langchain: Open Source AI Development Platforms

What is langchain?

What it is

LangChain is an open-source framework for building agents and LLM-powered applications, written in Python and released under the MIT license. It lives in the AI and machine learning ecosystem, specifically among AI development platforms, and it works by chaining together interoperable components and third-party integrations so that AI application development becomes simpler. The project describes itself as the agent engineering platform, and its abstractions are designed to keep applications working as the underlying technology evolves.

The concrete problem it solves is fragmentation. Model providers, embedding models, vector stores, tools and retrievers each arrive with their own interfaces, and swapping one for another normally forces a rewrite. LangChain provides a standard interface across those categories, so a development team can experiment with different models and data sources without rebuilding the application each time. It also offers real-time data augmentation, connecting LLMs to external and internal systems through its integration library, which addresses the gap between a model's training data and the live information an application needs. The repository has accumulated 146,351 stars and 24,453 forks over roughly four years, with 492 open issues at the time of writing.

Key capabilities

Standard interface for models, embeddings, vector stores and retrievers, allowing models to be swapped as teams experiment.
Real-time data augmentation that connects LLMs to diverse external and internal data sources through a library of integrations with model providers, tools, vector stores and retrievers.
Modular, component-based architecture for rapid prototyping and iteration without rebuilding from scratch.
Flexible abstraction layers, ranging from high-level chains for quick starts to low-level components for fine-grained control.
Agent building for LLM-powered applications, with agent orchestration available through the companion LangGraph framework.
Production-oriented features including monitoring, evaluation and debugging through LangSmith integrations.
First-class interoperability with the wider ecosystem, including Deep Agents, LangGraph, Integrations, LangSmith and LangSmith Deployment, plus an equivalent JS/TS library, LangChain.js.

Who uses it and how

Developers prototyping LLM applications who test different approaches and workflows with the component-based architecture.
Engineering teams comparing model providers, who swap models in and out as the industry frontier moves.
Teams connecting LLMs to internal and external systems for real-time data augmentation.
Builders of controllable agent workflows, who pair LangChain with LangGraph for low-level

project readme (upstream, from github) — read inline

The agent engineering platform.

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 capabilities 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

Documentation — conceptual overviews and guides
LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
API reference — complete reference for all public classes, functions, and types
Discussions — community forum for technical questions, ideas, and feedback
LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
Contributing Guide — how to contribute and find good first issues
Code of Conduct — community guidelines and standards

Frequently asked questions

Is langchain free to use?

langchain is open source under the MIT licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does langchain do?

The agent engineering platform.

What is langchain written in?

langchain is primarily written in Python. Its source is publicly available at https://github.com/langchain-ai/langchain, and it has 146,538 GitHub stars.