diff --git a/package-lock.json b/package-lock.json
index c7d6c33..421f5b3 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,24 +1,26 @@
 {
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+  "version": "2.3.0",
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   "packages": {
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@@ -1307,6 +1469,12 @@
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-        "form-data-encoder": "1.7.2",
-        "formdata-node": "^4.3.2",
-        "node-fetch": "^2.6.7"
-      },
-      "bin": {
-        "openai": "bin/cli"
-      },
-      "peerDependencies": {
-        "ws": "^8.18.0",
-        "zod": "^3.23.8"
-      },
-      "peerDependenciesMeta": {
-        "ws": {
-          "optional": true
-        },
-        "zod": {
-          "optional": true
-        }
-      }
-    },
-    "node_modules/openai/node_modules/@types/node": {
-      "version": "18.19.127",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.127.tgz",
-      "integrity": "sha512-gSjxjrnKXML/yo0BO099uPixMqfpJU0TKYjpfLU7TrtA2WWDki412Np/RSTPRil1saKBhvVVKzVx/p/6p94nVA==",
-      "license": "MIT",
-      "dependencies": {
-        "undici-types": "~5.26.4"
-      }
-    },
-    "node_modules/openai/node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
-      "license": "MIT"
-    },
     "node_modules/optionator": {
       "version": "0.9.4",
       "resolved": "https://registry.npmjs.org/optionator/-/optionator-0.9.4.tgz",
@@ -4607,6 +4457,16 @@
       ],
       "license": "MIT"
     },
+    "node_modules/react": {
+      "version": "19.2.4",
+      "resolved": "https://registry.npmjs.org/react/-/react-19.2.4.tgz",
+      "integrity": "sha512-9nfp2hYpCwOjAN+8TZFGhtWEwgvWHXqESH8qT89AT/lWklpLON22Lc8pEtnpsZz7VmawabSU0gCjnj8aC0euHQ==",
+      "license": "MIT",
+      "peer": true,
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
     "node_modules/readdirp": {
       "version": "4.1.2",
       "resolved": "https://registry.npmjs.org/readdirp/-/readdirp-4.1.2.tgz",
@@ -4752,6 +4612,12 @@
         "queue-microtask": "^1.2.2"
       }
     },
+    "node_modules/secure-json-parse": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/secure-json-parse/-/secure-json-parse-2.7.0.tgz",
+      "integrity": "sha512-6aU+Rwsezw7VR8/nyvKTx8QpWH9FrcYiXXlqC4z5d5XQBDRqtbfsRjnwGyqbi3gddNtWHuEk9OANUotL26qKUw==",
+      "license": "BSD-3-Clause"
+    },
     "node_modules/semver": {
       "version": "7.7.3",
       "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz",
@@ -5078,6 +4944,19 @@
         "node": ">=8"
       }
     },
+    "node_modules/swr": {
+      "version": "2.4.0",
+      "resolved": "https://registry.npmjs.org/swr/-/swr-2.4.0.tgz",
+      "integrity": "sha512-sUlC20T8EOt1pHmDiqueUWMmRRX03W7w5YxovWX7VR2KHEPCTMly85x05vpkP5i6Bu4h44ePSMD9Tc+G2MItFw==",
+      "license": "MIT",
+      "dependencies": {
+        "dequal": "^2.0.3",
+        "use-sync-external-store": "^1.6.0"
+      },
+      "peerDependencies": {
+        "react": "^16.11.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/tapable": {
       "version": "2.3.0",
       "resolved": "https://registry.npmjs.org/tapable/-/tapable-2.3.0.tgz",
@@ -5156,6 +5035,18 @@
         "node": ">=0.8"
       }
     },
+    "node_modules/throttleit": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/throttleit/-/throttleit-2.1.0.tgz",
+      "integrity": "sha512-nt6AMGKW1p/70DF/hGBdJB57B8Tspmbp5gfJ8ilhLnt7kkr2ye7hzD6NVG8GGErk2HWF34igrL2CXmNIkzKqKw==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
     "node_modules/tinybench": {
       "version": "2.9.0",
       "resolved": "https://registry.npmjs.org/tinybench/-/tinybench-2.9.0.tgz",
@@ -5260,12 +5151,6 @@
         "node": ">=8.0"
       }
     },
-    "node_modules/tr46": {
-      "version": "0.0.3",
-      "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz",
-      "integrity": "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw==",
-      "license": "MIT"
-    },
     "node_modules/tree-kill": {
       "version": "1.2.2",
       "resolved": "https://registry.npmjs.org/tree-kill/-/tree-kill-1.2.2.tgz",
@@ -5485,6 +5370,7 @@
       "version": "6.21.0",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
       "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
+      "dev": true,
       "license": "MIT"
     },
     "node_modules/unrs-resolver": {
@@ -5563,6 +5449,15 @@
         "punycode": "^2.1.0"
       }
     },
+    "node_modules/use-sync-external-store": {
+      "version": "1.6.0",
+      "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz",
+      "integrity": "sha512-Pp6GSwGP/NrPIrxVFAIkOQeyw8lFenOHijQWkUTrDvrF4ALqylP2C/KCkeS9dpUM3KvYRQhna5vt7IL95+ZQ9w==",
+      "license": "MIT",
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/vite": {
       "version": "5.4.21",
       "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz",
@@ -6142,31 +6037,6 @@
         }
       }
     },
-    "node_modules/web-streams-polyfill": {
-      "version": "4.0.0-beta.3",
-      "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-4.0.0-beta.3.tgz",
-      "integrity": "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug==",
-      "license": "MIT",
-      "engines": {
-        "node": ">= 14"
-      }
-    },
-    "node_modules/webidl-conversions": {
-      "version": "3.0.1",
-      "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",
-      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==",
-      "license": "BSD-2-Clause"
-    },
-    "node_modules/whatwg-url": {
-      "version": "5.0.0",
-      "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz",
-      "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==",
-      "license": "MIT",
-      "dependencies": {
-        "tr46": "~0.0.3",
-        "webidl-conversions": "^3.0.0"
-      }
-    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
@@ -6338,6 +6208,15 @@
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }
+    },
+    "node_modules/zod-to-json-schema": {
+      "version": "3.25.1",
+      "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.25.1.tgz",
+      "integrity": "sha512-pM/SU9d3YAggzi6MtR4h7ruuQlqKtad8e9S0fmxcMi+ueAK5Korys/aWcV9LIIHTVbj01NdzxcnXSN+O74ZIVA==",
+      "license": "ISC",
+      "peerDependencies": {
+        "zod": "^3.25 || ^4"
+      }
     }
   }
 }
diff --git a/package.json b/package.json
index ba1c5f6..2b6f7d2 100644
--- a/package.json
+++ b/package.json
@@ -54,16 +54,18 @@
     "node": ">=18.0.0"
   },
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.30.1",
-    "@google/generative-ai": "^0.24.1",
-    "@perplexity-ai/perplexity_ai": "^0.16.0",
+    "@ai-sdk/anthropic": "^1.0.0",
+    "@ai-sdk/azure": "^3.0.31",
+    "@ai-sdk/google": "^1.0.0",
+    "@ai-sdk/openai": "^1.0.0",
+    "@ai-sdk/perplexity": "^1.0.0",
     "@types/micromatch": "^4.0.9",
+    "ai": "^4.0.0",
     "chalk": "^5.3.0",
     "commander": "^12.0.0",
     "fast-glob": "^3.3.2",
     "fuzzball": "^2.2.3",
     "micromatch": "^4.0.5",
-    "openai": "^4.0.0",
     "smol-toml": "^1.6.0",
     "strip-ansi": "^7.1.0",
     "yaml": "^2.5.0",
@@ -86,4 +88,4 @@
     "typescript-eslint": "^8.46.1",
     "vitest": "^2.0.0"
   }
-}
\ No newline at end of file
+}
diff --git a/src/boundaries/api-client.ts b/src/boundaries/api-client.ts
deleted file mode 100644
index 7a4d98d..0000000
--- a/src/boundaries/api-client.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-import { 
-  OPENAI_RESPONSE_SCHEMA, 
-  ANTHROPIC_MESSAGE_SCHEMA,
-  type OpenAIResponse,
-  type AnthropicMessage 
-} from '../schemas/api-schemas';
-import { ValidationError, handleUnknownError } from '../errors/index';
-
-export function validateApiResponse(raw: unknown): OpenAIResponse {
-  try {
-    return OPENAI_RESPONSE_SCHEMA.parse(raw);
-  } catch (e: unknown) {
-    if (e instanceof Error && 'issues' in e) {
-      // Zod error
-      throw new ValidationError(`Invalid API response: ${e.message}`);
-    }
-    const err = handleUnknownError(e, 'API response validation');
-    throw new ValidationError(`API response validation failed: ${err.message}`);
-  }
-}
-
-export function validateAnthropicResponse(raw: unknown): AnthropicMessage {
-  try {
-    return ANTHROPIC_MESSAGE_SCHEMA.parse(raw);
-  } catch (e: unknown) {
-    if (e instanceof Error && 'issues' in e) {
-      // Zod error
-      throw new ValidationError(`Invalid Anthropic API response: ${e.message}`);
-    }
-    const err = handleUnknownError(e, 'Anthropic API response validation');
-    throw new ValidationError(`Anthropic API response validation failed: ${err.message}`);
-  }
-}
diff --git a/src/boundaries/index.ts b/src/boundaries/index.ts
index 467ffc1..c273c05 100644
--- a/src/boundaries/index.ts
+++ b/src/boundaries/index.ts
@@ -2,5 +2,4 @@
 export * from './cli-parser';
 export * from './config-loader';
 export * from './yaml-parser';
-export * from './api-client';
 export * from './env-parser';
diff --git a/src/cli/commands.ts b/src/cli/commands.ts
index 8629321..01309c7 100644
--- a/src/cli/commands.ts
+++ b/src/cli/commands.ts
@@ -26,6 +26,25 @@ const __filename = fileURLToPath(import.meta.url);
 // eslint-disable-next-line @typescript-eslint/naming-convention
 const __dirname = dirname(__filename);
 
+/**
+ * Resolves the presets directory for both dev and built modes.
+ * - Built mode: __dirname is `dist/`, so `../presets` resolves to project root `presets/`
+ * - Dev mode: __dirname is `src/cli/`, so `../../presets` resolves to project root `presets/`
+ */
+function resolvePresetsDir(dir: string): string {
+  const buildPath = path.resolve(dir, '../presets');
+  if (existsSync(path.join(buildPath, 'meta.json'))) {
+    return buildPath;
+  }
+  // Dev mode fallback: src/cli/ → ../../presets
+  const devPath = path.resolve(dir, '../../presets');
+  if (existsSync(path.join(devPath, 'meta.json'))) {
+    return devPath;
+  }
+
+  throw new Error(`Could not locate presets directory containing meta.json. Looked in ${buildPath} and ${devPath}`);
+}
+
 /*
  * Registers the main evaluation command with Commander.
  * This is the default command that runs content evaluations against target files.
@@ -117,7 +136,7 @@ export function registerMainCommand(program: Command): void {
 
       const prompts: PromptFile[] = [];
       try {
-        const presetsDir = path.resolve(__dirname, '../presets');
+        const presetsDir = resolvePresetsDir(__dirname);
         const presetLoader = new PresetLoader(presetsDir);
         const loader = new RulePackLoader(presetLoader);
 
