Moved docs into docs/

docs/API_EVOLUTION.md

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+# API Evolution Detection System
+
+This system automatically detects when your OpenAPI schema has new endpoints or changed parameters that need to be implemented in the `ApiClient` class.
+
+## How It Works
+
+### Automatic Detection
+- **Development Mode**: Automatically runs when `api-client.ts` is imported during development
+- **Runtime Checking**: Compares available endpoints in the OpenAPI schema with implemented methods
+- **Console Warnings**: Displays detailed warnings about unimplemented endpoints
+
+### Schema Comparison
+- **Hash-based Detection**: Detects when the OpenAPI schema file changes
+- **Endpoint Analysis**: Identifies new, changed, or unimplemented endpoints
+- **Parameter Validation**: Suggests checking for parameter changes
+
+## Usage
+
+### Automatic Checking
+The system runs automatically in development mode when you import from `api-client.ts`:
+
+```typescript
+import { apiClient } from './api-client';
+// Check runs automatically after 1 second delay
+```
+
+### Command Line Checking
+You can run API evolution checks from the command line:
+
+```bash
+# Full type generation with evolution check
+./generate-ts-types.sh
+
+# Quick evolution check only (without regenerating types)
+./check-api-evolution.sh
+
+# Or from within the client container
+npm run check-api-evolution
+```
+
+### Manual Checking
+You can manually trigger checks during development:
+
+```typescript
+import { devUtils } from './api-client';
+
+// Check for API evolution
+const evolution = await devUtils.checkApiEvolution();
+
+// Force recheck (bypasses once-per-session limit)
+devUtils.recheckEndpoints();
+```
+
+### Console Output
+When unimplemented endpoints are found, you'll see:
+
+**Browser Console (development mode):**
+```
+🚨 API Evolution Detection
+🆕 New API endpoints detected:
+  • GET /ai-voicebot/api/new-feature (get_new_feature_endpoint)
+⚠️  Unimplemented API endpoints:
+  • POST /ai-voicebot/api/admin/bulk-action
+💡 Implementation suggestions:
+Add these methods to ApiClient:
+  async adminBulkAction(): Promise<any> {
+    return this.request<any>('/ai-voicebot/api/admin/bulk-action', { method: 'POST' });
+  }
+```
+
+**Command Line:**
+```
+🔍 API Evolution Check
+==================================================
+📊 Summary:
+   Total endpoints: 8
+   Implemented: 7
+   Unimplemented: 1
+
+⚠️  Unimplemented API endpoints:
+   • POST /ai-voicebot/api/admin/bulk-action
+     Admin bulk action endpoint
+
+💡 Implementation suggestions:
+Add these methods to the ApiClient class:
+
+  async adminBulkAction(data?: any): Promise<any> {
+    return this.request<any>('/ai-voicebot/api/admin/bulk-action', { method: 'POST', body: data });
+  }
+```
+
+## Configuration
+
+### Implemented Endpoints Registry
+The system maintains a registry of implemented endpoints in `ApiClient`. When you add new methods, update the registry:
+
+```typescript
+// In api-evolution-checker.ts
+private getImplementedEndpoints(): Set<string> {
+  return new Set([
+    'GET:/ai-voicebot/api/admin/names',
+    'POST:/ai-voicebot/api/admin/set_password',
+    // Add new endpoints here:
+    'POST:/ai-voicebot/api/admin/bulk-action',
+  ]);
+}
+```
+
+### Schema Location
+The system attempts to load the OpenAPI schema from:
+- `/openapi-schema.json` (served by your development server)
+- Falls back to hardcoded endpoint list if schema file is unavailable
+
+## Development Workflow
+
+### When Adding New API Endpoints
+
+1. **Add endpoint to FastAPI server** (server/main.py)
+2. **Regenerate types**: Run `./generate-ts-types.sh`
+3. **Check console** for warnings about unimplemented endpoints
+4. **Implement methods** in `ApiClient` class
+5. **Update endpoint registry** in the evolution checker
+6. **Add convenience methods** to API namespaces if needed
+
+### Example Implementation
+
+When you see a warning like:
+```
+⚠️  Unimplemented: POST /ai-voicebot/api/admin/bulk-action
+```
+
+1. Add the method to `ApiClient`:
+```typescript
+async adminBulkAction(data: BulkActionRequest): Promise<BulkActionResponse> {
+  return this.request<BulkActionResponse>('/ai-voicebot/api/admin/bulk-action', { 
+    method: 'POST', 
+    body: data 
+  });
+}
+```
+
+2. Add to convenience API:
+```typescript
+export const adminApi = {
+  listNames: () => apiClient.adminListNames(),
+  setPassword: (data: AdminSetPassword) => apiClient.adminSetPassword(data),
+  clearPassword: (data: AdminClearPassword) => apiClient.adminClearPassword(data),
+  bulkAction: (data: BulkActionRequest) => apiClient.adminBulkAction(data), // New
+};
+```
+
+3. Update the registry:
+```typescript
+private getImplementedEndpoints(): Set<string> {
+  return new Set([
+    // ... existing endpoints ...
+    'POST:/ai-voicebot/api/admin/bulk-action', // Add this
+  ]);
+}
+```
+
+## Benefits
+
+- **Prevents Missing Implementations**: Never forget to implement new API endpoints
+- **Development Efficiency**: Automatic detection saves time during API evolution
+- **Type Safety**: Works with generated TypeScript types for full type safety
+- **Code Generation**: Provides implementation stubs to get started quickly
+- **Schema Validation**: Detects when OpenAPI schema changes
+
+## Production Considerations
+
+- **Development Only**: Evolution checking only runs in development mode
+- **Performance**: Minimal runtime overhead (single check per session)
+- **Error Handling**: Gracefully falls back if schema loading fails
+- **Console Logging**: All output goes to console.warn/info for easy filtering

docs/ARCHITECTURE_RECOMMENDATIONS.md

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+# Architecture Recommendations: Sessions, Lobbies, and WebSockets
+
+## Executive Summary
+
+The current architecture has grown organically into a monolithic structure that mixes concerns and creates maintenance challenges. This document outlines specific recommendations to improve maintainability, reduce complexity, and enhance the development experience.
+
+## Current Issues
+
+### 1. Server (`server/main.py`)
+- **Monolithic structure**: 2300+ lines in a single file
+- **Mixed concerns**: Session, lobby, WebSocket, bot, and admin logic intertwined
+- **Complex state management**: Multiple global dictionaries requiring manual synchronization
+- **WebSocket message handling**: Deep nested switch statements are hard to follow
+- **Threading complexity**: Multiple locks and shared state increase deadlock risk
+
+### 2. Client (`client/src/`)
+- **Fragmented connection logic**: WebSocket handling scattered across components
+- **Error handling complexity**: Different scenarios handled inconsistently
+- **State synchronization**: Multiple sources of truth for session/lobby state
+
+### 3. Voicebot (`voicebot/`)
+- **Duplicate patterns**: Similar WebSocket logic but different implementation
+- **Bot lifecycle complexity**: Complex orchestration with unclear state flow
+
+## Proposed Architecture
+
+### Server Refactoring
+
+#### 1. Extract Core Modules
+
+```
+server/
+├── main.py                 # FastAPI app setup and routing only
+├── core/
+│   ├── __init__.py
+│   ├── session_manager.py  # Session lifecycle and persistence
+│   ├── lobby_manager.py    # Lobby management and chat
+│   ├── bot_manager.py      # Bot provider and orchestration
+│   └── auth_manager.py     # Name/password authentication
+├── websocket/
+│   ├── __init__.py
+│   ├── connection.py       # WebSocket connection handling
+│   ├── message_handlers.py # Message type routing and handling
+│   └── signaling.py        # WebRTC signaling logic
+├── api/
+│   ├── __init__.py
+│   ├── admin.py           # Admin endpoints
+│   ├── sessions.py        # Session HTTP API
+│   ├── lobbies.py         # Lobby HTTP API
+│   └── bots.py            # Bot HTTP API
+└── models/
+    ├── __init__.py
+    ├── session.py         # Session and Lobby classes
+    └── events.py          # Event system for decoupled communication
+```
+
+#### 2. Event-Driven Architecture
+
+Replace direct method calls with an event system:
+
+```python
+from typing import Protocol
+from abc import ABC, abstractmethod
+
+class Event(ABC):
+    """Base event class"""
+    pass
+
+class SessionJoinedLobby(Event):
+    def __init__(self, session_id: str, lobby_id: str):
+        self.session_id = session_id
+        self.lobby_id = lobby_id
+
+class EventHandler(Protocol):
+    async def handle(self, event: Event) -> None: ...
+
+class EventBus:
+    def __init__(self):
+        self._handlers: dict[type[Event], list[EventHandler]] = {}
+    
+    def subscribe(self, event_type: type[Event], handler: EventHandler):
+        if event_type not in self._handlers:
+            self._handlers[event_type] = []
+        self._handlers[event_type].append(handler)
+    
+    async def publish(self, event: Event):
+        event_type = type(event)
+        if event_type in self._handlers:
+            for handler in self._handlers[event_type]:
+                await handler.handle(event)
+```
+
+#### 3. WebSocket Message Router
+
+Replace the massive switch statement with a clean router:
+
+```python
+from typing import Callable, Dict, Any
+from abc import ABC, abstractmethod
+
+class MessageHandler(ABC):
+    @abstractmethod
+    async def handle(self, session: Session, data: Dict[str, Any], websocket: WebSocket) -> None:
+        pass
+
+class SetNameHandler(MessageHandler):
+    async def handle(self, session: Session, data: Dict[str, Any], websocket: WebSocket) -> None:
+        # Handle set_name logic here
+        pass
+
+class WebSocketRouter:
+    def __init__(self):
+        self._handlers: Dict[str, MessageHandler] = {}
+    
+    def register(self, message_type: str, handler: MessageHandler):
+        self._handlers[message_type] = handler
+    
+    async def route(self, message_type: str, session: Session, data: Dict[str, Any], websocket: WebSocket):
+        if message_type in self._handlers:
+            await self._handlers[message_type].handle(session, data, websocket)
+        else:
+            await websocket.send_json({"type": "error", "data": {"error": f"Unknown message type: {message_type}"}})
+```
+
+### Client Refactoring
+
+#### 1. Centralized Connection Management
+
+Create a single WebSocket connection manager:
+
+```typescript
+// src/connection/WebSocketManager.ts
+export class WebSocketManager {
+  private ws: WebSocket | null = null;
+  private reconnectAttempts = 0;
+  private messageHandlers = new Map<string, (data: any) => void>();
+  
+  constructor(private url: string) {}
+  
+  async connect(): Promise<void> {
+    // Connection logic with automatic reconnection
+  }
+  
+  subscribe(messageType: string, handler: (data: any) => void): void {
+    this.messageHandlers.set(messageType, handler);
+  }
+  
+  send(type: string, data: any): void {
+    if (this.ws?.readyState === WebSocket.OPEN) {
+      this.ws.send(JSON.stringify({ type, data }));
+    }
+  }
+  
+  private handleMessage(event: MessageEvent): void {
+    const message = JSON.parse(event.data);
+    const handler = this.messageHandlers.get(message.type);
+    if (handler) {
+      handler(message.data);
+    }
+  }
+}
+```
+
+#### 2. Unified State Management
+
+Use a state management pattern (Context + Reducer or Zustand):
+
+```typescript
+// src/store/AppStore.ts
+interface AppState {
+  session: Session | null;
+  lobby: Lobby | null;
+  participants: Participant[];
+  connectionStatus: 'disconnected' | 'connecting' | 'connected';
+  error: string | null;
+}
+
+type AppAction = 
+  | { type: 'SET_SESSION'; payload: Session }
+  | { type: 'SET_LOBBY'; payload: Lobby }
+  | { type: 'UPDATE_PARTICIPANTS'; payload: Participant[] }
+  | { type: 'SET_CONNECTION_STATUS'; payload: AppState['connectionStatus'] }
+  | { type: 'SET_ERROR'; payload: string | null };
+
+const appReducer = (state: AppState, action: AppAction): AppState => {
+  switch (action.type) {
+    case 'SET_SESSION':
+      return { ...state, session: action.payload };
+    // ... other cases
+    default:
+      return state;
+  }
+};
+```
+
+### Voicebot Refactoring
+
+#### 1. Unified Connection Interface
+
+Create a common WebSocket interface used by both client and voicebot:
+
+```python
+# shared/websocket_client.py
+from abc import ABC, abstractmethod
+from typing import Dict, Any, Callable, Optional
+
+class WebSocketClient(ABC):
+    def __init__(self, url: str, session_id: str, lobby_id: str):
+        self.url = url
+        self.session_id = session_id
+        self.lobby_id = lobby_id
+        self.message_handlers: Dict[str, Callable[[Dict[str, Any]], None]] = {}
+    
+    @abstractmethod
+    async def connect(self) -> None:
+        pass
+    
+    @abstractmethod
+    async def send_message(self, message_type: str, data: Dict[str, Any]) -> None:
+        pass
+    
+    def register_handler(self, message_type: str, handler: Callable[[Dict[str, Any]], None]):
+        self.message_handlers[message_type] = handler
+    
+    async def handle_message(self, message_type: str, data: Dict[str, Any]):
+        handler = self.message_handlers.get(message_type)
+        if handler:
+            await handler(data)
+```
+
+## Implementation Plan
+
+### Phase 1: Server Foundation (Week 1-2)
+1. Extract `SessionManager` and `LobbyManager` classes
+2. Implement basic event system
+3. Create WebSocket message router
+4. Move admin endpoints to separate module
+
+### Phase 2: Server Completion (Week 3-4)
+1. Extract bot management functionality
+2. Implement remaining message handlers
+3. Add comprehensive testing
+4. Performance optimization
+
+### Phase 3: Client Refactoring (Week 5-6)
+1. Implement centralized WebSocket manager
+2. Create unified state management
+3. Refactor components to use new architecture
+4. Add error boundary and better error handling
+
+### Phase 4: Voicebot Integration (Week 7-8)
+1. Create shared WebSocket interface
+2. Refactor voicebot to use common patterns
+3. Improve bot lifecycle management
+4. Integration testing
+
+## Benefits of Proposed Architecture
+
+### Maintainability
+- **Single Responsibility**: Each module has a clear, focused purpose
+- **Testability**: Smaller, focused classes are easier to unit test
+- **Debugging**: Clear separation makes it easier to trace issues
+
+### Scalability
+- **Event-driven**: Loose coupling enables easier feature additions
+- **Modular**: New functionality can be added without touching core logic
+- **Performance**: Event system enables asynchronous processing
+
+### Developer Experience
+- **Code Navigation**: Easier to find relevant code
+- **Documentation**: Smaller modules are easier to document
+- **Onboarding**: New developers can understand individual components
+
+### Reliability
+- **Error Isolation**: Failures in one module don't cascade
+- **State Management**: Centralized state reduces synchronization bugs
+- **Connection Handling**: Robust reconnection and error recovery
+
+## Risk Mitigation
+
+### Breaking Changes
+- Implement changes incrementally
+- Maintain backward compatibility during transition
+- Comprehensive testing at each phase
+
+### Performance Impact
+- Benchmark before and after changes
+- Event system should be lightweight
+- Monitor memory usage and connection handling
+
+### Team Coordination
+- Clear communication about architecture changes
+- Code review process for architectural decisions
+- Documentation updates with each phase
+
+## Conclusion
+
+This refactoring will transform the current monolithic architecture into a maintainable, scalable system. The modular approach will reduce complexity, improve testability, and make the codebase more approachable for new developers while maintaining all existing functionality.

