diff --git a/config/gni/devtools_grd_files.gni b/config/gni/devtools_grd_files.gni
index ad056621f25..0417b2ccca7 100644
--- a/config/gni/devtools_grd_files.gni
+++ b/config/gni/devtools_grd_files.gni
@@ -655,6 +655,7 @@ grd_files_bundled_sources = [
   "front_end/panels/ai_chat/LLM/LLMClient.js",
   "front_end/panels/ai_chat/LLM/MessageSanitizer.js",
   "front_end/panels/ai_chat/tools/Tools.js",
+  "front_end/panels/ai_chat/tools/LLMTracingWrapper.js",
   "front_end/panels/ai_chat/tools/SequentialThinkingTool.js",
   "front_end/panels/ai_chat/tools/CombinedExtractionTool.js",
   "front_end/panels/ai_chat/tools/CritiqueTool.js",
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 41f08a1b465..78dca628a03 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -42,12 +42,21 @@ RUN /workspace/depot_tools/ensure_bootstrap
 # Build standard DevTools first
 RUN npm run build
 
-# Add Browser Operator fork and switch to it
+# Add Browser Operator fork and switch to it   
 RUN git remote add upstream https://github.com/BrowserOperator/browser-operator-core.git
 RUN git fetch upstream
 RUN git checkout upstream/main
 
-# Build Browser Operator version
+# Allow configurable automated mode
+ARG AUTOMATED_MODE=false
+
+# Set build-time flags based on Docker arg
+RUN if [ "$AUTOMATED_MODE" = "true" ]; then \
+      sed -i 's/AUTOMATED_MODE: false/AUTOMATED_MODE: true/' \
+        front_end/panels/ai_chat/core/BuildConfig.ts; \
+    fi
+
+# Build Browser Operator version with current changes
 RUN npm run build
 
 # Production stage
@@ -59,4 +68,4 @@ COPY --from=builder /workspace/devtools/devtools-frontend/out/Default/gen/front_
 # Copy nginx config
 COPY docker/nginx.conf /etc/nginx/conf.d/default.conf
 
-EXPOSE 8000
\ No newline at end of file
+EXPOSE 8000
diff --git a/docker/Dockerfile.local b/docker/Dockerfile.local
new file mode 100644
index 00000000000..17cc9c7712f
--- /dev/null
+++ b/docker/Dockerfile.local
@@ -0,0 +1,10 @@
+# Simple Dockerfile that uses pre-built local files
+FROM nginx:alpine
+
+# Copy the pre-built DevTools frontend from host
+COPY out/Default/gen/front_end /usr/share/nginx/html
+
+# Copy nginx config
+COPY docker/nginx.conf /etc/nginx/conf.d/default.conf
+
+EXPOSE 8000
\ No newline at end of file
diff --git a/docker/README.md b/docker/README.md
index 379527aceb5..ece2c08ebf0 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -22,8 +22,11 @@ The Docker setup uses a multi-stage build process:
 From the repository root directory:
 
 ```bash
-# Build the Docker image
-docker build -f docker/Dockerfile -t devtools-frontend .
+# Build with automated mode (default - bypasses OAuth, auto-enables evaluation)
+docker build -f docker/Dockerfile -t browser-operator-automated .
+
+# Build with normal mode (requires manual authentication)
+docker build -f docker/Dockerfile --build-arg AUTOMATED_MODE=false -t browser-operator-manual .
 
 # Or use docker-compose (recommended)
 docker-compose -f docker/docker-compose.yml build
@@ -32,8 +35,11 @@ docker-compose -f docker/docker-compose.yml build
 ### Running the Container
 
 ```bash
-# Using docker run
-docker run -d -p 8000:8000 --name devtools-frontend devtools-frontend
+# Automated mode (no authentication required, evaluation auto-enabled)
+docker run -d -p 8000:8000 --name browser-operator-automated browser-operator-automated
+
+# Manual mode (requires OAuth/API key setup)
+docker run -d -p 8000:8000 --name browser-operator-manual browser-operator-manual
 
 # Or using docker-compose (recommended)
 docker-compose -f docker/docker-compose.yml up -d
@@ -67,6 +73,29 @@ docker/
 └── README.md          # This file
 ```
 
+## Automated Mode vs Manual Mode
+
+### Automated Mode (Default)
+- **Purpose**: Optimized for Docker/CI environments and automated workflows
+- **Authentication**: Bypasses OAuth panel - no manual setup required  
+- **Evaluation**: Automatically enables evaluation mode for API wrapper connectivity
+- **Use cases**: Production deployments, CI/CD, headless automation, API integration
+
+### Manual Mode  
+- **Purpose**: Standard interactive usage
+- **Authentication**: Requires OAuth setup or API key configuration
+- **Evaluation**: Manual enable/disable in settings
+- **Use cases**: Development, interactive testing, manual usage
+
+```bash
+# Example automated mode workflow
+docker build -f docker/Dockerfile -t browser-operator-automated .
+docker run -d -p 8000:8000 --name browser-operator browser-operator-automated
+
+# Ready to use immediately - no authentication required!
+# Evaluation server can connect automatically via WebSocket (ws://localhost:8080)
+```
+
 ## Advanced Usage
 
 ### Development Mode
diff --git a/eval-server/README.md b/eval-server/README.md
index 88c852f46d8..db52ca346dc 100644
--- a/eval-server/README.md
+++ b/eval-server/README.md
@@ -15,6 +15,7 @@ Both implementations provide:
 - 📚 **Programmatic API** - Create and manage evaluations in code
 - ⚡ **Concurrent Support** - Handle multiple agents simultaneously
 - 📊 **Structured Logging** - Comprehensive evaluation tracking
+- 🌐 **OpenAI-Compatible API** - Standard REST endpoints for seamless integration
 
 ## Quick Start
 
@@ -56,6 +57,60 @@ python examples/basic_server.py
 
 See [`python/README.md`](python/README.md) for detailed usage.
 
+## OpenAI-Compatible API
+
+Both implementations now include OpenAI-compatible HTTP endpoints that provide seamless integration with existing OpenAI clients and tools.
+
+### Architecture
+
+```
+┌─────────────────┐    HTTP     ┌──────────────────┐    WebSocket    ┌─────────────────┐
+│ OpenAI Client   │ ──────────→ │ OpenAI HTTP      │ ──────────────→ │ WebSocket       │
+│ (External)      │             │ Wrapper          │                 │ Eval Server     │
+└─────────────────┘             └──────────────────┘                 └─────────────────┘
+                                                                               │
+                                                                               │ RPC
+                                                                               ▼
+                                                          ┌─────────────────────────────────────┐
+                                                          │ Connected DevTools Tabs             │
+                                                          │ ┌─────┐ ┌─────┐ ┌─────┐           │
+                                                          │ │Tab 1│ │Tab 2│ │Tab N│ ...       │
+                                                          │ └─────┘ └─────┘ └─────┘           │
+                                                          └─────────────────────────────────────┘
+```
+
+### Supported Endpoints
+
+- **`GET /v1/models`** - List available models from connected DevTools tabs
+- **`POST /v1/chat/completions`** - OpenAI-compatible chat completions
+- **`GET /health`** - Health check and status
+
+### Usage Example
+
+```bash
+# List available models
+curl http://localhost:8081/v1/models
+
+# Send a chat completion request
+curl -X POST http://localhost:8081/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4.1",
+    "messages": [
+      {"role": "user", "content": "Hello, how are you?"}
+    ]
+  }'
+```
+
+### Request Flow
+
+1. **External client** sends OpenAI-compatible HTTP request
+2. **HTTP wrapper** converts request to evaluation format  
+3. **WebSocket server** selects connected DevTools tab
+4. **RPC call** sent to tab via existing JSON-RPC protocol
+5. **Tab processes** request using Browser Operator's LLM system
+6. **Response flows back** through WebSocket → HTTP → OpenAI format
+
 ## Architecture Comparison
 
 | Feature | NodeJS | Python |
@@ -68,6 +123,7 @@ See [`python/README.md`](python/README.md) for detailed usage.
 | **Structured Logging** | ✅ (Winston) | ✅ (Loguru) |
 | **YAML Evaluations** | ✅ | ❌ |
 | **HTTP API Wrapper** | ✅ | ❌ |
+| **OpenAI-Compatible API** | ✅ | ✅ |
 | **CLI Interface** | ✅ | ❌ |
 | **LLM Judge System** | ✅ | ❌ |
 | **Type System** | TypeScript | Type Hints |
diff --git a/eval-server/nodejs/examples/openai-server-example.js b/eval-server/nodejs/examples/openai-server-example.js
new file mode 100644
index 00000000000..8d8e8505fa9
--- /dev/null
+++ b/eval-server/nodejs/examples/openai-server-example.js
@@ -0,0 +1,143 @@
+#!/usr/bin/env node
+
+// Copyright 2025 The Chromium Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+
+/**
+ * Example demonstrating how to use the OpenAI-compatible API server.
+ * 
+ * This example shows how to start both the WebSocket evaluation server
+ * and the OpenAI-compatible HTTP wrapper that multiplexes requests to
+ * connected DevTools tabs.
+ */
+
+import { EvalServer } from '../src/lib/EvalServer.js';
+import { OpenAICompatibleWrapper } from '../src/lib/OpenAICompatibleWrapper.js';
+
+console.log(`
+🌟 Browser Operator Evaluation Server with OpenAI-Compatible API
+
+This server provides:
+- WebSocket server for DevTools tab connections (port 8080)
+- OpenAI-compatible HTTP API (port 8081)
+
+To use:
+1. Start this server
+2. Connect DevTools tabs via WebSocket to ws://127.0.0.1:8080  
+3. Send OpenAI-compatible requests to http://127.0.0.1:8081
+
+Available endpoints:
+- GET  /v1/models           - List available models
+- POST /v1/chat/completions - Chat completions
+- GET  /health              - Health check
+
+Example usage:
+  curl http://127.0.0.1:8081/v1/models
+  
+  curl -X POST http://127.0.0.1:8081/v1/chat/completions \\
+    -H "Content-Type: application/json" \\
+    -d '{
+      "model": "gpt-4.1", 
+      "messages": [{"role": "user", "content": "Hello!"}]
+    }'
+
+Press Ctrl+C to stop the server.