diff --git a/src/providers/anthropic-provider.ts b/src/providers/anthropic-provider.ts
deleted file mode 100644
index 9dbaf4c..0000000
--- a/src/providers/anthropic-provider.ts
+++ /dev/null
@@ -1,241 +0,0 @@
-import Anthropic from '@anthropic-ai/sdk';
-import { z } from 'zod';
-import { LLMProvider, LLMResult } from './llm-provider';
-import { DefaultRequestBuilder, RequestBuilder } from './request-builder';
-import {
-  ANTHROPIC_RESPONSE_SCHEMA,
-  type AnthropicResponse,
-  type AnthropicToolUseBlock,
-  isToolUseBlock,
-  isTextBlock
-} from '../schemas/anthropic-responses';
-import { ValidationError, APIResponseError } from '../errors/validation-errors';
-import { handleUnknownError } from '../errors/index';
-
-export interface AnthropicConfig {
-  apiKey: string;
-  model?: string;
-  maxTokens?: number;
-  temperature?: number;
-  debug?: boolean;
-  showPrompt?: boolean;
-  showPromptTrunc?: boolean;
-}
-
-export const ANTHROPIC_DEFAULT_CONFIG = {
-  model: 'claude-3-sonnet-20240229',
-  maxTokens: 4096,
-  temperature: 0.2,
-};
-
-export class AnthropicProvider implements LLMProvider {
-  private client: Anthropic;
-  private config: AnthropicConfig;
-  private builder: RequestBuilder;
-
-  constructor(config: AnthropicConfig, builder?: RequestBuilder) {
-    this.client = new Anthropic({
-      apiKey: config.apiKey,
-      maxRetries: 2,
-    });
-    this.config = {
-      ...config,
-      model: config.model ?? ANTHROPIC_DEFAULT_CONFIG.model,
-      maxTokens: config.maxTokens ?? ANTHROPIC_DEFAULT_CONFIG.maxTokens,
-      temperature: config.temperature ?? ANTHROPIC_DEFAULT_CONFIG.temperature,
-    };
-    this.builder = builder ?? new DefaultRequestBuilder();
-  }
-
-  /**
-   * Validates Anthropic API response using schema validation
-   * Replaces unsafe type assertions with proper runtime validation
-   */
-  private validateResponse(response: unknown): AnthropicResponse {
-    try {
-      return ANTHROPIC_RESPONSE_SCHEMA.parse(response);
-    } catch (e: unknown) {
-      if (e instanceof z.ZodError) {
-        throw new APIResponseError(
-          `Invalid Anthropic API response structure: ${e.message}`,
-          response,
-          e
-        );
-      }
-      const err = handleUnknownError(e, 'Anthropic response validation');
-      throw new ValidationError(`Anthropic response validation failed: ${err.message}`, e);
-    }
-  }
-
-  async runPromptStructured<T = unknown>(
-    content: string,
-    promptText: string,
-    schema: { name: string; schema: Record<string, unknown> }
-  ): Promise<LLMResult<T>> {
-    const systemPrompt = this.builder.buildPromptBodyForStructured(promptText);
-
-    // Create tool schema for structured response
-    const toolSchema = this.convertToAnthropicToolSchema(schema);
-
-    // Create request with both official Anthropic fields and E2E mock compatibility aliases
-    const params: Anthropic.Messages.MessageCreateParams & Record<string, unknown> = {
-      // Official Anthropic fields (snake_case)
-      model: this.config.model!,
-      system: systemPrompt,
-      messages: [
-        {
-          role: 'user',
-          content: `Input:\n\n${content}`,
-        },
-      ],
-      max_tokens: this.config.maxTokens!,
-      tools: [toolSchema],
-      tool_choice: { type: 'tool', name: schema.name },
-      maxTokens: this.config.maxTokens!,
-      toolChoice: { type: 'tool', name: schema.name },
-    };
-
-    if (this.config.temperature !== undefined) {
-      params.temperature = this.config.temperature;
-    }
-
-    if (this.config.debug) {
-      console.log('[vectorlint] Sending request to Anthropic:', {
-        model: this.config.model,
-        maxTokens: this.config.maxTokens,
-        temperature: this.config.temperature,
-      });
-      if (this.config.showPrompt) {
-        console.log('[vectorlint] System prompt (full):');
-        console.log(systemPrompt);
-        console.log('[vectorlint] User content (full):');
-        console.log(content);
-      } else if (this.config.showPromptTrunc) {
-        console.log('[vectorlint] System prompt (first 500 chars):');
-        console.log(systemPrompt.slice(0, 500));
-        if (systemPrompt.length > 500) console.log('... [truncated]');
-        const preview = content.slice(0, 500);
-        console.log('[vectorlint] User content preview (first 500 chars):');
-        console.log(preview);
-        if (content.length > 500) console.log('... [truncated]');
-      }
-    }
-
-    // Create clean params for Anthropic API (remove E2E mock compatibility fields)
-    const anthropicParams: Anthropic.Messages.MessageCreateParams = {
-      model: params.model,
-      messages: params.messages,
-      max_tokens: params.max_tokens,
-      stream: false,
-      system: systemPrompt,
-      tools: [toolSchema],
-      tool_choice: { type: 'tool', name: schema.name },
-      ...(params.temperature !== undefined && { temperature: params.temperature }),
-    };
-
-    let rawResponse: unknown;
-    try {
-      rawResponse = await this.client.messages.create(anthropicParams);
-    } catch (e: unknown) {
-      // Handle specific Anthropic SDK errors
-      if (e instanceof Anthropic.APIError) {
-        throw new Error(`Anthropic API error (${e.status}): ${e.message}`);
-      }
-      if (e instanceof Anthropic.RateLimitError) {
-        throw new Error(`Anthropic rate limit exceeded: ${e.message}`);
-      }
-      if (e instanceof Anthropic.AuthenticationError) {
-        throw new Error(`Anthropic authentication failed: ${e.message}`);
-      }
-      if (e instanceof Anthropic.BadRequestError) {
-        throw new Error(`Anthropic bad request: ${e.message}`);
-      }
-
-      const err = handleUnknownError(e, 'Anthropic API call');
-      throw new Error(`Anthropic API call failed: ${err.message}`);
-    }
-
-    // Validate the API response structure using schema validation
-    const validatedResponse = this.validateResponse(rawResponse);
-
-    const data = this.extractStructuredResponse<T>(validatedResponse, schema.name);
-
-    return {
-      data,
-      usage: {
-        inputTokens: validatedResponse.usage.input_tokens,
-        outputTokens: validatedResponse.usage.output_tokens,
-      }
-    };
-  }
-
-  private convertToAnthropicToolSchema(schema: { name: string; schema: Record<string, unknown> }): Anthropic.Messages.Tool {
-    return {
-      name: schema.name,
-      description: `Submit ${schema.name} evaluation results`,
-      input_schema: {
-        type: 'object',
-        ...schema.schema,
-      },
-    };
-  }
-
-  private extractStructuredResponse<T>(response: AnthropicResponse, expectedToolName: string): T {
-    // Debug logging with type-safe property access
-    if (this.config.debug) {
-      const usage = response.usage;
-      const stopReason = response.stop_reason;
-      if (usage || stopReason) {
-        console.log('[vectorlint] LLM response meta:', {
-          usage: {
-            input_tokens: usage.input_tokens,
-            output_tokens: usage.output_tokens,
-          },
-          stop_reason: stopReason,
-        });
-      }
-
-    }
-
-    // Type-safe content validation - response is already validated by schema
-    const blocks = response.content;
-    if (blocks.length === 0) {
-      throw new Error('Empty response from Anthropic API (no content blocks).');
-    }
-
-    // Find the expected tool use block using type-safe filtering
-    const toolBlock = blocks.find((block): block is AnthropicToolUseBlock =>
-      isToolUseBlock(block) && block.name === expectedToolName
-    );
-
-    if (!toolBlock) {
-      // Check if there are any tool use blocks at all
-      const toolUseBlocks = blocks.filter(isToolUseBlock);
-      if (toolUseBlocks.length > 0) {
-        const availableTools = toolUseBlocks.map(block => block.name);
-        throw new Error(`Expected tool call '${expectedToolName}' but received: ${availableTools.join(', ')}`);
-      }
-
-      // Check if response contains text instead of tool use
-      const textBlocks = blocks.filter(isTextBlock);
-      const firstTextBlock = textBlocks[0];
-      if (firstTextBlock) {
-        const textContent = firstTextBlock.text.slice(0, 200);
-        throw new Error(`No tool call received for ${expectedToolName}. Response contains text instead: ${textContent}${textContent.length >= 200 ? '...' : ''}`);
-      }
-
-      throw new Error(`No tool call received for ${expectedToolName}. Response may not contain structured data.`);
-    }
-
-    const input = toolBlock.input;
-    if (input == null || (typeof input === 'object' && !Array.isArray(input) && Object.keys(input).length === 0)) {
-      throw new Error(`Tool call for ${expectedToolName} returned empty or null input.`);
-    }
-
-    if (typeof input !== 'object' || Array.isArray(input)) {
-      throw new Error(`Tool call for ${expectedToolName} returned invalid input type: ${typeof input}`);
-    }
-
-    return input as T;
-  }
-}
diff --git a/src/providers/azure-openai-provider.ts b/src/providers/azure-openai-provider.ts
deleted file mode 100644
index 403898a..0000000
--- a/src/providers/azure-openai-provider.ts
+++ /dev/null
@@ -1,141 +0,0 @@
-import { AzureOpenAI } from 'openai';
-import { LLMProvider, LLMResult } from './llm-provider';
-import { DefaultRequestBuilder, RequestBuilder } from './request-builder';
-import { validateApiResponse } from '../boundaries/api-client';
-import { handleUnknownError } from '../errors/index';
-
-export interface AzureOpenAIConfig {
-  apiKey: string;
-  endpoint: string;
-  deploymentName: string;
-  apiVersion?: string | undefined;
-  temperature?: number | undefined;
-  debug?: boolean | undefined;
-  showPrompt?: boolean | undefined; // full prompt and content
-  showPromptTrunc?: boolean | undefined; // truncated previews (500 chars)
-}
-
-export const AZURE_OPENAI_DEFAULT_CONFIG = {
-  apiVersion: '2024-02-15-preview',
-  temperature: 0.2,
-};
-
-export class AzureOpenAIProvider implements LLMProvider {
-  private client: AzureOpenAI;
-  private deploymentName: string;
-  private temperature?: number | undefined;
-  private apiVersion?: string | undefined;
-  private debug?: boolean | undefined;
-  private showPrompt?: boolean | undefined;
-  private showPromptTrunc?: boolean | undefined;
-  private builder: RequestBuilder;
-
-  constructor(config: AzureOpenAIConfig, builder?: RequestBuilder) {
-    this.client = new AzureOpenAI({
-      apiKey: config.apiKey,
-      endpoint: config.endpoint,
-      apiVersion: config.apiVersion || AZURE_OPENAI_DEFAULT_CONFIG.apiVersion,
-    });
-    this.deploymentName = config.deploymentName;
-    this.temperature = config.temperature;
-    this.apiVersion = config.apiVersion;
-    this.debug = config.debug;
-    this.showPrompt = config.showPrompt;
-    this.showPromptTrunc = config.showPromptTrunc;
-    this.builder = builder ?? new DefaultRequestBuilder();
-  }
-
-  async runPromptStructured<T = unknown>(content: string, promptText: string, schema: { name: string; schema: Record<string, unknown> }): Promise<LLMResult<T>> {
-    const prompt = this.builder.buildPromptBodyForStructured(promptText);
-
-    const params: Parameters<typeof this.client.chat.completions.create>[0] = {
-      model: this.deploymentName,
-      messages: [
-        { role: 'system', content: prompt },
-        { role: 'user', content: `Input:\n\n${content}` }
-      ],
-      response_format: {
-        type: 'json_schema',
-        json_schema: schema,
-      },
-    };
-    if (this.temperature !== undefined) {
-      params.temperature = this.temperature;
-    }
-
-    if (this.debug) {
-      console.log('[vectorlint] Sending request to Azure OpenAI:', {
-        model: this.deploymentName,
-        apiVersion: this.apiVersion || AZURE_OPENAI_DEFAULT_CONFIG.apiVersion,
-        temperature: this.temperature,
-      });
-      if (this.showPrompt) {
-        console.log('[vectorlint] Prompt (full):');
-        console.log(prompt);
-        console.log('[vectorlint] Injected content (full):');
-        console.log(content);
-      } else if (this.showPromptTrunc) {
-        console.log('[vectorlint] Prompt (first 500 chars):');
-        console.log(prompt.slice(0, 500));
-        if (prompt.length > 500) console.log('... [truncated]');
-        const preview = content.slice(0, 500);
-        console.log('[vectorlint] Injected content preview (first 500 chars):');
-        console.log(preview);
-        if (content.length > 500) console.log('... [truncated]');
-      }
-    }
-
-    let response;
-    try {
-      response = await this.client.chat.completions.create(params);
-    } catch (e: unknown) {
-      const err = handleUnknownError(e, 'OpenAI API call');
-      throw new Error(`OpenAI API call failed: ${err.message}`);
-    }
-
-    // Type guard to ensure we have a ChatCompletion, not a Stream
-    if (!('choices' in response)) {
-      throw new Error('Received streaming response when expecting structured response');
-    }
-
-    // Validate the API response structure
-    let validatedResponse;
-    try {
-      validatedResponse = validateApiResponse(response);
-    } catch (e: unknown) {
-      const err = handleUnknownError(e, 'API response validation');
-      throw new Error(`Invalid API response structure: ${err.message}`);
-    }
-
-    const responseTextRaw = validatedResponse.choices[0]?.message?.content;
-    const responseText = (responseTextRaw ?? '').trim();
-    if (this.debug) {
-      const usage = validatedResponse.usage;
-      const finish = validatedResponse.choices[0]?.finish_reason;
-      if (usage || finish) {
-        console.log('[vectorlint] LLM response meta:', { usage, finish_reason: finish });
-      }
-
-    }
-    if (!responseText) {
-      throw new Error('Empty response from LLM (no content).');
-    }
-    try {
-      const data = JSON.parse(responseText) as T;
-      const usage = validatedResponse.usage;
-
-      const result: LLMResult<T> = { data };
-      if (usage) {
-        result.usage = {
-          inputTokens: usage.prompt_tokens,
-          outputTokens: usage.completion_tokens,
-        };
-      }
-      return result;
-    } catch (e: unknown) {
-      const err = handleUnknownError(e, 'JSON parsing');
-      const preview = responseText.slice(0, 200);
-      throw new Error(`Failed to parse structured JSON response: ${err.message}. Preview: ${preview}${responseText.length > 200 ? ' ...' : ''}`);
-    }
-  }
-}
diff --git a/src/providers/gemini-provider.ts b/src/providers/gemini-provider.ts
deleted file mode 100644
index 53e87ed..0000000
--- a/src/providers/gemini-provider.ts
+++ /dev/null
@@ -1,95 +0,0 @@
-import { GoogleGenerativeAI, GenerativeModel } from '@google/generative-ai';
-import { LLMProvider, LLMResult } from './llm-provider';
-import { DefaultRequestBuilder, RequestBuilder } from './request-builder';
-import { handleUnknownError } from '../errors/index';
-
-export interface GeminiConfig {
-    apiKey: string;
-    model?: string;
-    temperature?: number;
-    debug?: boolean;
-    showPrompt?: boolean;
-    showPromptTrunc?: boolean;
-}
-
-export const GEMINI_DEFAULT_CONFIG = {
-    model: 'gemini-2.5-flash',
-    temperature: 0.2,
-};
-
-export class GeminiProvider implements LLMProvider {
-    private client: GoogleGenerativeAI;
-    private model: GenerativeModel;
-    private config: GeminiConfig;
-    private builder: RequestBuilder;
-
-    constructor(config: GeminiConfig, builder?: RequestBuilder) {
-        this.client = new GoogleGenerativeAI(config.apiKey);
-        this.config = {
-            ...config,
-            model: config.model ?? GEMINI_DEFAULT_CONFIG.model,
-            temperature: config.temperature ?? GEMINI_DEFAULT_CONFIG.temperature,
-        };
-        this.model = this.client.getGenerativeModel({
-            model: this.config.model!,
-            generationConfig: {
-                ...(this.config.temperature !== undefined && { temperature: this.config.temperature }),
-                responseMimeType: "application/json",
-            }
-        });
-        this.builder = builder ?? new DefaultRequestBuilder();
-    }
-
-    async runPromptStructured<T = unknown>(
-        content: string,
-        promptText: string,
-        schema: { name: string; schema: Record<string, unknown> }
-    ): Promise<LLMResult<T>> {
-        const systemPrompt = this.builder.buildPromptBodyForStructured(promptText);
-
-        const fullPrompt = `${systemPrompt}
-            You must output valid JSON that adheres to the following schema:
-            ${JSON.stringify(schema.schema, null, 2)}
-            Input:
-            ${content}
-        `;
-
-        if (this.config.debug) {
-            console.error('[vectorlint] Sending request to Gemini:', {
-                model: this.config.model,
-                temperature: this.config.temperature,
-            });
-            if (this.config.showPrompt) {
-                console.error('[vectorlint] Full prompt:');
-                console.error(fullPrompt);
-            } else if (this.config.showPromptTrunc) {
-                console.error('[vectorlint] Prompt preview (first 500 chars):');
-                console.error(fullPrompt.slice(0, 500));
-                if (fullPrompt.length > 500) console.error('... [truncated]');
-            }
-        }
-
-        try {
-            const result = await this.model.generateContent(fullPrompt);
-            const response = result.response;
-            const text = response.text();
-            const usageMetadata = response.usageMetadata;
-
-
-
-            const data = JSON.parse(text) as T;
-
-            const llmResult: LLMResult<T> = { data };
-            if (usageMetadata) {
-                llmResult.usage = {
-                    inputTokens: usageMetadata.promptTokenCount ?? 0,
-                    outputTokens: usageMetadata.candidatesTokenCount ?? 0,
-                };
-            }
-            return llmResult;
-        } catch (e: unknown) {
-            const err = handleUnknownError(e, 'Gemini API call');
-            throw new Error(`Gemini API call failed: ${err.message}`);
-        }
-    }
-}
diff --git a/src/providers/index.ts b/src/providers/index.ts
index 4c0ab21..4bdb538 100644
--- a/src/providers/index.ts
+++ b/src/providers/index.ts
@@ -1,6 +1,7 @@
-export { LLMProvider } from './llm-provider';
-export { AzureOpenAIProvider, type AzureOpenAIConfig } from './azure-openai-provider';
-export { AnthropicProvider, type AnthropicConfig } from './anthropic-provider';
-export { OpenAIProvider, type OpenAIConfig } from './openai-provider';
-export { createProvider, type ProviderOptions } from './provider-factory';
+export { LLMProvider, type LLMResult } from './llm-provider';
+export { VercelAIProvider, type VercelAIConfig } from './vercel-ai-provider';
+export { SearchProvider } from './search-provider';
+export { PerplexitySearchProvider, type PerplexitySearchConfig } from './perplexity-provider';
+export { createProvider, type ProviderOptions, ProviderType } from './provider-factory';
 export { RequestBuilder, DefaultRequestBuilder } from './request-builder';
+export { TokenUsage, TokenUsageStats, PricingConfig, calculateCost } from './token-usage';
diff --git a/src/providers/openai-provider.ts b/src/providers/openai-provider.ts
deleted file mode 100644
index c54ea9b..0000000
--- a/src/providers/openai-provider.ts
+++ /dev/null
@@ -1,180 +0,0 @@
-import OpenAI from 'openai';
-import { z } from 'zod';
-import { LLMProvider, LLMResult } from './llm-provider';
-import { DefaultRequestBuilder, RequestBuilder } from './request-builder';
-import { OPENAI_RESPONSE_SCHEMA, type OpenAIResponse } from '../schemas/openai-responses';
-import { ValidationError, APIResponseError } from '../errors/validation-errors';
-import { handleUnknownError } from '../errors/index';
-
-export interface OpenAIConfig {
-  apiKey: string;
-  model?: string;
-  temperature?: number;
-  debug?: boolean;
-  showPrompt?: boolean;
-  showPromptTrunc?: boolean;
-}
-
-export const OPENAI_DEFAULT_CONFIG = {
-  model: 'gpt-4o',
-  temperature: 0.2,
-};
-
-export class OpenAIProvider implements LLMProvider {
-  private client: OpenAI;
-  private config: OpenAIConfig;
-  private builder: RequestBuilder;
-
-  constructor(config: OpenAIConfig, builder?: RequestBuilder) {
-    this.client = new OpenAI({
-      apiKey: config.apiKey,
-      maxRetries: 2,
-    });
-    this.config = {
-      ...config,
-      model: config.model ?? OPENAI_DEFAULT_CONFIG.model,
-      temperature: config.temperature ?? OPENAI_DEFAULT_CONFIG.temperature,
-    };
-    this.builder = builder ?? new DefaultRequestBuilder();
-  }
-
-  /**
-   * Validates OpenAI API response using schema validation
-   * Replaces unsafe type assertions with proper runtime validation
-   */
-  private validateResponse(response: unknown): OpenAIResponse {
-    try {
-      return OPENAI_RESPONSE_SCHEMA.parse(response);
-    } catch (e: unknown) {
-      if (e instanceof z.ZodError) {
-        throw new APIResponseError(
-          `Invalid OpenAI API response structure: ${e.message}`,
-          response,
-          e
-        );
-      }
-      const err = handleUnknownError(e, 'OpenAI response validation');
-      throw new ValidationError(`OpenAI response validation failed: ${err.message}`, e);
-    }
-  }
-
-  async runPromptStructured<T = unknown>(
-    content: string,
-    promptText: string,
-    schema: { name: string; schema: Record<string, unknown> }
-  ): Promise<LLMResult<T>> {
-    const systemPrompt = this.builder.buildPromptBodyForStructured(promptText);
-
-    const params: OpenAI.Chat.Completions.ChatCompletionCreateParams = {
-      model: this.config.model!,
-      messages: [
-        { role: 'system', content: systemPrompt },
-        { role: 'user', content: `Input:\n\n${content}` }
-      ],
-      response_format: {
-        type: 'json_schema',
-        json_schema: {
-          name: schema.name,
-          schema: schema.schema,
-        },
-      },
-    };
-
-    if (this.config.temperature !== undefined) {
-      params.temperature = this.config.temperature;
-    }
-
-    if (this.config.debug) {
-      console.log('[vectorlint] Sending request to OpenAI:', {
-        model: this.config.model,
-        temperature: this.config.temperature,
-      });
-
-      if (this.config.showPrompt) {
-        console.log('[vectorlint] System prompt (full):');
-        console.log(systemPrompt);
-        console.log('[vectorlint] User content (full):');
-        console.log(content);
-      } else if (this.config.showPromptTrunc) {
-        console.log('[vectorlint] System prompt (first 500 chars):');
-        console.log(systemPrompt.slice(0, 500));
-        if (systemPrompt.length > 500) console.log('... [truncated]');
-        const preview = content.slice(0, 500);
-        console.log('[vectorlint] User content preview (first 500 chars):');
-        console.log(preview);
-        if (content.length > 500) console.log('... [truncated]');
-      }
-    }
-
-    let rawResponse: unknown;
-    try {
-      rawResponse = await this.client.chat.completions.create(params);
-    } catch (e: unknown) {
-      // Handle specific OpenAI SDK errors - check more specific errors first
-      if (e instanceof OpenAI.RateLimitError) {
-        throw new Error(`OpenAI rate limit exceeded: ${e.message}`);
-      }
-      if (e instanceof OpenAI.AuthenticationError) {
-        throw new Error(`OpenAI authentication failed: ${e.message}`);
-      }
-      if (e instanceof OpenAI.APIError) {
-        throw new Error(`OpenAI API error (${e.status}): ${e.message}`);
-      }
-
-      const err = handleUnknownError(e, 'OpenAI API call');
-      throw new Error(`OpenAI API call failed: ${err.message}`);
-    }
-
-    // Validate the API response structure using schema validation
-    const validatedResponse = this.validateResponse(rawResponse);
-
-    // Debug logging after successful response
-    if (this.config.debug) {
-      const usage = validatedResponse.usage;
-      const firstChoice = validatedResponse.choices[0];
-      if (usage || firstChoice) {
-        console.log('[vectorlint] LLM response meta:', {
-          usage: usage ? {
-            prompt_tokens: usage.prompt_tokens,
-            completion_tokens: usage.completion_tokens,
-            total_tokens: usage.total_tokens,
-          } : undefined,
-          finish_reason: firstChoice?.finish_reason,
-        });
-      }
-
-    }
-
-    // Type-safe property access with proper null checks
-    const firstChoice = validatedResponse.choices[0];
-    if (!firstChoice) {
-      throw new Error('Empty response from OpenAI API (no choices).');
-    }
-
-    const responseText = firstChoice.message.content?.trim();
-    if (!responseText) {
-      throw new Error('Empty response from OpenAI API (no content).');
-    }
-
-    try {
-      const data = JSON.parse(responseText) as T;
-      const usage = validatedResponse.usage;
-
-      const result: LLMResult<T> = { data };
-      if (usage) {
-        result.usage = {
-          inputTokens: usage.prompt_tokens,
-          outputTokens: usage.completion_tokens,
-        };
-      }
-      return result;
-    } catch (e: unknown) {
-      const err = handleUnknownError(e, 'JSON parsing');
-      const preview = responseText.slice(0, 200);
-      throw new ValidationError(
-        `Failed to parse structured JSON response: ${err.message}. Preview: ${preview}${responseText.length > 200 ? ' ...' : ''}`,
-        e
-      );
-    }
-  }
-}
diff --git a/src/providers/perplexity-provider.ts b/src/providers/perplexity-provider.ts
index 0f237eb..105f840 100644
--- a/src/providers/perplexity-provider.ts
+++ b/src/providers/perplexity-provider.ts
@@ -1,23 +1,38 @@
-import Perplexity from '@perplexity-ai/perplexity_ai';
+import { generateText } from 'ai';
+import { z } from 'zod';
+import { createPerplexity } from '@ai-sdk/perplexity';
 import type { SearchProvider } from './search-provider';
-import { PERPLEXITY_RESPONSE_SCHEMA, type PerplexityResult } from '../schemas/perplexity-responses';
+import type { PerplexityResult } from '../schemas/perplexity-responses';
+
+// Boundary validation schema for Perplexity source data.
+// The AI SDK's typed Source may not include provider-specific fields (text, publishedDate),
+// so we validate the raw data at the boundary to safely extract them.
+const PERPLEXITY_SOURCE_SCHEMA = z.object({
+  title: z.string().optional(),
+  text: z.string().optional(),
+  url: z.string().optional(),
+  publishedDate: z.string().optional(),
+}).passthrough();
 
 export interface PerplexitySearchConfig {
+  apiKey?: string;
   maxResults?: number;
-  maxTokensPerPage?: number;
   debug?: boolean;
 }
 
 export class PerplexitySearchProvider implements SearchProvider {
-  private client: Perplexity;
+  private client: ReturnType<typeof createPerplexity>;
   private maxResults: number;
-  private maxTokensPerPage: number;
   private debug: boolean;
 
   constructor(config: PerplexitySearchConfig = {}) {
-    this.client = new Perplexity();
+    // Use provided API key or fall back to environment variable
+    const apiKey = config.apiKey || process.env.PERPLEXITY_API_KEY;
+    if (!apiKey) {
+      throw new Error('Perplexity API key is required. Set PERPLEXITY_API_KEY environment variable or pass apiKey in config.');
+    }
+    this.client = createPerplexity({ apiKey });
     this.maxResults = config.maxResults ?? 5;
-    this.maxTokensPerPage = config.maxTokensPerPage ?? 1024;
     this.debug = config.debug ?? false;
   }
 
