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LangChain

モデルとツールの連携アダプタを最も広くカバーするライブラリ

公式MIT
スター
144k
フォーク
24k
オープンIssue
410
最終コミット
2026年8月15日

概要

本当の価値は網羅性にあります。必要なモデル、ベクトルストア、APIのアダプタはたいてい既に存在し、着想からプロトタイプまでの距離が短くなります。抽象化の階層が深いという評価は根強く、多くのチームは連携部分にこれを使い、制御フローが複雑になった段階でより明示的な実装に移行しています。

LangChainで何ができますか？

  • モデル差し替えの一元化 — init_chat_modelに「provider:model」形式の文字列を渡すだけで統一インターフェースのチャットモデルが得られ、プロバイダを変更しても呼び出し側は.invoke()のまま維持できます。
  • 上位パッケージからの着手 — LangChain上に構築されたDeep Agentsでは、計画立案、サブエージェント、ファイルシステム利用といった頻出パターンが組み込み済みの機能として提供されます。
  • 明示的な制御への移行 — 分岐や状態管理がchainレベルのAPIに収まらなくなった段階では、同系列の低レベルフレームワークであるLangGraphでワークフローを直接記述できます。
  • TypeScriptでの同等実装 — JS/TS向けには同等のライブラリとしてLangChain.jsがあり、npmのlangchainパッケージとして配布されています。Python版と同じ構成をTypeScriptでもそのまま書けます。

ドキュメント

langchain-ai/langchain のREADMEより転載（MIT）。 原文を読む ↗

LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.

[!TIP] Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.

Quickstart

uv add langchain
from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")

If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.

For an equivalent JS/TS library, check out LangChain.js.

[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

LangChain ecosystem

While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.

  • Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
  • LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
  • Integrations — Chat & embedding models, tools & toolkits, and more
  • LangSmith — Agent evals, observability, and debugging for LLM apps
  • LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows

Why use LangChain?

LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.

  • Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more
  • Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum
  • Rapid prototyping — Quickly build and iterate on LLM applications with LangChain’s modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
  • Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
  • Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
  • Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application’s complexity

Resources