+`);
+
+async function main() {
+  console.log('🚀 Starting Browser Operator evaluation server with OpenAI-compatible API...');
+  
+  // Create WebSocket evaluation server
+  const evalServer = new EvalServer({
+    authKey: 'hello',
+    host: '127.0.0.1',
+    port: 8080
+  });
+
+  // Set up client connection handlers
+  evalServer.onConnect((client) => {
+    console.log(`✅ DevTools tab connected: ${client.id}`);
+    console.log(`   Tab ID: ${client.tabId}`);
+    console.log(`   Base Client ID: ${client.baseClientId}`);
+    console.log(`   Connected at: ${new Date().toISOString()}`);
+    
+    // The client is now ready to receive evaluations via OpenAI API
+  });
+
+  evalServer.onDisconnect((clientInfo) => {
+    console.log(`❌ DevTools tab disconnected: ${clientInfo.clientId}`);
+  });
+
+  // Create OpenAI-compatible HTTP wrapper
+  const openaiWrapper = new OpenAICompatibleWrapper(evalServer, {
+    host: '127.0.0.1',
+    port: 8081,
+    modelCacheTTL: 300000 // 5 minutes
+  });
+
+  // Graceful shutdown handler
+  const shutdown = async () => {
+    console.log('\n🛑 Shutting down servers...');
+    try {
+      await openaiWrapper.stop();
+      await evalServer.stop();
+      console.log('✅ Servers stopped successfully');
+      process.exit(0);
+    } catch (error) {
+      console.error('❌ Error during shutdown:', error);
+      process.exit(1);
+    }
+  };
+
+  // Handle shutdown signals
+  process.on('SIGINT', shutdown);
+  process.on('SIGTERM', shutdown);
+
+  try {
+    // Start WebSocket server first
+    console.log('🔧 Starting WebSocket evaluation server on ws://127.0.0.1:8080');
+    await evalServer.start();
+    
+    // Start OpenAI-compatible HTTP wrapper
+    console.log('🔧 Starting OpenAI-compatible API server on http://127.0.0.1:8081');
+    await openaiWrapper.start();
+    
+    console.log('🎉 Both servers started successfully!');
+    console.log('');
+    console.log('📡 WebSocket Server: ws://127.0.0.1:8080 (for DevTools connections)');
+    console.log('🌐 OpenAI API Server: http://127.0.0.1:8081 (for HTTP requests)');
+    console.log('');
+    console.log('⏳ Waiting for DevTools tabs to connect...');
+    
+    // Monitor server status periodically
+    const statusInterval = setInterval(() => {
+      const evalStatus = evalServer.getStatus();
+      const openaiStatus = openaiWrapper.getStatus();
+      
+      console.log(`📊 Status - Connected clients: ${evalStatus.connectedClients}, Ready: ${evalStatus.readyClients}`);
+      console.log(`📊 OpenAI API: ${openaiStatus.isRunning ? 'running' : 'stopped'} on ${openaiStatus.url}`);
+    }, 30000); // Every 30 seconds
+    
+    // Keep the process running
+    process.on('beforeExit', () => {
+      clearInterval(statusInterval);
+    });
+    
+  } catch (error) {
+    console.error('❌ Failed to start servers:', error);
+    await shutdown();
+  }
+}
+
+// Handle unhandled promise rejections
+process.on('unhandledRejection', (reason, promise) => {
+  console.error('Unhandled Rejection at:', promise, 'reason:', reason);
+});
+
+process.on('uncaughtException', (error) => {
+  console.error('Uncaught Exception:', error);
+  process.exit(1);
+});
+
+main().catch(console.error);
\ No newline at end of file
diff --git a/eval-server/nodejs/src/lib/OpenAICompatibleWrapper.js b/eval-server/nodejs/src/lib/OpenAICompatibleWrapper.js
new file mode 100644
index 00000000000..816933ec8be
--- /dev/null
+++ b/eval-server/nodejs/src/lib/OpenAICompatibleWrapper.js
@@ -0,0 +1,700 @@
+// Copyright 2025 The Chromium Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+
+import { v4 as uuidv4 } from 'uuid';
+import logger from '../logger.js';
+
+/**
+ * OpenAI-compatible HTTP API wrapper that multiplexes requests to connected DevTools tabs.
+ * 
+ * This wrapper extends the existing HTTPWrapper functionality to provide OpenAI-compatible
+ * endpoints (/v1/models and /v1/chat/completions) while routing requests through the 
+ * existing WebSocket evaluation server infrastructure.
+ * 
+ * Example usage:
+ * ```js
+ * import { EvalServer } from './EvalServer.js';
+ * import { OpenAICompatibleWrapper } from './OpenAICompatibleWrapper.js';
+ * 
+ * const evalServer = new EvalServer({ port: 8080 });
+ * const openaiWrapper = new OpenAICompatibleWrapper(evalServer, { port: 8081 });
+ * 
+ * await evalServer.start();
+ * await openaiWrapper.start();
+ * ```
+ */
+export class OpenAICompatibleWrapper {
+  constructor(evalServer, options = {}) {
+    this.evalServer = evalServer;
+    this.config = {
+      port: options.port || 8081,
+      host: options.host || 'localhost',
+      modelCacheTTL: options.modelCacheTTL || 300000, // 5 minutes in ms
+      ...options
+    };
+    
+    this.server = null;
+    this.isRunning = false;
+    
+    // Model list cache
+    this.modelCache = null;
+    this.modelCacheTime = null;
+  }
+
+  /**
+   * Start the OpenAI-compatible HTTP server
+   */
+  async start() {
+    if (this.isRunning) {
+      throw new Error('OpenAI-compatible wrapper is already running');
+    }
+
+    if (!this.evalServer.isRunning) {
+      throw new Error('EvalServer must be started before starting OpenAI-compatible wrapper');
+    }
+
+    const http = await import('http');
+    
+    this.server = http.createServer((req, res) => {
+      // Enable CORS
+      res.setHeader('Access-Control-Allow-Origin', '*');
+      res.setHeader('Access-Control-Allow-Methods', 'GET, POST, OPTIONS');
+      res.setHeader('Access-Control-Allow-Headers', 'Content-Type, Authorization');
+
+      if (req.method === 'OPTIONS') {
+        res.writeHead(200);
+        res.end();
+        return;
+      }
+
+      this.handleRequest(req, res);
+    });
+
+    this.server.listen(this.config.port, this.config.host, () => {
+      logger.info(`OpenAI-compatible API started on http://${this.config.host}:${this.config.port}`);
+    });
+
+    this.isRunning = true;
+    return this;
+  }
+
+  /**
+   * Stop the OpenAI-compatible HTTP server
+   */
+  async stop() {
+    if (!this.isRunning || !this.server) {
+      return;
+    }
+
+    return new Promise((resolve) => {
+      this.server.close(() => {
+        logger.info('OpenAI-compatible API server stopped');
+        this.isRunning = false;
+        resolve();
+      });
+    });
+  }
+
+  /**
+   * Handle incoming HTTP requests
+   */
+  async handleRequest(req, res) {
+    const url = await import('url');
+    const parsedUrl = url.parse(req.url, true);
+    const pathname = parsedUrl.pathname;
+    const method = req.method;
+
+    try {
+      // Get body for POST requests
+      let body = '';
+      if (method === 'POST') {
+        for await (const chunk of req) {
+          body += chunk;
+        }
+      }
+
+      let result;
+
+      switch (pathname) {
+        case '/v1/models':
+          if (method !== 'GET') {
+            this.sendError(res, 405, 'Method not allowed');
+            return;
+          }
+          result = await this.handleModelsRequest();
+          break;
+
+        case '/v1/chat/completions':
+          if (method !== 'POST') {
+            this.sendError(res, 405, 'Method not allowed');
+            return;
+          }
+          result = await this.handleChatCompletionsRequest(JSON.parse(body));
+          break;
+
+        case '/v1/responses':
+          if (method !== 'POST') {
+            this.sendError(res, 405, 'Method not allowed');
+            return;
+          }
+          result = await this.handleResponsesRequest(JSON.parse(body));
+          break;
+
+        case '/health':
+          result = this.getHealthStatus();
+          break;
+
+        default:
+          this.sendError(res, 404, 'Not found');
+          return;
+      }
+
+      this.sendResponse(res, 200, result);
+
+    } catch (error) {
+      logger.error('OpenAI API error:', error);
+      
+      // Handle specific error types
+      if (error.message.includes('No connected')) {
+        this.sendError(res, 503, error.message);
+      } else if (error.name === 'SyntaxError') {
+        this.sendError(res, 400, 'Invalid JSON in request body');
+      } else {
+        this.sendError(res, 500, `Internal server error: ${error.message}`);
+      }
+    }
+  }
+
+  /**
+   * Handle GET /v1/models request
+   */
+  async handleModelsRequest() {
+    try {
+      const models = await this.getModelsFromTabs();
+      
+      return {
+        object: 'list',
+        data: models
+      };
+    } catch (error) {
+      logger.error('Error handling models request:', error);
+      throw new Error('Unable to fetch models from connected tabs');
+    }
+  }
+
+  /**
+   * Handle POST /v1/chat/completions request
+   */
+  async handleChatCompletionsRequest(requestBody) {
+    try {
+      // Validate required fields
+      if (!requestBody.model) {
+        throw new Error('Missing required field: model');
+      }
+      if (!requestBody.messages || !Array.isArray(requestBody.messages)) {
+        throw new Error('Missing or invalid required field: messages');
+      }
+
+      // Find a connected and ready client
+      const readyClient = this.findReadyClient();
+      if (!readyClient) {
+        throw new Error('No connected DevTools tabs available');
+      }
+
+      // Convert OpenAI request to evaluation format
+      const evaluation = this.convertOpenAIToEvaluation(requestBody);
+
+      // Execute via existing WebSocket multiplexing
+      logger.info(`Sending evaluation to client ${readyClient.clientId}`, {
+        evaluationId: evaluation.id,
+        model: requestBody.model
+      });
+
+      const result = await this.evalServer.executeEvaluation(readyClient, evaluation);
+
+      // Convert result back to OpenAI format
+      return this.convertResultToOpenAI(result, requestBody);
+
+    } catch (error) {
+      logger.error('Error handling chat completions request:', error);
+      throw error;
+    }
+  }
+
+  /**
+   * Handle POST /v1/responses request (OpenAI Responses API)
+   */
+  async handleResponsesRequest(requestBody) {
+    try {
+      // Validate required input field
+      if (!requestBody.input || typeof requestBody.input !== 'string') {
+        throw new Error('Missing or invalid "input" field. Expected a string.');
+      }
+
+      // Find a connected and ready client
+      const readyClient = this.findReadyClient();
+      if (!readyClient) {
+        throw new Error('No connected DevTools tabs available');
+      }
+
+      // Convert Responses API request to evaluation format
+      const evaluation = this.convertResponsesToEvaluation(requestBody);
+
+      // Execute via existing WebSocket multiplexing
+      logger.info(`Sending responses evaluation to client ${readyClient.clientId}`, {
+        evaluationId: evaluation.id,
+        input: requestBody.input
+      });
+
+      const result = await this.evalServer.executeEvaluation(readyClient, evaluation);
+
+      // Convert result back to Responses API format
+      return this.convertResultToResponses(result);
+
+    } catch (error) {
+      logger.error('Error handling responses request:', error);
+      throw error;
+    }
+  }
+
+  /**
+   * Get available models from connected DevTools tabs
+   */
+  async getModelsFromTabs() {
+    // Check cache first
+    const currentTime = Date.now();
+    if (this.modelCache && this.modelCacheTime && 
+        (currentTime - this.modelCacheTime < this.config.modelCacheTTL)) {
+      logger.debug('Returning cached model list');
+      return this.modelCache;
+    }
+
+    // Find a ready client
+    const readyClient = this.findReadyClient();
+    if (!readyClient) {
+      logger.warn('No connected tabs available for model listing');
+      return this.getDefaultModels();
+    }
+
+    try {
+      // Create evaluation request for model listing  
+      const evalId = `models-${uuidv4().substring(0, 8)}`;
+      const modelRequest = {
+        id: evalId,
+        evaluationId: evalId,
+        name: 'get_models',  // Name field is required for evaluation protocol
+        tool: 'get_models',
+        url: null,
+        input: {},
+        timeout: 10000, // 10 seconds
+      };
+
+      logger.info(`Requesting models from client ${readyClient.clientId}`);
+      const result = await this.evalServer.executeEvaluation(readyClient, modelRequest);
+
+      // Parse the result and convert to OpenAI format
+      const models = this.parseModelsResult(result);
+
