the budget for a given department.\n Arguments: {'department': {'type': 'string'}}\n 3) get_employee_salary\n Description: Gets the salary for a given employee.\n Arguments: {'employee_name': {'type': 'string'}}\n 4) finish\n Description: Marks the task as complete; signals that all information for producing the outputs (the answer) is now available.\n Arguments: {}\n\nInput you receive\n- Fields:\n - question: string\n\nOutput protocol (in each turn)\n- To request a tool, respond with exactly:\n {\n \"tool_name\": \"\",\n \"tool_args\": { ... JSON-encoded arguments ... }\n }\n (No extra text, no prose, no chain-of-thought.)\n\n- After you receive the tool\u2019s observation, continue by either:\n - requesting another tool (same JSON format as above), or\n - finishing the task by issuing:\n {\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n }\n\n- The final answer will be exposed by the system after you finish. When the final answer is produced, it should be the plain value (e.g., a number) with no extraneous text. If the answer is numeric (budget or salary), return just the digits (no currency symbols, no words).\n\nTask-specific guidelines\n- If the question asks for a salary, the final answer must be the plain numeric amount (e.g., 95000), not a sentence or formatted currency.\n- If the question requires a department budget, the final answer should be the numeric budget value (plain digits).\n- Use the minimum number of tool calls necessary to answer.\n- Maintain a concise interaction; avoid unnecessary steps.\n\nExample flow (correct and concise)\n1) Assistant outputs:\n{\n \"tool_name\": \"get_employee_department\",\n \"tool_args\": {\"employee_name\": \"John Smith\"}\n}\nObservation: \"Engineering\"\n\n2) Assistant outputs:\n{\n \"tool_name\": \"get_department_budget\",\n \"tool_args\": {\"department\": \"Engineering\"}\n}\nObservation: 250000\n\n3) Assistant outputs:\n{\n \"tool_name\": \"finish\",\n \"tool_args\": {}\n}\nFinal answer (system delivers): 250000\n\nNotes on common pitfalls\n- Do not return a natural-language sentence as the final answer when the data is numeric.\n- If a single tool yields the needed numeric value (salary or budget), you may finish immediately after receiving that observation.\n- If multiple steps are required (e.g., determine department, then budget), chain the tool calls in sequence as shown above.\n\n```\n" - ], - "usage": {}, - "cost": 0.0018137499999999998, - "timestamp": "2025-10-10T21:02:18.761810", - "uuid": "5f548fd8-70f7-4c77-817d-805ba8fafc6a", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": "I provided an assistant with the following instructions to perform a task for me:\n```\nGiven the fields `question`, produce the fields `answer`.\n```\n\nThe following are examples of different task inputs provided to the assistant along with the assistant's response for each of them, and some feedback on how the assistant's response could be better:\n```\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n[[ ## thought_1 ## ]]\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n[[ ## tool_name_1 ## ]]\nget_department_budget\n\n[[ ## tool_args_1 ## ]]\n{\"department\": \"Engineering\"}\n\n[[ ## observation_1 ## ]]\n500000\n\n[[ ## thought_2 ## ]]\nAll information gathered; budget obtained. Ready to finish and return the result.\n\n[[ ## tool_name_2 ## ]]\nfinish\n\n[[ ## tool_args_2 ## ]]\n{}\n\n[[ ## observation_2 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nBrief summary: The employee John Smith belongs to the Engineering department, whose budget is 500000.\n\n### answer\n500000\n\n## Feedback\nCorrect\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n### answer\nMary Johnson earns $95,000 per year.\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n[[ ## thought_1 ## ]]\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n[[ ## tool_name_1 ## ]]\nfinish\n\n[[ ## tool_args_1 ## ]]\n{}\n\n[[ ## observation_1 ## ]]\nCompleted.\n\n## Generated Outputs\n### reasoning\nI checked the latest salary record for Mary Johnson and found 95,000.\n\n### answer\nMary Johnson earns $95,000 per year.\n\n## Feedback\nWrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n```\n\nYour task is to write a new instruction for the assistant.\n\nRead the inputs carefully and identify the input format and infer detailed task description about the task I wish to solve with the assistant.\n\nRead all the assistant responses and the corresponding feedback. Identify all niche and domain specific factual information about the task and include it in the instruction, as a lot of it may not be available to the assistant in the future. The assistant may have utilized a generalizable strategy to solve the task, if so, include that in the instruction as well.\n\nProvide the new instructions within ``` blocks.", - "messages": null, - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKtQriStnHiU8jdA2NDSJdrt9Kk0", - "created": 1760154548, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "```\nYou are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n\nWhat to do\n- Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n- Use the latest/most recent available value if multiple exist.\n- Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n- If the requested value cannot be determined from available data, return 0.\n\nNotes\n- Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n- Do not reveal any chain-of-thought or tool usage in the output.\n- The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields).\n```", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "```\nYou are given a single input field named \"question\" and must output a single field named \"answer\" containing a numeric value.\n\nWhat to do\n- Determine what numeric value the question is asking for:\n - If the question asks for a department budget (e.g., contains \"budget\" related terms), find the stated employee\u2019s department and return that department's budget as an integer.\n - If the question asks for an employee\u2019s earnings/salary (e.g., contains \"earn\", \"salary\", \"pay\"), return the employee\u2019s salary as an integer.\n- Use the latest/most recent available value if multiple exist.\n- Return only the numeric value in the \"answer\" field. Do not include currency symbols, units, text, or explanations.\n- If the requested value cannot be determined from available data, return 0.\n\nNotes\n- Acceptable numeric formats should be converted to integers (e.g., \"95,000\" -> 95000).