@@ -27,18 +42,26 @@ export class PerplexitySearchProvider implements SearchProvider {
     if (this.debug) console.log(`[Perplexity] Searching: "${query}"`);
 
     try {
-      const rawResponse: unknown = await this.client.search.create({
-        query,
-        max_results: this.maxResults,
-        max_tokens_per_page: this.maxTokensPerPage,
+      const result = await generateText({
+        model: this.client('sonar-pro'),
+        prompt: query,
       });
 
-      /*
-       * Validate response with schema at boundary.
-       * Schema provides defaults for missing fields and ensures type safety.
-       */
-      const validated = PERPLEXITY_RESPONSE_SCHEMA.parse(rawResponse);
-      const results = validated.results;
+      // Validate sources at the boundary — the SDK may include provider-specific
+      // fields not present in the typed Source interface
+      const rawSources: unknown = result.sources ?? [];
+      const parseResult = z.array(PERPLEXITY_SOURCE_SCHEMA).safeParse(rawSources);
+      if (!parseResult.success && this.debug) {
+        console.warn(`[Perplexity] Source validation failed for raw sources:`, parseResult.error);
+      }
+      const sources = parseResult.success ? parseResult.data : [];
+
+      const results: PerplexityResult[] = sources.slice(0, this.maxResults).map(source => ({
+        title: source.title || 'Untitled',
+        snippet: source.text || '',
+        url: source.url || '',
+        date: source.publishedDate || '',
+      }));
 
       if (this.debug) {
         console.log(`[Perplexity] Found ${results.length} results`);
diff --git a/src/providers/provider-factory.ts b/src/providers/provider-factory.ts
index 5e276aa..9feac28 100644
--- a/src/providers/provider-factory.ts
+++ b/src/providers/provider-factory.ts
@@ -1,10 +1,12 @@
+import { createOpenAI } from '@ai-sdk/openai';
+import { createAzure } from '@ai-sdk/azure';
+import { createAnthropic } from '@ai-sdk/anthropic';
+import { createGoogleGenerativeAI } from '@ai-sdk/google';
+import type { LanguageModel } from 'ai';
 import { LLMProvider } from './llm-provider';
-import { AzureOpenAIProvider, type AzureOpenAIConfig } from './azure-openai-provider';
-import { AnthropicProvider, type AnthropicConfig } from './anthropic-provider';
-import { OpenAIProvider, type OpenAIConfig } from './openai-provider';
+import { VercelAIProvider, type VercelAIConfig } from './vercel-ai-provider';
 import { RequestBuilder } from './request-builder';
 import type { EnvConfig } from '../schemas/env-schemas';
-import { GeminiConfig, GeminiProvider } from './gemini-provider';
 
 export interface ProviderOptions {
   debug?: boolean;
@@ -31,60 +33,64 @@ export function createProvider(
   options: ProviderOptions = {},
   builder?: RequestBuilder
 ): LLMProvider {
+  let model: LanguageModel;
+  let temperature = 0.2;
+
   switch (envConfig.LLM_PROVIDER) {
     case ProviderType.AzureOpenAI: {
-      const azureConfig: AzureOpenAIConfig = {
+      const azure = createAzure({
         apiKey: envConfig.AZURE_OPENAI_API_KEY,
-        endpoint: envConfig.AZURE_OPENAI_ENDPOINT,
-        deploymentName: envConfig.AZURE_OPENAI_DEPLOYMENT_NAME,
-        apiVersion: envConfig.AZURE_OPENAI_API_VERSION,
-        ...(envConfig.AZURE_OPENAI_TEMPERATURE !== undefined && { temperature: envConfig.AZURE_OPENAI_TEMPERATURE }),
-        ...(options.debug !== undefined && { debug: options.debug }),
-        ...(options.showPrompt !== undefined && { showPrompt: options.showPrompt }),
-        ...(options.showPromptTrunc !== undefined && { showPromptTrunc: options.showPromptTrunc }),
-      };
-      return new AzureOpenAIProvider(azureConfig, builder);
+        baseURL: envConfig.AZURE_OPENAI_ENDPOINT,
+        apiVersion: envConfig.AZURE_OPENAI_API_VERSION ?? '2024-02-15-preview',
+      });
+      // Cast required: @ai-sdk/azure's factory returns a provider-specific type
+      // that is not directly assignable to the generic LanguageModel from 'ai'.
+      // Tested with @ai-sdk/azure@1.x — revisit if the SDK adds a typed adapter.
+      model = azure(envConfig.AZURE_OPENAI_DEPLOYMENT_NAME) as unknown as LanguageModel;
+      temperature = envConfig.AZURE_OPENAI_TEMPERATURE ?? 0.2;
+      break;
     }
 
     case ProviderType.Anthropic: {
-      const anthropicConfig: AnthropicConfig = {
+      const anthropic = createAnthropic({
         apiKey: envConfig.ANTHROPIC_API_KEY,
-        model: envConfig.ANTHROPIC_MODEL,
-        maxTokens: envConfig.ANTHROPIC_MAX_TOKENS,
-        ...(envConfig.ANTHROPIC_TEMPERATURE !== undefined && { temperature: envConfig.ANTHROPIC_TEMPERATURE }),
-        ...(options.debug !== undefined && { debug: options.debug }),
-        ...(options.showPrompt !== undefined && { showPrompt: options.showPrompt }),
-        ...(options.showPromptTrunc !== undefined && { showPromptTrunc: options.showPromptTrunc }),
-      };
-      return new AnthropicProvider(anthropicConfig, builder);
+      });
+      model = anthropic(envConfig.ANTHROPIC_MODEL);
+      temperature = envConfig.ANTHROPIC_TEMPERATURE ?? 0.2;
+      break;
     }
 
     case ProviderType.OpenAI: {
-      const openaiConfig: OpenAIConfig = {
+      const openai = createOpenAI({
         apiKey: envConfig.OPENAI_API_KEY,
-        model: envConfig.OPENAI_MODEL,
-        ...(envConfig.OPENAI_TEMPERATURE !== undefined && { temperature: envConfig.OPENAI_TEMPERATURE }),
-        ...(options.debug !== undefined && { debug: options.debug }),
-        ...(options.showPrompt !== undefined && { showPrompt: options.showPrompt }),
-        ...(options.showPromptTrunc !== undefined && { showPromptTrunc: options.showPromptTrunc }),
-      };
-      return new OpenAIProvider(openaiConfig, builder);
+      });
+      model = openai(envConfig.OPENAI_MODEL);
+      temperature = envConfig.OPENAI_TEMPERATURE ?? 0.2;
+      break;
     }
 
     case ProviderType.Gemini: {
-      const geminiConfig: GeminiConfig = {
+      const google = createGoogleGenerativeAI({
         apiKey: envConfig.GEMINI_API_KEY,
-        model: envConfig.GEMINI_MODEL,
-        ...(envConfig.GEMINI_TEMPERATURE !== undefined && { temperature: envConfig.GEMINI_TEMPERATURE }),
-        ...(options.debug !== undefined && { debug: options.debug }),
-        ...(options.showPrompt !== undefined && { showPrompt: options.showPrompt }),
-        ...(options.showPromptTrunc !== undefined && { showPromptTrunc: options.showPromptTrunc }),
-      };
-      return new GeminiProvider(geminiConfig, builder);
+      });
+      model = google(envConfig.GEMINI_MODEL);
+      temperature = envConfig.GEMINI_TEMPERATURE ?? 0.2;
+      break;
     }
 
     default:
       // TypeScript should prevent this, but add runtime safety
       throw new Error(`Unsupported provider type: ${(envConfig as { LLM_PROVIDER: string }).LLM_PROVIDER}`);
   }
+
+  const config: VercelAIConfig = {
+    model,
+    temperature,
+    ...(envConfig.LLM_PROVIDER === ProviderType.Anthropic && envConfig.ANTHROPIC_MAX_TOKENS !== undefined && { maxTokens: envConfig.ANTHROPIC_MAX_TOKENS }),
+    ...(options.debug !== undefined && { debug: options.debug }),
+    ...(options.showPrompt !== undefined && { showPrompt: options.showPrompt }),
+    ...(options.showPromptTrunc !== undefined && { showPromptTrunc: options.showPromptTrunc }),
+  };
+
+  return new VercelAIProvider(config, builder);
 }
diff --git a/src/providers/vercel-ai-provider.ts b/src/providers/vercel-ai-provider.ts
new file mode 100644
index 0000000..0350379
--- /dev/null
+++ b/src/providers/vercel-ai-provider.ts
@@ -0,0 +1,215 @@
+import { generateText, Output, NoObjectGeneratedError } from 'ai';
+import type { LanguageModel } from 'ai';
+import { z } from 'zod';
+import { LLMProvider, LLMResult } from './llm-provider';
+import { DefaultRequestBuilder, RequestBuilder } from './request-builder';
+
+export interface VercelAIConfig {
+  model: LanguageModel;
+  temperature?: number;
+  maxTokens?: number;
+  debug?: boolean;
+  showPrompt?: boolean;
+  showPromptTrunc?: boolean;
+}
+
+export class VercelAIProvider implements LLMProvider {
+  private config: VercelAIConfig;
+  private builder: RequestBuilder;
+
+  constructor(config: VercelAIConfig, builder?: RequestBuilder) {
+    this.config = {
+      ...config,
+      temperature: config.temperature ?? 0.2,
+      ...(config.maxTokens !== undefined && { maxTokens: config.maxTokens }),
+    };
+    this.builder = builder ?? new DefaultRequestBuilder();
+  }
+
+  async runPromptStructured<T = unknown>(
+    content: string,
+    promptText: string,
+    schema: { name: string; schema: Record<string, unknown> }
+  ): Promise<LLMResult<T>> {
+    const systemPrompt = this.builder.buildPromptBodyForStructured(promptText);
+
+    // Convert JSON Schema to Zod for Vercel AI SDK
+    const zodSchema = this.jsonSchemaToZod(schema);
+
+    if (this.config.debug) {
+      console.log('[vectorlint] Sending request via Vercel AI SDK:', {
+        model: this.config.model,
+        temperature: this.config.temperature,
+        ...(this.config.maxTokens !== undefined && { maxTokens: this.config.maxTokens }),
+      });
+
+      if (this.config.showPrompt) {
+        console.log('[vectorlint] System prompt (full):');
+        console.log(systemPrompt);
+        console.log('[vectorlint] User content (full):');
+        console.log(content);
+      } else if (this.config.showPromptTrunc) {
+        console.log('[vectorlint] System prompt (first 500 chars):');
+        console.log(systemPrompt.slice(0, 500));
+        if (systemPrompt.length > 500) console.log('... [truncated]');
+        const preview = content.slice(0, 500);
+        console.log('[vectorlint] User content preview (first 500 chars):');
+        console.log(preview);
+        if (content.length > 500) console.log('... [truncated]');
+      }
+    }
+
+    try {
+      const result = await generateText({
+        model: this.config.model,
+        system: systemPrompt,
+        prompt: `Input:\n\n${content}`,
+        ...(this.config.temperature !== undefined && { temperature: this.config.temperature }),
+        ...(this.config.maxTokens !== undefined && { maxTokens: this.config.maxTokens }),
+        experimental_output: Output.object({
+          schema: zodSchema,
+        }),
+      });
+
+      if (this.config.debug && result.usage) {
+        console.log('[vectorlint] LLM response meta:', {
+          usage: {
+            prompt_tokens: result.usage.promptTokens,
+            completion_tokens: result.usage.completionTokens,
+            total_tokens: result.usage.totalTokens,
+          },
+          finish_reason: result.finishReason,
+        });
+      }
+
+      // Map Vercel AI SDK usage (promptTokens/completionTokens)
+      // to VectorLint TokenUsage (inputTokens/outputTokens)
+      const usage = result.usage ? {
+        inputTokens: result.usage.promptTokens,
+        outputTokens: result.usage.completionTokens,
+      } : undefined;
+
+      // experimental_output is validated by the Zod schema passed to Output.object(),
+      // but can be undefined/null if the LLM response doesn't match the schema
+      const output: unknown = result.experimental_output;
+      if (output === undefined || output === null) {
+        throw new Error(
+          `LLM returned no structured output. Raw text: ${result.text?.slice(0, 500) ?? '(empty)'}`
+        );
+      }
+
+      const llmResult: LLMResult<T> = { data: output as T };
+      if (usage) {
+        llmResult.usage = usage;
+      }
+      return llmResult;
+    } catch (e: unknown) {
+      // Handle Vercel AI SDK's NoObjectGeneratedError with proper type narrowing
+      if (NoObjectGeneratedError.isInstance(e)) {
+        const rawText = e instanceof Error && 'text' in e ? String(e.text) : 'unknown';
+        throw new Error(
+          `LLM failed to generate valid structured output. Raw text: ${rawText}`
+        );
+      }
+      const err = e instanceof Error ? e : new Error(String(e));
+      throw new Error(`Vercel AI SDK call failed: ${err.message}`);
+    }
+  }
+
+  /**
+   * Entry point: converts the VectorLint schema wrapper to a Zod schema.
+   * Schema format: { name: string, schema: { type, properties, required, ... } }
+   */
+  private jsonSchemaToZod(schema: { name: string; schema: Record<string, unknown> }): z.ZodType {
+    return this.convertSchemaNode(schema.schema);
+  }
+
+  /**
+   * Recursively converts a JSON Schema node to a Zod schema.
+   * Handles nested objects, arrays with typed items, enums, and primitives.
+   */
+  private convertSchemaNode(node: Record<string, unknown>): z.ZodType {
+    let type = node.type as string | string[] | undefined;
+    const enumValues = node.enum as string[] | undefined;
+    const isNullable = node.nullable === true || (Array.isArray(type) && type.includes('null'));
+
+    // Normalize type array: remove 'null' (tracked via isNullable) and handle multi-type unions
+    if (Array.isArray(type)) {
+      const types = type.filter(t => t !== 'null');
+      if (types.length === 0) {
+        type = undefined;
+      } else if (types.length === 1) {
+        type = types[0];
+      } else {
+        // Multi-type (e.g. ['string','number']): build a union of each type's Zod schema
+        const schemas = types.map(t => this.convertSchemaNode({ ...node, type: t, enum: undefined }));
+        const unionSchema = z.union(schemas as unknown as [z.ZodTypeAny, z.ZodTypeAny, ...z.ZodTypeAny[]]);
+        return isNullable ? unionSchema.nullable() : unionSchema;
+      }
+    }
+
+    // Enums take priority over type (JSON Schema allows enum without type)
+    if (enumValues) {
+      let enumSchema: z.ZodType;
+      const allStrings = enumValues.every((v): v is string => typeof v === 'string');
+      if (allStrings && enumValues.length > 0) {
+        enumSchema = z.enum(enumValues as [string, ...string[]]);
+      } else {
+        // Mixed or non-string enums: build a union of literals
+        const literals = (enumValues as unknown[]).map(v => z.literal(v as z.Primitive));
+        enumSchema = literals.length === 1
+          ? literals[0]!
+          : z.union(literals as unknown as [z.ZodTypeAny, z.ZodTypeAny, ...z.ZodTypeAny[]]);
+      }
+      return isNullable ? enumSchema.nullable() : enumSchema;
+    }
+
+    let schema: z.ZodType;
+    switch (type) {
+      case 'string':
+        schema = z.string();
+        break;
+      case 'number':
+      case 'integer':
+        schema = z.number();
+        break;
+      case 'boolean':
+        schema = z.boolean();
+        break;
+      case 'array': {
+        const items = node.items as Record<string, unknown> | undefined;
+        if (items) {
+          schema = z.array(this.convertSchemaNode(items));
+        } else {
+          schema = z.array(z.unknown());
+        }
+        break;
+      }
+      case 'object': {
+        const properties = node.properties as Record<string, Record<string, unknown>> | undefined;
+        const required = (node.required as string[]) || [];
+        const additionalProperties = node.additionalProperties;
+
+        if (properties) {
+          const zodFields: Record<string, z.ZodTypeAny> = {};
+          for (const [key, value] of Object.entries(properties)) {
+            const fieldSchema = this.convertSchemaNode(value);
+            zodFields[key] = required.includes(key) ? fieldSchema : fieldSchema.optional();
+          }
+          let objSchema: z.ZodType = z.object(zodFields);
+          if (additionalProperties === false) {
+            objSchema = z.object(zodFields).strict();
+          }
+          schema = objSchema;
+        } else {
+          schema = z.record(z.unknown());
+        }
+        break;
+      }
+      default:
+        schema = z.unknown();
+    }
+
+    return isNullable ? schema.nullable() : schema;
+  }
+}
diff --git a/src/schemas/anthropic-responses.ts b/src/schemas/anthropic-responses.ts
deleted file mode 100644
index f96a65a..0000000
--- a/src/schemas/anthropic-responses.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-import { z } from 'zod';
-
-export const ANTHROPIC_TEXT_BLOCK_SCHEMA = z.object({
-  type: z.literal('text'),
-  text: z.string(),
-});
-
-export const ANTHROPIC_TOOL_USE_BLOCK_SCHEMA = z.object({
-  type: z.literal('tool_use'),
-  id: z.string(),
-  name: z.string(),
-  input: z.unknown(), // Tool input can be any valid JSON
-});
-
-export const ANTHROPIC_CONTENT_BLOCK_SCHEMA = z.discriminatedUnion('type', [
-  ANTHROPIC_TEXT_BLOCK_SCHEMA,
-  ANTHROPIC_TOOL_USE_BLOCK_SCHEMA,
-]);
-
-export const ANTHROPIC_USAGE_SCHEMA = z.object({
-  input_tokens: z.number(),
-  output_tokens: z.number(),
-});
-
-export const ANTHROPIC_RESPONSE_SCHEMA = z.object({
-  id: z.string(),
-  type: z.literal('message'),
-  role: z.literal('assistant'),
-  content: z.array(ANTHROPIC_CONTENT_BLOCK_SCHEMA),
-  model: z.string(),
-  stop_reason: z.enum(['max_tokens', 'end_turn', 'stop_sequence', 'tool_use']).nullable(),
-  stop_sequence: z.string().nullable(),
-  usage: ANTHROPIC_USAGE_SCHEMA,
-});
-
-// Inferred TypeScript types
-export type AnthropicTextBlock = z.infer<typeof ANTHROPIC_TEXT_BLOCK_SCHEMA>;
-export type AnthropicToolUseBlock = z.infer<typeof ANTHROPIC_TOOL_USE_BLOCK_SCHEMA>;
-export type AnthropicContentBlock = z.infer<typeof ANTHROPIC_CONTENT_BLOCK_SCHEMA>;
-export type AnthropicUsage = z.infer<typeof ANTHROPIC_USAGE_SCHEMA>;
-export type AnthropicResponse = z.infer<typeof ANTHROPIC_RESPONSE_SCHEMA>;
-
-// Helper type guards
-export function isTextBlock(block: AnthropicContentBlock): block is AnthropicTextBlock {
-  return block.type === 'text';
-}
-
-export function isToolUseBlock(block: AnthropicContentBlock): block is AnthropicToolUseBlock {
-  return block.type === 'tool_use';
-}
diff --git a/src/schemas/api-schemas.ts b/src/schemas/api-schemas.ts
deleted file mode 100644
index fbe2e88..0000000
--- a/src/schemas/api-schemas.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-import { z } from 'zod';
-
-// OpenAI API response schemas
-export const OPENAI_CHOICE_SCHEMA = z.object({
-  message: z.object({
-    content: z.string().nullable(),
-  }),
-  finish_reason: z.string(),
-});
-
-export const OPENAI_USAGE_SCHEMA = z.object({
-  prompt_tokens: z.number(),
-  completion_tokens: z.number(),
-  total_tokens: z.number(),
-});
-
-export const OPENAI_RESPONSE_SCHEMA = z.object({
-  choices: z.array(OPENAI_CHOICE_SCHEMA).min(1),
-  usage: OPENAI_USAGE_SCHEMA.optional(),
-});
-
-// Anthropic API response schemas
-export const ANTHROPIC_USAGE_SCHEMA = z.object({
-  input_tokens: z.number(),
-  output_tokens: z.number(),
-});
-
-export const ANTHROPIC_TEXT_BLOCK_SCHEMA = z.object({
-  type: z.literal('text'),
-  text: z.string(),
-});
-
-export const ANTHROPIC_TOOL_USE_BLOCK_SCHEMA = z.object({
-  type: z.literal('tool_use'),
-  id: z.string(),
-  name: z.string(),
-  input: z.record(z.unknown()),
-});
-
-export const ANTHROPIC_CONTENT_BLOCK_SCHEMA = z.discriminatedUnion('type', [
-  ANTHROPIC_TEXT_BLOCK_SCHEMA,
-  ANTHROPIC_TOOL_USE_BLOCK_SCHEMA,
-]);
-
-export const ANTHROPIC_MESSAGE_SCHEMA = z.object({
-  id: z.string(),
-  type: z.literal('message'),
-  role: z.literal('assistant'),
-  content: z.array(ANTHROPIC_CONTENT_BLOCK_SCHEMA),
-  model: z.string(),
-  stop_reason: z.enum(['max_tokens', 'end_turn', 'stop_sequence', 'tool_use']).nullable(),
-  stop_sequence: z.string().nullable(),
-  usage: ANTHROPIC_USAGE_SCHEMA,
-});
-
-// Inferred types
-export type OpenAIChoice = z.infer<typeof OPENAI_CHOICE_SCHEMA>;
-export type OpenAIUsage = z.infer<typeof OPENAI_USAGE_SCHEMA>;
-export type OpenAIResponse = z.infer<typeof OPENAI_RESPONSE_SCHEMA>;
-
-export type AnthropicUsage = z.infer<typeof ANTHROPIC_USAGE_SCHEMA>;
-export type AnthropicTextBlock = z.infer<typeof ANTHROPIC_TEXT_BLOCK_SCHEMA>;
-export type AnthropicToolUseBlock = z.infer<typeof ANTHROPIC_TOOL_USE_BLOCK_SCHEMA>;
-export type AnthropicContentBlock = z.infer<typeof ANTHROPIC_CONTENT_BLOCK_SCHEMA>;
-export type AnthropicMessage = z.infer<typeof ANTHROPIC_MESSAGE_SCHEMA>;
diff --git a/src/schemas/env-schemas.ts b/src/schemas/env-schemas.ts
index 54c0e63..ac849f2 100644
--- a/src/schemas/env-schemas.ts
+++ b/src/schemas/env-schemas.ts
@@ -1,9 +1,23 @@
 import { z } from 'zod';
 import { ProviderType } from '../providers/provider-factory';
-import { GEMINI_DEFAULT_CONFIG } from '../providers/gemini-provider';
-import { OPENAI_DEFAULT_CONFIG } from '../providers/openai-provider';
-import { ANTHROPIC_DEFAULT_CONFIG } from '../providers/anthropic-provider';
-import { AZURE_OPENAI_DEFAULT_CONFIG } from '../providers/azure-openai-provider';
+
+// Default configurations (previously from provider files)
+export const AZURE_OPENAI_DEFAULT_CONFIG = {
+  apiVersion: '2024-02-15-preview',
+};
+
+export const ANTHROPIC_DEFAULT_CONFIG = {
+  model: 'claude-3-sonnet-20240229',
+  maxTokens: 4096,
+};
+
+export const OPENAI_DEFAULT_CONFIG = {
+  model: 'gpt-4o',
+};
+
+export const GEMINI_DEFAULT_CONFIG = {
+  model: 'gemini-2.5-flash',
+};
 
