LangGraph AI
LangGraph AI Repository
What is LangGraph AI?
LangGraph AI is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to langgraph ai repository
LangGraph AI Repository
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- LangGraph AI Repository
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx langgraph-aiConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use LangGraph AI
The LangGraph AI repository is a collection of Jupyter notebooks and Python implementations demonstrating how to build agentic RAG (Retrieval-Augmented Generation) systems, MCP servers and clients, and advanced multi-step AI workflows using the LangGraph framework. It covers adaptive RAG with routing logic, self-correcting retrieval pipelines, human-in-the-loop patterns, and LangGraph's graph-based agent orchestration. Developers learning to build production-grade AI agents or experimenting with LangGraph's stateful workflow primitives will find this a hands-on reference implementation.
Prerequisites
- Python 3.10 or later
- uv package manager installed (https://astral.sh/uv/)
- An Anthropic API key (ANTHROPIC_API_KEY) for Claude-powered agents
- Optionally, a LangSmith API key (LANGCHAIN_API_KEY) for tracing and observability
- Jupyter or JupyterLab for running the notebook examples
Clone the repository
Clone the langgraph-ai repository to your local machine.
git clone https://github.com/piyushagni5/langgraph-ai
cd langgraph-aiCreate a Python virtual environment
Use uv to create an isolated Python 3.10 environment for the project.
uv venv --python 3.10
source .venv/bin/activate # macOS/Linux
# .venv\Scripts\activate # WindowsInstall dependencies
Install all required Python packages from the requirements file.
uv pip install -r requirements.txtConfigure environment variables
Create a .env file in the project root with your API keys.
# .env
ANTHROPIC_API_KEY=sk-ant-your-key-here
# Optional LangSmith tracing
LANGCHAIN_API_KEY=ls__your-key-here
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT=langgraph-aiRegister the Jupyter kernel
Register the virtual environment as a Jupyter kernel so notebooks can find the installed packages.
python -m ipykernel install --user --name=langgraph-aiRun an example workflow
Navigate to one of the example subdirectories and run the main workflow script.
cd agentic-rag/agentic-rag-systems/building-adaptive-rag
uv run main.pyLangGraph AI Examples
Client configuration
Example .env configuration for running LangGraph AI examples with Claude
{
"note": "This repo is a learning framework, not a standalone MCP server. Use the .env file below.",
"env_file": {
"ANTHROPIC_API_KEY": "sk-ant-your-key-here",
"LANGCHAIN_API_KEY": "ls__your-key-here",
"LANGCHAIN_TRACING_V2": "true",
"LANGCHAIN_PROJECT": "langgraph-ai"
}
}Prompts to try
Concepts and scenarios to explore with LangGraph AI implementations
- "Run the adaptive RAG example and ask: What are the key concepts in transformer architecture?"
- "Modify the routing logic to add a fallback to web search when the vector store returns low confidence."
- "Trace a multi-step agent workflow in LangSmith and identify which nodes ran most frequently."
- "Implement a self-correcting RAG loop that re-retrieves if the generated answer fails a fact-check step."Troubleshooting LangGraph AI
ModuleNotFoundError when running uv run main.py
Ensure you ran 'uv pip install -r requirements.txt' inside the activated virtual environment. If the error persists, check that the .venv is active with 'which python'.
ANTHROPIC_API_KEY not found error at runtime
Create a .env file in the root of the repository with your key and ensure the script loads it with python-dotenv. Some example scripts may require dotenv to be installed separately: 'uv pip install python-dotenv'.
Jupyter kernel does not find langgraph or anthropic packages
Re-register the kernel after installing packages: run 'python -m ipykernel install --user --name=langgraph-ai --force' and then select the langgraph-ai kernel from the Jupyter notebook kernel picker.
Frequently Asked Questions about LangGraph AI
What is LangGraph AI?
LangGraph AI is a Model Context Protocol (MCP) server that langgraph ai repository It connects AI assistants to external tools and data sources through a standardized interface.
How do I install LangGraph AI?
Follow the installation instructions on the LangGraph AI GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with LangGraph AI?
LangGraph AI works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is LangGraph AI free to use?
Yes, LangGraph AI is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up LangGraph AI in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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