\n- Do not reveal any chain-of-thought or tool usage in the output.\n- The output should be a valid single numeric value in the \"answer\" field (not a sentence or extra fields).\n```" - ], - "usage": {}, - "cost": 0.0010855, - "timestamp": "2025-10-10T21:02:18.762903", - "uuid": "554c9ada-96dc-4072-93b0-6a191b5e1132", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." - }, - { - "role": "user", - "content": "[[ ## current_tool_description ## ]]\nGets department.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_employee_department'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKtgoLekUHpjaiLtvbwis5RAvL65", - "created": 1760154564, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## improved_tool_description ## ]]\n```text\nTool name: get_employee_department\n\nPurpose:\nReturn the department name associated with a given employee.\n\nInput:\nA JSON object with a single field:\n{\"employee_name\": \"\"}\n\nOutput:\nA string containing the department name (e.g., \"Engineering\"). If the employee cannot be found or the department cannot be determined, it returns null (no department).\n\nUsage pattern:\n- Use this as the first step when answering questions about an employee's department.\n Example: To answer \"What is John's department?\" call\n get_employee_department with {\"employee_name\": \"John Smith\"} \u2192 \"Engineering\".\n Then, you can fetch department-level data (e.g., budget) by calling the appropriate tool with the returned department, e.g.,\n get_department_budget with {\"department\": \"Engineering\"}.\n\nNotes:\n- Do not use this tool for salary or compensation questions; use get_employee_salary for that.\n- This tool focuses strictly on department membership information, not other attributes like department head, location, or budget details.\n```\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## improved_tool_description ## ]]\n```text\nTool name: get_employee_department\n\nPurpose:\nReturn the department name associated with a given employee.\n\nInput:\nA JSON object with a single field:\n{\"employee_name\": \"\"}\n\nOutput:\nA string containing the department name (e.g., \"Engineering\"). If the employee cannot be found or the department cannot be determined, it returns null (no department).\n\nUsage pattern:\n- Use this as the first step when answering questions about an employee's department.\n Example: To answer \"What is John's department?\" call\n get_employee_department with {\"employee_name\": \"John Smith\"} \u2192 \"Engineering\".\n Then, you can fetch department-level data (e.g., budget) by calling the appropriate tool with the returned department, e.g.,\n get_department_budget with {\"department\": \"Engineering\"}.\n\nNotes:\n- Do not use this tool for salary or compensation questions; use get_employee_salary for that.\n- This tool focuses strictly on department membership information, not other attributes like department head, location, or budget details.\n```\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0012571499999999998, - "timestamp": "2025-10-10T21:02:18.803676", - "uuid": "c40e2775-52da-4984-93d2-7179af4852e9", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." - }, - { - "role": "user", - "content": "[[ ## current_tool_description ## ]]\nGets budget.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_department_budget'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKtv2ajGgxfaPcub3BieDIN3YnNp", - "created": 1760154579, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## improved_tool_description ## ]]\n```text\nBudget data retrieval tool. It provides access to monetary figures in the organization in two related forms:\n- Employee salary: by supplying an employee name, you can obtain the annual salary as a plain numeric value (e.g., 95000).\n- Department budget: by supplying a department name, you can obtain the department's annual budget as a plain numeric value (e.g., 120000).\n\nIf a question requires deriving a department budget from an employee context, first determine the employee's department with get_employee_department(employee_name), then fetch the budget with get_department_budget(department). All outputs are numbers; when presenting an answer, return only the numeric value and use a finish operation if needed to emit the final result without extra text.\n\nExamples (paraphrased):\n- \"What is John Smith's department budget?\" -> get_employee_department -> 'Engineering' -> get_department_budget -> 120000\n- \"How much does Mary Johnson earn?\" -> get_employee_salary -> 95000 -> finish with 95000\n```\n\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## improved_tool_description ## ]]\n```text\nBudget data retrieval tool. It provides access to monetary figures in the organization in two related forms:\n- Employee salary: by supplying an employee name, you can obtain the annual salary as a plain numeric value (e.g., 95000).\n- Department budget: by supplying a department name, you can obtain the department's annual budget as a plain numeric value (e.g., 120000).\n\nIf a question requires deriving a department budget from an employee context, first determine the employee's department with get_employee_department(employee_name), then fetch the budget with get_department_budget(department). All outputs are numbers; when presenting an answer, return only the numeric value and use a finish operation if needed to emit the final result without extra text.\n\nExamples (paraphrased):\n- \"What is John Smith's department budget?\" -> get_employee_department -> 'Engineering' -> get_department_budget -> 120000\n- \"How much does Mary Johnson earn?\" -> get_employee_salary -> 95000 -> finish with 95000\n```\n\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.0013079499999999998, - "timestamp": "2025-10-10T21:02:18.806193", - "uuid": "1c113b3c-124b-419d-9d50-5cf5e57d9ac7", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." - }, - { - "role": "user", - "content": "[[ ## current_tool_description ## ]]\nGets salary.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'get_employee_salary'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuCf9D1mICA1ISTsHnvFAS1fPc2", - "created": 1760154596, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## improved_tool_description ## ]]\n```text\nTool: get_employee_salary\nPurpose: Retrieve an employee's annual base salary as a numeric value (in USD) without formatting.\nInputs: {\"employee_name\": \"\"} e.g., {\"employee_name\": \"Mary Johnson\"}\nOutput: An integer representing the annual salary in USD (e.g., 95000). If unavailable, returns null/None.