LangChain — a framework for building LLM applications
langchain-ai/langchainLangChain is a widely used framework for building applications and agents on top of large language models. It provides standard interfaces and a large set of integrations so you do not reinvent common plumbing.
The default framework for many LLM projects, with the biggest ecosystem. Powerful and flexible — which also means abstractions to learn and a fast-moving API.
The problem it solves
Building with LLMs involves the same recurring pieces — prompting, model providers, retrieval, tools and agent loops. Rebuilding these for each project, across changing model APIs, is wasteful.
What is it?
LangChain gives you standard building blocks and interfaces for LLM apps — model wrappers, prompts, retrieval, tools and agent/graph orchestration (via LangGraph) — plus a very large library of integrations. You compose these instead of writing the plumbing yourself.
Why it's getting attention
With roughly 141k GitHub stars it is one of the most recognized names in AI development. Its popularity tracks the explosion of LLM apps and the value of a common framework with broad integrations.
Key features
- ✓Standard interfaces for models, prompts, retrieval and tools
- ✓Large ecosystem of integrations
- ✓Agent and graph orchestration (LangGraph)
- ✓Python and JavaScript implementations
- ✓MIT-licensed
Best use cases
- •Build RAG apps over your own data
- •Create tool-using agents and multi-step workflows
- •Swap model providers behind a common interface
- •Prototype LLM features quickly with ready integrations
How to install / try
LangChain is installed via pip (Python) or npm (JavaScript). See the repository for the current package layout and getting-started guide.
How to use
Compose components — a model, a prompt, retrieval and tools — into a chain or graph, then run it. For agents, define tools and let the orchestration drive multi-step reasoning. Refer to the docs for current APIs.
Strengths
- ✓Huge ecosystem and integration coverage
- ✓Standard interfaces reduce provider lock-in
- ✓Active development and large community
- ✓MIT-licensed
Limitations & risks
- △Abstractions add a learning curve versus calling a model API directly
- △Fast-moving APIs can change between versions
- △Flexibility can lead to over-engineering simple tasks
- △For very simple use cases the framework may be more than you need
Alternatives
Who should try it — and who should skip
Try it if you are building non-trivial LLM apps or agents and want a common framework with broad integrations. Skip it if your use case is a single, simple model call where a framework adds unnecessary weight.
Frequently asked questions
Yes. LangChain is open source under the MIT license.
No, but it provides ready-made building blocks and integrations that save you from writing the common plumbing yourself.
The core framework here is Python; a separate JavaScript/TypeScript version (LangChain.js) exists in its own repository.