Langgraph-complete-guide
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Overview
Demonstrates building a conversational AI agent using LangGraph that integrates external Model Context Protocol (MCP) servers for specialized functionalities like expense tracking and arithmetic calculations, featuring multi-turn conversations and persistent memory.
Installation
streamlit run 12.MCP/streamlit_frontend_mcp.pyEnvironment Variables
- COHERE_API_KEY
- LANGSMITH_API_KEY
- ALPHAVANTAGE_API_KEY
Security Notes
The `get_stock_price` tool within `langgraph_mcp_backend.py` contains a hardcoded Alpha Vantage API key (`apikey=19W1GEHJXPPUTKR2`). This is a critical security vulnerability as it exposes a sensitive credential directly in the source code. For production use, this key must be moved to an environment variable. SQL queries in `expenses_tracker_MCP_server.py` use parameterized inputs, which correctly mitigates SQL injection risks. Communication with MCP servers uses standard I/O ('stdio' transport), which is generally safe for local process interaction.
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