\nNotes:\n- The returned value is a raw number with no currency symbol or textual description.\n- Do not generate natural-language salary statements; formatting and any accompanying text should be handled by the caller.\n- This is for base salary only; bonuses, stock, or benefits are not included unless provided by the data source.\n```\n[[ ## completed ## ]]", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## improved_tool_description ## ]]\n```text\nTool: get_employee_salary\nPurpose: Retrieve an employee's annual base salary as a numeric value (in USD) without formatting.\nInputs: {\"employee_name\": \"\"} e.g., {\"employee_name\": \"Mary Johnson\"}\nOutput: An integer representing the annual salary in USD (e.g., 95000). If unavailable, returns null/None.\nNotes:\n- The returned value is a raw number with no currency symbol or textual description.\n- Do not generate natural-language salary statements; formatting and any accompanying text should be handled by the caller.\n- This is for base salary only; bonuses, stock, or benefits are not included unless provided by the data source.\n```\n[[ ## completed ## ]]" - ], - "usage": {}, - "cost": 0.00087075, - "timestamp": "2025-10-10T21:02:18.807604", - "uuid": "c3d8ffca-2fbc-4c76-8521-8a782e7e2623", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - }, - { - "prompt": null, - "messages": [ - { - "role": "system", - "content": "Your input fields are:\n1. `current_tool_description` (str): The current description of the tool\n2. `examples_with_feedback` (str): Examples showing tool usage decisions and feedback on correctness\nYour output fields are:\n1. `improved_tool_description` (str): An improved description that helps with tool selection decisions\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_tool_description ## ]]\n{current_tool_description}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_tool_description ## ]]\n{improved_tool_description}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with the following description for a tool:\n ```\n \n ```\n \n This tool is available to the assistant. The following are examples of task inputs provided to the assistant, the assistant's decisions about which tools to use, and feedback on whether those decisions were correct:\n ```\n \n ```\n \n Your task is to write a better description for this tool.\n \n Read the examples carefully and identify patterns in when the tool was used successfully versus when it was misused or overlooked. Identify any domain-specific information about the tool's capabilities or appropriate usage that may not be available to the assistant in the future. The assistant may have developed effective patterns for tool selection - if so, ensure the tool description supports those patterns.\n \n Provide the new tool description within ``` blocks." - }, - { - "role": "user", - "content": "[[ ## current_tool_description ## ]]\nMarks the task as complete. That is, signals that all information for producing the outputs, i.e. `answer`, are now available to be extracted.\n\n[[ ## examples_with_feedback ## ]]\n# Example 1\n## Inputs\n### question\nWhat is the budget of John Smith's department?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan: determine John Smith's department, then fetch that department's budget.\n\n[[ ## tool_name_0 ## ]]\nget_employee_department\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"John Smith\"}\n\n[[ ## observation_0 ## ]]\nEngineering\n\n## Generated Outputs\n### next_thought\nPlan: Use the known department (Engineering) to retrieve its budget.\n\n### next_tool_name\nget_department_budget\n\n### next_tool_args\n{'department': 'Engineering'}\n\n## Feedback\n[Optimizing tool: 'finish'] Correct\n\n\n\n# Example 2\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n[[ ## thought_0 ## ]]\nPlan to fetch Mary Johnson's salary.\n\n[[ ## tool_name_0 ## ]]\nget_employee_salary\n\n[[ ## tool_args_0 ## ]]\n{\"employee_name\": \"Mary Johnson\"}\n\n[[ ## observation_0 ## ]]\n95000\n\n## Generated Outputs\n### next_thought\nWe have Mary Johnson's salary from the last observation (95000). Ready to finalize.\n\n### next_tool_name\nfinish\n\n### next_tool_args\n{}\n\n## Feedback\n[Optimizing tool: 'finish'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\n# Example 3\n## Inputs\n### question\nHow much does Mary Johnson earn?\n\n### trajectory\n\n\n## Generated Outputs\n### next_thought\nPlan to fetch Mary Johnson's salary.\n\n### next_tool_name\nget_employee_salary\n\n### next_tool_args\n{'employee_name': 'Mary Johnson'}\n\n## Feedback\n[Optimizing tool: 'finish'] Wrong (got 'Mary Johnson earns $95,000 per year.', expected '95000')\n\n\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_tool_description ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`." - } - ], - "kwargs": {}, - "response": { - "id": "chatcmpl-CPKuP9AOMyaqrKPqDzNbxG2hAEjxc", - "created": 1760154609, - "model": "gpt-5-nano-2025-08-07", - "object": "chat.completion", - "system_fingerprint": null, - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "message": { - "content": "[[ ## improved_tool_description ## ]]\n``` \nFinish: Mark the task as complete and signal that the final answer should be extracted from the assistant's outputs.\n\nWhen to use:\n- Only call finish when you have determined the exact final value in the exact format expected by the evaluator (e.g., a plain number like 95000, or a plain string like 'Engineering').\n\nWhat happens after:\n- The system will treat the final value as the answer. No additional reasoning, editing, or commentary should accompany it.\n- Do not call finish to perform additional calculations or to reword the result.\n\nFormatting rules:\n- If the answer is numeric, provide digits only (no currency symbols, punctuation, or words).\n- If the answer is textual, provide the exact string with no surrounding quotes or extra punctuation.\n\nExamples:\n- Correct: 95000\n- Correct: Engineering\n- Incorrect: 'Mary Johnson earns 95000 per year.'\n```\n\n\n[[ ## completed ## ]]\nEnd of description.", - "role": "assistant", - "tool_calls": null, - "function_call": null, - "annotations": [] - }, - "provider_specific_fields": {} - } - ], - "usage": {}, - "service_tier": "default", - "cache_hit": true - }, - "outputs": [ - "[[ ## improved_tool_description ## ]]\n``` \nFinish: Mark the task as complete and signal that the final answer should be extracted from the assistant's outputs.