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LangChain

The broadest set of model and tool integrations in one library

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15 Aug 2026

Overview

Its real value is coverage: whatever model, vector store or API you need, an adapter probably already exists, which shortens the distance from idea to prototype. The abstractions have a reputation for indirection, so many teams use it for the integrations and reach for something more explicit once the control flow gets complicated.

What can you do with LangChain?

  • Swap model providers in place — init_chat_model takes a "provider:model" string and returns a chat model behind one interface, so call sites keep using .invoke() when the provider changes.
  • Start from prebuilt agent patterns — Deep Agents, a higher-level package built on LangChain, ships planning, subagents and file-system use as built-in capabilities instead of patterns you assemble yourself.
  • Move to explicit orchestration — When branching and state outgrow the chain-level API, LangGraph is the sibling low-level framework for writing controllable agent workflows directly.
  • Build the same design in TypeScript — LangChain.js is the equivalent JS/TS library, distributed as the langchain npm package alongside the Python one.

Documentation

Reproduced from the langchain-ai/langchain README, published under MIT. Read the original ↗

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