LangChain MCP Agentic Chatbot
2025 • Personal
AI Python LangChain ChatGPT MCP Model Context Protocol Agentic AI Chatbot AI Agents Zerodha Kite Trading
Agentic chatbot application built using LangChain in Python, powered by ChatGPT models and integrated with a Model Context Protocol (MCP) server for enhanced communication and agent capabilities.
Features
- LangChain Framework: Leverages LangChain for robust agent architecture
- ChatGPT Integration: Uses ChatGPT models for conversational intelligence
- MCP Server Communication: Integrated Model Context Protocol server for standardized agent interactions
- Zerodha Kite MCP Integration: Execute trading operations through natural language via MCP server
- Agentic Architecture: Autonomous agent capabilities for complex task handling
- Python-Based: Built entirely in Python for flexibility and ease of development
Implementation
Built with LangChain’s agent framework in Python, the chatbot communicates with an MCP server to handle various tasks including trading operations through Zerodha Kite integration. The ChatGPT model powers the conversational interface while the MCP server enables structured communication between the agent and external tools or services.
Technical Architecture
- LangChain Framework: Core framework for agent orchestration and chain management
- ChatGPT Model: Powers natural language understanding and generation
- MCP Server: Handles protocol-based communication and tool integration
- Zerodha Kite MCP: Trading operations integration via MCP protocol
- Python Runtime: Main development environment
- Agent System: Autonomous decision-making and task execution
Use Cases
- Conversational AI: Natural language interactions with users
- Trading Operations: Execute trades and manage portfolio through conversational interface
- Agentic Task Execution: Autonomous handling of complex multi-step tasks
- Tool Integration: Connect to external services via MCP protocol
- Intelligent Routing: Smart decision-making for task delegation
- Developer Experimentation: Platform for testing agent-based architectures
Capabilities
The chatbot can:
- Engage in natural conversations using ChatGPT
- Execute trading operations via Zerodha Kite MCP integration
- Execute autonomous tasks through agentic behavior
- Communicate with MCP server for tool access
- Handle complex queries requiring multi-step reasoning
- Integrate with external services through standardized protocol
Technologies
- Python
- LangChain
- ChatGPT Models
- Model Context Protocol (MCP)
- MCP Server
- Zerodha Kite MCP Integration
- Agent Architecture
- Natural Language Processing