 // Azure OpenAI configuration schema
 const AZURE_OPENAI_CONFIG_SCHEMA = z.object({
@@ -25,15 +39,10 @@ const ANTHROPIC_CONFIG_SCHEMA = z.object({
 // OpenAI configuration schema
 const OPENAI_CONFIG_SCHEMA = z.object({
   OPENAI_API_KEY: z.string().min(1),
-  OPENAI_MODEL: z.string().optional().default(OPENAI_DEFAULT_CONFIG.model),
+  OPENAI_MODEL: z.string().default(OPENAI_DEFAULT_CONFIG.model),
   OPENAI_TEMPERATURE: z.coerce.number().min(0).max(2).optional(),
 });
 
-// Base environment schema with shared optional variables
-const BASE_ENV_SCHEMA = z.object({
-  INPUT_PRICE_PER_MILLION: z.coerce.number().positive().optional(),
-  OUTPUT_PRICE_PER_MILLION: z.coerce.number().positive().optional(),
-});
 // Gemini configuration schema
 const GEMINI_CONFIG_SCHEMA = z.object({
   GEMINI_API_KEY: z.string().min(1),
@@ -41,6 +50,12 @@ const GEMINI_CONFIG_SCHEMA = z.object({
   GEMINI_TEMPERATURE: z.coerce.number().min(0).max(1).optional(),
 });
 
+// Base environment schema with shared optional variables
+const BASE_ENV_SCHEMA = z.object({
+  INPUT_PRICE_PER_MILLION: z.coerce.number().positive().optional(),
+  OUTPUT_PRICE_PER_MILLION: z.coerce.number().positive().optional(),
+});
+
 // Discriminated union based on provider type
 export const ENV_SCHEMA = z.discriminatedUnion('LLM_PROVIDER', [
   z.object({ LLM_PROVIDER: z.literal(ProviderType.AzureOpenAI) }).merge(AZURE_OPENAI_CONFIG_SCHEMA).merge(BASE_ENV_SCHEMA),
@@ -50,7 +65,7 @@ export const ENV_SCHEMA = z.discriminatedUnion('LLM_PROVIDER', [
 ]);
 
 export const GLOBAL_CONFIG_SCHEMA = z.object({
-    env: z.record(z.union([z.string(), z.number(), z.boolean()])).optional(),
+  env: z.record(z.union([z.string(), z.number(), z.boolean()])).optional(),
 });
 