+      // Cache the results
+      this.modelCache = models;
+      this.modelCacheTime = currentTime;
+
+      logger.info(`Retrieved ${models.length} models from connected tab`);
+      return models;
+
+    } catch (error) {
+      logger.error('Error fetching models from tab:', error);
+      return this.getDefaultModels();
+    }
+  }
+
+  /**
+   * Parse model result from DevTools tab into OpenAI format
+   */
+  parseModelsResult(result) {
+    const models = [];
+    const currentTime = Math.floor(Date.now() / 1000);
+    let currentSelection = null;
+
+    try {
+      // Extract models and current selection from various possible result formats
+      let modelData = null;
+
+      if (typeof result === 'object' && result !== null) {
+        // Check direct response format
+        if (result.models) {
+          modelData = result.models;
+          currentSelection = result.currentSelection;
+        } 
+        // Check nested output format
+        else if (result.output && result.output.models) {
+          modelData = result.output.models;
+          currentSelection = result.output.currentSelection;
+        } 
+        // Check response string format
+        else if (result.response) {
+          let response = result.response;
+          if (typeof response === 'string') {
+            try {
+              const parsed = JSON.parse(response);
+              if (parsed.models) {
+                modelData = parsed.models;
+                currentSelection = parsed.currentSelection;
+              }
+            } catch (jsonError) {
+              // Not JSON, continue
+            }
+          }
+        }
+      }
+
+      logger.info(`Current model selection from Browser Operator: ${currentSelection}`);
+
+      if (Array.isArray(modelData)) {
+        let selectedCount = 0;
+        for (const model of modelData) {
+          if (typeof model === 'object' && model.id) {
+            const isSelected = model.selected || false;
+            if (isSelected) {
+              selectedCount++;
+            }
+
+            models.push({
+              id: model.id,
+              object: 'model',
+              created: currentTime,
+              owned_by: model.provider || 'browser-operator'
+            });
+          } else if (typeof model === 'string') {
+            // Simple model name
+            models.push({
+              id: model,
+              object: 'model',
+              created: currentTime,
+              owned_by: 'browser-operator'
+            });
+          }
+        }
+        
+        logger.info(`Found ${models.length} models with ${selectedCount} selected`);
+      }
+
+      // Fallback to default models if nothing found
+      if (models.length === 0) {
+        logger.warn('No models found in result, providing defaults');
+        return this.getDefaultModels();
+      }
+
+    } catch (error) {
+      logger.error('Error parsing models result:', error);
+      logger.debug('Raw result:', result);
+      return this.getDefaultModels();
+    }
+
+    return models;
+  }
+
+  /**
+   * Get default model list as fallback
+   */
+  getDefaultModels() {
+    const currentTime = Math.floor(Date.now() / 1000);
+    
+    return [
+      {
+        id: 'gpt-4.1',
+        object: 'model',
+        created: currentTime,
+        owned_by: 'browser-operator'
+      },
+      {
+        id: 'gpt-4.1-mini',
+        object: 'model',
+        created: currentTime,
+        owned_by: 'browser-operator'
+      },
+      {
+        id: 'gpt-4.1-nano',
+        object: 'model',
+        created: currentTime,
+        owned_by: 'browser-operator'
+      }
+    ];
+  }
+
+  /**
+   * Convert OpenAI chat completion request to evaluation format
+   */
+  convertOpenAIToEvaluation(requestBody) {
+    // Convert OpenAI messages array to single message string
+    // The Browser Operator expects input.message (string), not input.messages (array)
+    const messageParts = [];
+    for (const msg of requestBody.messages) {
+      if (msg.role === 'system') {
+        messageParts.push(`System: ${msg.content}`);
+      } else if (msg.role === 'user') {
+        messageParts.push(`User: ${msg.content}`);
+      } else if (msg.role === 'assistant') {
+        messageParts.push(`Assistant: ${msg.content}`);
+      } else {
+        messageParts.push(`${msg.role}: ${msg.content}`);
+      }
+    }
+    
+    // Join all messages into a single conversation string
+    let conversationMessage = messageParts.join('\n\n');
+    
+    // If there's only a user message, use it directly
+    if (requestBody.messages.length === 1 && requestBody.messages[0].role === 'user') {
+      conversationMessage = requestBody.messages[0].content;
+    }
+
+    // Create evaluation object compatible with existing evaluation system
+    const evaluation = {
+      id: `openai-chat-${uuidv4().substring(0, 8)}`,
+      name: 'OpenAI Chat Completion',
+      description: `Chat completion using model ${requestBody.model}`,
+      enabled: true,
+      tool: 'chat',
+      timeout: 300000, // 5 minutes
+      input: {
+        message: conversationMessage,  // Single message string as expected by executeChatEvaluation
+        model: requestBody.model,
+        temperature: requestBody.temperature,
+        max_tokens: requestBody.max_tokens,
+        top_p: requestBody.top_p,
+        frequency_penalty: requestBody.frequency_penalty,
+        presence_penalty: requestBody.presence_penalty,
+      },
+      model: {
+        main_model: requestBody.model,
+        provider: 'openai'
+      },
+      validation: {
+        type: 'none' // No validation needed for API responses
+      },
+      metadata: {
+        tags: ['openai-api', 'chat-completion'],
+        source: 'openai-compatible-api',
+        originalModel: requestBody.model
+      }
+    };
+
+    return evaluation;
+  }
+
+  /**
+   * Convert OpenAI Responses API request to evaluation format
+   */
+  convertResponsesToEvaluation(requestBody) {
+    const evaluation = {
+      id: `openai-responses-${uuidv4().substring(0, 8)}`,
+      name: 'OpenAI Responses API Request',
+      description: 'Dynamic evaluation from OpenAI Responses API',
+      enabled: true,
+      tool: 'chat',
+      timeout: 300000, // 5 minutes
+      input: {
+        message: requestBody.input,  // Direct message from input
+        reasoning: 'OpenAI Responses API processing'
+      },
+      model: {
+        main_model: requestBody.main_model || 'gpt-4.1',
+        mini_model: requestBody.mini_model || 'gpt-4.1-mini', 
+        nano_model: requestBody.nano_model || 'gpt-4.1-nano',
+        provider: requestBody.provider || 'openai'
+      },
+      validation: {
+        type: 'none'
+      },
+      metadata: {
+        tags: ['openai-api', 'responses'],
+        source: 'openai-responses-api',
+        priority: 'high'
+      }
+    };
+
+    return evaluation;
+  }
+
+  /**
+   * Convert evaluation result to OpenAI Responses API format
+   */
+  convertResultToResponses(result) {
+    // Extract response text from evaluation result
+    const responseText = this.extractResponseText(result);
+
+    // Create message ID in OpenAI format
+    const messageId = `msg_${uuidv4().replace(/-/g, '').substring(0, 32)}`;
+
+    // Format in OpenAI Responses API format
+    return [
+      {
+        id: messageId,
+        type: 'message',
+        role: 'assistant',
+        content: [
+          {
+            type: 'output_text',
+            text: responseText,
+            annotations: []
+          }
+        ]
+      }
+    ];
+  }
+
+  /**
+   * Convert evaluation result back to OpenAI chat completion format
+   */
+  convertResultToOpenAI(result, originalRequest) {
+    // Extract response text from evaluation result
+    const responseText = this.extractResponseText(result);
+
+    // Create OpenAI-compatible response
+    const completionId = `chatcmpl-${uuidv4().replace(/-/g, '').substring(0, 29)}`;
+    const currentTime = Math.floor(Date.now() / 1000);
+
+    // Calculate rough token counts
+    const promptTokens = originalRequest.messages.reduce(
+      (acc, msg) => acc + (msg.content ? msg.content.split(' ').length : 0), 0
+    );
+    const completionTokens = responseText.split(' ').length;
+
+    return {
+      id: completionId,
+      object: 'chat.completion',
+      created: currentTime,
+      model: originalRequest.model,
+      choices: [{
+        index: 0,
+        message: {
+          role: 'assistant',
+          content: responseText
+        },
+        finish_reason: 'stop'
+      }],
+      usage: {
+        prompt_tokens: promptTokens,
+        completion_tokens: completionTokens,
+        total_tokens: promptTokens + completionTokens
+      }
+    };
+  }
+
+  /**
+   * Extract response text from evaluation result
+   */
+  extractResponseText(result) {
+    if (!result) {
+      return 'No response received from evaluation';
+    }
+
+    // Handle string results
+    if (typeof result === 'string') {
+      return result;
+    }
+
+    // Handle object results - try various common response fields
+    if (typeof result === 'object' && result !== null) {
+      // Direct fields
+      const responseFields = ['response', 'text', 'answer', 'content'];
+      for (const field of responseFields) {
+        if (result[field] && typeof result[field] === 'string' && result[field].trim()) {
+          return result[field];
+        }
+      }
+
+      // Nested fields
+      if (result.output) {
+        for (const field of responseFields) {
+          if (result.output[field] && typeof result.output[field] === 'string' && result.output[field].trim()) {
+            return result.output[field];
+          }
+        }
+      }
+
+      // If result is an object, return JSON representation
+      return JSON.stringify(result, null, 2);
+    }
+
+    return 'Unable to extract response text from evaluation result';
+  }
+
+  /**
+   * Find a connected and ready client (reusing logic from existing HTTPWrapper)
+   */
+  findReadyClient() {
+    for (const [clientId, connection] of this.evalServer.connectedClients) {
+      if (connection.ready && connection.registered) {
+        return connection;
+      }
+    }
+    return null;
+  }
+
+  /**
+   * Get health status of the OpenAI-compatible API
+   */
+  getHealthStatus() {
+    const connectedClients = Array.from(this.evalServer.connectedClients.values());
+    const readyClients = connectedClients.filter(client => client.ready).length;
+
+    return {
+      status: 'healthy',
+      openai_api_running: this.isRunning,
+      eval_server_running: this.evalServer.isRunning,
+      connected_clients: connectedClients.length,
+      ready_clients: readyClients,
+      model_cache_status: {
+        cached: !!this.modelCache,
+        cache_time: this.modelCacheTime,
+        cache_age_ms: this.modelCacheTime ? Date.now() - this.modelCacheTime : null
+      },
+      timestamp: new Date().toISOString(),
+      endpoints: [
+        'GET /v1/models',
+        'POST /v1/chat/completions', 
+        'POST /v1/responses',
+        'GET /health'
+      ]
+    };
+  }
+
+  /**
+   * Send JSON response
+   */
+  sendResponse(res, statusCode, data) {
+    res.writeHead(statusCode, { 'Content-Type': 'application/json' });
+    res.end(JSON.stringify(data, null, 2));
+  }
+
+  /**
+   * Send error response
+   */
+  sendError(res, statusCode, message) {
+    res.writeHead(statusCode, { 'Content-Type': 'application/json' });
+    res.end(JSON.stringify({ 
+      error: {
+        message: message,
+        type: 'api_error',
+        code: statusCode
+      }
+    }));
+  }
+
+  /**
+   * Get running status and configuration
+   */
+  getStatus() {
+    return {
+      isRunning: this.isRunning,
+      host: this.config.host,
+      port: this.config.port,
+      url: `http://${this.config.host}:${this.config.port}`,
+      modelCacheTTL: this.config.modelCacheTTL,
+      evalServerRunning: this.evalServer.isRunning
+    };
+  }
+}
\ No newline at end of file
diff --git a/eval-server/python/examples/openai_server_example.py b/eval-server/python/examples/openai_server_example.py
new file mode 100644
index 00000000000..dc320c544f0
--- /dev/null
+++ b/eval-server/python/examples/openai_server_example.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+
+"""
+Example demonstrating how to use the OpenAI-compatible API server.
