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Description

The agent engineering platform

README

The agent engineering platform.

PyPI - License PyPI - Downloads Version Twitter / XLangChain 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.

Quickstart

pip install langchain
# or
uv add langchain
from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.4")
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.

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

Documentation

Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.

Additional resources

  • Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
  • Code of Conduct – Our community guidelines and standards for participation.
  • LangChain Academy – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.

Release History

VersionChangesUrgencyDate
langchain-core==1.3.0Changes since langchain-core==1.2.31 release(core): release 1.3.0 (#36851) release(core): 1.3.0a3 (#36829) chore(core): keep checkpoint_ns behavior in streaming metadata for backwards compat (#36828) feat(core): Add chat model and LLM invocation params to traceable metadata (#36771) fix(core): restore cloud metadata IPs and link-local range in SSRF policy (#36816) chore(deps): bump pytest to `9.0.3` (#36801) chore(core): harden private SSRF utilities (#36768) fix(openai): handle content blocks High4/17/2026
langchain-core==1.2.30Changes since langchain-core==1.2.29 release(core): 1.2.30 (#36770) chore(core): harden private SSRF utilities (port 36768) (#36769)High4/15/2026
langchain-core==1.2.29Changes since langchain-core==1.2.28 release(core): 1.2.29 and also port #36725 (#36727)High4/14/2026
langchain-core==1.3.0a2Initial release release(core): 1.3.0.a2 (#36698) fix(core): Use reference counting for storing inherited run trees to support garbage collection (#36660) docs(core): nit (#36685) release(core): 1.3.0a1 (#36656) chore(core): reduce streaming metadata / perf (#36588) release(core): release 1.2.28 (#36614) fix(core): add more sanitization to templates (#36612) release(core): 1.2.27 (#36586) fix(core): handle symlinks in deprecated prompt save path (#36585) chore: add comment explaining `pygments>=High4/13/2026
langchain-core==1.3.0a1Initial release release(core): 1.3.0a1 (#36656) chore(core): reduce streaming metadata / perf (#36588) release(core): release 1.2.28 (#36614) fix(core): add more sanitization to templates (#36612) release(core): 1.2.27 (#36586) fix(core): handle symlinks in deprecated prompt save path (#36585) chore: add comment explaining `pygments>=2.20.0` (#36570) release(core): 1.2.26 (#36511) fix(core): add init validator and serialization mappings for Bedrock models (#34510) feat(core): add `ChatBaseten` High4/10/2026
langchain-core==0.3.84Changes since langchain-core==0.3.83 release(core): 0.3.84 (#36616) fix(core): sanitize prompts more (#36613)High4/8/2026

Dependencies & License Audit

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