\n\nWhen to use:\n- Only call finish when you have determined the exact final value in the exact format expected by the evaluator (e.g., a plain number like 95000, or a plain string like 'Engineering').\n\nWhat happens after:\n- The system will treat the final value as the answer. No additional reasoning, editing, or commentary should accompany it.\n- Do not call finish to perform additional calculations or to reword the result.\n\nFormatting rules:\n- If the answer is numeric, provide digits only (no currency symbols, punctuation, or words).\n- If the answer is textual, provide the exact string with no surrounding quotes or extra punctuation.\n\nExamples:\n- Correct: 95000\n- Correct: Engineering\n- Incorrect: 'Mary Johnson earns 95000 per year.'\n```\n\n\n[[ ## completed ## ]]\nEnd of description." - ], - "usage": {}, - "cost": 0.00145345, - "timestamp": "2025-10-10T21:02:18.809231", - "uuid": "4fc3542b-c13f-48da-b1ac-a6075c4ceab5", - "model": "openai/gpt-5-nano", - "response_model": "gpt-5-nano-2025-08-07", - "model_type": "chat" - } - ] -} \ No newline at end of file From 4df9ce568ad5c2494b5b4ed976d41cd5722ba719 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 16:58:18 -0700 Subject: [PATCH 12/19] docs(gepa): add tool-specific reflection prompt and metric example - Add GenerateImprovedToolDescriptionFromFeedback signature documentation - Include tool-aware metric example showing trajectory access - Document tool prefix annotation in feedback - Note component_selector applies to both signatures and tools - Fix 'fundamentally' language per reviewer feedback --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 67 ++++++++++++++++++- 1 file changed, 66 insertions(+), 1 deletion(-) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 0b25d3d182..f9a19d619e 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -450,7 +450,72 @@ gepa = dspy.GEPA( The `optimize_tool_descriptions` parameter enables GEPA to optimize tool descriptions in addition to signature instructions. This is particularly valuable for ReAct agents and other tool-using systems, where the quality of tool descriptions directly impacts the agent's ability to select appropriate tools for each task. -Unlike signature instructions that guide reasoning strategies, tool descriptions serve a fundamentally different purpose: they help agents decide **which tool to use** in a given situation. GEPA recognizes this categorical difference and applies a specialized reflection prompt tailored for tool selection decisions. +Unlike signature instructions that guide reasoning strategies, tool descriptions serve a different purpose: they help agents decide **which tool to use** in a given situation. GEPA applies a specialized reflection prompt tailored for tool selection decisions. + +### Tool-Specific Reflection Prompt + +GEPA uses a dedicated prompt for optimizing tool descriptions. The prompt receives the complete ReAct trajectory (all thoughts, actions, observations) from executions that used the tool being optimized: + +```python +class GenerateImprovedToolDescriptionFromFeedback(dspy.Signature): + """You are refining a tool description that the assistant currently uses. + + Review the current description along with examples of the assistant's tool decisions + and the feedback those decisions received. + + Read them together and refine the description. + So the agent understands when this tool actually helps, what argument or result matters, + and what misuse the feedback exposed. Keep the tool's voice and only change what the + evidence justifies. + + Return a refined description that helps the assistant quickly recognize good + opportunities for the tool.""" + + current_tool_description = dspy.InputField(desc="The current description of the tool") + examples_with_feedback = dspy.InputField( + desc="Examples showing tool usage decisions and feedback on correctness" + ) + + improved_tool_description = dspy.OutputField( + desc="An improved description that guides correct tool selection and usage" + ) +``` + +The `examples_with_feedback` contains full ReAct trajectories showing the complete context in which each tool was selected and used, enabling the reflection LM to understand tool selection patterns. + +**Example: Writing Tool-Aware Metrics** + +To provide effective feedback for tool optimization, write metrics that examine the trajectory: + +```python +def tool_feedback_metric(example, prediction, trace=None, pred_name=None, pred_trace=None): + """Metric that provides tool-specific feedback for GEPA optimization.""" + correct = prediction.answer == example.answer + score = 1.0 if correct else 0.0 + + # Generate tool-specific feedback if available + if hasattr(prediction, 'trajectory'): + tools_used = [ + prediction.trajectory[key] + for key in prediction.trajectory + if key.startswith('tool_name_') and prediction.trajectory[key] != 'finish' + ] + feedback = f"{'Correct' if correct else 'Wrong'}. Tools: {', '.join(tools_used)}" + else: + feedback = "Correct" if correct else "Wrong" + + return dspy.Prediction(score=score, feedback=feedback) +``` + +This produces feedback like: +``` +[Tool 'calculator' from 'agent'] Correct. Tools: calculator +[Tool 'search' from 'agent'] Wrong. Tools: search, calculator +``` + +The tool-specific prefix `[Tool 'calculator' from 'agent']` is automatically added by GEPA to focus the reflection LM on optimizing that particular tool's description. + +**Note:** Tool descriptions are treated as components in GEPA's optimization process. The `component_selector` parameter applies to both signature instructions and tool descriptions. For example, `component_selector="all"` optimizes all signatures and tools together, while `component_selector="round_robin"` cycles through them one at a time. ### Default Behavior From 4296ccf540b9dc9279ba16cb5b257adb049ab704 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 17:22:07 -0700 Subject: [PATCH 13/19] docs(gepa): fix implementation details with accurate code flow - Separate Pass 1 (predictor examples) and Pass 2 (tool aggregation) - Clarify Generated Outputs includes full trajectory for ReAct - Fix feedback annotation format to [Tool 'name' from 'predictor_key'] - Add Component Identification & Proposer Routing section - Explain dual-proposer independence (custom proposer doesn't affect tool proposer) - Use consistent terminology: 'predictor' and 'signature instructions' --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 46 +++++++++++++------ 1 file changed, 31 insertions(+), 15 deletions(-) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index f9a19d619e..08686d2534 