+
+This example shows how to start both the WebSocket evaluation server
+and the OpenAI-compatible HTTP wrapper that multiplexes requests to
+connected DevTools tabs.
+"""
+
+import asyncio
+import logging
+from bo_eval_server import EvalServer
+from bo_eval_server.openai_server import OpenAICompatibleServer
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+
+async def main():
+    print("🚀 Starting Browser Operator evaluation server with OpenAI-compatible API...")
+    
+    # Create WebSocket evaluation server
+    eval_server = EvalServer(
+        auth_key='hello',
+        host='127.0.0.1',
+        port=8080,
+        log_level='INFO'
+    )
+    
+    # Set up client connection handler
+    @eval_server.on_connect
+    async def handle_client_connect(client):
+        print(f"✅ DevTools tab connected: {client.id}")
+        print(f"   Tab ID: {client.tab_id}")
+        print(f"   Capabilities: {client.capabilities}")
+        print(f"   Connected at: {client._connected_at}")
+        
+        # The client is now ready to receive evaluations via OpenAI API
+    
+    @eval_server.on_disconnect  
+    async def handle_client_disconnect(client_info):
+        print(f"❌ DevTools tab disconnected: {client_info['id']}")
+    
+    # Create OpenAI-compatible HTTP server
+    openai_server = OpenAICompatibleServer(
+        eval_server=eval_server,
+        host='127.0.0.1',
+        port=8081,
+        model_cache_ttl=300  # 5 minutes
+    )
+    
+    try:
+        # Start WebSocket server first
+        print("🔧 Starting WebSocket evaluation server on ws://127.0.0.1:8080")
+        await eval_server.start()
+        
+        # Start OpenAI-compatible HTTP server
+        print("🔧 Starting OpenAI-compatible API server on http://127.0.0.1:8081")
+        
+        # Run the HTTP server (this will block)
+        await openai_server.start()
+        
+    except KeyboardInterrupt:
+        print("\n🛑 Shutting down servers...")
+    except Exception as e:
+        print(f"❌ Error: {e}")
+    finally:
+        # Clean shutdown
+        try:
+            await openai_server.stop()
+        except:
+            pass
+        try:
+            await eval_server.stop()  
+        except:
+            pass
+
+if __name__ == '__main__':
+    print("""
+🌟 Browser Operator Evaluation Server with OpenAI-Compatible API
+
+This server provides:
+- WebSocket server for DevTools tab connections (port 8080)
+- OpenAI-compatible HTTP API (port 8081)
+
+To use:
+1. Start this server
+2. Connect DevTools tabs via WebSocket to ws://127.0.0.1:8080  
+3. Send OpenAI-compatible requests to http://127.0.0.1:8081
+
+Available endpoints:
+- GET  /v1/models           - List available models
+- POST /v1/chat/completions - Chat completions
+- GET  /health              - Health check
+
+Example usage:
+  curl http://127.0.0.1:8081/v1/models
+  
+  curl -X POST http://127.0.0.1:8081/v1/chat/completions \\
+    -H "Content-Type: application/json" \\
+    -d '{
+      "model": "gpt-4.1", 
+      "messages": [{"role": "user", "content": "Hello!"}]
+    }'
+
+Press Ctrl+C to stop the server.
+""")
+    
+    asyncio.run(main())
\ No newline at end of file
diff --git a/eval-server/python/pyproject.toml b/eval-server/python/pyproject.toml
index 83d30eea069..4fe1eb31de8 100644
--- a/eval-server/python/pyproject.toml
+++ b/eval-server/python/pyproject.toml
@@ -32,6 +32,11 @@ dependencies = [
     "loguru>=0.7.0",
     "pandas>=2.0.0",
     "requests>=2.31.0",
+    "openai>=1.0.0",
+    "fastapi>=0.100.0",
+    "uvicorn>=0.23.0",
+    "pydantic>=2.0.0",
+    "httpx>=0.24.0",
 ]
 
 [project.optional-dependencies]
diff --git a/eval-server/python/src/bo_eval_server.egg-info/PKG-INFO b/eval-server/python/src/bo_eval_server.egg-info/PKG-INFO
new file mode 100644
index 00000000000..c0b05679e77
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/PKG-INFO
@@ -0,0 +1,407 @@
+Metadata-Version: 2.4
+Name: bo-eval-server
+Version: 1.0.0
+Summary: WebSocket server for evaluating LLM agents - Python implementation
+Author: Browser Operator Team
+License: MIT
+Project-URL: Homepage, https://github.com/chromium/devtools-frontend
+Project-URL: Repository, https://github.com/chromium/devtools-frontend
+Project-URL: Issues, https://github.com/chromium/devtools-frontend/issues
+Keywords: websocket,llm,evaluation,rpc,library,programmatic
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: System :: Networking
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Requires-Dist: websockets>=11.0.0
+Requires-Dist: loguru>=0.7.0
+Requires-Dist: pandas>=2.0.0
+Requires-Dist: requests>=2.31.0
+Requires-Dist: openai>=1.0.0
+Requires-Dist: fastapi>=0.100.0
+Requires-Dist: uvicorn>=0.23.0
+Requires-Dist: pydantic>=2.0.0
+Requires-Dist: httpx>=0.24.0
+Provides-Extra: dev
+Requires-Dist: pytest>=7.0.0; extra == "dev"
+Requires-Dist: pytest-asyncio>=0.21.0; extra == "dev"
+Requires-Dist: black>=23.0.0; extra == "dev"
+Requires-Dist: mypy>=1.0.0; extra == "dev"
+
+# bo-eval-server (Python)
+
+A minimal Python library for creating WebSocket-based evaluation servers for LLM agents.
+
+## Features
+
+- 🔌 **WebSocket Server**: Real-time agent connections with asyncio
+- 🤖 **Bidirectional RPC**: JSON-RPC 2.0 for calling methods on connected agents
+- 📚 **Programmatic API**: Create and manage evaluations in Python code
+- 📊 **Evaluation Stack**: LIFO stack for managing evaluation queues
+- ⚡ **Concurrent Support**: Full async/await support for multiple agents
+- 🔍 **Enhanced Logging**: Structured logging with loguru
+- ✨ **Minimal Dependencies**: Only websockets and loguru required
+
+## Quick Start
+
+### Basic WebSocket Server
+
+```python
+import asyncio
+from bo_eval_server import EvalServer
+
+async def main():
+    server = EvalServer(
+        auth_key='hello',
+        host='127.0.0.1',
+        port=8080
+    )
+    
+    @server.on_connect
+    async def handle_client(client):
+        print(f'Client connected: {client.id}')
+        
+        response = await client.evaluate({
+            "id": "test_eval",
+            "name": "Capital of France",
+            "tool": "chat",
+            "input": {"message": "What is the capital of France?"}
+        })
+        
+        print(f'Response: {response}')
+    
+    await server.start()
+    print('Server running on ws://127.0.0.1:8080')
+    
+    # Keep server running
+    await server.wait_closed()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Using Evaluation Stack
+
+```python
+import asyncio
+from bo_eval_server import EvalServer, EvaluationStack
+
+async def main():
+    server = EvalServer(auth_key='secret', port=8080)
+    stack = EvaluationStack()
+    
+    # Add evaluations to stack
+    stack.push({
+        "id": "eval_001",
+        "name": "Math Question",
+        "tool": "chat",
+        "input": {"message": "What is 2 + 2?"}
+    })
+    
+    stack.push({
+        "id": "eval_002", 
+        "name": "Science Question",
+        "tool": "chat",
+        "input": {"message": "What is the speed of light?"}
+    })
+    
+    @server.on_connect
+    async def handle_client(client):
+        print(f'Client connected: {client.id}')
+        
+        # Process evaluations from stack
+        while not stack.is_empty():
+            evaluation = stack.pop()
+            try:
+                result = await client.evaluate(evaluation)
+                print(f'✅ {evaluation["name"]}: {result["status"]}')
+            except Exception as e:
+                print(f'❌ {evaluation["name"]}: {e}')
+    
+    await server.start()
+    await server.wait_closed()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Installation
+
+### Using uv (Recommended)
+
+```bash
+# Install uv package manager (if not already installed)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Install dependencies and create virtual environment
+uv sync
+
+# Run examples using the convenient runner
+python run.py basic      # Basic server example
+python run.py stack      # Evaluation stack example  
+python run.py prog       # Programmatic evaluations example
+python run.py all        # Show all available examples
+
+# Or run examples directly with uv
+uv run python examples/basic_server.py
+uv run python examples/with_stack.py
+uv run python examples/programmatic_evals.py
+```
+
+### Using pip (Alternative)
+
+```bash
+# Install the package
+pip install -e .
+
+# Or install with development dependencies
+pip install -e ".[dev]"
+
+# Or install from requirements.txt
+pip install -r requirements.txt
+```
+
+## Library Usage
+
+### EvalServer API
+
+```python
+from bo_eval_server import EvalServer
+
+# Create server instance
+server = EvalServer(
+    auth_key='your-secret-key',  # Required: client authentication
+    host='127.0.0.1',           # Optional: default 'localhost'
+    port=8080,                  # Optional: default 8080
+)
+
+# Register event handlers
+@server.on_connect
+async def handle_connect(client):
+    # Called when client connects and is ready
+    pass
+
+@server.on_disconnect  
+async def handle_disconnect(client_info):
+    # Called when client disconnects
+    pass
+
+# Server lifecycle
+await server.start()        # Start the server
+await server.stop()         # Stop the server
+await server.wait_closed()  # Wait for server to close
+
+# Server status
+status = server.get_status()
+print(f"Server running: {status['running']}")
+```
+
+### Client Proxy API
+
+```python
+@server.on_connect
+async def handle_client(client):
+    # Client information
+    print(f'Client ID: {client.id}')
+    print(f'Tab ID: {client.tab_id}')
+    print(f'Base Client ID: {client.base_client_id}')
+    
+    # Execute evaluations
+    result = await client.evaluate({
+        "id": "eval_001",
+        "name": "Test Evaluation",
+        "description": "Optional description",
+        "tool": "chat",
+        "input": {"message": "Your question here"},
+        "timeout": 30.0,  # Optional timeout in seconds
+        "metadata": {"tags": ["api", "test"]}
+    })
+    
+    # Send custom messages
+    await client.send_message({
+        "type": "custom", 
+        "data": "Hello client!"