 // Inferred types
diff --git a/src/schemas/index.ts b/src/schemas/index.ts
index 7df61aa..d17675b 100644
--- a/src/schemas/index.ts
+++ b/src/schemas/index.ts
@@ -3,4 +3,4 @@ export * from './prompt-schemas';
 export * from './cli-schemas';
 export * from './config-schemas';
 export * from './env-schemas';
-export * from './api-schemas';
+export * from './perplexity-responses';
diff --git a/src/schemas/openai-responses.ts b/src/schemas/openai-responses.ts
deleted file mode 100644
index 90a16f6..0000000
--- a/src/schemas/openai-responses.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-import { z } from 'zod';
-
-export const OPENAI_MESSAGE_SCHEMA = z.object({
-  content: z.string().nullable(),
-  role: z.string().optional(),
-  tool_calls: z.array(z.object({
-    id: z.string().optional(),
-    type: z.literal('function').optional(),
-    function: z.object({
-      name: z.string().optional(),
-      arguments: z.string(),
-    }),
-  })).optional(),
-});
-
-export const OPENAI_CHOICE_SCHEMA = z.object({
-  index: z.number().optional(),
-  message: OPENAI_MESSAGE_SCHEMA,
-  finish_reason: z.string(),
-  logprobs: z.unknown().nullable().optional(),
-});
-
-export const OPENAI_USAGE_SCHEMA = z.object({
-  prompt_tokens: z.number(),
-  completion_tokens: z.number(),
-  total_tokens: z.number(),
-});
-
-export const OPENAI_RESPONSE_SCHEMA = z.object({
-  id: z.string().optional(),
-  object: z.string().optional(),
-  created: z.number().optional(),
-  model: z.string().optional(),
-  choices: z.array(OPENAI_CHOICE_SCHEMA).min(1),
-  usage: OPENAI_USAGE_SCHEMA.optional(),
-  system_fingerprint: z.string().optional(),
-});
-
-// Inferred TypeScript types
-export type OpenAIMessage = z.infer<typeof OPENAI_MESSAGE_SCHEMA>;
-export type OpenAIChoice = z.infer<typeof OPENAI_CHOICE_SCHEMA>;
-export type OpenAIUsage = z.infer<typeof OPENAI_USAGE_SCHEMA>;
-export type OpenAIResponse = z.infer<typeof OPENAI_RESPONSE_SCHEMA>;
-
-// Tool call specific types
-export type OpenAIToolCall = NonNullable<OpenAIMessage['tool_calls']>[number];
-export type OpenAIFunction = OpenAIToolCall['function'];
diff --git a/tests/anthropic-e2e.test.ts b/tests/anthropic-e2e.test.ts
deleted file mode 100644
index 11833bc..0000000
--- a/tests/anthropic-e2e.test.ts
+++ /dev/null
@@ -1,679 +0,0 @@
-import { describe, it, expect, vi, beforeEach } from 'vitest';
-import { createProvider, ProviderType } from '../src/providers/provider-factory';
-import { parseEnvironment } from '../src/boundaries/env-parser';
-import { ANTHROPIC_DEFAULT_CONFIG, AnthropicProvider } from '../src/providers/anthropic-provider';
-import type { AnthropicMessage } from '../src/schemas/api-schemas';
-import type {
-  MockAPIErrorParams,
-  MockAuthenticationErrorParams,
-  MockRateLimitErrorParams,
-  MockBadRequestErrorParams,
-  MockAnthropicClient
-} from './schemas/mock-schemas';
-
-// Create a shared mock function that all E2E instances will use
-const SHARED_E2E_MOCK_CREATE = vi.fn();
-
-// Mock the Anthropic SDK for E2E tests
-vi.mock('@anthropic-ai/sdk', () => {
-  return {
-    default: vi.fn((): MockAnthropicClient => ({
-      messages: {
-        create: SHARED_E2E_MOCK_CREATE,
-      },
-    })),
-    APIError: class APIError extends Error {
-      status: number;
-      constructor(params: MockAPIErrorParams) {
-        super(params.message);
-        this.status = params.status || 500;
-        this.name = 'APIError';
-      }
-    },
-    RateLimitError: class RateLimitError extends Error {
-      constructor(params: Partial<MockRateLimitErrorParams> = {}) {
-        super(params.message ?? 'Rate limit exceeded');
-        this.name = 'RateLimitError';
-      }
-    },
-    AuthenticationError: class AuthenticationError extends Error {
-      constructor(params: Partial<MockAuthenticationErrorParams> = {}) {
-        super(params.message ?? 'Authentication failed');
-        this.name = 'AuthenticationError';
-      }
-    },
-    BadRequestError: class BadRequestError extends Error {
-      constructor(params: Partial<MockBadRequestErrorParams> = {}) {
-        super(params.message ?? 'Bad request');
-        this.name = 'BadRequestError';
-      }
-    },
-  };
-});
-
-// Mock the API client validation
-vi.mock('../src/boundaries/api-client', () => ({
-  validateAnthropicResponse: vi.fn(),
-}));
-
-describe('Anthropic End-to-End Integration', () => {
-  let mockValidateAnthropicResponse: ReturnType<typeof vi.fn>;
-
-  beforeEach(async () => {
-    vi.clearAllMocks();
-
-    // Get reference to the mocked validation function
-    const apiClient = await import('../src/boundaries/api-client');
-    mockValidateAnthropicResponse = vi.mocked(apiClient.validateAnthropicResponse);
-
-    // Default mock behavior - return the response as-is
-    mockValidateAnthropicResponse.mockImplementation((response: unknown) => response as AnthropicMessage);
-  });
-
-  describe('Complete Flow from Environment to Provider', () => {
-    it('processes complete Anthropic configuration flow', async () => {
-      // Step 1: Environment configuration
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key-12345',
-        ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
-        ANTHROPIC_MAX_TOKENS: '4096',
-        ANTHROPIC_TEMPERATURE: '0.3',
-      };
-
-      // Step 2: Parse environment
-      const envConfig = parseEnvironment(env);
-      expect(envConfig.LLM_PROVIDER).toBe('anthropic');
-
-      // Step 3: Create provider via factory
-      const provider = createProvider(envConfig, {
-        debug: true,
-        showPrompt: false,
-      });
-      expect(provider).toBeInstanceOf(AnthropicProvider);
-
-      // Step 4: Mock successful API response
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_e2e_test',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_e2e_test',
-            name: 'content_evaluation',
-            input: {
-              score: 92,
-              feedback: 'Excellent content quality',
-              categories: ['clarity', 'accuracy'],
-            },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 150,
-          output_tokens: 75,
-        },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      // Step 5: Execute structured prompt
-      const schema = {
-        name: 'content_evaluation',
-        schema: {
-          properties: {
-            score: { type: 'number' },
-            feedback: { type: 'string' },
-            categories: { type: 'array', items: { type: 'string' } },
-          },
-          required: ['score', 'feedback'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Test content for evaluation',
-        'Evaluate this content for quality',
-        schema
-      );
-
-      // Step 6: Verify results
-      expect(result.data).toEqual({
-        score: 92,
-        feedback: 'Excellent content quality',
-        categories: ['clarity', 'accuracy'],
-      });
-      expect(result.usage).toEqual({
-        inputTokens: 150,
-        outputTokens: 75,
-      });
-
-      // Verify API was called with correct parameters
-      expect(SHARED_E2E_MOCK_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          model: 'claude-3-sonnet-20240229',
-          max_tokens: 4096,
-          temperature: 0.3,
-          tools: [
-            {
-              name: 'content_evaluation',
-              description: 'Submit content_evaluation evaluation results',
-              input_schema: {
-                type: 'object',
-                properties: {
-                  score: { type: 'number' },
-                  feedback: { type: 'string' },
-                  categories: { type: 'array', items: { type: 'string' } },
-                },
-                required: ['score', 'feedback'],
-              },
-            },
-          ],
-          tool_choice: { type: 'tool', name: 'content_evaluation' },
-        })
-      );
-    });
-
-    it('handles minimal Anthropic configuration with defaults', async () => {
-      // Step 1: Minimal environment configuration
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-minimal-key',
-      };
-
-      // Step 2: Parse environment (should apply defaults)
-      const envConfig = parseEnvironment(env);
-      expect(envConfig.LLM_PROVIDER).toBe(ProviderType.Anthropic);
-      if (envConfig.LLM_PROVIDER === ProviderType.Anthropic) {
-        expect(envConfig.ANTHROPIC_MODEL).toBe(ANTHROPIC_DEFAULT_CONFIG.model);
-        expect(envConfig.ANTHROPIC_MAX_TOKENS).toBe(ANTHROPIC_DEFAULT_CONFIG.maxTokens);
-      }
-
-      // Step 3: Create provider
-      const provider = createProvider(envConfig);
-      expect(provider).toBeInstanceOf(AnthropicProvider);
-
-      // Step 4: Mock response
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_minimal_test',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_minimal_test',
-            name: 'simple_eval',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 50,
-          output_tokens: 25,
-        },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      // Step 5: Execute with defaults
-      const schema = {
-        name: 'simple_eval',
-        schema: {
-          properties: { result: { type: 'string' } },
-          required: ['result'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Simple test',
-        'Simple prompt',
-        schema
-      );
-
-      expect(result.data).toEqual({ result: 'success' });
-      expect(result.usage).toEqual({
-        inputTokens: 50,
-        outputTokens: 25,
-      });
-
-      // Verify defaults were used
-      expect(SHARED_E2E_MOCK_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          model: 'claude-3-sonnet-20240229',
-          max_tokens: 4096,
-          temperature: 0.2, // Default temperature
-        })
-      );
-    });
-  });
-  // This closing brace was missing for the 'describe('Anthropic End-to-End Integration', () => {' block
-  // The 'describe('Error Scenarios and Recovery', () => {' block should be at the same level as 'describe('Complete Flow from Environment to Provider', () => {'
-  // and both should be nested within 'describe('Anthropic End-to-End Integration', () => {'
-  // The original code had an extra '});' after the 'it' block, and then another '});' for the 'describe' block.
-  // This structure implies that 'Error Scenarios and Recovery' was outside the main 'Anthropic End-to-End Integration' describe block.
-  // By adding this brace here, 'Error Scenarios and Recovery' is now correctly nested within the main describe block.
-
-  describe('Error Scenarios and Recovery', () => {
-    it('handles Anthropic API authentication errors end-to-end', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'invalid-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock authentication error
-      const anthropic = await import('@anthropic-ai/sdk');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_E2E_MOCK_CREATE.mockRejectedValue(new anthropic.AuthenticationError({
-        message: 'Invalid API key'
-      }));
-
-      const schema = {
-        name: 'test_eval',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles Anthropic API rate limit errors end-to-end', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock rate limit error
-      const anthropic = await import('@anthropic-ai/sdk');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_E2E_MOCK_CREATE.mockRejectedValue(new anthropic.RateLimitError({
-        message: 'Rate limit exceeded'
-      }));
-
-      const schema = {
-        name: 'test_eval',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles invalid environment configuration', () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        // Missing required ANTHROPIC_API_KEY
-      };
-
-      expect(() => parseEnvironment(env)).toThrow(/Missing required Anthropic environment variables/);
-    });
-
-    it('handles malformed API responses', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock malformed response (missing content)
-      const malformedResponse = {
-        id: 'msg_malformed',
-        type: 'message',
-        role: 'assistant',
-        content: [], // Empty content array
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'end_turn',
-        stop_sequence: null,
-        usage: { input_tokens: 10, output_tokens: 0 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(malformedResponse);
-
-      const schema = {
-        name: 'test_eval',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test', 'Test prompt', schema)
-      ).rejects.toThrow('Empty response from Anthropic API (no content blocks)');
-    });
-
-    it('handles response with wrong tool name', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock response with wrong tool name
-      const wrongToolResponse: AnthropicMessage = {
-        id: 'msg_wrong_tool',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_wrong',
-            name: 'unexpected_tool',
-            input: { result: 'wrong' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: { input_tokens: 50, output_tokens: 25 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(wrongToolResponse);
-
-      const schema = {
-        name: 'expected_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test', 'Test prompt', schema)
-      ).rejects.toThrow('Expected tool call \'expected_tool\' but received: unexpected_tool');
-    });
-  });
-
-  describe('Structured Response Parsing and Validation', () => {
-    it('correctly parses complex structured responses', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock complex structured response
-      const complexResponse: AnthropicMessage = {
-        id: 'msg_complex',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_complex',
-            name: 'detailed_evaluation',
-            input: {
-              overall_score: 87.5,
-              detailed_scores: {
-                clarity: 90,
-                accuracy: 85,
-                completeness: 88,
-              },
-              recommendations: [
-                'Improve technical accuracy',
-                'Add more examples',
-              ],
-              metadata: {
-                word_count: 1250,
-                reading_level: 'intermediate',
-                topics: ['technology', 'education'],
-              },
-            },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: { input_tokens: 200, output_tokens: 100 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(complexResponse);
-
-      const schema = {
-        name: 'detailed_evaluation',
-        schema: {
-          properties: {
-            overall_score: { type: 'number' },
-            detailed_scores: {
-              type: 'object',
-              properties: {
-                clarity: { type: 'number' },
-                accuracy: { type: 'number' },
-                completeness: { type: 'number' },
-              },
-            },
-            recommendations: {
-              type: 'array',
-              items: { type: 'string' },
-            },
-            metadata: {
-              type: 'object',
-              properties: {
-                word_count: { type: 'number' },
-                reading_level: { type: 'string' },
-                topics: { type: 'array', items: { type: 'string' } },
-              },
-            },
-          },
-          required: ['overall_score', 'detailed_scores'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Complex content to evaluate',
-        'Provide detailed evaluation',
-        schema
-      );
-
-      expect(result.data).toEqual({
-        overall_score: 87.5,
-        detailed_scores: {
-          clarity: 90,
-          accuracy: 85,
-          completeness: 88,
-        },
-        recommendations: [
-          'Improve technical accuracy',
-          'Add more examples',
-        ],
-        metadata: {
-          word_count: 1250,
-          reading_level: 'intermediate',
-          topics: ['technology', 'education'],
-        },
-      });
-      expect(result.usage).toEqual({
-        inputTokens: 200,
-        outputTokens: 100,
-      });
-    });
-
-    it('handles responses with text content alongside tool use', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      // Mock response with both text and tool use
-      const mixedResponse: AnthropicMessage = {
-        id: 'msg_mixed',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'text',
-            text: 'I\'ll evaluate this content for you.',
-          },
-          {
-            type: 'tool_use',
-            id: 'tool_mixed',
-            name: 'content_score',
-            input: {
-              score: 78,
-              notes: 'Good overall quality',
-            },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: { input_tokens: 80, output_tokens: 40 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(mixedResponse);
-
-      const schema = {
-        name: 'content_score',
-        schema: {
-          properties: {
-            score: { type: 'number' },
-            notes: { type: 'string' },
-          },
-          required: ['score'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Content to score',
-        'Score this content',
-        schema
-      );
-
-      // Should extract the tool use result, ignoring the text
-      expect(result.data).toEqual({
-        score: 78,
-        notes: 'Good overall quality',
-      });
-      expect(result.usage).toEqual({
-        inputTokens: 80,
-        outputTokens: 40,
-      });
-    });
-  });
-
-  describe('Configuration Integration', () => {
-    it('integrates debug options through the complete flow', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-debug-key',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig, {
-        debug: true,
-        showPrompt: true,
-      });
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_debug',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_debug',
-            name: 'debug_eval',
-            input: { status: 'debug_success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: { input_tokens: 30, output_tokens: 15 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const schema = {
-        name: 'debug_eval',
-        schema: { properties: { status: { type: 'string' } } },
-      };
-
-      // Mock console.log to verify debug output
-      const consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => { });
-
-      const result = await provider.runPromptStructured(
-        'Debug test content',
-        'Debug test prompt',
-        schema
-      );
-
-      expect(result.data).toEqual({ status: 'debug_success' });
-      expect(result.usage).toEqual({
-        inputTokens: 30,
-        outputTokens: 15,
-      });
-
-      // Verify debug logging occurred
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] Sending request to Anthropic:',
-        expect.any(Object)
-      );
-
-      consoleSpy.mockRestore();
-    });
-
-    it('handles temperature configuration through complete flow', async () => {
-      const env = {
-        LLM_PROVIDER: 'anthropic',
-        ANTHROPIC_API_KEY: 'sk-ant-temp-key',
-        ANTHROPIC_TEMPERATURE: '0.8',
-      };
-
-      const envConfig = parseEnvironment(env);
-      const provider = createProvider(envConfig);
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_temp',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_temp',
-            name: 'temp_eval',
-            input: { creativity: 'high' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: { input_tokens: 40, output_tokens: 20 },
-      };
-
-      SHARED_E2E_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const schema = {
-        name: 'temp_eval',
-        schema: { properties: { creativity: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured(
-        'Creative content',
-        'Creative prompt',
-        schema
-      );
-
-      // Verify temperature was passed through
-      expect(SHARED_E2E_MOCK_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          temperature: 0.8,
-        })
-      );
-    });
-  });
-});
diff --git a/tests/anthropic-provider.test.ts b/tests/anthropic-provider.test.ts
deleted file mode 100644
index 31f3f98..0000000
--- a/tests/anthropic-provider.test.ts
+++ /dev/null
@@ -1,848 +0,0 @@
-import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
-import { AnthropicProvider } from '../src/providers/anthropic-provider';
-import { DefaultRequestBuilder } from '../src/providers/request-builder';
-import type { AnthropicMessage } from '../src/schemas/api-schemas';
-import type {
-  MockAPIErrorParams,
-  MockAuthenticationErrorParams,
-  MockRateLimitErrorParams,
-  MockBadRequestErrorParams,
-  MockAnthropicClient
-} from './schemas/mock-schemas';
-
-// Create a shared mock function that all instances will use
-const SHARED_MOCK_CREATE = vi.fn();
-
-// Mock the Anthropic SDK module
-vi.mock('@anthropic-ai/sdk', () => {
-  // Create error classes inside the mock factory
-  class APIError extends Error {
-    status: number;
-    constructor(params: MockAPIErrorParams) {
-      super(params.message);
-      this.status = params.status || 500;
-      this.name = 'APIError';
-    }
-  }
-
-  class RateLimitError extends Error {
-    constructor(params: Partial<MockRateLimitErrorParams> = {}) {
-      super(params.message ?? 'Rate limit exceeded');
-      this.name = 'RateLimitError';
-    }
-  }
-
-  class AuthenticationError extends Error {
-    constructor(params: Partial<MockAuthenticationErrorParams> = {}) {
-      super(params.message ?? 'Authentication failed');
-      this.name = 'AuthenticationError';
-    }
-  }
-
-  class BadRequestError extends Error {
-    constructor(params: Partial<MockBadRequestErrorParams> = {}) {
-      super(params.message ?? 'Bad request');
-      this.name = 'BadRequestError';
-    }
-  }
-
-  return {
-    default: vi.fn((): MockAnthropicClient => ({
-      messages: {
-        create: SHARED_MOCK_CREATE,
-      },
-    })),
-    APIError,
-    RateLimitError,
-    AuthenticationError,
-    BadRequestError,
-  };
-});
-
-// Mock the API client validation
-vi.mock('../src/boundaries/api-client', () => ({
-  validateAnthropicResponse: vi.fn(),
-}));
-
-describe('AnthropicProvider', () => {
-  let mockValidateAnthropicResponse: ReturnType<typeof vi.fn>;
-
-  beforeEach(async () => {
-    vi.clearAllMocks();
-
-    // Get reference to the mocked validation function
-    const apiClient = await import('../src/boundaries/api-client');
-    mockValidateAnthropicResponse = vi.mocked(apiClient.validateAnthropicResponse);
-
-    // Default mock behavior - return the response as-is
-    mockValidateAnthropicResponse.mockImplementation((response: unknown) => response as AnthropicMessage);
-  });
-
-  describe('Constructor', () => {
-    it('creates provider with required config', () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const provider = new AnthropicProvider(config);
-      expect(provider).toBeInstanceOf(AnthropicProvider);
-    });
-
-    it('applies default values for optional config', () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      // Should not throw - defaults should be applied internally
-      expect(() => new AnthropicProvider(config)).not.toThrow();
-    });
-
-    it('accepts custom request builder', () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-      const customBuilder = new DefaultRequestBuilder('custom directive');
-
-      expect(() => new AnthropicProvider(config, customBuilder)).not.toThrow();
-    });
-
-    it('accepts all configuration options', () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        model: 'claude-3-haiku-20240307',
-        maxTokens: 2048,
-        temperature: 0.5,
-        debug: true,
-        showPrompt: true,
-        showPromptTrunc: false,
-      };
-
-      expect(() => new AnthropicProvider(config)).not.toThrow();
-    });
-  });
-
-  describe('Structured Response Handling', () => {
-    it('successfully extracts structured response from tool use', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'submit_evaluation',
-            input: {
-              score: 85,
-              feedback: 'Good content',
-            },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'submit_evaluation',
-        schema: {
-          properties: {
-            score: { type: 'number' },
-            feedback: { type: 'string' },
-          },
-          required: ['score', 'feedback'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Test content',
-        'Test prompt',
-        schema
-      );
-
-      expect(result.data).toEqual({
-        score: 85,
-        feedback: 'Good content',
-      });
-      expect(result.usage).toBeDefined();
-      if (result.usage) {
-        expect(result.usage.inputTokens).toBe(100);
-        expect(result.usage.outputTokens).toBe(50);
-      }
-    });
-
-    it('converts schema to Anthropic tool format correctly', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: {
-          properties: {
-            result: { type: 'string' },
-          },
-          required: ['result'],
-        },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(SHARED_MOCK_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          tools: [
-            {
-              name: 'test_tool',
-              description: 'Submit test_tool evaluation results',
-              input_schema: {
-                type: 'object',
-                properties: {
-                  result: { type: 'string' },
-                },
-                required: ['result'],
-              },
-            },
-          ],
-          tool_choice: { type: 'tool', name: 'test_tool' },
-        })
-      );
-    });
-
-    it('includes temperature in API call when configured', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        temperature: 0.7,
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(SHARED_MOCK_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          temperature: 0.7,
-        })
-      );
-    });
-
-    it('uses default temperature when not explicitly configured', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      const callArgs = SHARED_MOCK_CREATE.mock.calls[0]?.[0] as Record<string, unknown> | undefined;
-      expect(callArgs?.temperature).toBe(0.2); // Default temperature
-    });
-  });
-
-  describe('Error Handling', () => {
-    it('handles Anthropic API errors with status codes', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const anthropic = await import('@anthropic-ai/sdk');
-      const mockApiError = anthropic.APIError as unknown as new (params: MockAPIErrorParams) => Error;
-      SHARED_MOCK_CREATE.mockRejectedValue(new mockApiError({
-        message: 'Invalid request',
-        status: 400
-      }));
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles Anthropic rate limit errors', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const anthropic = await import('@anthropic-ai/sdk');
-      const mockRateLimitError = anthropic.RateLimitError as unknown as new (params: Partial<MockRateLimitErrorParams>) => Error;
-      SHARED_MOCK_CREATE.mockRejectedValue(new mockRateLimitError({
-        message: 'Rate limit exceeded'
-      }));
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles Anthropic authentication errors', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const anthropic = await import('@anthropic-ai/sdk');
-      const mockAuthenticationError = anthropic.AuthenticationError as unknown as new (params: Partial<MockAuthenticationErrorParams>) => Error;
-      SHARED_MOCK_CREATE.mockRejectedValue(new mockAuthenticationError({
-        message: 'Invalid API key'
-      }));
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles Anthropic bad request errors', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const anthropic = await import('@anthropic-ai/sdk');
-      const mockBadRequestError = anthropic.BadRequestError as unknown as new (params: Partial<MockBadRequestErrorParams>) => Error;
-      SHARED_MOCK_CREATE.mockRejectedValue(new mockBadRequestError({
-        message: 'Bad request'
-      }));
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles unknown errors', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      SHARED_MOCK_CREATE.mockRejectedValue(new Error('Unknown error'));
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow();
-    });
-
-    it('handles response validation errors', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      // Mock an invalid response that will fail schema validation
-      const invalidResponse = {
-        // Missing required fields like 'id', 'type', 'role', 'content', etc.
-        model: 'claude-3-sonnet-20240229',
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(invalidResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('API Response Error: Invalid Anthropic API response structure');
-    });
-
-    it('throws error when response has no content', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'end_turn',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 0,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Empty response from Anthropic API (no content blocks).');
-    });
-
-    it('throws error when no tool use is found', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'text',
-            text: 'I cannot provide structured output',
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'end_turn',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 20,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('No tool call received for test_tool. Response contains text instead: I cannot provide structured output');
-    });
-
-    it('throws error when wrong tool name is used', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'wrong_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'expected_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Expected tool call \'expected_tool\' but received: wrong_tool');
-    });
-
-    it('throws error when tool input is empty', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: {},
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Tool call for test_tool returned empty or null input.');
-    });
-
-    it('throws error when tool input is not an object', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-      };
-
-      const mockResponse = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: 'not an object',
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Tool call for test_tool returned invalid input type: string');
-    });
-  });
-
-  describe('Debugging and Logging', () => {
-    let consoleSpy: ReturnType<typeof vi.spyOn>;
-
-    beforeEach(() => {
-      consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => { });
-    });
-
-    afterEach(() => {
-      consoleSpy.mockRestore();
-    });
-
-    it('logs debug information when debug is enabled', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        debug: true,
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] Sending request to Anthropic:',
-        expect.objectContaining({
-          model: 'claude-3-sonnet-20240229',
-          maxTokens: 4096,
-          temperature: 0.2,
-        })
-      );
-
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] LLM response meta:',
-        expect.objectContaining({
-          usage: {
-            input_tokens: 100,
-            output_tokens: 50,
-          },
-          stop_reason: 'tool_use',
-        })
-      );
-    });
-
-    it('shows full prompt when showPrompt is enabled', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        debug: true,
-        showPrompt: true,
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] System prompt (full):');
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] User content (full):');
-      expect(consoleSpy).toHaveBeenCalledWith('Test content');
-    });
-
-    it('shows truncated prompt when showPromptTrunc is enabled', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        debug: true,
-        showPromptTrunc: true,
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      // Use long content to test truncation
-      const longContent = 'A'.repeat(600);
-      await provider.runPromptStructured(longContent, 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] System prompt (first 500 chars):');
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] User content preview (first 500 chars):');
-      expect(consoleSpy).toHaveBeenCalledWith('A'.repeat(500));
-      expect(consoleSpy).toHaveBeenCalledWith('... [truncated]');
-    });
-
-    it('does not log when debug is disabled', async () => {
-      const config = {
-        apiKey: 'sk-ant-test-key',
-        debug: false,
-      };
-
-      const mockResponse: AnthropicMessage = {
-        id: 'msg_123',
-        type: 'message',
-        role: 'assistant',
-        content: [
-          {
-            type: 'tool_use',
-            id: 'tool_123',
-            name: 'test_tool',
-            input: { result: 'success' },
-          },
-        ],
-        model: 'claude-3-sonnet-20240229',
-        stop_reason: 'tool_use',
-        stop_sequence: null,
-        usage: {
-          input_tokens: 100,
-          output_tokens: 50,
-        },
-      };
-
-      SHARED_MOCK_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new AnthropicProvider(config);
-      const schema = {
-        name: 'test_tool',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).not.toHaveBeenCalled();
-    });
-  });
-});
\ No newline at end of file
diff --git a/tests/env-parser.test.ts b/tests/env-parser.test.ts
index 369465a..fb6b50b 100644
--- a/tests/env-parser.test.ts
+++ b/tests/env-parser.test.ts
@@ -2,9 +2,7 @@ import { describe, it, expect } from 'vitest';
 import { parseEnvironment } from '../src/boundaries/env-parser';
 import { ValidationError } from '../src/errors/index';
 import { ProviderType } from '../src/providers/provider-factory';
-import { AZURE_OPENAI_DEFAULT_CONFIG } from '../src/providers/azure-openai-provider';
-import { ANTHROPIC_DEFAULT_CONFIG } from '../src/providers/anthropic-provider';
-import { OPENAI_DEFAULT_CONFIG } from '../src/providers/openai-provider';
+import { AZURE_OPENAI_DEFAULT_CONFIG, ANTHROPIC_DEFAULT_CONFIG, OPENAI_DEFAULT_CONFIG } from '../src/schemas/env-schemas';
 