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -557,25 +557,41 @@ When enabled, GEPA: **Reflective Dataset Construction:** -GEPA's approach to tool optimization is elegantly simple: +GEPA constructs the reflective dataset for tool optimization in two passes: -1. **ReAct predictors** generate reflective examples containing: - - Inputs: `question`, `trajectory` (full agent execution trace with thoughts, tool calls, observations) - - Generated Outputs: Agent's next action/tool selection decisions - - Feedback: Task outcome and evaluation from the metric +**Pass 1: Build reflective examples for predictors (used by instruction proposer)** -2. **Tools copy ReAct's data** with annotation: - - Each tool receives ReAct's complete reflective examples (same full trajectory context) - - Feedback is prefixed: `[Optimizing tool: 'tool_name'] {original_feedback}` - - This focuses the reflection LM on improving that specific tool's description +For each predictor (including ReAct modules), GEPA creates reflective examples containing: +- **Inputs**: The predictor's input fields (e.g., `{"question": "..."}`) +- **Generated Outputs**: ALL of the predictor's output fields converted to strings + - For ReAct: This includes both `answer` AND `trajectory` fields + - The trajectory contains the complete execution trace with all thoughts, actions, and observations +- **Feedback**: Text feedback returned by your metric function -3. **Reflection LM sees full context**: - - How the agent reasoned before selecting the tool - - What other tools were available and considered - - Whether the tool selection was successful - - Full multi-step trajectories showing tool composition patterns +These examples are used by the instruction proposer to optimize signature instructions. -This design allows the reflection LM to understand tool usage in context, leading to descriptions that clarify when and how each tool should be used +**Pass 2: Copy reflective examples to tools with annotation (used by tool proposer)** + +For each tool being optimized, GEPA: +- Identifies ALL ReAct predictors (across all nested modules) that have this tool in their toolset +- Takes ALL reflective examples from those predictors and makes a deep copy for the tool +- Annotates the feedback: `[Tool 'tool_name' from 'predictor_key'] {original_feedback}` +- If multiple ReAct modules use the same tool, their reflective examples are aggregated together + +These annotated examples are used by the tool proposer (with the tool-specific reflection prompt shown above) to optimize tool descriptions. + +This means: +- A tool receives the FULL ReAct trajectory (thoughts, actions, observations) in the "Generated Outputs" field +- The metric can optionally examine the trajectory and include tool-specific insights in the feedback text +- The reflection LM sees complete context about how and when the tool was used + +**Component Identification & Proposer Routing:** + +Tools are identified with a `tool:` prefix (e.g., `tool:calculator`). GEPA uses independent proposers: +- **Signature instructions** → Custom instruction proposer (if provided) OR default GEPA proposer +- **Tool descriptions** (prefixed with `tool:`) → Built-in `ToolProposer` (always used, not customizable) + +The custom instruction proposer affects ONLY signature instructions. Tools always use the built-in `ToolProposer`, regardless of whether you provide a custom instruction proposer. ### Usage Examples From ea1204a9a2c630f313db7fef7b49e7b72c577c50 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 17:22:51 -0700 Subject: [PATCH 14/19] docs(gepa): remove backward compatibility note Per reviewer feedback, backward compatibility should be implicit --- docs/docs/api/optimizers/GEPA/GEPA_Advanced.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 08686d2534..8efffc9bd8 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -718,5 +718,3 @@ gepa = dspy.GEPA( auto="medium" ) ``` - -**Note:** Tool optimization is fully backward compatible. Existing programs without tools, or with `optimize_tool_descriptions=False`, continue to work exactly as before. From 48d5cd61196146d35a5d2d5e0ee1b44eca9db807 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 17:28:44 -0700 Subject: [PATCH 15/19] docs(gepa): improve usage examples with optimization visualization - Add component_selector='all' to optimize all components together - Show how to view optimized tool descriptions - Add example output demonstrating improvement from vague to specific descriptions - Remove unnecessary comments for cleaner examples --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 64 +++++++++++-------- 1 file changed, 36 insertions(+), 28 deletions(-) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 8efffc9bd8..667a3f1b4f 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -601,80 +601,88 @@ The custom instruction proposer affects ONLY signature instructions. Tools alway import dspy def search_web(query: str) -> str: - """Search the web for information.""" - # Implementation here - return search_results + return f"Search results for: {query}" def calculate(expression: str) -> float: - """Evaluate a mathematical expression.""" - # Implementation here - return result + return eval(expression) -# Create ReAct agent with tools -search_tool = dspy.Tool(search_web, name="search", desc="Search the web") -calc_tool = dspy.Tool(calculate, name="calculator", desc="Do math") +# Create ReAct agent with tools (poor initial descriptions) +search_tool = dspy.Tool(search_web, name="search", desc="Finds things") +calc_tool = dspy.Tool(calculate, name="calculator", desc="Does calculations") agent = dspy.ReAct("question -> answer", tools=[search_tool, calc_tool]) # Enable tool optimization gepa = dspy.GEPA( metric=my_metric, - reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), - optimize_tool_descriptions=True, # Enable tool optimization + reflection_lm=dspy.LM(model="gpt-5-mini"), + optimize_tool_descriptions=True, + component_selector="all", # Optimize all components together auto="medium" ) optimized_agent = gepa.compile(agent, trainset=train_examples, valset=val_examples) + +# View optimized tool descriptions +print("Optimized search tool:", optimized_agent.tools["search"].desc) +print("Optimized calculator tool:", optimized_agent.tools["calculator"].desc) +``` + +**Example output after optimization:** +``` +Optimized search tool: Use when you need to find current information, facts, or data + from external sources. Provide specific search queries to get relevant