docs/AUTOMATED_API_CLIENT.md

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+# Automated API Client Generation System
+
+This document explains the automated TypeScript API client generation and update system for the AI Voicebot project.
+
+## Overview
+
+The system automatically:
+1. **Generates OpenAPI schema** from FastAPI server
+2. **Creates TypeScript types** from the schema 
+3. **Updates API client** with missing endpoint implementations using dynamic paths
+4. **Updates evolution checker** with current endpoint lists
+5. **Validates TypeScript** compilation
+6. **Runs evolution checks** to ensure completeness
+
+All generated API calls use the `PUBLIC_URL` environment variable to dynamically construct paths, making the system deployable to any base path without hardcoded `/ai-voicebot` prefixes.
+
+## Files in the System
+
+### Generated Files (Auto-updated)
+- `client/openapi-schema.json` - OpenAPI schema from server
+- `client/src/api-types.ts` - TypeScript type definitions
+- `client/src/api-client.ts` - API client (auto-sections updated)
+- `client/src/api-evolution-checker.ts` - Evolution checker (lists updated)
+
+### Manual Files 
+- `generate-ts-types.sh` - Main orchestration script
+- `client/update-api-client.js` - API client updater utility
+- `client/src/api-usage-examples.ts` - Usage examples and patterns
+
+## Configuration
+
+### Environment Variables
+
+The system uses environment variables for dynamic path configuration:
+
+- **`PUBLIC_URL`** - Base path for the application (e.g., `/ai-voicebot`, `/my-app`, etc.)
+  - Used in: API paths, schema loading, asset paths
+  - Default: `""` (empty string for root deployment)
+  - Set in: Docker environment, build process, or runtime
+
+### Dynamic Path Handling
+
+All API endpoints use dynamic path construction:
+
+```typescript
+// Instead of hardcoded paths:
+// "/ai-voicebot/api/health" 
+
+// The system uses:
+this.getApiPath("/ai-voicebot/api/health") 
+// Which becomes: `${PUBLIC_URL}/api/health`
+```
+
+This allows deployment to different base paths without code changes.
+
+## Usage
+
+### Full Generation (Recommended)
+```bash
+./generate-ts-types.sh
+```
+This runs the complete pipeline and is the primary way to use the system.
+
+### Individual Steps
+```bash
+# Inside client container
+npm run generate-schema        # Generate OpenAPI schema
+npm run generate-types         # Generate TypeScript types  
+npm run update-api-client      # Update API client
+npm run check-api-evolution    # Check for missing endpoints
+```
+
+## How Auto-Updates Work
+
+### API Client Updates
+
+The `update-api-client.js` script:
+
+1. **Parses OpenAPI schema** to find all available endpoints
+2. **Scans existing API client** to detect implemented methods
+3. **Identifies missing endpoints** by comparing the two
+4. **Generates method implementations** for missing endpoints
+5. **Updates the client class** by inserting new methods in designated section
+6. **Updates endpoint lists** used by evolution checking
+
+#### Auto-Generated Section
+```typescript
+export class ApiClient {
+  // ... manual methods ...
+
+  /**
+   * Construct API path using PUBLIC_URL environment variable
+   * Replaces hardcoded /ai-voicebot prefix with dynamic base from environment
+   */
+  private getApiPath(schemaPath: string): string {
+    return schemaPath.replace('/ai-voicebot', base);
+  }
+
+  // Auto-generated endpoints will be added here by update-api-client.js
+  // DO NOT MANUALLY EDIT BELOW THIS LINE
+  
+  // New endpoints automatically appear here using this.getApiPath()
+}
+```
+
+#### Method Generation
+- **Method names** derived from `operationId` or path/method combination
+- **Parameters** inferred from path parameters and request body
+- **Return types** use generic `Promise<any>` (can be enhanced)
+- **Path handling** supports both static and parameterized paths using `PUBLIC_URL`
+- **Dynamic paths** automatically replace hardcoded prefixes with environment-based values
+
+### Evolution Checker Updates
+
+The evolution checker tracks:
+- **Known schema endpoints** - updated from current OpenAPI schema
+- **Implemented endpoints** - updated from actual API client code
+- **Missing endpoints** - calculated difference for warnings
+
+## Customization
+
+### Adding Manual Endpoints
+
+For endpoints not in OpenAPI schema (e.g., external services), add them manually before the auto-generated section:
+
+```typescript
+// Manual endpoints (these won't be auto-generated)
+async getCustomData(): Promise<CustomResponse> {
+  return this.request<CustomResponse>("/custom/endpoint", { method: "GET" });
+}
+
+// Auto-generated endpoints will be added here by update-api-client.js
+// DO NOT MANUALLY EDIT BELOW THIS LINE
+```
+
+### Improving Generated Methods
+
+To enhance auto-generated methods:
+
+1. **Better Type Inference**: Modify `generateMethodSignature()` in `update-api-client.js` to use specific types from schema
+2. **Parameter Validation**: Add validation logic in method generation
+3. **Error Handling**: Customize error handling patterns
+4. **Documentation**: Add JSDoc generation from OpenAPI descriptions
+
+### Schema Evolution Detection
+
+The system detects:
+- **New endpoints** added to OpenAPI schema
+- **Changed endpoints** (parameter or response changes)  
+- **Deprecated endpoints** (with proper OpenAPI marking)
+
+## Development Workflow
+
+1. **Develop API endpoints** in FastAPI server with proper typing
+2. **Run generation script** to update client: `./generate-ts-types.sh` 
+3. **Use generated types** in React components
+4. **Manual customization** for complex endpoints if needed
+5. **Commit all changes** including generated and updated files
+
+## Best Practices
+
+### Server Development
+- Use **Pydantic models** for all request/response types
+- Add **proper OpenAPI metadata** (summary, description, tags)
+- Use **consistent naming** for operation IDs
+- **Version your API** to handle breaking changes
+
+### Client Development  
+- **Import from api-client.ts** rather than making raw fetch calls
+- **Use generated types** for type safety
+- **Avoid editing auto-generated sections** - they will be overwritten
+- **Add custom endpoints manually** when needed
+
+### Type Safety
+```typescript
+// Good: Using generated types and client
+import { apiClient, type LobbyModel, type LobbyCreateRequest } from './api-client';
+
+const createLobby = async (data: LobbyCreateRequest): Promise<LobbyModel> => {
+  const response = await apiClient.createLobby(sessionId, data);
+  return response.data; // Fully typed
+};
+
+// Avoid: Direct fetch calls
+const createLobbyRaw = async () => {
+  const response = await fetch('/api/lobby', { /* ... */ });
+  return response.json(); // No type safety
+};
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**"Could not find insertion marker"**
+- The API client file was manually edited and the auto-generation markers were removed
+- Restore the markers or regenerate the client file from template
+
+**"Missing endpoints detected"**  
+- New endpoints were added to the server but the generation script wasn't run
+- Run `./generate-ts-types.sh` to update the client
+
+**"Type errors after generation"**
+- Schema changes may have affected existing manual code
+- Check the TypeScript compiler output and update affected code
+
+**"Duplicate method names"**
+- Manual methods conflict with auto-generated ones
+- Rename manual methods or adjust the operation ID generation logic
+
+### Debug Mode
+
+Add debug logging by modifying `update-api-client.js`:
+
+```javascript
+// Add after parsing
+console.log('Schema endpoints:', this.endpoints.map(e => `${e.method}:${e.path}`));
+console.log('Implemented endpoints:', Array.from(this.implementedEndpoints));
+```
+
+## Future Enhancements
+
+- **Stronger type inference** from OpenAPI schema components
+- **Request/response validation** using schema definitions
+- **Mock data generation** for testing
+- **API versioning support** with backward compatibility
+- **Performance optimization** with request caching
+- **OpenAPI spec validation** before generation
+
+## Integration with Build Process
+
+The system integrates with:
+- **Docker Compose** for cross-container coordination
+- **npm scripts** for frontend build pipeline  
+- **TypeScript compilation** for type checking
+- **CI/CD workflows** for automated updates
+
+This ensures that API changes are automatically reflected in the frontend without manual intervention, reducing development friction and preventing API/client drift.

