This project provides an open-source ebook tutorial for LangGraph 1.0, designed to help Agent developers quickly master the framework's capabilities for workflow and state management. It targets developers seeking to build sophisticated agents by integrating LangGraph with LangChain, offering practical examples and explanations for core concepts. The primary benefit is accelerated learning and implementation of advanced agent architectures.

How It Works

The tutorial is structured into 11 chapters, systematically introducing LangGraph's features. It covers building agents with ReAct patterns, defining workflows using StateGraph, implementing custom middleware for features like PII detection and message truncation, and managing agent memory (short and long-term). The approach emphasizes LangGraph's role in orchestrating complex agent states and transitions, complementing LangChain's foundational components.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python environment. Dependencies include pydantic, langchain[openai], langgraph, langgraph-cli[inmem], langmem, fastmcp, and bs4.
• Resource Footprint: Setup involves installing Python packages; resource usage depends on the specific agent implementations explored.
• Links:
  • LangChain Docs: https://python.langchain.com/docs/get_started/introduction
  • LangGraph Docs: https://langchain-ai.github.io/langgraph/
  • Deep Agents: https://github.com/langchain-ai/deep-agents
  • LangMem: https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph/langgraph/mem

Highlighted Details

• Tutorial is based entirely on LangGraph v1.0, ensuring stability and avoiding legacy v0.6 code.
• Covers advanced agent patterns: Supervisor mode (tool-calling, langgraph-supervisor), concurrency (node concurrency, @task, Map-reduce), and Deep Agents.
• Includes practical examples for state management, custom middleware (budget control, PII detection), human-in-the-loop (HITL), and memory integration (e.g., RedisStore via tests/test_store.py).
• Features a langgraph-cli debugging page for development and visualization.

Maintenance & Community

The repository welcomes contributions via bug reports, feature suggestions, content improvements, and code optimizations. Specific details regarding core maintainers, active community channels (like Discord/Slack), or a public roadmap are not detailed in the README.

Licensing & Compatibility

• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
• Compatibility: The non-commercial clause significantly restricts use in commercial products or services. Derivative works must also be shared under the same license.

Limitations & Caveats

This repository serves as a tutorial and educational resource, not a production-ready framework. The CC BY-NC-SA 4.0 license imposes strict non-commercial usage limitations, potentially blocking adoption for business applications. The content focuses specifically on LangGraph 1.0, implying potential differences from earlier or future versions.