results. + +Optimized calculator tool: Use for arithmetic operations and mathematical expressions. + Accepts Python-compatible expressions with numbers and operators (+, -, *, /, **). + Do not use for date calculations or string manipulations. ``` #### Multi-Agent System -For systems with nested agents, GEPA automatically discovers and optimizes all tools: +GEPA automatically discovers and optimizes tools in nested agents: ```python import dspy def search_web(query: str) -> str: - """Search the web.""" - # Implementation here - return results + return f"Search results for: {query}" def calculate(expression: str) -> float: - """Evaluate math expression.""" - # Implementation here - return result + return eval(expression) -# Define tools -search_tool = dspy.Tool(search_web, name="search", desc="Searches web") -calc_tool = dspy.Tool(calculate, name="calculator", desc="Does math") +search_tool = dspy.Tool(search_web, name="search", desc="Searches") +calc_tool = dspy.Tool(calculate, name="calculator", desc="Computes") class ResearchAssistant(dspy.Module): def __init__(self): super().__init__() - # Sub-agent with search tool self.researcher = dspy.ReAct("query -> findings", tools=[search_tool]) - # Delegation tool wraps sub-agent def delegate_research(query: str) -> str: return self.researcher(query=query).findings - research_tool = dspy.Tool(delegate_research, name="research", desc="Research things") - - # Main agent with calculator and research delegation + research_tool = dspy.Tool(delegate_research, name="research", desc="Helps with questions") self.assistant = dspy.ReAct("question -> answer", tools=[research_tool, calc_tool]) def forward(self, question): return self.assistant(question=question) -# GEPA optimizes ALL tools (calculator, research, search) together +# Optimizes ALL tools: calculator, research, search gepa = dspy.GEPA( metric=my_metric, - reflection_lm=dspy.LM(model="gpt-5", temperature=1.0, max_tokens=32000, api_key=api_key), + reflection_lm=dspy.LM(model="gpt-5-mini"), optimize_tool_descriptions=True, + component_selector="all", auto="medium" ) optimized_system = gepa.compile(ResearchAssistant(), trainset=train, valset=val) + +# View optimized nested tool descriptions +print(optimized_system.researcher.tools["search"].desc) +print(optimized_system.assistant.tools["research"].desc) +print(optimized_system.assistant.tools["calculator"].desc) ``` ### Inspecting Optimized Tool Descriptions From 548d9b68055ead82e489631293d766ac059226dd Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 17:46:25 -0700 Subject: [PATCH 16/19] docs(gepa): add design rationale comments for tool context sharing - Document why full ReAct trajectory is shared with all tools - Explain rationale: tool interdependencies, selection patterns, workflow context - Add concrete example of optimization benefit - Describe alternative considered (tool-specific filtering) and rejection reasoning - Add future work section on joint tool optimization - Present two architectural approaches: separate proposer vs extending ReAct proposer - Include implementation details, benefits, challenges, and decision rationale --- dspy/teleprompt/gepa/gepa_utils.py | 84 ++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index 4bd594628a..d64b28115e 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -379,6 +379,26 @@ def make_reflective_dataset( tool_examples = defaultdict(list) if self.optimize_tool_descriptions: + # Design Decision: Full ReAct Trajectory Sharing for Tools + # + # Each tool receives the COMPLETE ReAct trajectory (all thoughts, actions, observations) + # rather than only the segments where that tool was used. This trades token efficiency + # for richer optimization context. + # + # Rationale: + # 1. Tools are interdependent: search results inform calculator usage, API responses + # guide follow-up queries. Full trajectory shows these dependencies. + # 2. Reflection LM needs context to understand tool SELECTION patterns: + # - Why did the agent choose this tool over alternatives? + # - When in the reasoning process is this tool most useful? + # - What prior information typically triggers this tool's usage? + # 3. Goal is descriptions that guide "when to use" not just "what it does" + # + # Trade-offs: + # - Cost: N tools = N copies of same trajectory (5 tools = 5x duplication) + # - Benefit: Descriptions capture tool's role in multi-step workflows + # Example: "Use after search when numerical analysis is needed" vs "Does math" + # for module_path, sub_module in program.named_sub_modules(): # Walk each sub-module to locate its tools and remember the predictor scope # so we can share those reflections with the tool descriptions below @@ -396,6 +416,7 @@ def make_reflective_dataset( if not reflections: continue + # Share the FULL ReAct trajectory with each tool for tool_name, _ in tool_entries: tool_key = f"tool:{tool_name}" for item in reflections: @@ -411,6 +432,69 @@ def make_reflective_dataset( return ret_d + # Future Work: Joint Tool Optimization with ReAct for Token Efficiency + # =========================================================== + # Current approach duplicates the same trajectory N times for N tools in a ReAct module. + # For multi-tool agents, we could optimize all tools simultaneously to reduce token usage. + # + # Assumption: + # - ReAct module is the only module that uses the tools + # - When optimizing tool descriptions of ReAct, reflection LM would capture general pattern of tools and ReAct's decision making process + # - It's probably better to holistically optimize all tools and ReAct together + + # Proposed Architecture: + # 1. During reflective dataset construction, group tools by their parent ReAct module: + # - Walk program.named_sub_modules() to find ReAct predictors + # - Extract tools from each ReAct module via getattr(module, "tools", None) + # - Build mapping: {module_path: [tool_name1, tool_name2, ...]