LangGraph Learning Repository

Welcome to the LangGraph Learning Repository! This comprehensive collection contains hands-on examples, tutorials, and practical implementations of LangGraph - a powerful library for building stateful, multi-actor applications with LLMs.

🚀 What is LangGraph?

LangGraph is a library for building stateful, multi-actor applications with Large Language Models (LLMs). It extends LangChain with the ability to coordinate multiple actors in a structured graph format, enabling complex workflows, agent interactions, and stateful conversations.

📁 Repository Structure

Folder	Description	Key Features
LangGraph Basics	Foundation concepts and basic implementations	Core concepts, state management, simple graphs
LangGraph Debugging	Debugging tools and techniques	LangSmith integration, debugging workflows
LangGraph Multi Agents	Multi-agent systems and coordination	Agent communication, supervised workflows
LangGraph RAG Agents	Retrieval-Augmented Generation implementations	Document retrieval, knowledge-based agents
LangGraph SQL Agent	Database interaction and SQL query agents	Database connectivity, SQL generation
LangGraph Tools	Custom tools and integrations	Tool creation, external API integration
Curie Radiation Documents	Sample documents for RAG examples	Historical documents, text corpus

🛠️ Quick Start

Prerequisites

• Python 3.11+
• Virtual environment (recommended)
• API keys for LLM services (OpenAI, Groq, etc.)

Setup

1. Clone or navigate to this repository
2. Activate your virtual environment (langgraph-handson folder contains the virtual environment)

   # Windows
   .\langgraph-handson\Scripts\Activate.ps1
   
   # Or activate.bat for Command Prompt
   .\langgraph-handson\Scripts\activate.bat

3. Set up environment variables
   • Copy .env.example to .env (if available)
   • Add your API keys:

     OPENAI_API_KEY=your_openai_key
     GROQ_API_KEY=your_groq_key
     LANGSMITH_API_KEY=your_langsmith_key
     

Learning Path

🌱 Beginner (Start Here)

1. LangGraph Basics - Learn core concepts
2. LangGraph Tools - Understand tool integration

🌿 Intermediate

3. LangGraph Multi Agents - Multi-agent coordination
4. LangGraph RAG Agents - Document-based reasoning

🌳 Advanced

5. LangGraph SQL Agent - Database interactions
6. LangGraph Debugging - Production debugging

🎯 Key Learning Outcomes

By working through this repository, you'll learn:

• State Management: How to maintain conversation and application state
• Graph Construction: Building complex workflows as directed graphs
• Agent Coordination: Managing multiple AI agents working together
• Tool Integration: Creating and using custom tools with agents
• RAG Implementation: Building retrieval-augmented generation systems
• Database Integration: Creating SQL-capable AI agents
• Debugging & Monitoring: Using LangSmith for debugging and observability

🔄 Common Workflows

Basic Agent Workflow

1. Define state structure
2. Create nodes (functions)
3. Build the graph
4. Add edges and conditions
5. Compile and run

Multi-Agent Workflow

1. Define shared state
2. Create specialized agents
3. Set up communication patterns
4. Implement supervision logic
5. Handle conflicts and coordination

RAG Workflow

1. Prepare document corpus
2. Set up vector store
3. Create retrieval tools
4. Build reasoning agent
5. Implement answer synthesis

📚 Additional Resources

• LangGraph Documentation
• LangChain Documentation
• LangSmith for Debugging

Happy learning! 🎉