LangGraph Solution Template for MCP based agents

This project provides multiple LangGraph framework based implementations (strategies) for a universal assistant powered by MCP (Model Context Protocol) servers. It aims to showcase how LangGraph and MCP can be combined to build modular and extensible AI agents capable of interacting with various tools and data sources.

This documentation provides details necessary for Large Language Models (LLMs) and AI Agents to understand, diagnose, and enhance this project workspace.

Project Overview

• Project Structure and Purpose: High-level description of the project, its goals, and the directory structure.

Dependencies

• Key Dependencies: Information about project dependencies, Python version, and build system details extracted from pyproject.toml.

LangGraph Integration

• LangGraph Setup: Details on how LangGraph is configured and integrated, based on langgraph.json.

Strategies

• Implementation Strategies: Overview of the different LangGraph-based assistant strategies implemented and links to their specific documentation.

Code Patterns

• Common Code Patterns: Documentation of recurring code patterns and conventions used throughout the project, including LLM node implementation, model loading, and MCP interaction.

Setting it up

1. Create and activate a virtual environment

   git clone {{REPO_URL}}
   cd langgraph-mcp
   python3 -m venv .venv
   source .venv/bin/activate

2. Install Langgraph CLI

   pip install -U "langgraph-cli[inmem]"

   Note: "inmem" extra(s) are needed to run LangGraph API server in development mode (without requiring Docker installation)

3. Install the dependencies

   source .venv/bin/activate
   pip install -e .
   pip install -e ".[dev]"
   pip install -e ".[test]"

4. Configure environment variables

   cp env.example .env

5. Run tests

   source .venv/bin/activate
   pytest tests/test_assistant_with_planner.py
   pytest tests/test_planner_style_agent.py