 describe('Environment Parser', () => {
   describe('Azure OpenAI Configuration', () => {
diff --git a/tests/openai-provider.test.ts b/tests/openai-provider.test.ts
deleted file mode 100644
index 93eff8e..0000000
--- a/tests/openai-provider.test.ts
+++ /dev/null
@@ -1,1019 +0,0 @@
-import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
-import type {
-  MockAPIErrorParams,
-  MockAuthenticationErrorParams,
-  MockRateLimitErrorParams,
-  MockOpenAIClient
-} from './schemas/mock-schemas';
-
-// Shared spy used by all tests
-const SHARED_CREATE = vi.fn();
-
-// Hoist error classes to avoid TDZ issues
-const ERRORS = vi.hoisted(() => {
-  class APIError extends Error {
-    status: number;
-    constructor(params: MockAPIErrorParams) {
-      super(params.message);
-      this.name = 'APIError';
-      this.status = params.status ?? 500;
-    }
-  }
-
-  class AuthenticationError extends APIError {
-    constructor(params: Partial<MockAuthenticationErrorParams> = {}) {
-      super({
-        message: params.message ?? 'Unauthorized',
-        status: 401,
-        options: params.options,
-        body: params.body,
-      });
-      this.name = 'AuthenticationError';
-    }
-  }
-
-  class RateLimitError extends APIError {
-    constructor(params: Partial<MockRateLimitErrorParams> = {}) {
-      super({
-        message: params.message ?? 'Rate Limited',
-        status: 429,
-        options: params.options,
-        body: params.body,
-      });
-      this.name = 'RateLimitError';
-    }
-  }
-
-  return { APIError, AuthenticationError, RateLimitError };
-});
-
-// Mock OpenAI SDK - must come before importing SUT
-vi.mock('openai', () => {
-  // Pull hoisted classes so we reuse the same identities everywhere
-  const { APIError, AuthenticationError, RateLimitError } = ERRORS;
-
-  // Default export client with proper typing
-  const openAI = vi.fn((): MockOpenAIClient => ({
-    // Support either surface your provider might call
-    chat: { completions: { create: SHARED_CREATE } },
-    responses: { create: SHARED_CREATE },
-  }));
-
-  // Attach error classes on the default export too
-  // @ts-expect-error - Mock needs to add error classes to constructor function
-  openAI.APIError = APIError;
-  // @ts-expect-error - Mock needs to add error classes to constructor function
-  openAI.AuthenticationError = AuthenticationError;
-  // @ts-expect-error - Mock needs to add error classes to constructor function
-  openAI.RateLimitError = RateLimitError;
-
-  // Some codebases read from OpenAI.errors.*
-  // @ts-expect-error - Mock needs to add errors object to constructor function
-  openAI.errors = {
-    APIError,
-    AuthenticationError,
-    RateLimitError,
-  };
-
-  return {
-    __esModule: true,
-    default: openAI,
-    // Also expose named exports in case the provider uses named imports
-    APIError,
-    AuthenticationError,
-    RateLimitError,
-  };
-});
-
-// Mock the API client validation
-vi.mock('../src/boundaries/api-client', () => ({
-  validateApiResponse: vi.fn(),
-}));
-
-// Now import SUT after mocks are set up
-import { OpenAIProvider } from '../src/providers/openai-provider';
-import { DefaultRequestBuilder } from '../src/providers/request-builder';
-import type { OpenAIResponse } from '../src/schemas/api-schemas';
-
-describe('OpenAIProvider', () => {
-  let mockValidateApiResponse: ReturnType<typeof vi.fn>;
-
-  beforeEach(async () => {
-    vi.clearAllMocks();
-
-    // Get reference to the mocked validation function
-    const apiClient = await import('../src/boundaries/api-client');
-    mockValidateApiResponse = vi.mocked(apiClient.validateApiResponse);
-
-    // Default mock behavior - return the response as-is
-    mockValidateApiResponse.mockImplementation((response: unknown) => response as OpenAIResponse);
-  });
-
-  describe('Constructor', () => {
-    it('creates provider with required config', () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const provider = new OpenAIProvider(config);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
-    });
-
-    it('applies default values for optional config', () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      // Should not throw - defaults should be applied internally
-      expect(() => new OpenAIProvider(config)).not.toThrow();
-    });
-
-    it('accepts custom request builder', () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-      const customBuilder = new DefaultRequestBuilder('custom directive');
-
-      expect(() => new OpenAIProvider(config, customBuilder)).not.toThrow();
-    });
-
-    it('accepts all configuration options', () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        model: 'gpt-4o-mini',
-        temperature: 0.5,
-        debug: true,
-        showPrompt: true,
-        showPromptTrunc: false,
-      };
-
-      expect(() => new OpenAIProvider(config)).not.toThrow();
-    });
-  });
-
-  describe('Structured Response Handling', () => {
-    it('successfully parses structured JSON response', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({
-                score: 85,
-                feedback: 'Good content',
-              }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-        usage: {
-          prompt_tokens: 100,
-          completion_tokens: 50,
-          total_tokens: 150,
-        },
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'submit_evaluation',
-        schema: {
-          properties: {
-            score: { type: 'number' },
-            feedback: { type: 'string' },
-          },
-          required: ['score', 'feedback'],
-        },
-      };
-
-      const result = await provider.runPromptStructured(
-        'Test content',
-        'Test prompt',
-        schema
-      );
-
-      expect(result.data).toEqual({
-        score: 85,
-        feedback: 'Good content',
-      });
-
-      expect(result.usage).toBeDefined();
-      if (result.usage) {
-        expect(result.usage.inputTokens).toBe(100);
-        expect(result.usage.outputTokens).toBe(50);
-      }
-    });
-
-    it('configures OpenAI API call with JSON schema response format', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: {
-          properties: {
-            result: { type: 'string' },
-          },
-          required: ['result'],
-        },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(SHARED_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          model: 'gpt-4o',
-          messages: [
-            { role: 'system', content: expect.any(String) as string },
-            { role: 'user', content: 'Input:\n\nTest content' },
-          ],
-          response_format: {
-            type: 'json_schema',
-            json_schema: {
-              name: 'test_schema',
-              schema: {
-                properties: {
-                  result: { type: 'string' },
-                },
-                required: ['result'],
-              },
-            },
-          },
-          temperature: 0.2,
-        })
-      );
-    });
-
-    it('properly formats complex JSON schema for OpenAI', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({
-                score: 85,
-                feedback: 'Good',
-                categories: ['content', 'style']
-              }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const complexSchema = {
-        name: 'evaluation_result',
-        schema: {
-          type: 'object',
-          properties: {
-            score: {
-              type: 'number',
-              minimum: 0,
-              maximum: 100
-            },
-            feedback: {
-              type: 'string',
-              minLength: 1
-            },
-            categories: {
-              type: 'array',
-              items: { type: 'string' }
-            }
-          },
-          required: ['score', 'feedback'],
-          additionalProperties: false
-        },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', complexSchema);
-
-      const callArgs = SHARED_CREATE.mock.calls[0]?.[0] as Record<string, unknown> | undefined;
-      expect(callArgs?.response_format).toEqual({
-        type: 'json_schema',
-        json_schema: {
-          name: 'evaluation_result',
-          schema: complexSchema.schema,
-        },
-      });
-    });
-
-    it('includes temperature in API call when configured', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        temperature: 0.7,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(SHARED_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          temperature: 0.7,
-        })
-      );
-    });
-
-    it('uses default temperature when not explicitly configured', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      const callArgs = SHARED_CREATE.mock.calls[0]?.[0] as Record<string, unknown> | undefined;
-      expect(callArgs?.temperature).toBe(0.2); // Default temperature
-    });
-
-    it('uses default model when not explicitly configured', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      const callArgs = SHARED_CREATE.mock.calls[0]?.[0] as Record<string, unknown> | undefined;
-      expect(callArgs?.model).toBe('gpt-4o'); // Default model
-    });
-
-    it('uses custom model when configured', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        model: 'gpt-4o-mini',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      const callArgs = SHARED_CREATE.mock.calls[0]?.[0] as Record<string, unknown> | undefined;
-      expect(callArgs?.model).toBe('gpt-4o-mini');
-    });
-  });
-
-  describe('Error Handling', () => {
-    it('mock sanity check', async () => {
-      const mod = await import('openai');
-      expect(mod.default.APIError).toBe(mod.APIError);
-      expect(typeof mod.default.APIError).toBe('function');
-      expect(typeof new mod.default().chat.completions.create).toBe('function');
-    });
-
-    it('handles OpenAI API errors', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const openAI = await import('openai');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_CREATE.mockRejectedValue(new openAI.APIError({
-        message: 'API request failed',
-        status: 429
-      }));
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('OpenAI API error (429): API request failed');
-    });
-
-    it('handles OpenAI rate limit errors', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const openAI = await import('openai');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_CREATE.mockRejectedValue(new openAI.RateLimitError({
-        message: 'Rate limit exceeded'
-      }));
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('OpenAI rate limit exceeded: Rate limit exceeded');
-    });
-
-    it('handles OpenAI authentication errors', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const openAI = await import('openai');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_CREATE.mockRejectedValue(new openAI.AuthenticationError({
-        message: 'Invalid API key'
-      }));
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('OpenAI authentication failed: Invalid API key');
-    });
-
-    it('handles unknown errors', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      SHARED_CREATE.mockRejectedValue(new Error('Unknown error'));
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('OpenAI API call failed: Unknown error');
-    });
-
-    it('handles response validation errors', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      // Mock an invalid response that will fail schema validation
-      const invalidResponse = {
-        // Missing required 'choices' field
-        usage: {
-          prompt_tokens: 10,
-          completion_tokens: 20,
-          total_tokens: 30,
-        },
-      };
-
-      SHARED_CREATE.mockResolvedValue(invalidResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('API Response Error: Invalid OpenAI API response structure');
-    });
-
-    it('throws error when response has no content', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: null,
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Empty response from OpenAI API (no content).');
-    });
-
-    it('throws error when response has empty content', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: '   ',
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Empty response from OpenAI API (no content).');
-    });
-
-    it('throws error when JSON parsing fails', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: 'invalid json content',
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow('Failed to parse structured JSON response');
-    });
-
-    it('includes response preview in JSON parsing error', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const longInvalidJson = 'invalid json content that is longer than 200 characters '.repeat(10);
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: longInvalidJson,
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await expect(
-        provider.runPromptStructured('Test content', 'Test prompt', schema)
-      ).rejects.toThrow(/Preview:.*\.\.\./);
-    });
-  });
-
-  describe('Debugging and Logging', () => {
-    let consoleSpy: ReturnType<typeof vi.spyOn>;
-
-    beforeEach(() => {
-      consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => { });
-    });
-
-    afterEach(() => {
-      consoleSpy.mockRestore();
-    });
-
-    it('logs debug information when debug is enabled', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        debug: true,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-        usage: {
-          prompt_tokens: 100,
-          completion_tokens: 50,
-          total_tokens: 150,
-        },
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] Sending request to OpenAI:',
-        expect.objectContaining({
-          model: 'gpt-4o',
-          temperature: 0.2,
-        })
-      );
-
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] LLM response meta:',
-        expect.objectContaining({
-          usage: {
-            prompt_tokens: 100,
-            completion_tokens: 50,
-            total_tokens: 150,
-          },
-          finish_reason: 'stop',
-        })
-      );
-    });
-
-    it('shows full prompt when showPrompt is enabled', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        debug: true,
-        showPrompt: true,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] System prompt (full):');
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] User content (full):');
-      expect(consoleSpy).toHaveBeenCalledWith('Test content');
-    });
-
-    it('shows truncated prompt when showPromptTrunc is enabled', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        debug: true,
-        showPromptTrunc: true,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      // Use long content to test truncation
-      const longContent = 'A'.repeat(600);
-      await provider.runPromptStructured(longContent, 'Test prompt', schema);
-
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] System prompt (first 500 chars):');
-      expect(consoleSpy).toHaveBeenCalledWith('[vectorlint] User content preview (first 500 chars):');
-      expect(consoleSpy).toHaveBeenCalledWith('A'.repeat(500));
-      expect(consoleSpy).toHaveBeenCalledWith('... [truncated]');
-    });
-
-    it('does not log when debug is disabled', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-        debug: false,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(consoleSpy).not.toHaveBeenCalled();
-    });
-
-    it('never exposes API keys in debug logs', async () => {
-      const sensitiveApiKey = 'sk-very-secret-api-key-12345';
-      const config = {
-        apiKey: sensitiveApiKey,
-        debug: true,
-        showPrompt: true,
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-        usage: {
-          prompt_tokens: 100,
-          completion_tokens: 50,
-          total_tokens: 150,
-        },
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      // Check all console.log calls to ensure API key is never exposed
-      const allLogCalls = consoleSpy.mock.calls.flat();
-      const allLogContent = allLogCalls.join(' ');
-
-      expect(allLogContent).not.toContain(sensitiveApiKey);
-      expect(allLogContent).not.toContain('sk-very-secret-api-key');
-      expect(allLogContent).not.toContain('very-secret-api-key');
-    });
-
-    it('never exposes API keys in debug logs even during errors', async () => {
-      const sensitiveApiKey = 'sk-another-secret-key-67890';
-      const config = {
-        apiKey: sensitiveApiKey,
-        debug: true,
-      };
-
-      const openAI = await import('openai');
-      // @ts-expect-error - Mock constructor signature differs from real SDK
-      SHARED_CREATE.mockRejectedValue(new openAI.APIError({
-        message: 'Authentication failed',
-        status: 401
-      }));
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      try {
-        await provider.runPromptStructured('Test content', 'Test prompt', schema);
-      } catch {
-        // Error handling is expected, we're testing debug logs
-      }
-
-      // Check console logs don't contain API key - this is the main security test
-      const allLogCalls = consoleSpy.mock.calls.flat();
-      const allLogContent = allLogCalls.join(' ');
-
-      expect(allLogContent).not.toContain(sensitiveApiKey);
-      expect(allLogContent).not.toContain('sk-another-secret-key');
-      expect(allLogContent).not.toContain('another-secret-key');
-
-      // Verify debug logs were actually called (so test is meaningful)
-      expect(consoleSpy).toHaveBeenCalledWith(
-        '[vectorlint] Sending request to OpenAI:',
-        expect.objectContaining({
-          model: 'gpt-4o',
-          temperature: 0.2,
-        })
-      );
-    });
-  });
-
-  describe('Request Building', () => {
-    it('uses request builder to build system prompt', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const mockBuilder = {
-        buildPromptBodyForStructured: vi.fn().mockReturnValue('Built system prompt'),
-      };
-
-      const provider = new OpenAIProvider(config, mockBuilder);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('Test content', 'Test prompt', schema);
-
-      expect(mockBuilder.buildPromptBodyForStructured).toHaveBeenCalledWith('Test prompt');
-
-      expect(SHARED_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          messages: [
-            { role: 'system', content: 'Built system prompt' },
-            { role: 'user', content: 'Input:\n\nTest content' },
-          ],
-        })
-      );
-    });
-
-    it('formats user message correctly', async () => {
-      const config = {
-        apiKey: 'sk-test-key',
-      };
-
-      const mockResponse: OpenAIResponse = {
-        choices: [
-          {
-            message: {
-              content: JSON.stringify({ result: 'success' }),
-            },
-            finish_reason: 'stop',
-          },
-        ],
-      };
-
-      SHARED_CREATE.mockResolvedValue(mockResponse);
-
-      const provider = new OpenAIProvider(config);
-      const schema = {
-        name: 'test_schema',
-        schema: { properties: { result: { type: 'string' } } },
-      };
-
-      await provider.runPromptStructured('User input content', 'Test prompt', schema);
-
-      expect(SHARED_CREATE).toHaveBeenCalledWith(
-        expect.objectContaining({
-          messages: expect.arrayContaining([
-            { role: 'user', content: 'Input:\n\nUser input content' },
-          ]) as unknown[],
-        })
-      );
-    });
-  });
-});
\ No newline at end of file
diff --git a/tests/perplexity-provider.test.ts b/tests/perplexity-provider.test.ts
index d49315c..3140736 100644
--- a/tests/perplexity-provider.test.ts
+++ b/tests/perplexity-provider.test.ts
@@ -1,22 +1,34 @@
-import { describe, it, expect, vi, beforeEach } from 'vitest';
+import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
 import { PerplexitySearchProvider } from '../src/providers/perplexity-provider';
-import { createMockPerplexityClient } from './schemas/mock-schemas';
-
-const SHARED_CREATE = vi.fn();
-
-// Mock the Perplexity SDK before importing SUT to avoid TDZ issues
-vi.mock('@perplexity-ai/perplexity_ai', () => {
-  return {
-    __esModule: true,
-    default: vi.fn(() => ({
-      search: {
-        create: SHARED_CREATE,
-      },
-    })),
-  };
-});
 
-const MOCK_RESULTS = [
+
+// Mock the Vercel AI SDK — use vi.hoisted so the mock is available in the vi.mock factory
+const MOCK_GENERATE_TEXT = vi.hoisted(() => vi.fn());
+
+vi.mock('ai', () => ({
+  generateText: MOCK_GENERATE_TEXT,
+}));
+
+vi.mock('@ai-sdk/perplexity', () => ({
+  createPerplexity: vi.fn(() => vi.fn((model: string) => ({ _type: 'perplexity', model }))),
+}));
+
+// Mock data matching the raw Perplexity API source shape (uses `text`, not `snippet`)
+const MOCK_SOURCES = [
+  {
+    title: 'AI Overview',
+    text: 'AI tools in 2025 are evolving fast.',
+    url: 'https://example.com/ai-overview',
+  },
+  {
+    title: 'Developer Productivity',
+    text: 'AI improves developer efficiency by 40%.',
+    url: 'https://example.com/dev-productivity',
+  },
+];
+
+// Expected mapped output (provider maps `text` → `snippet`, adds `date` default)
+const EXPECTED_RESULTS = [
   {
     title: 'AI Overview',
     snippet: 'AI tools in 2025 are evolving fast.',
@@ -32,114 +44,218 @@ const MOCK_RESULTS = [
 ];
 
 describe('PerplexitySearchProvider', () => {
+  const ORIGINAL_ENV = { ...process.env };
+
   beforeEach(() => {
     vi.clearAllMocks();
+    // Mock process.env.PERPLEXITY_API_KEY for tests
+    process.env.PERPLEXITY_API_KEY = 'test-api-key';
+  });
+
+  afterEach(() => {
+    process.env = { ...ORIGINAL_ENV };
   });
 
   describe('Constructor', () => {
-    it('initializes with defaults', () => {
+    it('initializes with defaults using environment variable', () => {
       const provider = new PerplexitySearchProvider();
       expect(provider).toBeInstanceOf(PerplexitySearchProvider);
     });
 
-    it('accepts override config', () => {
-      const provider = new PerplexitySearchProvider({ maxResults: 10, maxTokensPerPage: 512, debug: true });
+    it('accepts override config with apiKey', () => {
+      const provider = new PerplexitySearchProvider({ apiKey: 'custom-key', maxResults: 10, debug: true });
       expect(provider).toBeInstanceOf(PerplexitySearchProvider);
     });
-  });
 
