LangGraph AI Repository

A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns.

Overview

This repository serves as a implementation guide for building sophisticated AI applications using LangGraph. It contains practical examples, tutorials, and production-ready implementations across multiple domains:

• Agentic RAG Systems: Advanced retrieval-augmented generation with adaptive routing and self-correction mechanisms
• MCP Development: Complete Model Context Protocol server and client implementations
• Workflow Patterns: Orchestration patterns for complex AI workflows
• Human-in-the-Loop Systems: Interactive AI systems with human oversight
• Advanced RAG Agents: Sophisticated retrieval and generation systems

Repository Structure

langgraph-ai/
├── rag/
│   ├── rag-from-scratch/
│   │   └── 1_rag_overview.ipynb
│   ├── rag-agents/
│   │   ├── Building an Advanced RAG Agent.ipynb
│   │   └── rag-as-tool-in-langgraph-agents.ipynb
│   ├── agentic-rag/
│   │   ├── agentic-rag-systems/
│   │   │   └── building-adaptive-rag/
│   │   └── agentic-workflow-pattern/
│   │       ├── 1-prompting_chaining.ipynb
│   │       ├── 2-routing.ipynb
│   │       ├── 3-parallelization.ipynb
│   │       ├── 4-orchestrator-worker.ipynb
│   │       └── 5-Evaluator-optimizer.ipynb
├── mcp/
│   ├── 01-build-your-own-server-client/
│   ├── 02-build-mcp-client-with-multiple-server-support/
│   ├── 03-build-mcp-server-client-using-sse/
│   └── 04-build-streammable-http-mcp-client/
├── langgraph-cookbook/
│   ├── human-in-the-loop/
│   │   ├── 01-human-in-the-loop.ipynb
│   │   ├── 02-human-in-the-loop.ipynb
│   │   └── 03-human-in-the-loop.ipynb
│   └── tool-calling -vs-react.ipynb
├── .gitignore
├── .gitmodules
├── README.md
└── requirements.txt