docs/BACKEND_RESTART_FIX.md

@@ -0,0 +1,261 @@
+# Backend Restart Issue Fix
+
+## Problem Description
+
+When backend services (server or voicebot) restart, active frontend UIs become unable to add bots, resulting in:
+
+```
+POST https://ketrenos.com/ai-voicebot/api/bots/ai_chatbot/join 404 (Not Found)
+```
+
+## Root Cause Analysis
+
+The issue was caused by three main problems:
+
+1. **Incorrect Provider Registration Check**: The voicebot service was checking provider registration using the wrong API endpoint (`/api/bots` instead of `/api/bots/providers`)
+
+2. **No Persistence for Bot Providers**: Bot providers were stored only in memory and lost on server restart, requiring re-registration
+
+3. **AsyncIO Task Initialization Issue**: The cleanup task was being created during `__init__` when no event loop was running, causing FastAPI route registration failures
+
+## Fixes Implemented
+
+### 1. Fixed Provider Registration Check Endpoint
+
+**File**: `voicebot/bot_orchestrator.py`
+
+**Problem**: The `check_provider_registration` function was calling `/api/bots` (which returns available bots) instead of `/api/bots/providers` (which returns registered providers).
+
+**Fix**: Updated the function to use the correct endpoint and parse the response properly:
+
+```python
+async def check_provider_registration(server_url: str, provider_id: str, insecure: bool = False) -> bool:
+    """Check if the bot provider is still registered with the server."""
+    try:
+        import httpx
+        
+        verify = not insecure
+        async with httpx.AsyncClient(verify=verify) as client:
+            # Check if our provider is still in the provider list
+            response = await client.get(f"{server_url}/api/bots/providers", timeout=5.0)
+            if response.status_code == 200:
+                data = response.json()
+                providers = data.get("providers", [])
+                # providers is a list of BotProviderModel objects, check if our provider_id is in the list
+                is_registered = any(provider.get("provider_id") == provider_id for provider in providers)
+                logger.debug(f"Registration check: provider_id={provider_id}, found_providers={len(providers)}, is_registered={is_registered}")
+                return is_registered
+            else:
+                logger.warning(f"Registration check failed: HTTP {response.status_code}")
+                return False
+    except Exception as e:
+        logger.debug(f"Provider registration check failed: {e}")
+    return False
+```
+
+### 2. Added Bot Provider Persistence
+
+**File**: `server/core/bot_manager.py`
+
+**Problem**: Bot providers were stored only in memory and lost on server restart.
+
+**Fix**: Added persistence functionality to save/load bot providers to/from `bot_providers.json`:
+
+```python
+def _save_bot_providers(self):
+    """Save bot providers to disk"""
+    try:
+        with self.lock:
+            providers_data = {}
+            for provider_id, provider in self.bot_providers.items():
+                providers_data[provider_id] = provider.model_dump()
+        
+        with open(self.bot_providers_file, 'w') as f:
+            json.dump(providers_data, f, indent=2)
+        logger.debug(f"Saved {len(providers_data)} bot providers to {self.bot_providers_file}")
+    except Exception as e:
+        logger.error(f"Failed to save bot providers: {e}")
+
+def _load_bot_providers(self):
+    """Load bot providers from disk"""
+    try:
+        if not os.path.exists(self.bot_providers_file):
+            logger.debug(f"No bot providers file found at {self.bot_providers_file}")
+            return
+            
+        with open(self.bot_providers_file, 'r') as f:
+            providers_data = json.load(f)
+        
+        with self.lock:
+            for provider_id, provider_dict in providers_data.items():
+                try:
+                    provider = BotProviderModel.model_validate(provider_dict)
+                    self.bot_providers[provider_id] = provider
+                except Exception as e:
+                    logger.warning(f"Failed to load bot provider {provider_id}: {e}")
+        
+        logger.info(f"Loaded {len(self.bot_providers)} bot providers from {self.bot_providers_file}")
+    except Exception as e:
+        logger.error(f"Failed to load bot providers: {e}")
+```
+
+**Integration**: The persistence functions are automatically called:
+- `_load_bot_providers()` during `BotManager.__init__()`
+- `_save_bot_providers()` when registering new providers or removing stale ones
+
+### 3. Fixed AsyncIO Task Initialization Issue
+
+**File**: `server/core/bot_manager.py`
+
+**Problem**: The cleanup task was being created during `BotManager.__init__()` when no event loop was running, causing the FastAPI application to fail to register routes properly.
+
+**Fix**: Deferred the cleanup task creation until it's actually needed:
+
+```python
+def __init__(self):
+    # ... other initialization ...
+    # Load persisted bot providers
+    self._load_bot_providers()
+    
+    # Note: Don't start cleanup task here - will be started when needed
+
+def start_cleanup(self):
+    """Start the cleanup task"""
+    try:
+        if self.cleanup_task is None:
+            self.cleanup_task = asyncio.create_task(self._periodic_cleanup())
+            logger.debug("Bot provider cleanup task started")
+    except RuntimeError:
+        # No event loop running yet, cleanup will be started later
+        logger.debug("No event loop available for bot provider cleanup task")
+
+async def register_provider(self, request: BotProviderRegisterRequest) -> BotProviderRegisterResponse:
+    # ... registration logic ...
+    
+    # Start cleanup task if not already running
+    self.start_cleanup()
+    
+    return BotProviderRegisterResponse(provider_id=provider_id)
+```
+
+### 4. Added Periodic Cleanup for Stale Providers
+
+**File**: `server/core/bot_manager.py`
+
+**Enhancement**: Added a background task that periodically removes providers that haven't been seen in 15 minutes:
+
+```python
+async def _periodic_cleanup(self):
+    """Periodically clean up stale bot providers"""
+    cleanup_interval = 300  # 5 minutes
+    stale_threshold = 900   # 15 minutes
+    
+    while not self._shutdown_event.is_set():
+        try:
+            await asyncio.sleep(cleanup_interval)
+            
+            now = time.time()
+            providers_to_remove = []
+            
+            with self.lock:
+                for provider_id, provider in self.bot_providers.items():
+                    if now - provider.last_seen > stale_threshold:
+                        providers_to_remove.append(provider_id)
+                        logger.info(f"Marking stale bot provider for removal: {provider.name} (ID: {provider_id}, last_seen: {now - provider.last_seen:.1f}s ago)")
+            
+            if providers_to_remove:
+                with self.lock:
+                    for provider_id in providers_to_remove:
+                        if provider_id in self.bot_providers:
+                            del self.bot_providers[provider_id]
+                
+                self._save_bot_providers()
+                logger.info(f"Cleaned up {len(providers_to_remove)} stale bot providers")
+                
+        except asyncio.CancelledError:
+            break
+        except Exception as e:
+            logger.error(f"Error in bot provider cleanup: {e}")
+```
+
+### 5. Added Client-Side Retry Logic
+
+**File**: `client/src/BotManager.tsx`
+
+**Enhancement**: Added retry logic to handle temporary 404s during service restarts:
+
+```typescript
+// Retry logic for handling service restart scenarios
+let retries = 3;
+let response;
+
+while (retries > 0) {
+  try {
+    response = await botsApi.requestJoinLobby(selectedBot, request);
+    break; // Success, exit retry loop
+  } catch (err: any) {
+    retries--;
+    
+    // If it's a 404 error and we have retries left, wait and retry
+    if (err?.status === 404 && retries > 0) {
+      console.log(`Bot join failed with 404, retrying... (${retries} attempts left)`);
+      await new Promise(resolve => setTimeout(resolve, 1000)); // Wait 1 second
+      continue;
+    }
+    
+    // If it's not a 404 or we're out of retries, throw the error
+    throw err;
+  }
+}
+```
+
+## Benefits
+
+1. **Persistence**: Bot providers now survive server restarts and don't need to re-register immediately
+2. **Correct Registration Checks**: Provider registration checks use the correct API endpoint
+3. **Proper AsyncIO Task Management**: Cleanup tasks are started only when an event loop is available
+4. **Automatic Cleanup**: Stale providers are automatically removed to prevent accumulation of dead entries
+5. **Client Resilience**: Frontend can handle temporary 404s during service restarts with automatic retries
+6. **Reduced Downtime**: Users experience fewer failed bot additions during service restarts
+
+## Testing
+
+After implementing these fixes:
+
+1. Bot providers are correctly persisted in `bot_providers.json`
+2. Server restarts load existing providers from disk
+3. Provider registration checks use the correct `/api/bots/providers` endpoint
+4. AsyncIO cleanup tasks start properly without interfering with route registration
+5. Client retries failed requests with 404 errors
+6. Periodic cleanup prevents accumulation of stale providers
+7. Bot join requests work correctly: `POST /api/bots/{bot_name}/join` returns 200 OK
+
+## Verification Commands
+
+Test the fix with these commands:
+
+```bash
+# Check available lobbies
+curl -k https://ketrenos.com/ai-voicebot/api/lobby
+
+# Test bot join (replace lobby_id and provider_id with actual values)
+curl -k -X POST https://ketrenos.com/ai-voicebot/api/bots/ai_chatbot/join \
+  -H "Content-Type: application/json" \
+  -d '{"lobby_id":"<lobby_id>","nick":"test-bot","provider_id":"<provider_id>"}'
+
+# Check bot providers
+curl -k https://ketrenos.com/ai-voicebot/api/bots/providers
+
+# Check available bots
+curl -k https://ketrenos.com/ai-voicebot/api/bots
+```
+
+## Files Modified
+
+1. `voicebot/bot_orchestrator.py` - Fixed registration check endpoint
+2. `server/core/bot_manager.py` - Added persistence and cleanup
+3. `client/src/BotManager.tsx` - Added retry logic
+
+## Configuration
+
+No additional configuration is required. The fixes work with existing environment variables and settings.

docs/CHAT_INTEGRATION.md

@@ -0,0 +1,220 @@
+# Chat Integration for AI Voicebot System
+
+This document describes the chat functionality that has been integrated into the AI voicebot system, allowing bots to send and receive chat messages through the WebSocket signaling server.
+
+## Overview
+
+The chat integration enables bots to:
+1. **Receive chat messages** from other participants in the lobby
+2. **Send chat messages** back to the lobby
+3. **Process and respond** to specific commands or keywords
+4. **Integrate seamlessly** with the existing WebRTC signaling infrastructure
+
+## Architecture
+
+### Core Components
+
+1. **WebRTC Signaling Client** (`webrtc_signaling.py`)
+   - Extended with chat message handling capabilities
+   - Added `on_chat_message_received` callback for bots
+   - Added `send_chat_message()` method for sending messages
+
+2. **Bot Orchestrator** (`bot_orchestrator.py`)
+   - Enhanced bot discovery to detect chat handlers
+   - Sets up chat message callbacks when bots join lobbies
+   - Manages the connection between WebRTC client and bot chat handlers
+
+3. **Chat Models** (`shared/models.py`)
+   - `ChatMessageModel`: Structure for chat messages
+   - `ChatMessagesListModel`: For message lists
+   - `ChatMessagesSendModel`: For sending messages
+
+### Bot Interface
+
+Bots can now implement an optional `handle_chat_message` function:
+
+```python
+async def handle_chat_message(
+    chat_message: ChatMessageModel, 
+    send_message_func: Callable[[str], Awaitable[None]]
+) -> Optional[str]:
+    """
+    Handle incoming chat messages and optionally return a response.
+    
+    Args:
+        chat_message: The received chat message
+        send_message_func: Function to send messages back to the lobby
+    
+    Returns:
+        Optional response message to send back to the lobby
+    """
+    # Process the message and return a response
+    return "Hello! I received your message."
+```
+
+## Implementation Details
+
+### 1. WebSocket Message Handling
+
+The WebRTC signaling client now handles `chat_message` type messages:
+
+```python
+elif msg_type == "chat_message":
+    try:
+        validated = ChatMessageModel.model_validate(data)
+    except ValidationError as e:
+        logger.error(f"Invalid chat_message payload: {e}", exc_info=True)
+        return
+    logger.info(f"Received chat message from {validated.sender_name}: {validated.message[:50]}...")
+    # Call the callback if it's set
+    if self.on_chat_message_received:
+        try:
+            await self.on_chat_message_received(validated)
+        except Exception as e:
+            logger.error(f"Error in chat message callback: {e}", exc_info=True)
+```
+
+### 2. Bot Discovery Enhancement
+
+The bot orchestrator now detects chat handlers during discovery:
+
+```python
+if hasattr(mod, "handle_chat_message") and callable(getattr(mod, "handle_chat_message")):
+    chat_handler = getattr(mod, "handle_chat_message")
+
+bots[info.get("name", name)] = {
+    "module": name, 
+    "info": info, 
+    "create_tracks": create_tracks,
+    "chat_handler": chat_handler
+}
+```
+
+### 3. Chat Handler Setup
+
+When a bot joins a lobby, the orchestrator sets up the chat handler:
+
+```python
+if chat_handler:
+    async def bot_chat_handler(chat_message: ChatMessageModel):
+        """Wrapper to call the bot's chat handler and optionally send responses"""
+        try:
+            response = await chat_handler(chat_message, client.send_chat_message)
+            if response and isinstance(response, str):
+                await client.send_chat_message(response)
+        except Exception as e:
+            logger.error(f"Error in bot chat handler for {bot_name}: {e}", exc_info=True)
+    
+    client.on_chat_message_received = bot_chat_handler
+```
+
+## Example Bots
+
+### 1. Chatbot (`bots/chatbot.py`)
+
+A simple conversational bot that responds to greetings and commands:
+
+- Responds to keywords like "hello", "how are you", "goodbye"
+- Provides time information when asked
+- Tells jokes on request
+- Handles direct mentions intelligently
+
+Example interactions:
+- User: "hello" → Bot: "Hi there!"
+- User: "time" → Bot: "Let me check... it's currently 2025-09-03 23:45:12"
+- User: "joke" → Bot: "Why don't scientists trust atoms? Because they make up everything!"
+
+### 2. Enhanced Whisper Bot (`bots/whisper.py`)
+
+The existing speech recognition bot now also handles chat commands:
+
+- Responds to messages starting with "whisper:"
+- Provides help and status information
+- Echoes back commands for demonstration
+
+Example interactions:
+- User: "whisper: hello" → Bot: "Hello UserName! I'm the Whisper speech recognition bot."
+- User: "whisper: help" → Bot: "I can process speech and respond to simple commands..."
+- User: "whisper: status" → Bot: "Whisper bot is running and ready to process audio and chat messages."
+
+## Server Integration
+
+The server (`server/main.py`) already handles chat messages through WebSocket:
+
+1. **Receiving messages**: `send_chat_message` message type
+2. **Broadcasting**: `broadcast_chat_message` method distributes messages to all lobby participants
+3. **Storage**: Messages are stored in lobby's `chat_messages` list
+
+## Testing
+
+The implementation has been tested with:
+
+1. **Bot Discovery**: All bots are correctly discovered with chat capabilities detected
+2. **Message Processing**: Both chatbot and whisper bot respond correctly to test messages
+3. **Integration**: The WebRTC signaling client properly routes messages to bot handlers
+
+Test results:
+```
+Discovered 3 bots:
+  Bot: chatbot
+    Has chat handler: True
+  Bot: synthetic_media  
+    Has chat handler: False
+  Bot: whisper
+    Has chat handler: True
+
+Chat functionality test:
+- Chatbot response to "hello": "Hey!"
+- Whisper response to "whisper: hello": "Hello TestUser! I'm the Whisper speech recognition bot."
+✅ Chat functionality test completed!
+```
+
+## Usage
+
+### For Bot Developers
+
+To add chat capabilities to a bot:
+
+1. Import the required types:
+```python
+from typing import Dict, Optional, Callable, Awaitable
+from shared.models import ChatMessageModel
+```
+
+2. Implement the chat handler:
+```python
+async def handle_chat_message(
+    chat_message: ChatMessageModel, 
+    send_message_func: Callable[[str], Awaitable[None]]
+) -> Optional[str]:
+    # Your chat logic here
+    if "hello" in chat_message.message.lower():
+        return f"Hello {chat_message.sender_name}!"
+    return None
+```
+
+3. The bot orchestrator will automatically detect and wire up the chat handler when the bot joins a lobby.
+
+### For System Integration
+
+The chat system integrates seamlessly with the existing voicebot infrastructure:
+
+1. **No breaking changes** to existing bots without chat handlers
+2. **Automatic discovery** of chat capabilities
+3. **Error isolation** - chat handler failures don't affect WebRTC functionality
+4. **Logging** provides visibility into chat message flow
+
+## Future Enhancements
+
+Potential improvements for the chat system:
+
+1. **Message History**: Bots could access recent chat history
+2. **Rich Responses**: Support for formatted messages, images, etc.
+3. **Private Messaging**: Direct messages between participants
+4. **Chat Commands**: Standardized command parsing framework
+5. **Persistence**: Long-term storage of chat interactions
+6. **Analytics**: Message processing metrics and bot performance monitoring
+
+## Conclusion
+
+The chat integration provides a powerful foundation for creating interactive AI bots that can engage with users through text while maintaining their audio/video capabilities. The implementation is robust, well-tested, and ready for production use.

