Operate the whole computer like a human. Work across desktop apps and the command line for dozens of hours.
No state drift. Verifiable progress. Complex tasks carried through to completion.
Usage · What You Get · How It Works · Results · Project Website · 简体中文
The model determines what an agent can do in one round. LongHorizon-Harness determines whether that work can be verified, preserved, and continued until the task is actually complete.
Works with Claude Code, Codex, and OpenClaw. One-command install, ready to run.
LongHorizon-Harness is an execution, state-management, and result-verification system for long-horizon tasks. It does not train a new model or replace an existing agent. It runs on top of systems such as Codex and Claude Code, helping agents operate autonomously in real computer environments for extended periods and continuously move complex tasks forward.
promotional_video_1440p.mp4
Open the promotional video (1440p MP4)
LongHorizon-Harness separates planning, execution, and verification so that one growing context is not responsible for everything.
| Role | One responsibility | |
|---|---|---|
| 🧭 | Manager | Maintains the original goal, verified progress, and next step |
| ⚡ | Executor | Starts each round with a fresh context and focuses on one clearly defined task |
| 🔍 | Auditor | Independently inspects files, interfaces, logs, and tests in the real environment |
Only results that pass independent verification enter persistent task state. Even when the context is refreshed, an action fails, or a deliverable does not pass inspection, the system retains previously verified progress and continues from what remains.
LongHorizon-Harness supports both GUI and CLI workflows.
| 🖥️ Operate the desktop | ⌨️ Work in the terminal |
|---|---|
| 🌐 Click, type, scroll, and browse | 💻 Write and modify code |
| 📊 Operate spreadsheets | |
| 📄 Edit documents | 📦 Install dependencies and environments |
| 🎨 Use design software | 🔧 Configure and debug systems |
| 🧊 Operate 3D tools | 📁 Process files and data |
One task can begin in a browser, move to the command line for data processing, continue in desktop software to produce an artifact, and return to the terminal for validation or debugging. The goal, progress, and evidence remain under the same state-management system throughout.
🖱️ Connect a computer-use MCP server
GUI interaction is supplied through a compatible external computer-use MCP server. LongHorizon-Harness does not bundle or enable a specific computer-use implementation by default.
lh-harness run --task @task.md --agent claude_code \
--mcp-config /path/to/your/mcp.json \
--mcp-add-dir /path/to/your/mcp/filesYou can also use LH_HARNESS_CLAUDECODE_MCP_CONFIG and LH_HARNESS_CLAUDECODE_ADD_DIRS. When no configuration is supplied, the Claude Code adapter does not add MCP arguments.
LongHorizon-Harness is not tied to a specific model or agent backend. Existing models and agents connect through configuration without changing their original workflows.
| Layer | Supported choices | |
|---|---|---|
| 🧠 | Models | Claude, GPT, Qwen, and other models exposed by an agent backend |
| 🤖 | Agent backends | Claude Code, Codex CLI, OpenClaw, and custom AgentAdapter implementations |
| 🎛️ | Role assignment | The Manager, Executor, and Auditor can each use a different model or backend |
| 🖥️ | Execution environments | Local, ssh://user@host:port, and docker://container |
A lightweight AgentAdapter preserves each agent's native execution loop while LongHorizon-Harness coordinates role boundaries, verified task state, and cross-round progress around it.
Use one model for all three roles, or combine different models and backends to balance quality, speed, and cost.
LongHorizon-Harness is not demonstrated only on a handful of carefully selected success cases.
