# LangchainMCPAdapter Documentation

## Overview
The `LangchainMCPAdapter` enables LangChain agents to work within the Model Context Protocol (MCP) framework. It provides seamless integration between LangChain agents and other framework agents through the MCP transport layer.

## Core Components

### Initialization Parameters
- `name`: Agent identifier
- `transport`: Optional MCPTransport instance
- `client_mode`: Client mode flag
- `langchain_agent`: OpenAIFunctionsAgent instance
- `agent_executor`: AgentExecutor instance
- `system_message`: Custom system message

### Key Features
1. **LangChain Integration**
   - Wraps LangChain agents
   - Manages agent executor
   - Handles async execution

2. **Message Processing**
   - Handles incoming messages
   - Manages task queue
   - Processes tasks asynchronously

3. **Transport Layer**
   - Server connection management
   - Message routing
   - Task coordination

## Core Functionality

### Message Handling
1. `handle_incoming_message()`
   - Validates message format
   - Processes message types
   - Manages task queueing
   - Handles acknowledgments

2. `process_messages()`
   - Continuous message monitoring
   - Error handling
   - Message routing
   - Queue management

### Task Processing
1. `process_tasks()`
   - Task queue management
   - LangChain execution
   - Result handling
   - Error recovery

2. Task Components:
   - Task validation
   - Executor invocation
   - Result formatting
   - Response routing

### Server Connection
1. `connect_to_server()`
   - Server registration
   - Capability announcement
   - Connection management
   - Error handling

## Best Practices

1. **Configuration**
   - Set appropriate mode
   - Configure transport
   - Initialize agents properly
   - Handle connections

2. **Error Handling**
   - Validate messages
   - Handle task failures
   - Manage timeouts
   - Log errors

3. **Resource Management**
   - Clean up connections
   - Manage task queue
   - Handle cancellation
   - Monitor performance

## Usage Example
```python
# Initialize LangChain components
langchain_agent = OpenAIFunctionsAgent(...)
agent_executor = AgentExecutor(...)

# Create adapter
adapter = LangchainMCPAdapter(
    name="langchain_agent",
    transport=HTTPTransport(),
    client_mode=True,
    langchain_agent=langchain_agent,
    agent_executor=agent_executor
)

# Connect to MCP network
await adapter.connect_to_server("http://localhost:8000")

# Start processing
await adapter.process_messages()
```

## Security Considerations

1. **Message Security**
   - Validate message sources
   - Sanitize inputs
   - Handle sensitive data

2. **Task Security**
   - Validate permissions
   - Protect resources
   - Monitor execution

3. **Network Security**
   - Secure connections
   - Manage timeouts
   - Handle authentication