docs/MULTI_PEER_WHISPER_ARCHITECTURE.md

@@ -0,0 +1,216 @@
+# Multi-Peer Whisper ASR Architecture
+
+## Overview
+
+The Whisper ASR system has been redesigned to handle multiple audio tracks from different WebRTC peers simultaneously, with proper speaker identification and isolated audio processing.
+
+## Architecture Changes
+
+### Before (Single AudioProcessor)
+```
+Peer A Audio → |
+Peer B Audio → | → Single AudioProcessor → Mixed Transcription
+Peer C Audio → |
+```
+
+**Problems:**
+- Mixed audio streams from all speakers
+- No speaker identification
+- Poor transcription quality when multiple people speak
+- Audio interference between speakers
+
+### After (Per-Peer AudioProcessor)
+```
+Peer A Audio → AudioProcessor A → "🎤 Alice: Hello there"
+Peer B Audio → AudioProcessor B → "🎤 Bob: How are you?"
+Peer C Audio → AudioProcessor C → "🎤 Charlie: Good morning"
+```
+
+**Benefits:**
+- Isolated audio processing per speaker
+- Clear speaker identification in transcriptions
+- No audio interference between speakers
+- Better transcription quality
+- Scalable to many speakers
+
+## Key Components
+
+### 1. Per-Peer Audio Processors
+- **Global Dictionary**: `_audio_processors: Dict[str, AudioProcessor]`
+- **Automatic Creation**: New AudioProcessor created when peer connects
+- **Peer Identification**: Each processor tagged with peer name
+- **Independent Processing**: Separate audio buffers, queues, and transcription threads
+
+### 2. Enhanced AudioProcessor Class
+```python
+class AudioProcessor:
+    def __init__(self, peer_name: str, send_chat_func: Callable):
+        self.peer_name = peer_name  # NEW: Peer identification
+        # ... rest of initialization
+```
+
+### 3. Speaker-Tagged Transcriptions
+- **Final transcriptions**: `"🎤 Alice: Hello there"`
+- **Partial transcriptions**: `"🎤 Alice [partial]: Hello th..."`
+- **Clear attribution**: Always know who said what
+
+### 4. Peer Management
+- **Connection**: AudioProcessor created on first audio track
+- **Disconnection**: Cleanup via `cleanup_peer_processor(peer_name)`
+- **Status Monitoring**: `get_active_processors()` for debugging
+
+## API Changes
+
+### New Functions
+```python
+def cleanup_peer_processor(peer_name: str):
+    """Clean up audio processor for disconnected peer."""
+
+def get_active_processors() -> Dict[str, AudioProcessor]:
+    """Get currently active audio processors."""
+```
+
+### Modified Functions
+```python
+# Old
+AudioProcessor(send_chat_func)
+
+# New
+AudioProcessor(peer_name, send_chat_func)
+```
+
+## Usage Examples
+
+### 1. Multiple Speakers Scenario
+```
+# In a 3-person meeting:
+🎤 Alice: I think we should start with the quarterly review
+🎤 Bob [partial]: That sounds like a good...
+🎤 Bob: That sounds like a good idea to me
+🎤 Charlie: I agree, let's begin
+```
+
+### 2. Debugging Multiple Processors
+```bash
+# Check status of all active processors
+python force_transcription.py stats
+
+# Force transcription for all peers
+python force_transcription.py
+```
+
+### 3. Monitoring Active Connections
+```python
+from bots.whisper import get_active_processors
+
+processors = get_active_processors()
+print(f"Active speakers: {list(processors.keys())}")
+```
+
+## Performance Considerations
+
+### Resource Usage
+- **Memory**: Linear scaling with number of speakers
+- **CPU**: Parallel processing threads (one per speaker)
+- **Model**: Shared Whisper model across all processors (efficient)
+
+### Scalability
+- **Small groups (2-5 people)**: Excellent performance
+- **Medium groups (6-15 people)**: Good performance
+- **Large groups (15+ people)**: May need optimization
+
+### Optimization Strategies
+1. **Silence Detection**: Skip processing for quiet/inactive speakers
+2. **Dynamic Cleanup**: Remove processors for disconnected peers
+3. **Configurable Thresholds**: Adjust per-speaker sensitivity
+4. **Resource Limits**: Max concurrent processors if needed
+
+## Debugging Tools
+
+### 1. Force Transcription (Enhanced)
+```bash
+# Shows status for all active peers
+python force_transcription.py
+
+# Output example:
+🔍 Found 3 active audio processors:
+
+👤 Alice:
+  - Running: True
+  - Buffer size: 5 frames
+  - Queue size: 1
+  - Current phrase length: 8000 samples
+
+👤 Bob:
+  - Running: True  
+  - Buffer size: 0 frames
+  - Queue size: 0
+  - Current phrase length: 0 samples
+```
+
+### 2. Audio Statistics (Per-Peer)
+```bash
+python force_transcription.py stats
+
+# Shows detailed metrics for each peer
+📊 Detailed Audio Statistics for 2 processors:
+
+👤 Alice:
+Sample rate: 16000Hz
+Current buffer size: 3
+Processing queue size: 0
+  Current phrase:
+    Duration: 1.25s
+    RMS: 0.0234
+    Peak: 0.1892
+```
+
+### 3. Enhanced Logging
+```
+INFO - Creating new AudioProcessor for Alice
+INFO - AudioProcessor initialized for Alice - sample_rate: 16000Hz
+INFO - ✅ Transcribed (final) for Alice: 'Hello everyone'
+INFO - Cleaning up AudioProcessor for disconnected peer: Bob
+```
+
+## Migration Guide
+
+### For Existing Code
+- **No changes needed** for basic usage
+- **Enhanced debugging** with per-peer information
+- **Better transcription quality** automatically
+
+### For Advanced Usage
+- Use `get_active_processors()` to monitor speakers
+- Call `cleanup_peer_processor()` on peer disconnect
+- Check peer-specific statistics in force_transcription.py
+
+## Error Handling
+
+### Common Issues
+1. **No AudioProcessor for peer**: Automatically created on first audio
+2. **Peer disconnection**: Manual cleanup recommended
+3. **Resource exhaustion**: Monitor with `get_active_processors()`
+
+### Error Messages
+```
+ERROR - Cannot create AudioProcessor for Alice: no send_chat_func available
+WARNING - No audio processor available to handle audio data for Bob
+INFO - Cleaning up AudioProcessor for disconnected peer: Charlie
+```
+
+## Future Enhancements
+
+### Planned Features
+1. **Voice Activity Detection**: Only process when speaker is active
+2. **Speaker Diarization**: Merge multiple audio sources per speaker
+3. **Language Detection**: Per-speaker language settings
+4. **Quality Metrics**: Per-speaker transcription confidence scores
+
+### Possible Optimizations
+1. **Shared Processing**: Batch multiple speakers in single inference
+2. **Dynamic Model Loading**: Different models per speaker/language
+3. **Audio Mixing**: Optional mixed transcription for meeting notes
+4. **Real-time Adaptation**: Adjust thresholds per speaker automatically
+
+This new architecture provides a robust foundation for multi-speaker ASR with clear attribution, better quality, and comprehensive debugging capabilities.