+    })
+```
+
+### EvaluationStack API
+
+```python
+from bo_eval_server import EvaluationStack
+
+stack = EvaluationStack()
+
+# Add evaluations (LIFO - Last In, First Out)
+stack.push({
+    "id": "eval_001",
+    "name": "Test",
+    "tool": "chat", 
+    "input": {"message": "Hello"}
+})
+
+# Remove and get evaluation
+evaluation = stack.pop()  # Returns dict or None if empty
+
+# Stack operations
+size = stack.size()           # Get number of evaluations
+is_empty = stack.is_empty()   # Check if empty
+top = stack.peek()            # View top without removing
+stack.clear()                 # Remove all evaluations
+all_evals = stack.to_array()  # Get copy as list
+```
+
+## Agent Protocol
+
+Your agent needs to implement the WebSocket protocol:
+
+### 1. Connect to WebSocket
+```python
+import websockets
+import json
+
+ws = await websockets.connect('ws://localhost:8080')
+```
+
+### 2. Receive Authentication Challenge
+The server sends an authentication challenge with the secret key:
+```python
+challenge = json.loads(await ws.recv())
+# Expected: {"type": "auth_challenge", "secretKey": "hello", "connectionId": "uuid"}
+```
+
+### 3. Send Registration Response
+Client validates the secret key and responds:
+```python
+await ws.send(json.dumps({
+    "type": "register",
+    "clientId": "your-client-id",
+    "acceptAuth": True,  # True if secret key is acceptable
+    "connectionId": challenge["connectionId"],
+    "capabilities": ["chat", "action"]
+}))
+```
+
+### 4. Receive Registration Confirmation
+```python
+confirmation = json.loads(await ws.recv())
+# Expected: {"type": "registered", "clientId": "your-client-id", "serverTime": 123456}
+```
+
+### 5. Send Ready Signal
+```python
+await ws.send(json.dumps({"type": "ready"}))
+```
+
+### 6. Handle RPC Calls
+```python
+async for message in ws:
+    data = json.loads(message)
+    
+    if data.get("jsonrpc") == "2.0" and data.get("method") == "evaluate":
+        # Handle evaluation request
+        result = await handle_evaluation(data["params"])
+        
+        # Send response
+        await ws.send(json.dumps({
+            "jsonrpc": "2.0",
+            "id": data["id"],
+            "result": result
+        }))
+```
+
+## Architecture
+
+```
+src/bo_eval_server/
+├── __init__.py           # Package exports
+├── eval_server.py        # Main EvalServer class
+├── evaluation_stack.py   # EvaluationStack implementation
+├── client_manager.py     # Client connection management
+├── rpc_client.py         # JSON-RPC client implementation
+├── config.py             # Configuration management
+└── logger.py             # Enhanced logging setup
+```
+
+## Design Principles
+
+- **Async-First**: Built on asyncio for high concurrency
+- **Minimal Dependencies**: Only essential packages required
+- **Type Hints**: Full typing support for better development experience
+- **Event-Driven**: React to client connections with decorators
+- **Programmatic**: Full control through Python code
+- **Clean API**: Simple, Pythonic interface
+
+## Examples
+
+See the `examples/` directory for complete working examples:
+
+- `basic_server.py` - Simple WebSocket server setup
+- `with_stack.py` - Using evaluation stack for queuing
+- `programmatic_evals.py` - Creating evaluations in code
+
+## Evaluation Scripts
+
+The `evals/` directory contains ready-to-use evaluation scripts for various benchmarks:
+
+- `browsecomp_eval_server.py` - Browsecomp benchmark server (1,266 web browsing questions)
+  - Run with: `./evals/run_browsecomp_eval_server.sh`
+  - See `evals/README.md` for detailed usage
+
+## Development
+
+### Using uv
+
+```bash
+# Install with development dependencies
+uv sync --dev
+
+# Run tests
+uv run pytest
+
+# Format code
+uv run black src/ examples/
+
+# Type checking
+uv run mypy src/
+
+# Run all development commands
+uv run pytest && uv run black src/ examples/ && uv run mypy src/
+```
+
+### Using pip
+
+```bash
+# Install in development mode
+pip install -e ".[dev]"
+
+# Run tests
+pytest
+
+# Format code
+black src/ examples/
+
+# Type checking
+mypy src/
+```
+
+## Environment Variables
+
+```bash
+# Optional configuration
+BO_EVAL_SERVER_HOST=localhost
+BO_EVAL_SERVER_PORT=8080
+BO_EVAL_SERVER_LOG_LEVEL=INFO
+```
+
+---
+
+This Python implementation provides the core WebSocket evaluation server functionality with a clean, async API for programmatic evaluation management.
diff --git a/eval-server/python/src/bo_eval_server.egg-info/SOURCES.txt b/eval-server/python/src/bo_eval_server.egg-info/SOURCES.txt
new file mode 100644
index 00000000000..301f8b4b09c
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/SOURCES.txt
@@ -0,0 +1,17 @@
+README.md
+pyproject.toml
+src/bo_eval_server/__init__.py
+src/bo_eval_server/client_manager.py
+src/bo_eval_server/config.py
+src/bo_eval_server/eval_server.py
+src/bo_eval_server/evaluation_stack.py
+src/bo_eval_server/logger.py
+src/bo_eval_server/openai_server.py
+src/bo_eval_server/rpc_client.py
+src/bo_eval_server.egg-info/PKG-INFO
+src/bo_eval_server.egg-info/SOURCES.txt
+src/bo_eval_server.egg-info/dependency_links.txt
+src/bo_eval_server.egg-info/entry_points.txt
+src/bo_eval_server.egg-info/requires.txt
+src/bo_eval_server.egg-info/top_level.txt
+tests/test_openai_compatibility.py
\ No newline at end of file
diff --git a/eval-server/python/src/bo_eval_server.egg-info/dependency_links.txt b/eval-server/python/src/bo_eval_server.egg-info/dependency_links.txt
new file mode 100644
index 00000000000..8b137891791
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/eval-server/python/src/bo_eval_server.egg-info/entry_points.txt b/eval-server/python/src/bo_eval_server.egg-info/entry_points.txt
new file mode 100644
index 00000000000..bf7b94bb7be
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/entry_points.txt
@@ -0,0 +1,4 @@
+[console_scripts]
+bo-eval-basic = scripts:run_basic_server
+bo-eval-programmatic = scripts:run_programmatic_evals
+bo-eval-stack = scripts:run_with_stack
diff --git a/eval-server/python/src/bo_eval_server.egg-info/requires.txt b/eval-server/python/src/bo_eval_server.egg-info/requires.txt
new file mode 100644
index 00000000000..f49308bd49d
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/requires.txt
@@ -0,0 +1,15 @@
+websockets>=11.0.0
+loguru>=0.7.0
+pandas>=2.0.0
+requests>=2.31.0
+openai>=1.0.0
+fastapi>=0.100.0
+uvicorn>=0.23.0
+pydantic>=2.0.0
+httpx>=0.24.0
+
+[dev]
+pytest>=7.0.0
+pytest-asyncio>=0.21.0
+black>=23.0.0
+mypy>=1.0.0
diff --git a/eval-server/python/src/bo_eval_server.egg-info/top_level.txt b/eval-server/python/src/bo_eval_server.egg-info/top_level.txt
new file mode 100644
index 00000000000..90a889c9a6b
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server.egg-info/top_level.txt
@@ -0,0 +1 @@
+bo_eval_server
diff --git a/eval-server/python/src/bo_eval_server/openai_server.py b/eval-server/python/src/bo_eval_server/openai_server.py
new file mode 100644
index 00000000000..c6379db6bff
--- /dev/null
+++ b/eval-server/python/src/bo_eval_server/openai_server.py
@@ -0,0 +1,644 @@
+"""
+OpenAI-compatible HTTP API server that multiplexes requests to connected DevTools tabs.
+
+This module provides an HTTP server that implements OpenAI-compatible endpoints
+(/v1/models and /v1/chat/completions) while routing requests through the existing
+WebSocket evaluation server to connected Browser Operator tabs.
+"""
+
+import asyncio
+import json
+import time
+import uuid
+from typing import Dict, Any, List, Optional
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+import uvicorn
+from loguru import logger
+
+from .eval_server import EvalServer
+
+
+class OpenAIMessage(BaseModel):
+    """OpenAI chat message format"""
+    role: str = Field(..., description="Message role (system, user, assistant)")
+    content: str = Field(..., description="Message content")
+
+
+class OpenAIChatCompletionRequest(BaseModel):
+    """OpenAI chat completion request format"""
+    model: str = Field(..., description="Model to use for completion")
+    messages: List[OpenAIMessage] = Field(..., description="List of messages")
+    temperature: Optional[float] = Field(None, description="Sampling temperature")
+    max_tokens: Optional[int] = Field(None, description="Maximum tokens to generate")
+    top_p: Optional[float] = Field(None, description="Top-p sampling parameter")
+    frequency_penalty: Optional[float] = Field(None, description="Frequency penalty")
+    presence_penalty: Optional[float] = Field(None, description="Presence penalty")
+    stream: Optional[bool] = Field(False, description="Whether to stream responses")
+
+
+class OpenAIModelInfo(BaseModel):
+    """OpenAI model information format"""
+    id: str = Field(..., description="Model identifier")
+    object: str = Field(default="model", description="Object type")
+    created: int = Field(..., description="Creation timestamp")
+    owned_by: str = Field(..., description="Model owner")
+
+
+class OpenAIModelsResponse(BaseModel):
+    """OpenAI models list response format"""
+    object: str = Field(default="list", description="Object type")
+    data: List[OpenAIModelInfo] = Field(..., description="List of available models")
+
+
+class OpenAIChatChoice(BaseModel):
+    """OpenAI chat completion choice"""
+    index: int = Field(..., description="Choice index")
+    message: OpenAIMessage = Field(..., description="Generated message")
+    finish_reason: str = Field(..., description="Reason for completion finish")
+
+
+class OpenAIChatCompletionResponse(BaseModel):
+    """OpenAI chat completion response format"""
+    id: str = Field(..., description="Completion ID")
+    object: str = Field(default="chat.completion", description="Object type")
+    created: int = Field(..., description="Creation timestamp")
+    model: str = Field(..., description="Model used")
+    choices: List[OpenAIChatChoice] = Field(..., description="Generated choices")
+    usage: Optional[Dict[str, int]] = Field(None, description="Token usage statistics")
+
+
+class OpenAICompatibleServer:
+    """
+    OpenAI-compatible HTTP API server that multiplexes requests to connected DevTools tabs.
+    
+    This server provides OpenAI-compatible endpoints while routing requests through
+    the existing WebSocket evaluation server infrastructure.
+    """
+    
+    def __init__(
+        self,
+        eval_server: EvalServer,
+        host: str = "localhost",
+        port: int = 8081,
+        model_cache_ttl: int = 300,  # 5 minutes
+    ):
+        """
+        Initialize the OpenAI-compatible server.