} + # - Detect when a module has multiple tools + # + # 2. For multi-tool ReAct modules, choose architectural approach: + # + # Option A: Separate tool-specific proposer signature + # - Create custom signature extending GenerateImprovedToolDescriptionFromFeedback + # - Use dspy.Signature.append_field() to add one output field per tool + # - Example: For 3 tools, add fields "improved_search_desc", "improved_calc_desc", "improved_api_desc" + # - Pro: Clean separation between instruction and tool optimization + # - Con: Separate LM call from ReAct instruction optimization + # + # Option B: Extend ReAct instruction proposer directly + # - Append tool description fields to existing ReAct instruction proposer + # - Update proposer instructions/docstring to include tool optimization guidance + # - Use dspy.Signature's helper functions to add output fields for each tool + # - Aggregate all tools' input/output fields expected to be updated from that ReAct module + # - Pro: Single LM call optimizes ReAct instructions AND tool descriptions together + # - Pro: Reflection LM sees relationship between instructions and tools holistically + # - Con: More complex signature modification, harder to maintain separation of concerns + # + # 3. Pass the ReAct trajectory ONCE to generate all tool descriptions and ReAct instruction simultaneously: + # - Single LM call with multi-field output instead of N separate calls + # - Proposer prompt instructs LM to consider tool interactions + # + # 4. Parse the multi-field output and update each tool's description: + # - Extract each field from the prediction + # - Map back to tool names using the grouping from step 1 + # - Handle parsing errors with fallback to current one-at-a-time approach + # + # Benefits: + # - Eliminates trajectory duplication: 1x token cost instead of Nx + # - Reflection LM sees all tools holistically, can coordinate descriptions + # - Tool descriptions can complement each other ("use search before calculator") + # - Scales better for agents with 10+ tools + # + # Challenges: + # - Signature modification at runtime requires careful field naming/parsing + # - More output fields → higher chance of LM parsing errors + # - Need robust fallback when multi-field output fails + # - Requires refactoring GEPA's "one component at a time" architecture + # - Tool proposer prompt becomes more complex with multiple tools + # + # Implementation Notes: + # - Start with simple case: all tools from one ReAct module + # - Add retry logic for malformed multi-field outputs + # - Consider hybrid approach: joint optimization for <5 tools, separate for more + # - May need different proposer prompt template for joint vs. individual optimization + # TODO: The current DSPyAdapter implementation uses the GEPA default propose_new_texts. # We can potentially override this, to use the instruction proposal similar to MIPROv2. From e61d0a10b77cea44ae73a1eef4428d42d10c5417 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 17:53:19 -0700 Subject: [PATCH 17/19] docs(gepa): add tool optimization links to overview and parameter docs - Add Tool Description Optimization section to GEPA overview.md with link to advanced guide - Add documentation link to optimize_tool_descriptions parameter in gepa.py - Addresses reviewer feedback to make tool optimization more discoverable --- docs/docs/api/optimizers/GEPA/overview.md | 6 ++++++ dspy/teleprompt/gepa/gepa.py | 4 +++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/docs/api/optimizers/GEPA/overview.md b/docs/docs/api/optimizers/GEPA/overview.md index 0125702bea..b830ee3985 100644 --- a/docs/docs/api/optimizers/GEPA/overview.md +++ b/docs/docs/api/optimizers/GEPA/overview.md @@ -117,6 +117,12 @@ Practical Recipe for GEPA-Friendly Feedback: - **Multi-Objective Tasks** (e.g., PUPA): Decompose aggregate scores to reveal contributions from each objective, highlighting tradeoffs (e.g., quality vs. privacy). - **Stacked Pipelines** (e.g., code generation: parse → compile → run → profile → evaluate): Expose stage-specific failures; natural-language traces often suffice for LLM self-correction. +## Tool Description Optimization + +GEPA can optimize tool descriptions for ReAct agents. When `optimize_tool_descriptions=True`, GEPA discovers all tools in your program (including nested multi-agent systems) and applies a specialized reflection prompt to improve how tools are described. This helps agents make better tool selection decisions by learning from execution traces which tools work well in which contexts. + +For details on how tool optimization works, when to use it, and usage examples, see [Tool Description Optimization](GEPA_Advanced.md#tool-description-optimization) in the Advanced Features guide. + ## Custom Instruction Proposal For advanced customization of GEPA's instruction proposal mechanism, including custom instruction proposers and component selectors, see [Advanced Features](GEPA_Advanced.md). diff --git a/dspy/teleprompt/gepa/gepa.py b/dspy/teleprompt/gepa/gepa.py index e19188f757..e4c4d40862 100644 --- a/dspy/teleprompt/gepa/gepa.py +++ b/dspy/teleprompt/gepa/gepa.py @@ -275,7 +275,9 @@ def metric( raised if a mismatch in module-level and predictor-level score is detected. optimize_tool_descriptions: Whether to optimize tool descriptions for modules with tools (e.g., ReAct agents). When enabled, tool descriptions are included in the optimization - process alongside signature instructions. Default is False. + process alongside signature instructions. See the + [Tool Description Optimization guide](https://dspy.ai/api/optimizers/GEPA/GEPA_Advanced/#tool-description-optimization) + for details on when to use this feature and how it works. Default is False. seed: The random seed to use for reproducibility. Default is 0. gepa_kwargs: (Optional) provide additional kwargs to be passed to [gepa.optimize](https://github.com/gepa-ai/gepa/blob/main/src/gepa/api.py) method From 5c9541234a40ea5c1db0e1d208c5bb5033b7fc9f Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 18:15:31 -0700 Subject: [PATCH 18/19] docs(gepa): refine tool optimization scenarios and remove implementation details - Restructure 'When to Use' as numbered list (1-5) per reviewer feedback - Move section after implementation details for better flow - Remove tool: prefix implementation detail from component identification - Explain tool discovery via ReAct modules in user-friendly terms - Add custom proposer compatibility clarification - Address optional PR feedback items (11 & 13) --- .../docs/api/optimizers/GEPA/GEPA_Advanced.md | 47 ++++++++++++------- 1 file changed, 31 insertions(+), 16 deletions(-) diff --git a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md index 667a3f1b4f..da4b4b8157 100644 --- a/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md +++ b/docs/docs/api/optimizers/GEPA/GEPA_Advanced.md @@ -532,16 +532,6 @@ gepa = dspy.GEPA( optimized_program = gepa.compile(student, trainset=examples) ``` -### When to Use optimize_tool_descriptions - -Consider enabling `optimize_tool_descriptions=True` when: - -- **Building ReAct agents**: ReAct agents rely on tool descriptions to make action selection decisions -- **Multi-agent