docs/README.md

@@ -0,0 +1,302 @@
+# AI Voicebot
+
+A WebRTC-enabled AI voicebot system with speech recognition and synthetic media capabilities. The voicebot can run in two modes: as a client connecting to lobbies or as a provider serving bots to other applications.
+
+## Features
+
+- **Speech Recognition**: Uses Whisper models for real-time audio transcription
+- **Synthetic Media**: Generates animated video and audio tracks
+- **WebRTC Integration**: Real-time peer-to-peer communication
+- **Bot Provider System**: Can register with a main server to provide bot services
+- **Flexible Deployment**: Docker-based with development and production modes
+
+## Quick Start
+
+### Prerequisites
+
+- Docker and Docker Compose
+- Python 3.12+ (if running locally)
+- Access to a compatible signaling server
+
+### Running with Docker
+
+#### 1. Bot Provider Mode (Recommended)
+
+Run the voicebot as a bot provider that registers with the main server:
+
+```bash
+# Development mode with auto-reload
+VOICEBOT_MODE=provider PRODUCTION=false docker-compose up voicebot
+
+# Production mode
+VOICEBOT_MODE=provider PRODUCTION=true docker-compose up voicebot
+```
+
+#### 2. Direct Client Mode
+
+Run the voicebot as a direct client connecting to a lobby:
+
+```bash
+# Development mode
+VOICEBOT_MODE=client PRODUCTION=false docker-compose up voicebot
+
+# Production mode  
+VOICEBOT_MODE=client PRODUCTION=true docker-compose up voicebot
+```
+
+### Running Locally
+
+#### 1. Setup Environment
+
+```bash
+cd voicebot/
+
+# Create virtual environment
+uv init --python /usr/bin/python3.12 --name "ai-voicebot-agent"
+uv add -r requirements.txt
+
+# Activate environment
+source .venv/bin/activate
+```
+
+#### 2. Bot Provider Mode
+
+```bash
+# Development with auto-reload
+python main.py --mode provider --server-url https://your-server.com/ai-voicebot --reload --insecure
+
+# Production
+python main.py --mode provider --server-url https://your-server.com/ai-voicebot
+```
+
+#### 3. Direct Client Mode
+
+```bash
+python main.py --mode client \
+    --server-url https://your-server.com/ai-voicebot \
+    --lobby "my-lobby" \
+    --session-name "My Bot" \
+    --insecure
+```
+
+## Configuration
+
+### Environment Variables
+
+| Variable | Description | Default | Example |
+|----------|-------------|---------|---------|
+| `VOICEBOT_MODE` | Operating mode: `client` or `provider` | `client` | `provider` |
+| `PRODUCTION` | Production mode flag | `false` | `true` |
+
+### Command Line Arguments
+
+#### Common Arguments
+- `--mode`: Run as `client` or `provider`
+- `--server-url`: Main server URL
+- `--insecure`: Allow insecure SSL connections
+- `--help`: Show all available options
+
+#### Provider Mode Arguments
+- `--host`: Host to bind the provider server (default: `0.0.0.0`)
+- `--port`: Port for the provider server (default: `8788`)
+- `--reload`: Enable auto-reload for development
+
+#### Client Mode Arguments
+- `--lobby`: Lobby name to join (default: `default`)
+- `--session-name`: Display name for the bot (default: `Python Bot`)
+- `--session-id`: Existing session ID to reuse
+- `--password`: Password for protected names
+- `--private`: Create/join private lobby
+
+## Available Bots
+
+The voicebot system includes the following bot types:
+
+### 1. Whisper Bot
+- **Name**: `whisper`
+- **Description**: Speech recognition agent using OpenAI Whisper models
+- **Capabilities**: Real-time audio transcription, multiple language support
+- **Models**: Supports various Whisper and Distil-Whisper models
+
+### 2. Synthetic Media Bot
+- **Name**: `synthetic_media`
+- **Description**: Generates animated video and audio tracks
+- **Capabilities**: Animated video generation, synthetic audio, edge detection on incoming video
+
+## Architecture
+
+### Bot Provider System
+
+```
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   Main Server   │    │   Bot Provider   │    │   Client App    │
+│                 │◄───┤   (Voicebot)     │    │                 │
+│ - Bot Registry  │    │ - Whisper Bot    │    │ - Bot Manager   │
+│ - Lobby Management   │ - Synthetic Bot  │    │ - UI Controls   │
+│ - API Endpoints │    │ - API Server     │    │ - Lobby View    │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+```
+
+### Flow
+1. Voicebot registers as bot provider with main server
+2. Main server discovers available bots from providers
+3. Client requests bot to join lobby via main server
+4. Main server forwards request to appropriate provider
+5. Provider creates bot instance that connects to the lobby
+
+## Development
+
+### Auto-Reload
+
+In development mode, the bot provider supports auto-reload using uvicorn:
+
+```bash
+# Watches /voicebot and /shared directories for changes
+python main.py --mode provider --reload
+```
+
+### Adding New Bots
+
+1. Create a new module in `voicebot/bots/`
+2. Implement required functions:
+   ```python
+   def agent_info() -> dict:
+       return {"name": "my_bot", "description": "My custom bot"}
+   
+   def create_agent_tracks(session_name: str) -> dict:
+       # Return MediaStreamTrack instances
+       return {"audio": my_audio_track, "video": my_video_track}
+   ```
+3. The bot will be automatically discovered and available
+
+### Testing
+
+```bash
+# Test bot discovery
+python test_bot_api.py
+
+# Test client connection
+python main.py --mode client --lobby test --session-name "Test Bot"
+```
+
+## Production Deployment
+
+### Docker Compose
+
+```yaml
+version: '3.8'
+services:
+  voicebot-provider:
+    build: .
+    environment:
+      - VOICEBOT_MODE=provider
+      - PRODUCTION=true
+    ports:
+      - "8788:8788"
+    volumes:
+      - ./cache:/voicebot/cache
+```
+
+### Kubernetes
+
+```yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: voicebot-provider
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: voicebot-provider
+  template:
+    metadata:
+      labels:
+        app: voicebot-provider
+    spec:
+      containers:
+      - name: voicebot
+        image: ai-voicebot:latest
+        env:
+        - name: VOICEBOT_MODE
+          value: "provider"
+        - name: PRODUCTION
+          value: "true"
+        ports:
+        - containerPort: 8788
+```
+
+## API Reference
+
+### Bot Provider Endpoints
+
+The voicebot provider exposes the following HTTP API:
+
+- `GET /bots` - List available bots
+- `POST /bots/{bot_name}/join` - Request bot to join lobby
+- `GET /bots/runs` - List active bot instances
+- `POST /bots/runs/{run_id}/stop` - Stop a bot instance
+
+### Example API Usage
+
+```bash
+# List available bots
+curl http://localhost:8788/bots
+
+# Request whisper bot to join lobby
+curl -X POST http://localhost:8788/bots/whisper/join \
+  -H "Content-Type: application/json" \
+  -d '{
+    "lobby_id": "lobby-123",
+    "session_id": "session-456", 
+    "nick": "Speech Bot",
+    "server_url": "https://server.com/ai-voicebot"
+  }'
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Bot provider not registering:**
+- Check server URL is correct and accessible
+- Verify network connectivity between provider and server
+- Check logs for registration errors
+
+**Auto-reload not working:**
+- Ensure `--reload` flag is used in development
+- Check file permissions on watched directories
+- Verify uvicorn version supports reload functionality
+
+**WebRTC connection issues:**
+- Check STUN/TURN server configuration
+- Verify network ports are not blocked
+- Check browser console for ICE connection errors
+
+### Logs
+
+Logs are written to stdout and include:
+- Bot registration status
+- WebRTC connection events
+- Media track creation/destruction
+- API request/response details
+
+### Debug Mode
+
+Enable verbose logging:
+
+```bash
+python main.py --mode provider --server-url https://server.com --debug
+```
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Add tests for new functionality
+5. Submit a pull request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

docs/REFACTORING_STEP1_COMPLETE.md

@@ -0,0 +1,190 @@
+"""
+Documentation for the Server Refactoring Step 1 Implementation
+
+This document outlines what was accomplished in Step 1 of the server refactoring
+and how to verify the implementation works.
+"""
+
+# STEP 1 IMPLEMENTATION SUMMARY
+
+## What Was Accomplished
+
+### 1. Created Modular Architecture
+- **server/core/**: Core business logic modules
+  - `session_manager.py`: Session lifecycle and persistence
+  - `lobby_manager.py`: Lobby management and chat functionality  
+  - `auth_manager.py`: Authentication and name protection
+
+- **server/models/**: Event system and data models
+  - `events.py`: Event-driven architecture foundation
+
+- **server/websocket/**: WebSocket handling
+  - `message_handlers.py`: Clean message routing (replaces massive switch statement)
+  - `connection.py`: WebSocket connection management
+
+- **server/api/**: HTTP API endpoints  
+  - `admin.py`: Admin endpoints (extracted from main.py)
+  - `sessions.py`: Session management endpoints
+  - `lobbies.py`: Lobby management endpoints
+
+### 2. Key Improvements
+- **Separation of Concerns**: Each module has a single responsibility
+- **Event-Driven Architecture**: Decoupled communication between components
+- **Clean Message Routing**: Replaced 200+ line switch statement with handler pattern
+- **Thread Safety**: Proper locking and state management
+- **Type Safety**: Better type annotations and error handling
+- **Testability**: Modules can be tested independently
+
+### 3. Backward Compatibility
+- All existing endpoints work unchanged
+- Same WebSocket message protocols
+- Same session/lobby behavior
+- Same authentication mechanisms
+
+## File Structure Created
+
+```
+server/
+├── main_refactored.py          # New main file using modular architecture
+├── core/
+│   ├── __init__.py
+│   ├── session_manager.py      # Session lifecycle management
+│   ├── lobby_manager.py        # Lobby and chat management  
+│   └── auth_manager.py         # Authentication and passwords
+├── websocket/
+│   ├── __init__.py
+│   ├── message_handlers.py     # WebSocket message routing
+│   └── connection.py           # Connection management
+├── api/
+│   ├── __init__.py
+│   ├── admin.py               # Admin HTTP endpoints
+│   ├── sessions.py            # Session HTTP endpoints  
+│   └── lobbies.py             # Lobby HTTP endpoints
+└── models/
+    ├── __init__.py
+    └── events.py              # Event system
+```
+
+## How to Test/Verify
+
+### 1. Syntax Verification
+The modules can be imported and instantiated:
+
+```python
+# In server/ directory:
+python3 -c "
+import sys; sys.path.append('.')
+from core.session_manager import SessionManager
+from core.lobby_manager import LobbyManager  
+from core.auth_manager import AuthManager
+print('✓ All modules import successfully')
+"
+```
+
+### 2. Basic Functionality Test
+```python
+# Test basic object creation (no FastAPI dependencies)
+python3 -c "
+import sys; sys.path.append('.')
+from core.auth_manager import AuthManager
+auth = AuthManager()
+auth.set_password('test', 'password')
+assert auth.verify_password('test', 'password')
+assert not auth.verify_password('test', 'wrong')
+print('✓ AuthManager works correctly')
+"
+```
+
+### 3. Server Startup Test
+To test the full refactored server:
+
+```bash
+# Start the refactored server
+cd server/
+python3 main_refactored.py
+```
+
+Expected output:
+```
+INFO - Starting AI Voice Bot server with modular architecture...
+INFO - Loaded 0 sessions from sessions.json
+INFO - AI Voice Bot server started successfully!
+INFO - Server URL: /
+INFO - Sessions loaded: 0
+INFO - Lobbies available: 0
+INFO - Protected names: 0
+```
+
+### 4. API Endpoints Test
+```bash
+# Test health endpoint
+curl http://localhost:8000/api/system/health
+
+# Expected response:
+{
+  "status": "ok",
+  "architecture": "modular",
+  "version": "2.0.0",
+  "managers": {
+    "session_manager": "active",
+    "lobby_manager": "active", 
+    "auth_manager": "active",
+    "websocket_manager": "active"
+  },
+  "statistics": {
+    "sessions": 0,
+    "lobbies": 0,
+    "protected_names": 0
+  }
+}
+```
+
+## Benefits Achieved
+
+### Maintainability
+- **Reduced Complexity**: Original 2300-line main.py split into focused modules
+- **Clear Dependencies**: Each module has explicit dependencies
+- **Easier Debugging**: Issues can be isolated to specific modules
+
+### Testability  
+- **Unit Testing**: Each module can be tested independently
+- **Mocking**: Dependencies can be easily mocked for testing
+- **Integration Testing**: Components can be tested together
+
+### Developer Experience
+- **Code Navigation**: Easy to find relevant functionality
+- **Onboarding**: New developers can understand individual components
+- **Documentation**: Smaller modules are easier to document
+
+### Scalability
+- **Event System**: Enables loose coupling and async processing
+- **Modular Growth**: New features can be added without touching core logic
+- **Performance**: Better separation allows for targeted optimizations
+
+## Next Steps (Future Phases)
+
+### Phase 2: Complete WebSocket Extraction
+- Extract remaining WebSocket message types (WebRTC signaling)
+- Add comprehensive error handling
+- Implement message validation
+
+### Phase 3: Enhanced Event System
+- Add event persistence for reliability
+- Implement event replay capabilities
+- Add monitoring and metrics
+
+### Phase 4: Advanced Features
+- Plugin architecture for bots
+- Rate limiting and security enhancements
+- Advanced admin capabilities
+
+## Migration Path
+
+The refactored architecture can be adopted gradually:
+
+1. **Testing**: Use `main_refactored.py` in development
+2. **Validation**: Verify all functionality works correctly  
+3. **Deployment**: Replace `main.py` with `main_refactored.py`
+4. **Cleanup**: Remove old monolithic code after verification
+
+The modular design ensures that each component can evolve independently while maintaining system stability.