+        
+        Args:
+            eval_server: The WebSocket evaluation server to route requests through
+            host: HTTP server host
+            port: HTTP server port
+            model_cache_ttl: Model list cache TTL in seconds
+        """
+        self.eval_server = eval_server
+        self.host = host
+        self.port = port
+        self.model_cache_ttl = model_cache_ttl
+        
+        # Model list cache
+        self._model_cache: Optional[List[OpenAIModelInfo]] = None
+        self._model_cache_time: Optional[float] = None
+        
+        # Create FastAPI app
+        self.app = FastAPI(
+            title="OpenAI Compatible API",
+            description="OpenAI-compatible API that routes requests to Browser Operator tabs",
+            version="1.0.0",
+        )
+        
+        # Setup routes
+        self._setup_routes()
+    
+    def _setup_routes(self):
+        """Setup FastAPI routes"""
+        
+        @self.app.get("/v1/models", response_model=OpenAIModelsResponse)
+        async def list_models():
+            """List available models from connected DevTools tabs"""
+            try:
+                models = await self._get_models_from_tabs()
+                return OpenAIModelsResponse(data=models)
+            except Exception as e:
+                logger.error(f"Error listing models: {e}")
+                raise HTTPException(status_code=503, detail="Unable to fetch models from connected tabs")
+        
+        @self.app.post("/v1/chat/completions", response_model=OpenAIChatCompletionResponse)
+        async def chat_completions(request: OpenAIChatCompletionRequest):
+            """Handle chat completion requests"""
+            try:
+                # Get available connected tabs
+                clients = self.eval_server.get_clients()
+                ready_client = next((c for c in clients if c.is_connected()), None)
+                
+                if not ready_client:
+                    raise HTTPException(status_code=503, detail="No connected DevTools tabs available")
+                
+                # Convert OpenAI request to evaluation format
+                evaluation = self._convert_openai_to_evaluation(request)
+                
+                # Send RPC to selected tab
+                logger.info(f"Sending evaluation to client {ready_client.id}")
+                result = await ready_client.evaluate(evaluation)
+                
+                # Convert result back to OpenAI format
+                return self._convert_result_to_openai(result, request)
+                
+            except HTTPException:
+                raise
+            except Exception as e:
+                logger.error(f"Error processing chat completion: {e}")
+                raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+        
+        @self.app.post("/v1/responses")
+        async def responses_api(request: Dict[str, Any]):
+            """Handle OpenAI Responses API requests"""
+            try:
+                # Validate required input field
+                if "input" not in request or not isinstance(request["input"], str):
+                    raise HTTPException(
+                        status_code=400, 
+                        detail="Missing or invalid 'input' field. Expected a string."
+                    )
+                
+                # Get available connected tabs
+                clients = self.eval_server.get_clients()
+                ready_client = next((c for c in clients if c.is_connected()), None)
+                
+                if not ready_client:
+                    raise HTTPException(status_code=503, detail="No connected DevTools tabs available")
+                
+                # Convert Responses API request to evaluation format
+                evaluation = self._convert_responses_to_evaluation(request)
+                
+                # Send RPC to selected tab
+                logger.info(f"Sending responses evaluation to client {ready_client.id}")
+                result = await ready_client.evaluate(evaluation)
+                
+                # Convert result back to Responses API format
+                return self._convert_result_to_responses(result)
+                
+            except HTTPException:
+                raise
+            except Exception as e:
+                logger.error(f"Error processing responses request: {e}")
+                raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+        
+        @self.app.get("/health")
+        async def health_check():
+            """Health check endpoint"""
+            connected_clients = len([c for c in self.eval_server.get_clients() if c.is_connected()])
+            return {
+                "status": "healthy",
+                "eval_server_running": self.eval_server.is_running(),
+                "connected_clients": connected_clients,
+                "timestamp": time.time(),
+            }
+    
+    async def _get_models_from_tabs(self) -> List[OpenAIModelInfo]:
+        """
+        Get available models from connected DevTools tabs.
+        
+        Returns:
+            List of OpenAI-compatible model information
+        """
+        # Check cache first
+        current_time = time.time()
+        if (self._model_cache is not None and 
+            self._model_cache_time is not None and 
+            current_time - self._model_cache_time < self.model_cache_ttl):
+            logger.debug("Returning cached model list")
+            return self._model_cache
+        
+        # Get models from any connected tab
+        clients = self.eval_server.get_clients()
+        ready_client = next((c for c in clients if c.is_connected()), None)
+        
+        if not ready_client:
+            logger.warning("No connected tabs available for model listing")
+            return []
+        
+        try:
+            # Create a get_models request to the DevTools tab
+            eval_id = f"get_models_{uuid.uuid4().hex}"
+            evaluation = {
+                "id": eval_id,  # Add the required id field
+                "evaluationId": eval_id,
+                "name": "get_models",  # Add the required name field
+                "tool": "get_models",
+                "url": None,
+                "input": {},
+                "timeout": 10000  # 10 second timeout for model listing
+            }
+            
+            logger.info(f"Requesting models from connected client {ready_client.id}")
+            result = await ready_client.evaluate(evaluation)
+            
+            # Parse the result from Browser Operator
+            models = self._parse_models_result(result)
+            
+            # Cache the results
+            self._model_cache = models
+            self._model_cache_time = current_time
+            
+            logger.info(f"Retrieved {len(models)} models from Browser Operator")
+            return models
+            
+        except Exception as e:
+            logger.error(f"Error getting models from Browser Operator: {e}")
+            logger.info("Falling back to static model list")
+            
+            # Fallback to static list if dynamic fetching fails
+            models = [
+                OpenAIModelInfo(
+                    id="gpt-4.1",
+                    created=int(current_time),
+                    owned_by="browser-operator"
+                ),
+                OpenAIModelInfo(
+                    id="gpt-4.1-mini", 
+                    created=int(current_time),
+                    owned_by="browser-operator"
+                ),
+                OpenAIModelInfo(
+                    id="gpt-4.1-nano",
+                    created=int(current_time),
+                    owned_by="browser-operator"
+                ),
+                OpenAIModelInfo(
+                    id="claude-3-5-sonnet",
+                    created=int(current_time),
+                    owned_by="anthropic"
+                ),
+                OpenAIModelInfo(
+                    id="claude-3-5-haiku",
+                    created=int(current_time), 
+                    owned_by="anthropic"
+                )
+            ]
+            
+            # Cache the fallback results
+            self._model_cache = models
+            self._model_cache_time = current_time
+            
+            return models
+    
+    def _parse_models_result(self, result: Dict[str, Any]) -> List[OpenAIModelInfo]:
+        """
+        Parse model result from DevTools tab into OpenAI format.
+        
+        Args:
+            result: Raw result from evaluation
+            
+        Returns:
+            List of OpenAI-compatible model info
+        """
+        models = []
+        current_selection = None
+        
+        try:
+            # Extract models and current selection from various possible result formats
+            model_data = None
+            
+            if isinstance(result, dict):
+                # Check direct response format
+                if 'models' in result:
+                    model_data = result['models']
+                    current_selection = result.get('currentSelection')
+                # Check nested output format  
+                elif 'output' in result and isinstance(result['output'], dict):
+                    output = result['output']
+                    if 'models' in output:
+                        model_data = output['models']
+                        current_selection = output.get('currentSelection')
+                # Check response string format
+                elif 'response' in result:
+                    response = result['response']
+                    if isinstance(response, str):
+                        try:
+                            parsed = json.loads(response)
+                            if 'models' in parsed:
+                                model_data = parsed['models']
+                                current_selection = parsed.get('currentSelection')
+                        except json.JSONDecodeError:
+                            pass
+            
+            logger.info(f"Current model selection from Browser Operator: {current_selection}")
+            
+            if model_data and isinstance(model_data, list):
+                selected_count = 0
+                for model in model_data:
+                    if isinstance(model, dict) and 'id' in model:
+                        is_selected = model.get('selected', False)
+                        if is_selected:
+                            selected_count += 1
+                            
+                        models.append(OpenAIModelInfo(
+                            id=model['id'],
+                            created=int(time.time()),
+                            owned_by=model.get('provider', 'browser-operator'),
+                        ))
+                    elif isinstance(model, str):
+                        # Simple model name
+                        models.append(OpenAIModelInfo(
+                            id=model,
+                            created=int(time.time()),
+                            owned_by='browser-operator',
+                        ))
+                
+                logger.info(f"Found {len(models)} models with {selected_count} selected")
+            
+            # Fallback: provide some default models if nothing found
+            if not models:
+                logger.warning("No models found in result, providing defaults")
+                default_models = ['gpt-4.1', 'gpt-4.1-mini', 'gpt-4.1-nano']
+                for model_name in default_models:
+                    models.append(OpenAIModelInfo(
+                        id=model_name,
+                        created=int(time.time()),
+                        owned_by='browser-operator',
+                    ))
+            
+        except Exception as e:
+            logger.error(f"Error parsing models result: {e}")
+            logger.debug(f"Raw result: {result}")
+            # Provide fallback models
+            models = [
+                OpenAIModelInfo(
+                    id='gpt-4.1',
+                    created=int(time.time()),
+                    owned_by='browser-operator',
+                )
+            ]
+        
+        return models
+    
+    def _convert_openai_to_evaluation(self, request: OpenAIChatCompletionRequest) -> Dict[str, Any]:
+        """
+        Convert OpenAI chat completion request to evaluation format.
+        
+        Args:
+            request: OpenAI chat completion request
+            
+        Returns:
+            Evaluation object for Browser Operator
+        """
+        # Convert OpenAI messages array to single message string
+        # The Browser Operator expects input.message (string), not input.messages (array)
+        message_parts = []
+        for msg in request.messages:
+            if msg.role == "system":
+                message_parts.append(f"System: {msg.content}")
+            elif msg.role == "user":
+                message_parts.append(f"User: {msg.content}")
+            elif msg.role == "assistant":
+                message_parts.append(f"Assistant: {msg.content}")
+            else:
+                message_parts.append(f"{msg.role}: {msg.content}")
+        
+        # Join all messages into a single conversation string
+        conversation_message = "\n\n".join(message_parts)
+        
+        # If there's only a user message, use it directly
+        if len(request.messages) == 1 and request.messages[0].role == "user":
+            conversation_message = request.messages[0].content
+        
+        # Create evaluation object
+        evaluation = {
+            "id": f"openai-chat-{uuid.uuid4().hex[:8]}",
+            "name": "OpenAI Chat Completion",
+            "description": f"Chat completion using model {request.model}",
+            "tool": "chat",
+            "input": {
+                "message": conversation_message,  # Single message string as expected by executeChatEvaluation
+                "model": request.model,
+                "temperature": request.temperature,
+                "max_tokens": request.max_tokens,
+                "top_p": request.top_p,
+                "frequency_penalty": request.frequency_penalty,
+                "presence_penalty": request.presence_penalty,
+            },
+            "timeout": 300000,  # 5 minutes
+            "metadata": {
+                "tags": ["openai-api", "chat-completion"],
+                "source": "openai-compatible-api",
+                "original_model": request.model,
+            },
+        }
+        
+        return evaluation
+    
+    def _convert_responses_to_evaluation(self, request: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Convert OpenAI Responses API request to evaluation format.