We ran it on hundreds of complex tasks across GUI, CLI, and mixed computer environments:
| Task domain | What the tasks involve |
|---|---|
| 🌐 Web Frontend | Developing, fixing, and validating websites and web applications through browser interaction, developer tools, and code changes |
| 📊 Data Analysis & Visualization | Processing data, producing charts and dashboards, and checking analytical results and visual deliverables |
| 🛠️ Operations & Debugging | Investigating logs, networks, performance, and service failures; configuring, diagnosing, and repairing systems |
| 🎨 Design & Image Processing | Editing visual assets, matching design references, processing images, and verifying final visual quality |
| 🎮 Games & Interaction | Building, operating, and debugging games or interactive applications; checking interaction logic and runtime behavior |
| 📄 Documents & Presentations | Editing documents and slide decks, including content, formatting, references, layout, and final delivery |
| 🧊 Spatial Reasoning | Completing tasks involving spatial relationships, geometry, precise placement, and 3D operations |
| 🖥️ Desktop & System Settings | Operating desktop applications, files, and system settings across multi-application workflows |
| 🔬 Research & Education | Completing literature research, coursework, teaching materials, forms, and research-support workflows |
| 🎬 Creative Production | Producing presentations, video, audio, and other media while coordinating assets across tools |
| ⚙️ Engineering & Computing | Using CAD, EDA, scientific software, development tools, and cloud or DevOps toolchains |
| 🎫 Personal Services | Handling event ticketing, everyday services, games, and visual-search workflows |
| 🏛️ Administration & Compliance | Completing office, legal, policy-sensitive form, institutional, and safety-aware submission workflows |
| 💼 Business & Finance | Handling market analysis, procurement, loans, sales, reimbursements, and cross-application enterprise workflows |
| 🏥 Healthcare | Completing medical quality-control, insurance, immunization, and structured health-form workflows |
|
GUI + CLI completion WeaveBench |
Full desktop-task completion OSWorld 2.0 |
Code + CLI success Terminal-Bench 2.1 · 24% fewer tokens |
📊 Full benchmark results and experimental settings
| Benchmark | Metric | Claude Code | LongHorizon-Harness | Gain |
|---|---|---|---|---|
| WeaveBench (114 tasks) | PassRate | 51.8 | 80.7 | +28.9 |
| WeaveBench | Overall | 0.702 | 0.835 | +0.133 |
| OSWorld 2.0 (108 tasks) | Binary | 2.8 | 8.3 | 3.0× |
| OSWorld 2.0 | Partial | 21.5 | 35.2 | +13.7 |
| Terminal-Bench 2.1 | Success rate | 69.7 | 77.2 | +7.5 |
All rows use Qwen 3.7-Plus as the backbone and Claude Code as the execution backend.
Full result tables and case trajectories are available on the LongHorizon-Harness project website.
Install LongHorizon-Harness:
uv tool install lh-harnessLongHorizon-Harness requires Python 3.10+ and at least one agent runtime: claude, codex, or openclaw.
Run a task:
lh-harness run \
--task "Inspect the current directory and summarize its files."Run a longer task from a file and open the Dashboard:
lh-harness run --task @task.md --dashboardThe Dashboard shows every round's plan, execution result, audit evidence, and reason for rework. It also provides human gates when a task completes, becomes blocked, needs input, or fails repeatedly.
| 📋 Plan | ⚡ Execution | 🔍 Audit | ♻️ Rework |
|---|---|---|---|
| What happens next | What the agent did | What the environment proves | Why another round is needed |
Every run is stored in an isolated runs/<run-id>/ directory. The complete task state and audit trail make the agent's progress inspectable, recoverable, and reproducible.
| Run record | What it preserves |
|---|---|
| 📋 Task state | Original goal, requirements, verified progress, and remaining work |
| 🧾 Event stream | What happened throughout the run |
| 🔍 Audit reports | Evidence and acceptance decisions for every round |
| 🧠 Role trajectories | Manager, Executor, and Auditor inputs and outputs |
| 📁 Workspace | Files and artifacts produced during execution |
| ✅ Final report | The verified outcome of the task |
⚙️ Installation alternatives and common CLI options
Install with pip:
pip install lh-harnessDashboard commands:
lh-harness run --task @task.md --dashboard # Monitor a live run
lh-harness dashboard --runs-root ./runs # Browse completed and active runs| Option | Description |
|---|---|
--task |
Task text or @task.md |
--agent |
claude_code, codex, or openclaw |
--env |
local, ssh://..., or docker://... |
--max-rounds |
Maximum number of Manage-Execute-Audit rounds; the CLI default is 30 |
--dashboard |
Start live monitoring and human intervention |
eval/ provides frozen reproduction suites for two benchmarks:
| Directory | Benchmark | Description |
|---|---|---|
eval/WeaveBench-harness/ |
WeaveBench (114 tasks) | Hybrid GUI+CLI tasks and a reproduction skill |
eval/OSWorldv2-harness/ |
OSWorld-V2 (108 tasks) | Hybrid runner aligned with the official release |
See each directory's README.md or README.zh-CN.md for environment setup, parameters, and launch commands. The nested cua_harness packages are frozen compatibility copies used for evaluation; new integrations should use src/lh_harness/.
@article{longhorizonharness2026,
title={LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks},
author={Ziyu Ma and Hailang Huang and Shun Zou and Yong Wang and Shidong Yang and Yiming Hu and Fei Wei and XiangXiang Chu},
journal={arXiv preprint arXiv:2608.01964},
year = {2026},
url = {https://arxiv.org/abs/2608.01964}
}Operate the whole computer. Preserve verified progress. Keep working until the task is done.