docs/REFACTORING_STEP1_SUCCESS.md

@@ -0,0 +1,153 @@
+🎉 SERVER REFACTORING STEP 1 - SUCCESSFULLY COMPLETED!
+
+## Summary of Implementation
+
+### ✅ What Was Accomplished
+
+**1. Modular Architecture Created**
+```
+server/
+├── core/                    # Business logic modules
+│   ├── session_manager.py   # Session lifecycle & persistence
+│   ├── lobby_manager.py     # Lobby management & chat
+│   └── auth_manager.py      # Authentication & passwords
+├── websocket/               # WebSocket handling
+│   ├── message_handlers.py  # Message routing (replaces switch statement)
+│   └── connection.py        # Connection management
+├── api/                     # HTTP endpoints
+│   ├── admin.py            # Admin endpoints
+│   ├── sessions.py         # Session endpoints
+│   └── lobbies.py          # Lobby endpoints
+├── models/                  # Events & data models
+│   └── events.py           # Event-driven architecture
+└── main_refactored.py       # New modular main file
+```
+
+**2. Key Improvements Achieved**
+- ✅ **Separation of Concerns**: 2300-line monolith split into focused modules
+- ✅ **Event-Driven Architecture**: Decoupled communication via event bus
+- ✅ **Clean Message Routing**: Replaced massive switch statement with handler pattern
+- ✅ **Thread Safety**: Proper locking and state management maintained
+- ✅ **Dependency Injection**: Managers can be configured and swapped
+- ✅ **Testability**: Each module can be tested independently
+
+**3. Backward Compatibility Maintained**
+- ✅ **Same API endpoints**: All existing HTTP endpoints work unchanged
+- ✅ **Same WebSocket protocol**: All message types work identically
+- ✅ **Same authentication**: Password and name protection unchanged
+- ✅ **Same session persistence**: Existing sessions.json format preserved
+
+### 🧪 Verification Results
+
+**Architecture Structure**: ✅ All directories and files created correctly
+**Module Imports**: ✅ All core modules import successfully in proper environment
+**Server Startup**: ✅ Refactored server starts and initializes all components
+**Session Loading**: ✅ Successfully loaded 4 existing sessions from disk
+**Background Tasks**: ✅ Cleanup and validation tasks start properly
+**Session Integrity**: ✅ Detected and logged duplicate session names
+**Graceful Shutdown**: ✅ All components shut down cleanly
+
+### 📊 Test Results
+
+```
+INFO - Starting AI Voice Bot server with modular architecture...
+INFO - Loaded 4 sessions from sessions.json
+INFO - Starting session background tasks...
+INFO - AI Voice Bot server started successfully!
+INFO - Server URL: /ai-voicebot/
+INFO - Sessions loaded: 4
+INFO - Lobbies available: 0
+INFO - Protected names: 0
+INFO - Session background tasks started
+```
+
+**Session Integrity Validation Working**:
+```
+WARNING - Session integrity issues found: 3 issues
+WARNING - Integrity issue: Duplicate name 'whisper-bot' found in 3 sessions
+```
+
+### 🔧 Technical Achievements
+
+**1. SessionManager**
+- Extracted all session lifecycle management
+- Background cleanup and validation tasks
+- Thread-safe operations with proper locking
+- Event publishing for session state changes
+
+**2. LobbyManager** 
+- Extracted lobby creation and management
+- Chat message handling and persistence
+- Event-driven participant updates
+- Automatic empty lobby cleanup
+
+**3. AuthManager**
+- Extracted password hashing and verification
+- Name protection and takeover logic
+- Integrity validation for auth data
+- Clean separation from session logic
+
+**4. WebSocket Message Router**
+- Replaced 200+ line switch statement
+- Handler pattern for clean message processing
+- Easy to extend with new message types
+- Proper error handling and validation
+
+**5. Event System**
+- Decoupled component communication
+- Async event processing
+- Error isolation and logging
+- Foundation for future enhancements
+
+### 🚀 Benefits Realized
+
+**Maintainability**
+- Code is now organized into logical, focused modules
+- Much easier to locate and modify specific functionality
+- Reduced cognitive load when working on individual features
+
+**Testability**
+- Each module can be unit tested independently
+- Dependencies can be mocked easily
+- Integration tests can focus on specific interactions
+
+**Scalability**
+- Event system enables loose coupling
+- New features can be added without touching core logic
+- Components can be optimized independently
+
+**Developer Experience**
+- New developers can understand individual components
+- Clear separation of responsibilities
+- Better error messages and logging
+
+### 🎯 Next Steps (Future Phases)
+
+**Phase 2: Complete WebSocket Extraction**
+- Extract WebRTC signaling handlers
+- Add comprehensive message validation
+- Implement rate limiting
+
+**Phase 3: Enhanced Event System**
+- Add event persistence
+- Implement event replay capabilities
+- Add metrics and monitoring
+
+**Phase 4: Advanced Features**
+- Plugin architecture for bots
+- Advanced admin capabilities
+- Performance optimizations
+
+### 🏁 Conclusion
+
+**Step 1 of the server refactoring is COMPLETE and SUCCESSFUL!**
+
+The monolithic `main.py` has been successfully transformed into a clean, modular architecture that:
+- Maintains 100% backward compatibility
+- Significantly improves code organization
+- Provides a solid foundation for future development
+- Reduces maintenance burden and technical debt
+
+The refactored server is ready for production use and provides a much better foundation for continued development and feature additions.
+
+**Ready to proceed to Phase 2 or continue with other improvements! 🚀**

docs/REFACTORING_SUMMARY.md

@@ -0,0 +1,82 @@
+# Voicebot Module Refactoring
+
+The voicebot/main.py functionality has been broken down into individual Python files for better organization and maintainability:
+
+## New File Structure
+
+### Core Modules
+
+1. **`models.py`** - Data models and configuration
+   - `VoicebotArgs` - Pydantic model for CLI arguments and configuration
+   - `VoicebotMode` - Enum for client/provider modes
+   - `Peer` - WebRTC peer representation
+   - `JoinRequest` - Request model for joining lobbies
+   - `MessageData` - Type alias for message payloads
+
+2. **`webrtc_signaling.py`** - WebRTC signaling client functionality
+   - `WebRTCSignalingClient` - Main WebRTC signaling client class
+   - Handles peer connection management, ICE candidates, session descriptions
+   - Registration status tracking and reconnection logic
+   - Message processing and event handling
+
+3. **`session_manager.py`** - Session and lobby management
+   - `create_or_get_session()` - Session creation/retrieval
+   - `create_or_get_lobby()` - Lobby creation/retrieval
+   - HTTP API communication utilities
+
+4. **`bot_orchestrator.py`** - FastAPI bot orchestration service
+   - Bot discovery and management
+   - FastAPI endpoints for bot operations
+   - Provider registration with main server
+   - Bot instance lifecycle management
+
+5. **`client_main.py`** - Main client logic
+   - `main_with_args()` - Core client functionality
+   - `start_client_with_reload()` - Development mode with reload
+   - Event handlers for peer and track management
+
+6. **`client_app.py`** - Client FastAPI application
+   - `create_client_app()` - Creates FastAPI app for client mode
+   - Health check and status endpoints
+   - Process isolation and locking
+
+7. **`utils.py`** - Utility functions
+   - URL conversion utilities (`http_base_url`, `ws_url`)
+   - SSL context creation
+   - Network information logging
+
+8. **`main.py`** - Main orchestration and entry point
+   - Command-line argument parsing
+   - Mode selection (client vs provider)
+   - Entry points for both modes
+
+### Key Improvements
+
+- **Separation of Concerns**: Each file handles specific functionality
+- **Better Maintainability**: Smaller, focused modules are easier to understand and modify
+- **Reduced Coupling**: Dependencies between components are more explicit
+- **Type Safety**: Proper type hints and Pydantic models throughout
+- **Error Handling**: Centralized error handling and logging
+
+### Usage
+
+The refactored code maintains the same CLI interface:
+
+```bash
+# Client mode
+python voicebot/main.py --mode client --server-url http://localhost:8000/ai-voicebot
+
+# Provider mode  
+python voicebot/main.py --mode provider --host 0.0.0.0 --port 8788
+```
+
+### Import Structure
+
+```python
+from voicebot import VoicebotArgs, VoicebotMode, WebRTCSignalingClient
+from voicebot.models import Peer, JoinRequest
+from voicebot.session_manager import create_or_get_session, create_or_get_lobby
+from voicebot.client_main import main_with_args
+```
+
+The original `main_old.py` contains the monolithic implementation for reference.

docs/STEP4_COMPLETE.md

@@ -0,0 +1,123 @@
+# Step 4 Complete: Enhanced Error Handling and Recovery
+
+## Summary
+
+Step 4 has been successfully completed! We've implemented a comprehensive error handling and recovery system that significantly enhances the robustness and maintainability of the AI VoiceBot server.
+
+## What Was Implemented
+
+### 1. Custom Exception Hierarchy
+- **VoiceBotError**: Base exception class with categorization and severity
+- **WebSocketError**: WebSocket-specific errors
+- **WebRTCError**: WebRTC connection and signaling errors  
+- **SessionError**: Session management errors
+- **LobbyError**: Lobby management errors
+- **AuthError**: Authentication and authorization errors
+- **PersistenceError**: Data persistence errors
+- **ValidationError**: Input validation errors
+
+### 2. Error Classification System
+- **Severity Levels**: LOW, MEDIUM, HIGH, CRITICAL
+- **Categories**: websocket, webrtc, session, lobby, auth, persistence, network, validation, system
+
+### 3. Resilience Patterns
+
+#### Circuit Breaker Pattern
+```python
+@CircuitBreaker(failure_threshold=5, recovery_timeout=30.0)
+async def critical_operation():
+    # Automatically prevents cascading failures
+    pass
+```
+
+#### Retry Strategy with Exponential Backoff
+```python
+@RetryStrategy(max_attempts=3, base_delay=1.0)
+async def retryable_operation():
+    # Automatic retry with increasing delays
+    pass
+```
+
+### 4. Centralized Error Handler
+- Context tracking and correlation
+- Error statistics and monitoring
+- Client notification with appropriate messages
+- Recovery action coordination
+
+### 5. Enhanced WebSocket Message Handling
+- Structured error handling for all message types
+- Automatic recovery actions for connection issues
+- Validation error handling with user feedback
+
+### 6. WebRTC Signaling Error Handling
+- All signaling methods decorated with error handling
+- Peer connection failure recovery
+- ICE candidate error handling
+- Session description negotiation error recovery
+
+## Key Files Modified
+
+### Created
+- `server/core/error_handling.py` - Complete error handling framework (400+ lines)
+
+### Enhanced
+- `server/websocket/message_handlers.py` - Added structured error handling to MessageRouter
+- `server/websocket/webrtc_signaling.py` - Added error handling decorators to all signaling methods
+
+## Verification Results
+
+✅ **All Tests Passed:**
+- Custom exception classes working correctly
+- Error handler tracking and statistics functional
+- Circuit breaker pattern preventing cascading failures
+- Retry strategy with exponential backoff working
+- Enhanced message router with error recovery
+- WebRTC signaling with error handling active
+- Error classification and severity working
+- Live error handling test successful
+
+## Benefits Achieved
+
+1. **Improved Reliability**: Circuit breakers prevent cascading failures
+2. **Better User Experience**: Appropriate error messages and recovery actions
+3. **Enhanced Debugging**: Detailed error context and correlation tracking
+4. **Operational Visibility**: Error statistics and monitoring capabilities
+5. **Automatic Recovery**: Retry strategies and recovery mechanisms
+6. **Maintainability**: Centralized error handling reduces code duplication
+
+## Performance Impact
+
+- **Minimal Overhead**: Error handling adds < 1% performance overhead
+- **Early Failure Detection**: Circuit breakers prevent wasted resources
+- **Efficient Recovery**: Exponential backoff prevents resource storms
+
+## Next Steps Available
+
+### Step 5: Performance Optimization and Monitoring
+- Implement caching strategies for frequently accessed data
+- Add performance metrics and monitoring endpoints
+- Optimize database queries and WebSocket message handling
+- Implement load balancing for multiple bot instances
+
+### Step 6: Advanced Bot Management
+- Enhanced bot orchestration with multiple AI providers
+- Bot personality and behavior customization
+- Advanced conversation context management
+- Bot performance analytics
+
+### Step 7: Security Enhancements
+- Rate limiting and DDoS protection
+- Enhanced authentication mechanisms
+- Data encryption and privacy features
+- Security audit logging
+
+## Migration Notes
+
+- **Backward Compatibility**: All existing functionality preserved
+- **Gradual Adoption**: Error handling can be adopted incrementally
+- **Configuration**: Error thresholds and retry policies are configurable
+- **Monitoring**: Error statistics available via error_handler.get_error_statistics()
+
+---
+
+The server is now significantly more robust and ready for production use. The enhanced error handling provides both immediate benefits and a foundation for future reliability improvements.

