LangGraph
状態を持つエージェントのためのグラフ型ランタイム
概要
エージェントを「中身の見えないループ」ではなく、ノードとエッジの明示的なグラフとして表現します。永続チェックポイントにより実行を人間の承認待ちで中断し、後から再開できるため、本番システムに触れるワークフローで現実的な選択肢になります。反面、単純なツール呼び出しだけのエージェントには記述量が多く、他のフレームワークより初期セットアップが重くなります。
LangGraphで何ができますか？
- 障害後に途中から再開 — Durable executionが実行状態を永続化するため、長時間動くエージェントが停止しても最初からやり直さず、中断したステップから再開します。
- 人間の承認を挟んで中断 — Interruptsで任意のノードで実行を止め、人がエージェントの状態を確認・修正してから続行させられます。
- 実行の中身はLangSmithで見る — READMEがデバッグ手段として挙げるのはLangChainの別プロダクトであるLangSmithで、実行経路の可視化、状態遷移の記録、実行時メトリクスの取得を担います。どのノードで結果が崩れたかはこの画面から辿ります。
- Deep Agentsで上位から始める — LangGraph上に構築されたDeep Agentsを使うと、計画立案・サブエージェント・ファイルシステムを前提としたエージェントを、グラフを自分で組まずに作れます。
- TypeScriptでも同じ構成 — JS/TS向けにはLangGraph.jsが提供されており、NodeのサービスからPythonプロセスを別途立てる必要がありません。
ドキュメント
langchain-ai/langgraph のREADMEより転載（MIT）。 原文を読む ↗
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph[!TIP] If you’re looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.