systems**: Systems with nested agents and delegated tools benefit from holistic optimization -- **Poor tool selection**: Your agent frequently selects wrong tools or overlooks appropriate ones -- **Complex tool sets**: When managing many tools with overlapping capabilities -- **Domain-specific tools**: Tools requiring specialized knowledge or context for proper usage - ### How It Works When enabled, GEPA: @@ -587,11 +577,27 @@ This means: **Component Identification & Proposer Routing:** -Tools are identified with a `tool:` prefix (e.g., `tool:calculator`). GEPA uses independent proposers: +GEPA discovers tools by traversing ReAct modules and extracting their associated `dspy.Tool` instances. Once identified, GEPA routes components to appropriate proposers: - **Signature instructions** → Custom instruction proposer (if provided) OR default GEPA proposer -- **Tool descriptions** (prefixed with `tool:`) → Built-in `ToolProposer` (always used, not customizable) +- **Tool descriptions** → Built-in `ToolProposer` (always used, not customizable) + +The custom instruction proposer affects ONLY signature instructions. Tools always use the specialized `ToolProposer` with the tool-specific reflection prompt, regardless of whether you provide a custom instruction proposer. + +### When to Use optimize_tool_descriptions + +Enable `optimize_tool_descriptions=True` when you use `dspy.Tool` in your program and need better tool selection. Here are common scenarios: -The custom instruction proposer affects ONLY signature instructions. Tools always use the built-in `ToolProposer`, regardless of whether you provide a custom instruction proposer. +1. **ReAct agents with multiple tools** - Agent with `search` and `calculator` tools keeps searching when it should calculate, or vice versa. GEPA learns from execution feedback to clarify "use search for factual queries, calculator for numerical analysis." + +2. **Multi-agent systems with delegation** - Parent agent has delegation tools to specialized sub-agents but doesn't understand when to use each. GEPA optimizes both delegation tools and sub-agent internal tools holistically. + +3. **Sequential tool workflows** - Tools like `query_database` → `analyze_results` have dependencies but descriptions don't capture this. GEPA learns the sequence and timing from successful executions. + +4. **Domain-specific tools** - Tools like legal vs. medical document search have overlapping but domain-specific purposes. GEPA discovers usage patterns and adds context: "for legal precedents" vs. "for patient records." + +5. **Tools with limitations** - Initial description "Does calculations" is too vague. GEPA adds specificity from observed usage: "Use for arithmetic (+, -, *, /, **). Not for date math or string operations." + +See the usage examples below for implementations of scenarios 1 and 2. ### Usage Examples @@ -710,10 +716,19 @@ print(optimized_system.assistant.tools["calculator"].desc) ### Compatibility with Custom Instruction Proposers -Tool optimization works seamlessly with custom instruction proposers. When both are provided: +Tool optimization works seamlessly with custom instruction proposers. When you provide a custom instruction proposer AND enable `optimize_tool_descriptions=True`: + +**Component routing:** +- **Signature instructions** → Your custom instruction proposer +- **Tool descriptions** → Built-in `ToolProposer` with specialized tool reflection prompt + +**Key points:** +- Both operate independently during the same GEPA run +- Tools receive domain-appropriate optimization guidance (tool selection patterns, usage context) +- Signatures use your custom logic (task-specific reasoning, formatting, etc.) +- The built-in tool proposer is not customizable - it always uses `GenerateImprovedToolDescriptionFromFeedback` -- Signature instructions → Custom instruction proposer -- Tool descriptions → Built-in `ToolProposer` +This separation ensures tools and signatures get appropriate optimization strategies without interference. ```python from dspy.teleprompt.gepa.instruction_proposal import MultiModalInstructionProposer From 19d7717499cb706b739244b51761a64d3cee1ed0 Mon Sep 17 00:00:00 2001 From: Juyoung Lee Date: Sun, 12 Oct 2025 20:20:23 -0700 Subject: [PATCH 19/19] docs(gepa): clarify future work section in code comments - Add note that proposed architecture details may change - Expand challenges with counterpoints and questions - Mark implementation notes as optional to avoid overengineering --- dspy/teleprompt/gepa/gepa_utils.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/dspy/teleprompt/gepa/gepa_utils.py b/dspy/teleprompt/gepa/gepa_utils.py index d64b28115e..6e6eaf4a7d 100644 --- a/dspy/teleprompt/gepa/gepa_utils.py +++ b/dspy/teleprompt/gepa/gepa_utils.py @@ -442,7 +442,7 @@ def make_reflective_dataset( # - When optimizing tool descriptions of ReAct, reflection LM would capture general pattern of tools and ReAct's decision making process # - It's probably better to holistically optimize all tools and ReAct together - # Proposed Architecture: + # Proposed Architecture (Exact details may change): # 1. During reflective dataset construction, group tools by their parent ReAct module: # - Walk program.named_sub_modules() to find ReAct predictors # - Extract tools from each ReAct module via getattr(module, "tools", None) @@ -483,13 +483,12 @@ def make_reflective_dataset( # - Scales better for agents with 10+ tools # # Challenges: - # - Signature modification at runtime requires careful field naming/parsing - # - More output fields → higher chance of LM parsing errors - # - Need robust fallback when multi-field output fails - # - Requires refactoring GEPA's "one component at a time" architecture - # - Tool proposer prompt becomes more complex with multiple tools + # - Signature modification at runtime may require careful field naming/parsing + # - More output fields → higher chance of LM parsing errors (but user will likely to use powerful LMs for ReAct + tools prompts optimization) + # - Need robust fallback when multi-field output fails (DSPy natively implemented fallback logic for this?) + # - Requires refactoring GEPA's "one component at a time" architecture (but we can treat ReAct + tools as "one component") # - # Implementation Notes: + # Implementation Notes (Ignore if it's too overengineering): # - Start with simple case: all tools from one ReAct module # - Add retry logic for malformed multi-field outputs # - Consider hybrid approach: joint optimization for <5 tools, separate for more