docs/STEP5_PLANNING.md

@@ -0,0 +1,134 @@
+# Server Refactoring Roadmap - Step 5 Planning
+
+## Current Status: Step 4 COMPLETED ✅
+
+**Enhanced Error Handling and Recovery** has been successfully implemented with comprehensive error handling framework, resilience patterns, and recovery mechanisms.
+
+## Step 5 Options: Performance Optimization and Monitoring
+
+Based on the current architecture, here are the recommended paths for Step 5:
+
+### Option A: Performance Optimization Focus
+
+#### 1. Caching Layer Implementation
+- **Redis Integration**: Add Redis for session and lobby state caching
+- **In-Memory Caching**: Implement LRU cache for frequently accessed data
+- **WebSocket Message Caching**: Cache repeated WebRTC signaling messages
+- **Bot Response Caching**: Cache common bot responses and interactions
+
+#### 2. Database Optimization
+- **Connection Pooling**: Implement async database connection pooling
+- **Query Optimization**: Add database indexes and optimize frequent queries
+- **Batch Operations**: Implement batch updates for session persistence
+- **Read Replicas**: Support for read-only database replicas
+
+#### 3. WebSocket Performance
+- **Message Compression**: Implement WebSocket message compression
+- **Connection Pooling**: Optimize WebSocket connection management
+- **Async Processing**: Move heavy operations to background tasks
+- **Message Queuing**: Implement message queues for high-traffic scenarios
+
+### Option B: Monitoring and Observability Focus
+
+#### 1. Performance Metrics
+- **Real-time Metrics**: CPU, memory, network, and application metrics
+- **Custom Metrics**: Session counts, message rates, error rates
+- **Performance Baselines**: Establish and track performance benchmarks
+- **Alert Thresholds**: Automated alerts for performance degradation
+
+#### 2. Health Check System
+- **Deep Health Checks**: Database, Redis, external service connectivity
+- **Readiness Probes**: Kubernetes-ready health endpoints
+- **Graceful Degradation**: Service health status with fallback modes
+- **Dependency Monitoring**: Track health of all system dependencies
+
+#### 3. Logging and Tracing
+- **Structured Logging**: JSON logging with correlation IDs
+- **Distributed Tracing**: Request tracing across services
+- **Log Aggregation**: Centralized log collection and analysis
+- **Performance Profiling**: Built-in profiling endpoints
+
+### Option C: Hybrid Approach (Recommended)
+
+Combine the most impactful elements from both options:
+
+1. **Quick Wins** (1-2 hours):
+   - Add performance metrics endpoints
+   - Implement basic caching for sessions
+   - Add health check endpoints
+
+2. **Medium Impact** (2-4 hours):
+   - Redis integration for distributed caching
+   - Enhanced monitoring dashboard
+   - WebSocket performance optimizations
+
+3. **High Impact** (4+ hours):
+   - Complete observability stack
+   - Advanced caching strategies
+   - Performance testing suite
+
+## Recommended: Step 5A - Essential Performance and Monitoring
+
+### Scope
+- **Performance Metrics**: Real-time application metrics
+- **Caching Layer**: Redis-based caching for sessions and lobbies
+- **Health Monitoring**: Comprehensive health check system
+- **WebSocket Optimization**: Message compression and connection pooling
+
+### Benefits
+- 20-50% performance improvement for high-traffic scenarios
+- Real-time visibility into system health and performance
+- Proactive issue detection and resolution
+- Foundation for auto-scaling and load balancing
+
+### Implementation Plan
+1. **Metrics Collection**: Add performance metrics endpoints
+2. **Redis Integration**: Implement distributed caching
+3. **Health Checks**: Add comprehensive health monitoring
+4. **WebSocket Optimization**: Improve message handling efficiency
+
+## Alternative Paths
+
+### Step 5B: Bot Management Enhancement
+If performance is sufficient, focus on advanced bot features:
+- Multi-provider AI integration (OpenAI, Claude, local models)
+- Bot personality customization
+- Advanced conversation context
+- Bot analytics and insights
+
+### Step 5C: Security and Compliance
+For production-ready security:
+- Rate limiting and DDoS protection
+- Enhanced authentication (OAuth, JWT, multi-factor)
+- Data encryption and privacy compliance
+- Security audit logging
+
+## Decision Factors
+
+Choose **Step 5A (Performance & Monitoring)** if:
+- You expect high user traffic
+- You need production-grade observability
+- You want to optimize resource usage
+- You plan to scale horizontally
+
+Choose **Step 5B (Bot Management)** if:
+- Performance is currently adequate
+- You want to enhance user experience
+- You need multiple AI provider support
+- Bot capabilities are the primary focus
+
+Choose **Step 5C (Security)** if:
+- You're preparing for production deployment
+- You handle sensitive user data
+- Compliance requirements are critical
+- Security is the top priority
+
+## Recommendation
+
+**Proceed with Step 5A: Performance Optimization and Monitoring**
+
+This provides the best foundation for production deployment while maintaining the momentum of infrastructure improvements. The performance and monitoring capabilities will be essential regardless of which features are added later.
+
+---
+
+**Ready to proceed?** Let me know which Step 5 option you'd like to implement, and I'll begin the detailed implementation.

docs/STEP_5B_IMPLEMENTATION.md

@@ -0,0 +1,278 @@
+# Step 5B: Advanced Bot Management Implementation
+
+This document describes the implementation of **Step 5B: Advanced Bot Management** as part of the server refactoring roadmap. This step enhances the existing voicebot system with multi-provider AI integration, personality-driven bot behavior, and conversation context management.
+
+## Overview
+
+Step 5B adds sophisticated bot management capabilities to the AI voicebot system, enabling:
+
+- **Multi-Provider AI Integration**: Support for OpenAI, Anthropic, and local AI models
+- **Personality System**: Configurable bot personalities with distinct traits and communication styles
+- **Conversation Context Management**: Persistent conversation memory and context tracking
+- **Enhanced Bot Orchestration**: Dynamic configuration and health monitoring
+- **Backward Compatibility**: Full compatibility with existing bot implementations
+
+## Architecture Components
+
+### 1. AI Provider System (`ai_providers.py`)
+
+The AI provider system provides a unified interface for multiple AI backends:
+
+```python
+# Abstract base class for all AI providers
+class AIProvider:
+    async def generate_response(self, context: ConversationContext, message: str) -> str
+    async def stream_response(self, context: ConversationContext, message: str) -> AsyncIterator[str]
+    async def health_check(self) -> bool
+
+# Concrete implementations
+- OpenAIProvider: GPT-4, GPT-3.5-turbo integration
+- AnthropicProvider: Claude integration  
+- LocalProvider: Local model integration (Ollama, etc.)
+```
+
+**Key Features:**
+- Unified API across different AI providers
+- Streaming response support
+- Health monitoring and retry logic
+- Conversation context integration
+- Provider-specific configuration
+
+### 2. Personality System (`personality_system.py`)
+
+The personality system enables bots to have distinct behavioral characteristics:
+
+```python
+class BotPersonality:
+    traits: List[PersonalityTrait]
+    communication_style: CommunicationStyle
+    behavior_guidelines: List[str]
+    response_patterns: Dict[str, str]
+```
+
+**Available Personality Templates:**
+- **Helpful Assistant**: Balanced, professional, and supportive
+- **Technical Expert**: Detailed, precise, and thorough explanations
+- **Creative Companion**: Imaginative, inspiring, and artistic
+- **Business Advisor**: Strategic, professional, and results-oriented
+- **Comedy Bot**: Humorous, casual, and entertaining
+- **Wise Mentor**: Thoughtful, philosophical, and guidance-focused
+
+**Key Features:**
+- Template-based personality creation
+- Configurable traits and communication styles
+- System prompt generation for AI providers
+- Dynamic personality switching
+
+### 3. Conversation Context Management (`conversation_context.py`)
+
+The context system provides persistent conversation memory:
+
+```python
+class ConversationMemory:
+    turns: List[ConversationTurn]
+    facts_learned: List[str]
+    emotional_context: Dict[str, Any]
+    persistent_context: Dict[str, Any]
+```
+
+**Key Features:**
+- Turn-by-turn conversation tracking
+- Fact extraction and learning
+- Emotional context analysis
+- Persistent storage with JSON serialization
+- Context summarization for AI providers
+
+### 4. Enhanced Bot Implementation (`bots/ai_chatbot.py`)
+
+Example implementation of an enhanced bot using all Step 5B features:
+
+```python
+class EnhancedAIChatbot:
+    def __init__(self, session_name: str):
+        self.ai_provider = ai_provider_manager.create_provider(provider_type)
+        self.personality = personality_manager.create_personality_from_template(template)
+        self.conversation_context = context_manager.get_or_create_context(session_id)
+```
+
+**Key Features:**
+- Multi-provider AI integration
+- Personality-driven responses
+- Conversation memory
+- Health monitoring
+- Runtime configuration
+- Graceful fallback when AI features unavailable
+
+## Configuration
+
+### Environment Variables
+
+Configure AI providers and bot behavior through environment variables:
+
+```bash
+# AI Provider Configuration
+OPENAI_API_KEY=your_openai_key
+ANTHROPIC_API_KEY=your_anthropic_key
+
+# Bot-Specific Configuration
+AI_CHATBOT_PERSONALITY=helpful_assistant
+AI_CHATBOT_PROVIDER=openai
+AI_CHATBOT_STREAMING=true
+AI_CHATBOT_MEMORY=true
+```
+
+### Bot Configuration File (`enhanced_bot_configs.json`)
+
+Define bot configurations in JSON format:
+
+```json
+{
+  "ai_chatbot": {
+    "personality": "helpful_assistant",
+    "ai_provider": "openai",
+    "streaming": true,
+    "memory_enabled": true,
+    "advanced_features": true
+  }
+}
+```
+
+## Integration with Existing System
+
+### Bot Orchestrator Enhancement
+
+The enhanced orchestrator (`step_5b_integration_demo.py`) extends existing functionality:
+
+```python
+class EnhancedBotOrchestrator:
+    async def discover_enhanced_bots(self) -> Dict[str, Dict[str, Any]]
+    async def create_enhanced_bot_instance(self, bot_name: str, session_name: str)
+    async def monitor_bot_health(self) -> Dict[str, Any]
+    async def configure_bot_runtime(self, bot_name: str, new_config: Dict[str, Any])
+```
+
+### Backward Compatibility
+
+- Existing bots continue to work without modification
+- Enhanced features are opt-in through configuration
+- Graceful degradation when AI providers unavailable
+- Standard bot interface maintained
+
+## Usage Examples
+
+### Creating an Enhanced Bot
+
+```python
+# Create bot with specific configuration
+bot_instance = await enhanced_orchestrator.create_enhanced_bot_instance(
+    "ai_chatbot", 
+    "user_session_123"
+)
+
+# Bot automatically configured with:
+# - OpenAI provider
+# - Helpful assistant personality  
+# - Conversation memory enabled
+# - Streaming responses
+```
+
+### Runtime Configuration
+
+```python
+# Switch bot personality at runtime
+await enhanced_orchestrator.configure_bot_runtime("ai_chatbot", {
+    "personality": "technical_expert",
+    "ai_provider": "anthropic"
+})
+```
+
+### Health Monitoring
+
+```python
+# Get comprehensive health report
+health_report = await enhanced_orchestrator.monitor_bot_health()
+
+# Includes:
+# - AI provider status
+# - Personality system health
+# - Conversation context statistics
+# - Individual bot instance status
+```
+
+## Implementation Status
+
+### ✅ Completed Components
+
+- **AI Provider System**: Multi-provider abstraction with OpenAI, Anthropic, Local support
+- **Personality System**: 6 personality templates with configurable traits
+- **Conversation Context**: Memory management with persistent storage
+- **Enhanced Bot Example**: Fully functional AI chatbot implementation
+- **Configuration System**: JSON-based bot configuration with environment variable support
+- **Integration Demo**: Shows how to integrate with existing bot orchestrator
+
+### 🔄 Integration Points
+
+- **Bot Orchestrator Integration**: Enhance existing `bot_orchestrator.py` with new capabilities
+- **Configuration Loading**: Integrate configuration system with bot discovery
+- **Health Monitoring**: Add health endpoints to existing FastAPI server
+
+### 📋 Next Steps
+
+1. **Integration with Existing System**:
+   ```python
+   # Modify bot_orchestrator.py to use enhanced features
+   from step_5b_integration_demo import enhanced_orchestrator
+   ```
+
+2. **Add Health Monitoring Endpoints**:
+   ```python
+   # Add to main.py FastAPI server
+   @app.get("/api/bots/health")
+   async def get_bot_health():
+       return await enhanced_orchestrator.monitor_bot_health()
+   ```
+
+3. **Environment Setup**:
+   ```bash
+   # Install additional dependencies
+   pip install openai anthropic aiohttp
+   
+   # Configure API keys
+   export OPENAI_API_KEY=your_key
+   export ANTHROPIC_API_KEY=your_key
+   ```
+
+4. **Testing Enhanced Bots**:
+   ```python
+   # Run integration demo
+   python voicebot/step_5b_integration_demo.py
+   ```
+
+## Performance Considerations
+
+- **Streaming Responses**: Reduces perceived latency for long AI responses
+- **Conversation Context**: JSON storage for persistence, in-memory for active sessions
+- **Health Monitoring**: Cached health checks to avoid excessive API calls
+- **Provider Fallback**: Graceful degradation when primary AI provider unavailable
+
+## Security Considerations
+
+- **API Key Management**: Secure storage of AI provider API keys
+- **Rate Limiting**: Implement rate limiting for AI provider calls
+- **Context Storage**: Secure storage of conversation data
+- **Input Validation**: Sanitize user inputs before sending to AI providers
+
+## Monitoring and Analytics
+
+The system provides comprehensive monitoring:
+
+- **Bot Usage Analytics**: Track which personalities and providers are most used
+- **Health Trends**: Historical health data for system reliability
+- **Conversation Statistics**: Metrics on conversation length and context usage
+- **Performance Metrics**: Response times and success rates per provider
+
+## Conclusion
+
+Step 5B transforms the voicebot system from a simple bot orchestrator into a sophisticated AI-powered conversation platform. The modular design ensures that existing functionality remains intact while providing powerful new capabilities for AI-driven interactions.
+
+The implementation provides a solid foundation for advanced conversational AI while maintaining the flexibility to add new providers, personalities, and features in the future.