+        
+        Args:
+            request: Responses API request with input field
+            
+        Returns:
+            Evaluation object for Browser Operator
+        """
+        # Create evaluation object for responses API
+        evaluation = {
+            "id": f"openai-responses-{uuid.uuid4().hex[:8]}",
+            "name": "OpenAI Responses API Request",
+            "description": "Dynamic evaluation from OpenAI Responses API",
+            "tool": "chat",
+            "input": {
+                "message": request["input"],  # Direct message from input
+                "reasoning": "OpenAI Responses API processing",
+            },
+            "timeout": 300000,  # 5 minutes
+            "metadata": {
+                "tags": ["openai-api", "responses"],
+                "source": "openai-responses-api",
+                "priority": "high",
+            },
+        }
+        
+        # Merge any model configuration from request
+        if "main_model" in request:
+            evaluation["input"]["main_model"] = request["main_model"]
+        if "mini_model" in request:
+            evaluation["input"]["mini_model"] = request["mini_model"]
+        if "nano_model" in request:
+            evaluation["input"]["nano_model"] = request["nano_model"]
+        if "provider" in request:
+            evaluation["input"]["provider"] = request["provider"]
+        
+        return evaluation
+    
+    def _convert_result_to_responses(self, result: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Convert evaluation result to OpenAI Responses API format.
+        
+        Args:
+            result: Result from Browser Operator evaluation
+            
+        Returns:
+            OpenAI Responses API compatible response
+        """
+        # Extract response text from evaluation result
+        response_text = self._extract_response_text(result)
+        
+        # Create message ID in OpenAI format
+        message_id = f"msg_{uuid.uuid4().hex[:32]}"
+        
+        # Format in OpenAI Responses API format
+        return [
+            {
+                "id": message_id,
+                "type": "message",
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "output_text",
+                        "text": response_text,
+                        "annotations": []
+                    }
+                ]
+            }
+        ]
+    
+    def _convert_result_to_openai(
+        self,
+        result: Dict[str, Any],
+        request: OpenAIChatCompletionRequest,
+    ) -> OpenAIChatCompletionResponse:
+        """
+        Convert evaluation result back to OpenAI chat completion format.
+        
+        Args:
+            result: Result from Browser Operator evaluation
+            request: Original OpenAI request for context
+            
+        Returns:
+            OpenAI-compatible response
+        """
+        # Extract response text from various possible result formats
+        response_text = self._extract_response_text(result)
+        
+        # Create OpenAI-compatible response
+        completion_id = f"chatcmpl-{uuid.uuid4().hex[:29]}"
+        
+        choice = OpenAIChatChoice(
+            index=0,
+            message=OpenAIMessage(
+                role="assistant",
+                content=response_text,
+            ),
+            finish_reason="stop",
+        )
+        
+        return OpenAIChatCompletionResponse(
+            id=completion_id,
+            created=int(time.time()),
+            model=request.model,
+            choices=[choice],
+            usage={
+                "prompt_tokens": sum(len(msg.content.split()) for msg in request.messages),
+                "completion_tokens": len(response_text.split()),
+                "total_tokens": sum(len(msg.content.split()) for msg in request.messages) + len(response_text.split()),
+            },
+        )
+    
+    def _extract_response_text(self, result: Dict[str, Any]) -> str:
+        """
+        Extract response text from evaluation result.
+        
+        Args:
+            result: Evaluation result from Browser Operator
+            
+        Returns:
+            Extracted response text
+        """
+        if not result:
+            return "No response received from evaluation"
+        
+        # Handle different result formats
+        if isinstance(result, str):
+            return result
+        
+        # Check for nested evaluation result structure
+        if isinstance(result, dict):
+            # Try various common response fields
+            response_fields = [
+                'response', 'text', 'answer', 'content',
+                'output.response', 'output.text', 'output.answer',
+            ]
+            
+            for field in response_fields:
+                if '.' in field:
+                    # Handle nested fields
+                    parts = field.split('.')
+                    current = result
+                    try:
+                        for part in parts:
+                            current = current[part]
+                        if isinstance(current, str) and current.strip():
+                            return current
+                    except (KeyError, TypeError):
+                        continue
+                else:
+                    # Handle top-level fields
+                    if field in result and isinstance(result[field], str) and result[field].strip():
+                        return result[field]
+            
+            # If result is an object, try to extract meaningful content
+            return json.dumps(result, indent=2)
+        
+        return "Unable to extract response text from evaluation result"
+    
+    async def start(self):
+        """Start the HTTP server"""
+        if not self.eval_server.is_running():
+            raise RuntimeError("EvalServer must be started before starting OpenAI-compatible server")
+        
+        logger.info(f"Starting OpenAI-compatible server on {self.host}:{self.port}")
+        
+        config = uvicorn.Config(
+            self.app,
+            host=self.host,
+            port=self.port,
+            log_level="info",
+            access_log=True,
+        )
+        
+        self.server = uvicorn.Server(config)
+        await self.server.serve()
+    
+    async def stop(self):
+        """Stop the HTTP server"""
+        if hasattr(self, 'server'):
+            logger.info("Stopping OpenAI-compatible server")
+            await self.server.shutdown()
+
+
+async def main():
+    """Main entry point for running the OpenAI-compatible server standalone"""
+    from .eval_server import EvalServer
+    from .config import Config
+    
+    # Create config
+    config = Config()
+    
+    # Create and start evaluation server
+    eval_server = EvalServer(config)
+    await eval_server.start()
+    
+    # Create and start OpenAI-compatible server
+    openai_server = OpenAICompatibleServer(eval_server)
+    
+    try:
+        await openai_server.start()
+    except KeyboardInterrupt:
+        logger.info("Received shutdown signal")
+    finally:
+        await openai_server.stop()
+        await eval_server.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
\ No newline at end of file
diff --git a/eval-server/python/uv.lock b/eval-server/python/uv.lock
index 2da956841a5..0a21e2ef35a 100644
--- a/eval-server/python/uv.lock
+++ b/eval-server/python/uv.lock
@@ -9,6 +9,57 @@ resolution-markers = [
     "python_full_version < '3.9'",
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
+[[package]]
+name = "anyio"
+version = "4.5.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.9'",
+]
+dependencies = [
+    { name = "exceptiongroup", marker = "python_full_version < '3.9'" },
+    { name = "idna", marker = "python_full_version < '3.9'" },
+    { name = "sniffio", marker = "python_full_version < '3.9'" },
+    { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4d/f9/9a7ce600ebe7804daf90d4d48b1c0510a4561ddce43a596be46676f82343/anyio-4.5.2.tar.gz", hash = "sha256:23009af4ed04ce05991845451e11ef02fc7c5ed29179ac9a420e5ad0ac7ddc5b", size = 171293, upload-time = "2024-10-13T22:18:03.307Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1b/b4/f7e396030e3b11394436358ca258a81d6010106582422f23443c16ca1873/anyio-4.5.2-py3-none-any.whl", hash = "sha256:c011ee36bc1e8ba40e5a81cb9df91925c218fe9b778554e0b56a21e1b5d4716f", size = 89766, upload-time = "2024-10-13T22:18:01.524Z" },
+]
+
+[[package]]
+name = "anyio"
+version = "4.10.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version == '3.9.*'",
+]
+dependencies = [
+    { name = "exceptiongroup", marker = "python_full_version >= '3.9' and python_full_version < '3.11'" },
+    { name = "idna", marker = "python_full_version >= '3.9'" },
+    { name = "sniffio", marker = "python_full_version >= '3.9'" },
+    { name = "typing-extensions", version = "4.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9' and python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
+]
+
 [[package]]
 name = "backports-asyncio-runner"
 version = "1.2.0"
@@ -109,10 +160,17 @@ name = "bo-eval-server"
 version = "1.0.0"
 source = { editable = "." }
 dependencies = [
+    { name = "fastapi" },
+    { name = "httpx" },
     { name = "loguru" },
+    { name = "openai" },
     { name = "pandas", version = "2.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
     { name = "pandas", version = "2.3.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "pydantic", version = "2.10.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "pydantic", version = "2.11.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
     { name = "requests" },
+    { name = "uvicorn", version = "0.33.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "uvicorn", version = "0.35.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
     { name = "websockets", version = "13.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
     { name = "websockets", version = "15.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
 ]
@@ -144,12 +202,17 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "black", marker = "extra == 'dev'", specifier = ">=23.0.0" },
+    { name = "fastapi", specifier = ">=0.100.0" },
+    { name = "httpx", specifier = ">=0.24.0" },
     { name = "loguru", specifier = ">=0.7.0" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.0.0" },
+    { name = "openai", specifier = ">=1.0.0" },
     { name = "pandas", specifier = ">=2.0.0" },
+    { name = "pydantic", specifier = ">=2.0.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0.0" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.21.0" },
     { name = "requests", specifier = ">=2.31.0" },
+    { name = "uvicorn", specifier = ">=0.23.0" },
     { name = "websockets", specifier = ">=11.0.0" },
 ]
 provides-extras = ["dev"]
@@ -300,6 +363,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -313,6 +385,61 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674, upload-time = "2025-05-10T17:42:49.33Z" },
 ]
 
+[[package]]
+name = "fastapi"
+version = "0.116.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic", version = "2.10.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "pydantic", version = "2.11.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "starlette", version = "0.44.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "starlette", version = "0.47.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "typing-extensions", version = "4.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/78/d7/6c8b3bfe33eeffa208183ec037fee0cce9f7f024089ab1c5d12ef04bd27c/fastapi-0.116.1.tar.gz", hash = "sha256:ed52cbf946abfd70c5a0dccb24673f0670deeb517a88b3544d03c2a6bf283143", size = 296485, upload-time = "2025-07-11T16:22:32.057Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e5/47/d63c60f59a59467fda0f93f46335c9d18526d7071f025cb5b89d5353ea42/fastapi-0.116.1-py3-none-any.whl", hash = "sha256:c46ac7c312df840f0c9e220f7964bada936781bc4e2e6eb71f1c4d7553786565", size = 95631, upload-time = "2025-07-11T16:22:30.485Z" },
+]
+
+[[package]]
+name = "h11"
+version = "0.16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
+wheels = [
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+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "anyio", version = "4.10.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "certifi" },
+    { name = "httpcore" },
+    { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
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+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -331,6 +458,182 @@ wheels = [