-  describe('Search behavior', () => {
-    it('calls the Perplexity API with valid parameters and normalizes results', async () => {
-      SHARED_CREATE.mockResolvedValueOnce({ results: MOCK_RESULTS });
+    it('throws error when no API key is provided', () => {
+      delete process.env.PERPLEXITY_API_KEY;
+      expect(() => new PerplexitySearchProvider()).toThrow('Perplexity API key is required');
+    });
 
-      const provider = new PerplexitySearchProvider({ maxResults: 2, maxTokensPerPage: 512 });
-      const res = await provider.search('AI tools in 2025');
+    it('accepts partial config with maxResults', () => {
+      const provider = new PerplexitySearchProvider({ maxResults: 2 });
+      expect(provider).toBeInstanceOf(PerplexitySearchProvider);
+    });
+  });
 
-      expect(SHARED_CREATE).toHaveBeenCalledWith({
-        query: 'AI tools in 2025',
-        max_results: 2,
-        max_tokens_per_page: 512,
+  describe('search', () => {
+    it('executes search query successfully', async () => {
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: MOCK_SOURCES,
       });
 
-      expect(res).toEqual(MOCK_RESULTS);
-    });
+      const provider = new PerplexitySearchProvider({ maxResults: 2 });
+      const results = await provider.search('AI tools for developers');
 
-    it('handles optional max_results and tokens by sending defaults when not specified', async () => {
-      // provider default constructor sets maxResults=5 and maxTokensPerPage=1024
-      SHARED_CREATE.mockResolvedValueOnce({ results: MOCK_RESULTS });
+      expect(results).toHaveLength(2);
+      expect(results[0]).toEqual(EXPECTED_RESULTS[0]);
+    });
 
+    it('throws error for empty query', async () => {
       const provider = new PerplexitySearchProvider();
-      await provider.search('modern LLM architectures');
-
-      const args = SHARED_CREATE.mock.calls[0]?.[0] as {
-        query: string;
-        max_results?: number;
-        max_tokens_per_page?: number;
-      } | undefined;
 
-      expect(args).toBeDefined();
-      expect(args).toHaveProperty('query', 'modern LLM architectures');
-      // provider sets defaults; tests should expect those defaults to be present
-      expect(args).toHaveProperty('max_results', 5);
-      expect(args).toHaveProperty('max_tokens_per_page', 1024);
+      await expect(provider.search('')).rejects.toThrow('Search query cannot be empty');
+      await expect(provider.search('   ')).rejects.toThrow('Search query cannot be empty');
     });
 
-    it('returns empty array if Perplexity returns no results', async () => {
-      SHARED_CREATE.mockResolvedValueOnce({ results: [] });
+    it('handles empty sources array', async () => {
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: [],
+      });
 
       const provider = new PerplexitySearchProvider();
-      const results = await provider.search('nonexistent query');
-      expect(results).toEqual([]);
+      const results = await provider.search('unknown topic');
+
+      expect(results).toHaveLength(0);
     });
 
-    it('throws helpful error when API call fails', async () => {
-      SHARED_CREATE.mockRejectedValueOnce(new Error('Network error'));
+    it('limits results to maxResults', async () => {
+      const manyResults = Array.from({ length: 10 }, (_, i) => ({
+        title: `Result ${i}`,
+        text: `Snippet ${i}`,
+        url: `https://example.com/${i}`,
+        publishedDate: '',
+      }));
+
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: manyResults,
+      });
+
+      const provider = new PerplexitySearchProvider({ maxResults: 5 });
+      const results = await provider.search('test query');
+
+      expect(results).toHaveLength(5);
+    });
+
+    // This test relies on PERPLEXITY_SOURCE_SCHEMA marking all fields as optional
+    // with .passthrough(), so objects with missing fields still pass validation.
+    it('handles missing fields gracefully', async () => {
+      const incompleteResults = [
+        {
+          // Missing all fields
+        },
+        {
+          title: 'Has Title',
+          // Missing other fields
+        },
+        {
+          text: 'Has snippet',
+          url: 'https://example.com',
+          publishedDate: '2025-01-01',
+        },
+      ];
+
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: incompleteResults,
+      });
 
       const provider = new PerplexitySearchProvider();
-      await expect(provider.search('AI')).rejects.toThrow('Perplexity API call failed: Network error');
+      const results = await provider.search('test');
+
+      expect(results).toHaveLength(3);
+      expect(results[0]!.title).toBe('Untitled');
+      expect(results[0]!.snippet).toBe('');
+      expect(results[1]!.title).toBe('Has Title');
+      expect(results[1]!.snippet).toBe('');
     });
   });
 
-  describe('Error and schema validation', () => {
-    it('throws when query is empty', async () => {
-      const provider = new PerplexitySearchProvider();
-      await expect(provider.search('')).rejects.toThrow('Search query cannot be empty.');
+  describe('Debug Logging', () => {
+    let consoleSpy: ReturnType<typeof vi.spyOn>;
+
+    beforeEach(() => {
+      consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => { });
     });
 
-    it('validates mock schema using createMockPerplexityClient', async () => {
-      const fn = vi.fn().mockResolvedValue({});
-      const client = createMockPerplexityClient(fn);
-      await client.search.create({ query: 'test query', max_results: 3 });
-      expect(fn).toHaveBeenCalledWith({ query: 'test query', max_results: 3 });
+    afterEach(() => {
+      consoleSpy.mockRestore();
     });
-  });
 
-  describe('Debug logging', () => {
-    it('logs debug info when debug is enabled', async () => {
-      SHARED_CREATE.mockResolvedValueOnce({ results: MOCK_RESULTS });
-      const logSpy = vi.spyOn(console, 'log').mockImplementation(() => {});
-      const provider = new PerplexitySearchProvider({ debug: true });
+    it('logs search query when debug is enabled', async () => {
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: MOCK_SOURCES,
+      });
 
+      const provider = new PerplexitySearchProvider({ debug: true });
       await provider.search('test query');
 
-      // provider emits Perplexity-specific debug messages
-      expect(logSpy).toHaveBeenCalledWith('[Perplexity] Searching: "test query"');
-      expect(logSpy).toHaveBeenCalledWith('[Perplexity] Found 2 results');
-      // provider also logs a preview of results (array), accept any array
-      expect(logSpy).toHaveBeenCalledWith(expect.any(Array));
+      expect(consoleSpy).toHaveBeenCalledWith('[Perplexity] Searching: "test query"');
+    });
+
+    it('logs results count when debug is enabled', async () => {
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: MOCK_SOURCES,
+      });
 
-      logSpy.mockRestore();
+      const provider = new PerplexitySearchProvider({ debug: true });
+      await provider.search('test query');
+
+      expect(consoleSpy).toHaveBeenCalledWith('[Perplexity] Found 2 results');
     });
 
     it('does not log when debug is disabled', async () => {
-      SHARED_CREATE.mockResolvedValueOnce({ results: MOCK_RESULTS });
-      const logSpy = vi.spyOn(console, 'log').mockImplementation(() => {});
-      const provider = new PerplexitySearchProvider({ debug: false });
+      MOCK_GENERATE_TEXT.mockResolvedValue({
+        sources: MOCK_SOURCES,
+      });
 
+      const provider = new PerplexitySearchProvider({ debug: false });
       await provider.search('test query');
 
-      expect(logSpy).not.toHaveBeenCalled();
-      logSpy.mockRestore();
+      expect(consoleSpy).not.toHaveBeenCalled();
+    });
+  });
+
+  describe('Error Handling', () => {
+    it('throws descriptive error for API failures', async () => {
+      MOCK_GENERATE_TEXT.mockRejectedValue(new Error('Network error'));
+
+      const provider = new PerplexitySearchProvider();
+
+      await expect(provider.search('test')).rejects.toThrow('Perplexity API call failed: Network error');
+    });
+
+    it('handles unknown error types', async () => {
+      MOCK_GENERATE_TEXT.mockRejectedValue('String error');
+
+      const provider = new PerplexitySearchProvider();
+
+      await expect(provider.search('test')).rejects.toThrow('Perplexity API call failed: String error');
+    });
+  });
+
+  describe('Configuration', () => {
+    it('returns all sources when maxResults exceeds available count', async () => {
+      const results = Array.from({ length: 3 }, (_, i) => ({
+        title: `Result ${i}`,
+        text: `Snippet ${i}`,
+        url: `https://example.com/${i}`,
+        publishedDate: '',
+      }));
+
+      MOCK_GENERATE_TEXT.mockResolvedValue({ sources: results });
+
+      const provider = new PerplexitySearchProvider({ maxResults: 10 });
+      const searchResults = await provider.search('test');
+
+      // maxResults (10) > available sources (3), so all 3 should be returned
+      expect(searchResults).toHaveLength(3);
+    });
+
+    it('uses default maxResults when not specified', async () => {
+      const results = Array.from({ length: 10 }, (_, i) => ({
+        title: `Result ${i}`,
+        text: `Snippet ${i}`,
+        url: `https://example.com/${i}`,
+        publishedDate: '',
+      }));
+
+      MOCK_GENERATE_TEXT.mockResolvedValue({ sources: results });
+
+      const provider = new PerplexitySearchProvider();
+      const searchResults = await provider.search('test');
+
+      // Default maxResults is 5
+      expect(searchResults).toHaveLength(5);
     });
   });
 });
diff --git a/tests/provider-factory.test.ts b/tests/provider-factory.test.ts
index 8e009f0..0d2abb8 100644
--- a/tests/provider-factory.test.ts
+++ b/tests/provider-factory.test.ts
@@ -1,14 +1,29 @@
-import { describe, it, expect } from 'vitest';
+import { describe, it, expect, vi } from 'vitest';
 import { createProvider, ProviderType } from '../src/providers/provider-factory';
-import { AzureOpenAIProvider } from '../src/providers/azure-openai-provider';
-import { AnthropicProvider } from '../src/providers/anthropic-provider';
-import { OpenAIProvider } from '../src/providers/openai-provider';
+import { VercelAIProvider } from '../src/providers/vercel-ai-provider';
 import { DefaultRequestBuilder } from '../src/providers/request-builder';
 import type { EnvConfig } from '../src/schemas/env-schemas';
 
+// Mock the Vercel AI SDK provider creators
+vi.mock('@ai-sdk/openai', () => ({
+  createOpenAI: vi.fn(() => vi.fn((model: string) => ({ _type: 'openai', model }))),
+}));
+
+vi.mock('@ai-sdk/azure', () => ({
+  createAzure: vi.fn(() => vi.fn((model: string) => ({ _type: 'azure', model }))),
+}));
+
+vi.mock('@ai-sdk/anthropic', () => ({
+  createAnthropic: vi.fn(() => vi.fn((model: string) => ({ _type: 'anthropic', model }))),
+}));
+
+vi.mock('@ai-sdk/google', () => ({
+  createGoogleGenerativeAI: vi.fn(() => vi.fn((model: string) => ({ _type: 'google', model }))),
+}));
+
 describe('Provider Factory', () => {
   describe('Provider Instantiation', () => {
-    it('creates Azure OpenAI provider when configured', () => {
+    it('creates VercelAIProvider for Azure OpenAI when configured', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.AzureOpenAI,
         AZURE_OPENAI_API_KEY: 'test-key',
@@ -18,10 +33,10 @@ describe('Provider Factory', () => {
       };
 
       const provider = createProvider(envConfig, { debug: true });
-      expect(provider).toBeInstanceOf(AzureOpenAIProvider);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
     });
 
-    it('creates Anthropic provider when configured', () => {
+    it('creates VercelAIProvider for Anthropic when configured', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.Anthropic,
         ANTHROPIC_API_KEY: 'sk-ant-test-key',
@@ -30,10 +45,10 @@ describe('Provider Factory', () => {
       };
 
       const provider = createProvider(envConfig, { debug: true });
-      expect(provider).toBeInstanceOf(AnthropicProvider);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
     });
 
-    it('creates OpenAI provider when configured', () => {
+    it('creates VercelAIProvider for OpenAI when configured', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
         OPENAI_API_KEY: 'sk-test-key',
@@ -42,57 +57,18 @@ describe('Provider Factory', () => {
       };
 
       const provider = createProvider(envConfig, { debug: true });
-      expect(provider).toBeInstanceOf(OpenAIProvider);
-    });
-
-    it('creates Azure OpenAI provider with minimal configuration', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      const provider = createProvider(envConfig);
-      expect(provider).toBeInstanceOf(AzureOpenAIProvider);
-    });
-
-    it('creates Anthropic provider with minimal configuration', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.Anthropic,
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-        ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
-        ANTHROPIC_MAX_TOKENS: 4096,
-      };
-
-      const provider = createProvider(envConfig);
-      expect(provider).toBeInstanceOf(AnthropicProvider);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
     });
 
-    it('creates OpenAI provider with minimal configuration', () => {
+    it('creates VercelAIProvider for Gemini when configured', () => {
       const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
+        LLM_PROVIDER: ProviderType.Gemini,
+        GEMINI_API_KEY: 'test-key',
+        GEMINI_MODEL: 'gemini-2.5-flash',
       };
 
       const provider = createProvider(envConfig);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
-    });
-
-    it('creates provider with custom request builder', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      const customBuilder = new DefaultRequestBuilder('Custom directive');
-      const provider = createProvider(envConfig, {}, customBuilder);
-      expect(provider).toBeInstanceOf(AzureOpenAIProvider);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
     });
   });
 
@@ -107,7 +83,6 @@ describe('Provider Factory', () => {
         AZURE_OPENAI_TEMPERATURE: 0.8,
       };
 
-      // Should not throw - configuration should be valid
       expect(() => createProvider(envConfig)).not.toThrow();
     });
 
@@ -120,7 +95,6 @@ describe('Provider Factory', () => {
         ANTHROPIC_TEMPERATURE: 0.5,
       };
 
-      // Should not throw - configuration should be valid
       expect(() => createProvider(envConfig)).not.toThrow();
     });
 
@@ -132,53 +106,14 @@ describe('Provider Factory', () => {
         OPENAI_TEMPERATURE: 0.8,
       };
 
-      // Should not throw - configuration should be valid
       expect(() => createProvider(envConfig)).not.toThrow();
     });
 
-    it('passes debug options to Azure OpenAI provider', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      const options = {
-        debug: true,
-        showPrompt: true,
-        showPromptTrunc: false,
-      };
-
-      // Should not throw - provider creation should work with options
-      expect(() => createProvider(envConfig, options)).not.toThrow();
-    });
-
-    it('passes debug options to Anthropic provider', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.Anthropic,
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-        ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
-        ANTHROPIC_MAX_TOKENS: 4096,
-      };
-
-      const options = {
-        debug: true,
-        showPrompt: false,
-        showPromptTrunc: true,
-      };
-
-      // Should not throw - provider creation should work with options
-      expect(() => createProvider(envConfig, options)).not.toThrow();
-    });
-
-    it('passes debug options to OpenAI provider', () => {
+    it('passes debug options to provider', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
         OPENAI_API_KEY: 'sk-test-key',
         OPENAI_MODEL: 'gpt-4o',
-        OPENAI_TEMPERATURE: 0.3,
       };
 
       const options = {
@@ -187,7 +122,6 @@ describe('Provider Factory', () => {
         showPromptTrunc: false,
       };
 
-      // Should not throw - provider creation should work with options
       expect(() => createProvider(envConfig, options)).not.toThrow();
     });
   });
@@ -203,7 +137,7 @@ describe('Provider Factory', () => {
 
     it('throws descriptive error for invalid provider type', () => {
       const envConfig = {
-        LLM_PROVIDER: 'unsupported-provider', // Not supported
+        LLM_PROVIDER: 'unsupported-provider',
       } as unknown as EnvConfig;
 
       expect(() => createProvider(envConfig)).toThrow('Unsupported provider type: unsupported-provider');
@@ -224,161 +158,50 @@ describe('Provider Factory', () => {
     });
   });
 
-  describe('Backward Compatibility', () => {
-    it('maintains consistent interface for Azure OpenAI provider', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'legacy-key',
-        AZURE_OPENAI_ENDPOINT: 'https://legacy.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'legacy-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      const provider = createProvider(envConfig);
-
-      // Should implement the LLMProvider interface
-      expect(provider).toHaveProperty('runPromptStructured');
-      expect(typeof provider.runPromptStructured).toBe('function');
-    });
-
-    it('maintains consistent interface for Anthropic provider', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.Anthropic,
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-        ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
-        ANTHROPIC_MAX_TOKENS: 4096,
-      };
-
-      const provider = createProvider(envConfig);
-
-      // Should implement the LLMProvider interface
-      expect(provider).toHaveProperty('runPromptStructured');
-      expect(typeof provider.runPromptStructured).toBe('function');
-    });
-
-    it('maintains consistent interface for OpenAI provider', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
-      };
-
-      const provider = createProvider(envConfig);
-
-      // Should implement the LLMProvider interface
-      expect(provider).toHaveProperty('runPromptStructured');
-      expect(typeof provider.runPromptStructured).toBe('function');
-    });
-
-    it('works with existing Azure OpenAI configurations without changes', () => {
-      // Simulate an existing configuration that worked before Anthropic support
-      const existingConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'existing-api-key',
-        AZURE_OPENAI_ENDPOINT: 'https://existing.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'existing-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-        AZURE_OPENAI_TEMPERATURE: 1.0,
-      };
-
-      const provider = createProvider(existingConfig, {
-        debug: false,
-        showPrompt: false,
-        showPromptTrunc: false,
-      });
-
-      expect(provider).toBeInstanceOf(AzureOpenAIProvider);
-    });
-  });
-
-  describe('Provider-Specific Configuration', () => {
-    it('handles Azure OpenAI specific fields correctly', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-        AZURE_OPENAI_TEMPERATURE: 1.5,
-      };
-
-      // Should create provider successfully with Azure-specific config
-      expect(() => createProvider(envConfig)).not.toThrow();
-    });
-
-    it('handles Anthropic specific fields correctly', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.Anthropic,
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-        ANTHROPIC_MODEL: 'claude-3-opus-20240229',
-        ANTHROPIC_MAX_TOKENS: 8192,
-        ANTHROPIC_TEMPERATURE: 0.9,
-      };
-
-      // Should create provider successfully with Anthropic-specific config
-      expect(() => createProvider(envConfig)).not.toThrow();
-    });
-
-    it('handles OpenAI specific fields correctly', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4-turbo',
-        OPENAI_TEMPERATURE: 1.5,
-      };
-
-      // Should create provider successfully with OpenAI-specific config
-      expect(() => createProvider(envConfig)).not.toThrow();
-    });
-
-    it('creates providers with different temperature ranges', () => {
-      // Azure OpenAI supports 0-2 temperature range
+  describe('Interface Consistency', () => {
+    it('maintains consistent LLMProvider interface for all providers', () => {
       const azureConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.AzureOpenAI,
         AZURE_OPENAI_API_KEY: 'test-key',
         AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
         AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
         AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-        AZURE_OPENAI_TEMPERATURE: 2.0,
       };
 