docs/TYPESCRIPT_GENERATION.md

@@ -0,0 +1,168 @@
+# OpenAPI TypeScript Generation
+
+This project now supports automatic TypeScript type generation from the FastAPI server's Pydantic models using OpenAPI schema generation.
+
+## Overview
+
+The implementation follows the "OpenAPI Schema Generation (Recommended for FastAPI)" approach:
+
+1. **Server-side**: FastAPI automatically generates OpenAPI schema from Pydantic models
+2. **Generation**: Python script extracts the schema and saves it as JSON
+3. **TypeScript**: `openapi-typescript` converts the schema to TypeScript types
+4. **Client**: Typed API client provides type-safe server communication
+
+## Generated Files
+
+- `client/openapi-schema.json` - OpenAPI schema extracted from FastAPI
+- `client/src/api-types.ts` - TypeScript interfaces generated from OpenAPI schema
+- `client/src/api-client.ts` - Typed API client with convenience methods
+
+## How It Works
+
+### 1. Schema Generation
+The `server/generate_schema_simple.py` script:
+- Imports the FastAPI app from `main.py`
+- Extracts the OpenAPI schema using `app.openapi()`
+- Saves the schema as JSON in `client/openapi-schema.json`
+
+### 2. TypeScript Generation
+The `openapi-typescript` package:
+- Reads the OpenAPI schema JSON
+- Generates TypeScript interfaces in `client/src/api-types.ts`
+- Creates type-safe definitions for all Pydantic models
+
+### 3. API Client
+The `client/src/api-client.ts` file provides:
+- Type-safe API client class
+- Convenience functions for each endpoint
+- Proper error handling with custom `ApiError` class
+- Re-exported types for easy importing
+
+## Usage in React Components
+
+```typescript
+import { apiClient, adminApi, healthApi, lobbiesApi, sessionsApi } from './api-client';
+import type { LobbyModel, SessionModel, AdminSetPassword } from './api-client';
+
+// Using the convenience APIs
+const healthStatus = await healthApi.check();
+const lobbies = await lobbiesApi.getAll();
+const session = await sessionsApi.getCurrent();
+
+// Using the main client
+const adminNames = await apiClient.adminListNames();
+
+// With type safety for request data
+const passwordData: AdminSetPassword = {
+  name: "admin",
+  password: "newpassword"
+};
+const result = await adminApi.setPassword(passwordData);
+
+// Type-safe lobby creation
+const lobbyRequest: LobbyCreateRequest = {
+  type: "lobby_create",
+  data: {
+    name: "My Lobby",
+    private: false
+  }
+};
+const newLobby = await sessionsApi.createLobby("session-id", lobbyRequest);
+```
+
+## Regenerating Types
+
+### Manual Generation
+```bash
+# Generate schema from server
+docker compose exec server uv run python3 generate_schema_simple.py
+
+# Generate TypeScript types
+docker compose exec client npx openapi-typescript openapi-schema.json -o src/api-types.ts
+
+# Type check
+docker compose exec client npm run type-check
+```
+
+### Automated Generation
+```bash
+# Run the comprehensive generation script
+./generate-ts-types.sh
+```
+
+### NPM Scripts (in frontend container)
+```bash
+# Generate just the schema
+npm run generate-schema
+
+# Generate just the TypeScript types (requires schema to exist)
+npm run generate-types
+
+# Generate both schema and types
+npm run generate-api-types
+```
+
+## Development Workflow
+
+1. **Modify Pydantic models** in `shared/models.py`
+2. **Regenerate types** using one of the methods above
+3. **Update React components** to use the new types
+4. **Type check** to ensure everything compiles
+
+## Benefits
+
+- ✅ **Type Safety**: Full TypeScript type checking for API requests/responses
+- ✅ **Auto-completion**: IDE support with auto-complete for API methods and data structures
+- ✅ **Error Prevention**: Catch type mismatches at compile time
+- ✅ **Documentation**: Self-documenting API with TypeScript interfaces
+- ✅ **Sync Guarantee**: Types are always in sync with server models
+- ✅ **Refactoring Safety**: IDE can safely refactor across frontend/backend
+
+## File Structure
+
+```
+server/
+├── main.py                    # FastAPI app with Pydantic models
+├── generate_schema_simple.py  # Schema extraction script
+└── generate_api_client.py     # Enhanced generator (backup)
+
+shared/
+└── models.py                  # Pydantic models (source of truth)
+
+client/
+├── openapi-schema.json        # Generated OpenAPI schema
+├── package.json              # Updated with openapi-typescript dependency
+└── src/
+    ├── api-types.ts          # Generated TypeScript interfaces
+    └── api-client.ts         # Typed API client
+```
+
+## Troubleshooting
+
+### Container Issues
+If the frontend container has dependency conflicts:
+```bash
+# Rebuild the frontend container
+docker compose build client
+docker compose up -d client
+```
+
+### TypeScript Errors
+Ensure the generated types are up to date:
+```bash
+./generate-ts-types.sh
+```
+
+### Module Not Found Errors
+Check that the volume mounts are working correctly and files are synced between host and container.
+
+## API Evolution Detection
+
+The system now includes automatic detection of API changes:
+
+- **Automatic Checking**: In development mode, the system automatically warns about unimplemented endpoints
+- **Console Warnings**: Clear warnings appear in the browser console when new API endpoints are available
+- **Implementation Stubs**: Provides ready-to-use code stubs for new endpoints
+- **Schema Monitoring**: Detects when the OpenAPI schema changes
+
+See `client/src/API_EVOLUTION.md` for detailed documentation on using this feature.

docs/WHISPER_LOGGING_GUIDE.md

@@ -0,0 +1,118 @@
+# Whisper ASR Enhanced Logging
+
+This enhancement adds detailed logging to the Whisper ASR system to help debug and monitor speech recognition performance.
+
+## New Logging Features
+
+### 1. Model Loading
+- Logs when the Whisper model is being loaded
+- Shows which model variant is being used
+- Confirms successful processor and model initialization
+
+### 2. Audio Frame Processing
+- **Frame-by-frame details**: Sample rate, format, layout, shape, and data type
+- **Audio quality metrics**: RMS level and peak amplitude for each frame
+- **Format conversions**: Logs when converting stereo to mono, resampling, or normalizing
+- **Frame counting**: Reduced noise by logging full details every 20 frames
+
+### 3. Audio Buffer Management
+- **Buffer status**: Shows buffer size in frames and milliseconds
+- **Queue management**: Tracks when audio is queued for processing
+- **Audio metrics**: RMS, peak amplitude, and duration for queued chunks
+- **Queue size monitoring**: Shows processing queue depth
+
+### 4. ASR Processing Pipeline
+- **Processing timing**: Separate timing for feature extraction, model inference, and decoding
+- **Audio analysis**: Duration, RMS, and peak levels for audio being transcribed
+- **Phrase detection**: Logs when phrases are considered complete
+- **Streaming vs final**: Clear distinction between partial and final transcriptions
+
+### 5. Performance Metrics
+- **Processing time**: How long each transcription takes
+- **Audio-to-text ratio**: Processing time vs audio duration
+- **Queue depth**: Processing backlog monitoring
+
+## Log Levels
+
+### DEBUG Level
+- Individual audio frame details
+- Buffer management operations
+- Processing queue status
+- Detailed timing information
+- Audio quality metrics for each chunk
+
+### INFO Level  
+- Model loading status
+- Track connection events
+- Completed transcriptions with timing
+- Periodic audio frame summaries (every 20 frames)
+- Major processing events
+
+### WARNING Level
+- Missing audio processor
+- Event loop issues
+- Queue full conditions
+- Non-audio frame reception
+
+### ERROR Level
+- Model loading failures
+- Transcription errors
+- Processing loop crashes
+- Track handling exceptions
+
+## Usage
+
+### Enable Debug Logging
+```bash
+# From the voicebot directory
+python set_whisper_debug.py
+```
+
+### Return to Normal Logging
+```bash
+python set_whisper_debug.py info
+```
+
+### Sample Enhanced Log Output
+
+```
+INFO - Loading Whisper model: distil-whisper/distil-large-v3
+INFO - Whisper processor loaded successfully  
+INFO - Whisper model loaded and set to evaluation mode
+INFO - AudioProcessor initialized - sample_rate: 16000Hz, frame_size: 480, phrase_timeout: 3.0s
+INFO - Received audio track from user_123, starting transcription (processor available: True)
+DEBUG - Received audio frame from user_123: 48000Hz, s16, stereo
+DEBUG - Audio frame data: shape=(1440, 2), dtype=int16
+DEBUG - Converted stereo to mono: (1440, 2) -> (1440,)
+DEBUG - Normalized int16 audio to float32
+DEBUG - Resampled audio: 48000Hz -> 16000Hz, 1440 -> 480 samples
+DEBUG - Audio frame #1: RMS: 0.0234, Peak: 0.1892
+DEBUG - Added audio chunk: 480 samples, buffer size: 1 frames (30ms)
+INFO - Audio frame #20 from user_123: 48000Hz, s16, stereo, 480 samples, RMS: 0.0156, Peak: 0.2103
+DEBUG - Buffer threshold reached, queuing for processing
+DEBUG - Queuing audio chunk: 4800 samples, 0.30s duration, RMS: 0.0189, Peak: 0.2103
+DEBUG - Added to processing queue, queue size: 1
+DEBUG - Retrieved audio chunk from queue, remaining queue size: 0
+INFO - Starting streaming transcription: 2.10s audio, RMS: 0.0245, Peak: 0.3456
+DEBUG - ASR timing - Feature extraction: 0.045s, Model inference: 0.234s, Decoding: 0.012s, Total: 0.291s
+INFO - Transcribed (streaming): 'Hello there, how are you doing today?' (processing time: 0.291s, audio duration: 2.10s)
+```
+
+## Troubleshooting
+
+### No Transcriptions Appearing
+- Check if AudioProcessor is created: Look for "AudioProcessor initialized" message
+- Verify audio quality: Look for RMS levels > 0.001 and reasonable peak values
+- Check processing queue: Should show "Added to processing queue" messages
+
+### Poor Recognition Quality  
+- Monitor RMS and peak levels - very low values indicate quiet audio
+- Check processing timing - slow inference may indicate resource issues
+- Look for resampling messages - frequent resampling can degrade quality
+
+### Performance Issues
+- Monitor "ASR timing" logs for slow components
+- Check queue depth - high values indicate processing backlog
+- Look for "queue full" warnings indicating dropped audio
+
+This enhanced logging provides comprehensive visibility into the ASR pipeline, making it much easier to diagnose audio quality issues, performance problems, and configuration errors.