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+name = "jiter"
+version = "0.9.1"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
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+    { url = "https://files.pythonhosted.org/packages/86/0d/aac9eafc5d46bdf5c4f127ac1ce85e434d003bb5e3ae886f5e726a988cf6/jiter-0.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:514d4dd845e0af4da15112502e6fcb952f0721f27f17e530454e379472b90c14", size = 311743, upload-time = "2025-05-18T17:45:16.196Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/54/fab1f4d8634af7bb1ad6dc49bee50ea9f649de0e5309c80192ace739f968/jiter-0.9.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b879faee1cc1a67fde3f3f370041239fd260ac452bd53e861aa4a94a51e3fd02", size = 1085889, upload-time = "2025-05-18T17:45:17.883Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/86/bf4ed251d8035d5d72a46c8f9969bd5054fad052371cbea0cb161060e660/jiter-0.9.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:20a5ce641f93bfb8d8e336f8c4a045e491652f41eaacc707b15b245ece611e72", size = 1117896, upload-time = "2025-05-18T17:45:19.82Z" },
+    { url = "https://files.pythonhosted.org/packages/62/40/b04c40deccd5edd5f2a3853f4a80dc0ddbe157d1d523a573fb3d224315fc/jiter-0.9.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8575b1d2b49df04ca82d658882f4a432b7ed315a69126a379df4d10aeb416021", size = 1211956, upload-time = "2025-05-18T17:45:21.606Z" },
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+    { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" },
+]
+
+[[package]]
+name = "starlette"
+version = "0.44.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.9'",
+]
+dependencies = [
+    { name = "anyio", version = "4.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8d/b4/910f693584958b687b8f9c628f8217cfef19a42b64d2de7840814937365c/starlette-0.44.0.tar.gz", hash = "sha256:e35166950a3ccccc701962fe0711db0bc14f2ecd37c6f9fe5e3eae0cbaea8715", size = 2575579, upload-time = "2024-12-28T07:32:56.003Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b6/c5/7ae467eeddb57260c8ce17a3a09f9f5edba35820fc022d7c55b7decd5d3a/starlette-0.44.0-py3-none-any.whl", hash = "sha256:19edeb75844c16dcd4f9dd72f22f9108c1539f3fc9c4c88885654fef64f85aea", size = 73412, upload-time = "2024-12-28T07:32:53.871Z" },
+]
+
+[[package]]
+name = "starlette"
+version = "0.47.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version == '3.9.*'",
+]
+dependencies = [
+    { name = "anyio", version = "4.10.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "typing-extensions", version = "4.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9' and python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/04/57/d062573f391d062710d4088fa1369428c38d51460ab6fedff920efef932e/starlette-0.47.2.tar.gz", hash = "sha256:6ae9aa5db235e4846decc1e7b79c4f346adf41e9777aebeb49dfd09bbd7023d8", size = 2583948, upload-time = "2025-07-20T17:31:58.522Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f7/1f/b876b1f83aef204198a42dc101613fefccb32258e5428b5f9259677864b4/starlette-0.47.2-py3-none-any.whl", hash = "sha256:c5847e96134e5c5371ee9fac6fdf1a67336d5815e09eb2a01fdb57a351ef915b", size = 72984, upload-time = "2025-07-20T17:31:56.738Z" },
+]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -1055,6 +1691,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.13.2"
@@ -1082,6 +1730,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b5/00/d631e67a838026495268c2f6884f3711a15a9a2a96cd244fdaea53b823fb/typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76", size = 43906, upload-time = "2025-07-04T13:28:32.743Z" },
 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions", version = "4.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f8/b1/0c11f5058406b3af7609f121aaa6b609744687f1d158b3c3a5bf4cc94238/typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28", size = 75726, upload-time = "2025-05-21T18:55:23.885Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51", size = 14552, upload-time = "2025-05-21T18:55:22.152Z" },
+]
+
 [[package]]
 name = "tzdata"
 version = "2025.2"
@@ -1118,6 +1778,44 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
 ]
 
+[[package]]
+name = "uvicorn"
+version = "0.33.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.9'",
+]
+dependencies = [
+    { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "h11", marker = "python_full_version < '3.9'" },
+    { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cb/81/a083ae41716b00df56d45d4b5f6ca8e90fc233a62e6c04ab3ad3c476b6c4/uvicorn-0.33.0.tar.gz", hash = "sha256:3577119f82b7091cf4d3d4177bfda0bae4723ed92ab1439e8d779de880c9cc59", size = 76590, upload-time = "2024-12-14T11:14:46.526Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/79/2e2620337ef1e4ef7a058b351603b765f59ac28e6e3ac7c5e7cdee9ea1ab/uvicorn-0.33.0-py3-none-any.whl", hash = "sha256:2c30de4aeea83661a520abab179b24084a0019c0c1bbe137e5409f741cbde5f8", size = 62297, upload-time = "2024-12-14T11:14:43.408Z" },
+]
+
+[[package]]
+name = "uvicorn"
+version = "0.35.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version == '3.9.*'",
+]
+dependencies = [
+    { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
+    { name = "click", version = "8.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "h11", marker = "python_full_version >= '3.9'" },
+    { name = "typing-extensions", version = "4.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9' and python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/42/e0e305207bb88c6b8d3061399c6a961ffe5fbb7e2aa63c9234df7259e9cd/uvicorn-0.35.0.tar.gz", hash = "sha256:bc662f087f7cf2ce11a1d7fd70b90c9f98ef2e2831556dd078d131b96cc94a01", size = 78473, upload-time = "2025-06-28T16:15:46.058Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/e2/dc81b1bd1dcfe91735810265e9d26bc8ec5da45b4c0f6237e286819194c3/uvicorn-0.35.0-py3-none-any.whl", hash = "sha256:197535216b25ff9b785e29a0b79199f55222193d47f820816e7da751e9bc8d4a", size = 66406, upload-time = "2025-06-28T16:15:44.816Z" },
+]
+
 [[package]]
 name = "websockets"
 version = "13.1"
diff --git a/front_end/panels/ai_chat/BUILD.gn b/front_end/panels/ai_chat/BUILD.gn
index 1fea191b0c1..5e364879389 100644
--- a/front_end/panels/ai_chat/BUILD.gn
+++ b/front_end/panels/ai_chat/BUILD.gn
@@ -48,6 +48,7 @@ devtools_module("ai_chat") {
     "core/Types.ts",
     "core/AgentService.ts",
     "core/Constants.ts",
+    "core/BuildConfig.ts",
     "core/structured_response.ts",
     "core/GraphConfigs.ts",
     "core/ConfigurableGraph.ts",
@@ -176,6 +177,7 @@ _ai_chat_sources = [
     "core/Types.ts",
     "core/AgentService.ts",
     "core/Constants.ts",
+    "core/BuildConfig.ts",
     "core/structured_response.ts",
     "core/GraphConfigs.ts",
     "core/ConfigurableGraph.ts",
diff --git a/front_end/panels/ai_chat/common/EvaluationConfig.ts b/front_end/panels/ai_chat/common/EvaluationConfig.ts
index 66ea9134ec3..be5169fe6cb 100644
--- a/front_end/panels/ai_chat/common/EvaluationConfig.ts
+++ b/front_end/panels/ai_chat/common/EvaluationConfig.ts
@@ -4,6 +4,7 @@
 
 import { createLogger } from '../core/Logger.js';
 import { WebSocketRPCClient } from './WebSocketRPCClient.js';
+import { BUILD_CONFIG } from '../core/BuildConfig.js';
 
 const logger = createLogger('EvaluationConfig');
 
@@ -49,6 +50,13 @@ class EvaluationConfigStore {
 
   private loadFromLocalStorage(): void {
     try {
+      // In automated mode, set default to enabled if not already set
+      if (BUILD_CONFIG.AUTOMATED_MODE && 
+          localStorage.getItem('ai_chat_evaluation_enabled') === null) {
+        localStorage.setItem('ai_chat_evaluation_enabled', 'true');
+        logger.info('Automated mode: defaulted evaluation to enabled');
+      }
+
       const enabled = localStorage.getItem('ai_chat_evaluation_enabled') === 'true';
       const endpoint = localStorage.getItem('ai_chat_evaluation_endpoint') || 'ws://localhost:8080';
       const secretKey = localStorage.getItem('ai_chat_evaluation_secret_key') || '';
diff --git a/front_end/panels/ai_chat/core/BuildConfig.ts b/front_end/panels/ai_chat/core/BuildConfig.ts
new file mode 100644
index 00000000000..91d89fec191
--- /dev/null
+++ b/front_end/panels/ai_chat/core/BuildConfig.ts
@@ -0,0 +1,20 @@
+// Copyright 2025 The Chromium Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+
+/**
+ * Build-time configuration for automated deployments and Docker environments.
+ * 
+ * This file contains constants that are set during the build process to configure
+ * behavior for different deployment scenarios.
+ */
+export const BUILD_CONFIG = {
+  /**
+   * Automated mode flag for Docker/CI deployments.
+   * When true:
+   * - Bypasses OAuth authentication panel
+   * - Automatically enables evaluation mode
+   * - Optimized for headless/automated usage
+   */
+  AUTOMATED_MODE: false,  // Will be set to true during Docker build
+} as const;
\ No newline at end of file
diff --git a/front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts b/front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts
index 56ff739b9ff..61413acb0a6 100644
--- a/front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts
+++ b/front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts
@@ -456,7 +456,7 @@ export class EvaluationAgent {
         
         toolResult = await this.executeChatEvaluation(
           mergedInput,
-          params.timeout || 300000, // Default 5 minutes for chat
+          params.timeout || 2700000, // Default 15 minutes for chat
           tracingContext
         );
       } else {
@@ -466,7 +466,7 @@ export class EvaluationAgent {
         toolResult = await this.executeToolWithTimeout(
           tool,
           params.input,
-          params.timeout || 30000,
+          params.timeout || 2700000,
           tracingContext,
           params.tool
         );
diff --git a/front_end/panels/ai_chat/ui/AIChatPanel.ts b/front_end/panels/ai_chat/ui/AIChatPanel.ts
index ec1a673f090..6dd9055451a 100644
--- a/front_end/panels/ai_chat/ui/AIChatPanel.ts
+++ b/front_end/panels/ai_chat/ui/AIChatPanel.ts
@@ -20,6 +20,7 @@ import { OpenRouterProvider } from '../LLM/OpenRouterProvider.js';
 import { createLogger } from '../core/Logger.js';
 import { isEvaluationEnabled, getEvaluationConfig } from '../common/EvaluationConfig.js';
 import { EvaluationAgent } from '../evaluation/remote/EvaluationAgent.js';
+import { BUILD_CONFIG } from '../core/BuildConfig.js';
 // Import of LiveAgentSessionComponent is not required here; the element is
 // registered by ChatView where it is used.
 
@@ -1395,9 +1396,8 @@ export class AIChatPanel extends UI.Panel.Panel {
    * @returns true if at least one provider has valid credentials
    */
   #hasAnyProviderCredentials(): boolean {
-    logger.info('=== CHECKING ALL PROVIDER CREDENTIALS ===');
+    
     const selectedProvider = localStorage.getItem(PROVIDER_SELECTION_KEY) || 'openai';
-    logger.info('Currently selected provider:', selectedProvider);
     
     // Check all providers except LiteLLM (unless LiteLLM is selected)
     const providers = ['openai', 'groq', 'openrouter'];
@@ -1407,19 +1407,13 @@ export class AIChatPanel extends UI.Panel.Panel {
       providers.push('litellm');
     }
     
-    logger.info('Providers to check:', providers);
-    
     for (const provider of providers) {
-      logger.info(`Checking provider: ${provider}`);
       const validation = LLMClient.validateProviderCredentials(provider);
-      logger.info(`Provider ${provider} validation result:`, validation);
       if (validation.isValid) {
-        logger.info(`✅ Found valid credentials for provider: ${provider}`);
         return true;
       }
     }
     
-    logger.info('❌ No valid credentials found for any provider');
     return false;
   }
 
@@ -1997,12 +1991,11 @@ export class AIChatPanel extends UI.Panel.Panel {
         inputPlaceholder: this.#getInputPlaceholderText(),
         // Add OAuth login state
         showOAuthLogin: (() => {
+          if (BUILD_CONFIG.AUTOMATED_MODE) {
+            return false;
+          }
           const hasCredentials = this.#hasAnyProviderCredentials();
-          const showOAuth = !hasCredentials;
-          logger.info('=== OAUTH LOGIN UI DECISION ===');
-          logger.info('hasAnyProviderCredentials:', hasCredentials);
-          logger.info('showOAuthLogin will be set to:', showOAuth);
-          return showOAuth;
+          return !hasCredentials;
         })(),
         onOAuthLogin: this.#handleOAuthLogin.bind(this),
       };