-      // Anthropic supports 0-1 temperature range
       const anthropicConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.Anthropic,
         ANTHROPIC_API_KEY: 'sk-ant-test-key',
         ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
         ANTHROPIC_MAX_TOKENS: 4096,
-        ANTHROPIC_TEMPERATURE: 1.0,
       };
 
-      // OpenAI supports 0-2 temperature range
       const openaiConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
         OPENAI_API_KEY: 'sk-test-key',
         OPENAI_MODEL: 'gpt-4o',
-        OPENAI_TEMPERATURE: 2.0,
       };
 
-      expect(() => createProvider(azureConfig)).not.toThrow();
-      expect(() => createProvider(anthropicConfig)).not.toThrow();
-      expect(() => createProvider(openaiConfig)).not.toThrow();
+      const geminiConfig: EnvConfig = {
+        LLM_PROVIDER: ProviderType.Gemini,
+        GEMINI_API_KEY: 'test-key',
+        GEMINI_MODEL: 'gemini-2.5-flash',
+      };
+
+      const azureProvider = createProvider(azureConfig);
+      const anthropicProvider = createProvider(anthropicConfig);
+      const openaiProvider = createProvider(openaiConfig);
+      const geminiProvider = createProvider(geminiConfig);
+
+      // All should implement the LLMProvider interface
+      for (const provider of [azureProvider, anthropicProvider, openaiProvider, geminiProvider]) {
+        expect(provider).toHaveProperty('runPromptStructured');
+        expect(typeof provider.runPromptStructured).toBe('function');
+      }
     });
   });
 
   describe('Options Handling', () => {
     it('works without options parameter', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      expect(() => createProvider(envConfig)).not.toThrow();
-    });
-
-    it('works without options parameter for OpenAI', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
         OPENAI_API_KEY: 'sk-test-key',
@@ -399,16 +222,6 @@ describe('Provider Factory', () => {
       expect(() => createProvider(envConfig, {})).not.toThrow();
     });
 
-    it('works with empty options object for OpenAI', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
-      };
-
-      expect(() => createProvider(envConfig, {})).not.toThrow();
-    });
-
     it('works with partial options', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.AzureOpenAI,
@@ -422,17 +235,6 @@ describe('Provider Factory', () => {
       expect(() => createProvider(envConfig, { showPrompt: true })).not.toThrow();
     });
 
-    it('works with partial options for OpenAI', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
-      };
-
-      expect(() => createProvider(envConfig, { debug: true })).not.toThrow();
-      expect(() => createProvider(envConfig, { showPrompt: true })).not.toThrow();
-    });
-
     it('handles all debug options for all providers', () => {
       const azureConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.AzureOpenAI,
@@ -455,6 +257,12 @@ describe('Provider Factory', () => {
         OPENAI_MODEL: 'gpt-4o',
       };
 
+      const geminiConfig: EnvConfig = {
+        LLM_PROVIDER: ProviderType.Gemini,
+        GEMINI_API_KEY: 'test-key',
+        GEMINI_MODEL: 'gemini-2.5-flash',
+      };
+
       const allOptions = {
         debug: true,
         showPrompt: true,
@@ -464,127 +272,70 @@ describe('Provider Factory', () => {
       expect(() => createProvider(azureConfig, allOptions)).not.toThrow();
       expect(() => createProvider(anthropicConfig, allOptions)).not.toThrow();
       expect(() => createProvider(openaiConfig, allOptions)).not.toThrow();
+      expect(() => createProvider(geminiConfig, allOptions)).not.toThrow();
     });
   });
 
-  describe('OpenAI Configuration Mapping Integration', () => {
-    it('correctly maps OpenAI environment variables to provider config', () => {
-      const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-integration-test-key',
-        OPENAI_MODEL: 'gpt-4o-mini',
-        OPENAI_TEMPERATURE: 0.7,
-      };
-
-      const options = {
-        debug: true,
-        showPrompt: false,
-        showPromptTrunc: true,
-      };
-
-      // Should create provider without throwing
-      const provider = createProvider(envConfig, options);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
-    });
-
-    it('handles OpenAI configuration with undefined optional fields', () => {
+  describe('Custom Request Builder', () => {
+    it('passes custom request builder to provider', () => {
       const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
         OPENAI_API_KEY: 'sk-test-key',
         OPENAI_MODEL: 'gpt-4o',
-        // OPENAI_TEMPERATURE is undefined
       };
 
-      const provider = createProvider(envConfig);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
+      const customBuilder = new DefaultRequestBuilder('Custom directive');
+      const provider = createProvider(envConfig, {}, customBuilder);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
     });
+  });
 
-    it('passes request builder to OpenAI provider correctly', () => {
+  describe('Provider-Specific Configuration', () => {
+    it('handles Azure OpenAI specific fields correctly', () => {
       const envConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
+        LLM_PROVIDER: ProviderType.AzureOpenAI,
+        AZURE_OPENAI_API_KEY: 'test-key',
+        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
+        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
+        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
+        AZURE_OPENAI_TEMPERATURE: 1.5,
       };
 
-      const customBuilder = new DefaultRequestBuilder('Custom OpenAI directive');
-      const provider = createProvider(envConfig, {}, customBuilder);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
+      expect(() => createProvider(envConfig)).not.toThrow();
     });
 
-    it('correctly handles OpenAI temperature edge values', () => {
-      // Test minimum temperature (0)
-      const minTempConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
-        OPENAI_TEMPERATURE: 0,
-      };
-
-      expect(() => createProvider(minTempConfig)).not.toThrow();
-
-      // Test maximum temperature (2)
-      const maxTempConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
-        OPENAI_TEMPERATURE: 2,
+    it('handles Anthropic specific fields correctly', () => {
+      const envConfig: EnvConfig = {
+        LLM_PROVIDER: ProviderType.Anthropic,
+        ANTHROPIC_API_KEY: 'sk-ant-test-key',
+        ANTHROPIC_MODEL: 'claude-3-opus-20240229',
+        ANTHROPIC_MAX_TOKENS: 8192,
+        ANTHROPIC_TEMPERATURE: 0.9,
       };
 
-      expect(() => createProvider(maxTempConfig)).not.toThrow();
+      expect(() => createProvider(envConfig)).not.toThrow();
     });
 
-    it('creates OpenAI provider with all configuration combinations', () => {
-      // Test with all optional fields present
-      const fullConfig: EnvConfig = {
+    it('handles OpenAI specific fields correctly', () => {
+      const envConfig: EnvConfig = {
         LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-full-config-key',
+        OPENAI_API_KEY: 'sk-test-key',
         OPENAI_MODEL: 'gpt-4-turbo',
-        OPENAI_TEMPERATURE: 1.2,
-      };
-
-      const fullOptions = {
-        debug: true,
-        showPrompt: true,
-        showPromptTrunc: false,
+        OPENAI_TEMPERATURE: 1.5,
       };
 
-      const provider = createProvider(fullConfig, fullOptions);
-      expect(provider).toBeInstanceOf(OpenAIProvider);
+      expect(() => createProvider(envConfig)).not.toThrow();
     });
 
-    it('maintains consistent option passing across provider types', () => {
-      const debugOptions = {
-        debug: true,
-        showPrompt: false,
-        showPromptTrunc: true,
-      };
-
-      // Test that the same options work for all provider types
-      const azureConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.AzureOpenAI,
-        AZURE_OPENAI_API_KEY: 'test-key',
-        AZURE_OPENAI_ENDPOINT: 'https://test.openai.azure.com',
-        AZURE_OPENAI_DEPLOYMENT_NAME: 'test-deployment',
-        AZURE_OPENAI_API_VERSION: '2024-02-15-preview',
-      };
-
-      const anthropicConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.Anthropic,
-        ANTHROPIC_API_KEY: 'sk-ant-test-key',
-        ANTHROPIC_MODEL: 'claude-3-sonnet-20240229',
-        ANTHROPIC_MAX_TOKENS: 4096,
-      };
-
-      const openaiConfig: EnvConfig = {
-        LLM_PROVIDER: ProviderType.OpenAI,
-        OPENAI_API_KEY: 'sk-test-key',
-        OPENAI_MODEL: 'gpt-4o',
+    it('handles Gemini specific fields correctly', () => {
+      const envConfig: EnvConfig = {
+        LLM_PROVIDER: ProviderType.Gemini,
+        GEMINI_API_KEY: 'test-key',
+        GEMINI_MODEL: 'gemini-pro',
+        GEMINI_TEMPERATURE: 0.5,
       };
 
-      // All should create successfully with the same options
-      expect(() => createProvider(azureConfig, debugOptions)).not.toThrow();
-      expect(() => createProvider(anthropicConfig, debugOptions)).not.toThrow();
-      expect(() => createProvider(openaiConfig, debugOptions)).not.toThrow();
+      expect(() => createProvider(envConfig)).not.toThrow();
     });
   });
-});
\ No newline at end of file
+});
diff --git a/tests/vercel-ai-provider.test.ts b/tests/vercel-ai-provider.test.ts
new file mode 100644
index 0000000..8e0868d
--- /dev/null
+++ b/tests/vercel-ai-provider.test.ts
@@ -0,0 +1,493 @@
+import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
+
+
+// Mock the Vercel AI SDK — use vi.hoisted so the mock is available in the vi.mock factory
+const MOCK_GENERATE_TEXT = vi.hoisted(() => vi.fn());
+
+// Hoist error class for NoObjectGeneratedError
+const ERROR_CLASSES = vi.hoisted(() => {
+  class NoObjectGeneratedError extends Error {
+    text: string;
+    constructor(message: string, text: string) {
+      super(message);
+      this.name = 'NoObjectGeneratedError';
+      this.text = text;
+    }
+    static isInstance(error: unknown): error is NoObjectGeneratedError {
+      return error instanceof NoObjectGeneratedError;
+    }
+  }
+  return { NoObjectGeneratedError };
+});
+
+// Mock Vercel AI SDK - must come before importing SUT
+vi.mock('ai', () => {
+  const { NoObjectGeneratedError } = ERROR_CLASSES;
+
+  return {
+    generateText: MOCK_GENERATE_TEXT,
+    Output: {
+      object: vi.fn((schema: unknown) => ({
+        _outputType: 'object',
+        schema,
+      })),
+    },
+    NoObjectGeneratedError,
+  };
+});
+
+// Import SUT after mocks are set up
+import { VercelAIProvider, type VercelAIConfig } from '../src/providers/vercel-ai-provider';
+import { DefaultRequestBuilder, type RequestBuilder } from '../src/providers/request-builder';
+import type { LanguageModel } from 'ai';
+
+// Mock model stub — only stored in config and passed through to the mocked
+// generateText function, so it doesn't need to implement the full interface.
+const MOCK_MODEL = {} as unknown as LanguageModel;
+
+describe('VercelAIProvider', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  describe('Constructor', () => {
+    it('creates provider with required config', () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL, // Mock LanguageModel
+      };
+
+      const provider = new VercelAIProvider(config);
+      expect(provider).toBeInstanceOf(VercelAIProvider);
+    });
+
+    it('applies default temperature when not provided', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { result: 'ok' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await provider.runPromptStructured('content', 'prompt', schema);
+
+      expect(MOCK_GENERATE_TEXT).toHaveBeenCalledWith(
+        expect.objectContaining({ temperature: 0.2 })
+      );
+    });
+
+    it('accepts custom request builder', () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+      const customBuilder = new DefaultRequestBuilder('custom directive');
+
+      expect(() => new VercelAIProvider(config, customBuilder)).not.toThrow();
+    });
+
+    it('accepts all configuration options', () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+        temperature: 0.7,
+        debug: true,
+        showPrompt: true,
+        showPromptTrunc: false,
+      };
+
+      expect(() => new VercelAIProvider(config)).not.toThrow();
+    });
+  });
+
+  describe('Structured Response Handling', () => {
+    it('successfully parses structured JSON response', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockOutput = {
+        score: 85,
+        feedback: 'Good content',
+      };
+
+      const mockResult = {
+        experimental_output: mockOutput,
+        usage: {
+          promptTokens: 100,
+          completionTokens: 50,
+          totalTokens: 150,
+        },
+        finishReason: 'stop',
+      };
+
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'submit_evaluation',
+        schema: {
+          properties: {
+            score: { type: 'number' },
+            feedback: { type: 'string' },
+          },
+          required: ['score', 'feedback'],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured(
+        'Test content',
+        'Test prompt',
+        schema
+      );
+
+      expect(result.data).toEqual({
+        score: 85,
+        feedback: 'Good content',
+      });
+
+      expect(result.usage).toBeDefined();
+      if (result.usage) {
+        expect(result.usage.inputTokens).toBe(100);
+        expect(result.usage.outputTokens).toBe(50);
+      }
+    });
+
+    it('configures Vercel AI SDK with Output.object()', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = {
+        experimental_output: { result: 'success' },
+      };
+
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            result: { type: 'string' },
+          },
+          required: ['result'],
+          type: 'object',
+        },
+      };
+
+      await provider.runPromptStructured('Test content', 'Test prompt', schema);
+
+      expect(MOCK_GENERATE_TEXT).toHaveBeenCalledWith(
+        expect.objectContaining({
+          system: expect.any(String) as string,
+          prompt: 'Input:\n\nTest content',
+          temperature: 0.2,
+          experimental_output: expect.objectContaining({
+            _outputType: 'object',
+          }) as Record<string, unknown>,
+        })
+      );
+    });
+
+    it('includes temperature in API call when configured', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+        temperature: 0.7,
+      };
+
+      const mockResult = { experimental_output: { result: 'success' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await provider.runPromptStructured('Test content', 'Test prompt', schema);
+
+      expect(MOCK_GENERATE_TEXT).toHaveBeenCalledWith(
+        expect.objectContaining({
+          temperature: 0.7,
+        })
+      );
+    });
+  });
+
+  describe('Error Handling', () => {
+    it('handles NoObjectGeneratedError', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const { NoObjectGeneratedError } = ERROR_CLASSES;
+      MOCK_GENERATE_TEXT.mockRejectedValue(
+        new NoObjectGeneratedError('Failed to generate object', 'Raw text here')
+      );
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await expect(
+        provider.runPromptStructured('Test content', 'Test prompt', schema)
+      ).rejects.toThrow('LLM failed to generate valid structured output');
+    });
+
+    it('handles unknown errors', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      MOCK_GENERATE_TEXT.mockRejectedValue(new Error('Unknown error'));
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await expect(
+        provider.runPromptStructured('Test content', 'Test prompt', schema)
+      ).rejects.toThrow('Vercel AI SDK call failed: Unknown error');
+    });
+  });
+
+  describe('Debugging and Logging', () => {
+    let consoleSpy: ReturnType<typeof vi.spyOn>;
+
+    beforeEach(() => {
+      consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => { });
+    });
+
+    afterEach(() => {
+      consoleSpy.mockRestore();
+    });
+
+    it('logs debug information when debug is enabled', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+        debug: true,
+      };
+
+      const mockResult = {
+        experimental_output: { result: 'success' },
+        usage: {
+          promptTokens: 100,
+          completionTokens: 50,
+          totalTokens: 150,
+        },
+        finishReason: 'stop',
+      };
+
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await provider.runPromptStructured('Test content', 'Test prompt', schema);
+
+      expect(consoleSpy).toHaveBeenCalledWith(
+        '[vectorlint] Sending request via Vercel AI SDK:',
+        expect.any(Object)
+      );
+
+      expect(consoleSpy).toHaveBeenCalledWith(
+        '[vectorlint] LLM response meta:',
+        expect.objectContaining({
+          usage: expect.objectContaining({
+            prompt_tokens: 100,
+            completion_tokens: 50,
+            total_tokens: 150,
+          }) as Record<string, unknown>,
+        })
+      );
+    });
+
+    it('does not log when debug is disabled', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+        debug: false,
+      };
+
+      const mockResult = { experimental_output: { result: 'success' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await provider.runPromptStructured('Test content', 'Test prompt', schema);
+
+      expect(consoleSpy).not.toHaveBeenCalled();
+    });
+  });
+
+  describe('Request Building', () => {
+    it('uses request builder to build system prompt', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { result: 'success' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const buildPromptBodyForStructuredFn = vi.fn().mockReturnValue('Built system prompt');
+      const mockBuilder: RequestBuilder = {
+        buildPromptBodyForStructured: buildPromptBodyForStructuredFn,
+      };
+
+      const provider = new VercelAIProvider(config, mockBuilder);
+      const schema = {
+        name: 'test_schema',
+        schema: { properties: { result: { type: 'string' } }, type: 'object' },
+      };
+
+      await provider.runPromptStructured('Test content', 'Test prompt', schema);
+
+      expect(buildPromptBodyForStructuredFn).toHaveBeenCalledWith('Test prompt');
+
+      expect(MOCK_GENERATE_TEXT).toHaveBeenCalledWith(
+        expect.objectContaining({
+          system: 'Built system prompt',
+          prompt: 'Input:\n\nTest content',
+        })
+      );
+    });
+  });
+
+  describe('JSON Schema to Zod Conversion', () => {
+    it('converts simple string properties', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { name: 'test' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            name: { type: 'string' },
+          },
+          required: ['name'] as string[],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured('Test content', 'Test prompt', schema);
+      expect(result.data).toEqual({ name: 'test' });
+    });
+
+    it('converts number properties', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { score: 42 } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            score: { type: 'number' },
+          },
+          required: ['score'] as string[],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured('Test content', 'Test prompt', schema);
+      expect(result.data).toEqual({ score: 42 });
+    });
+
+    it('handles optional fields', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { requiredField: 'value' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            requiredField: { type: 'string' },
+            optionalField: { type: 'string' },
+          },
+          required: ['requiredField'] as string[],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured('Test content', 'Test prompt', schema);
+      expect(result.data).toEqual({ requiredField: 'value' });
+    });
+
+    it('converts union type arrays (e.g. [string, number])', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { value: 'hello' } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            value: { type: ['string', 'number'] },
+          },
+          required: ['value'] as string[],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured('Test content', 'Test prompt', schema);
+      expect(result.data).toEqual({ value: 'hello' });
+    });
+
+    it('handles nullable types (e.g. [null, string])', async () => {
+      const config: VercelAIConfig = {
+        model: MOCK_MODEL,
+      };
+
+      const mockResult = { experimental_output: { name: null } };
+      MOCK_GENERATE_TEXT.mockResolvedValue(mockResult);
+
+      const provider = new VercelAIProvider(config);
+      const schema = {
+        name: 'test_schema',
+        schema: {
+          properties: {
+            name: { type: ['null', 'string'] },
+          },
+          required: ['name'] as string[],
+          type: 'object',
+        },
+      };
+
+      const result = await provider.runPromptStructured('Test content', 'Test prompt', schema);
+      expect(result.data).toEqual({ name: null });
+    });
+  });
+});