langchain
The toolkit for wiring language models into real applications.
What it is
LangChain is a framework for building software that uses language models as one component rather than just chatting with one. Instead of calling an API and parsing the reply by hand, you compose models, prompts, retrievers, tools and memory into a pipeline and let the framework handle the plumbing between them.
It has grown into a family of packages: a core library of interfaces, a set of integrations with model providers and data stores, and higher-level layers for agents and orchestration. Much of the value is that the same abstractions work across providers, so swapping a model or a vector database does not mean rewriting your application.
It suits developers building something that chains several steps or several data sources together. It is not the lightest way to make a single API call — for that, a plain client library is simpler — and it comes with a genuine learning curve before the abstractions pay off.
Why we recommend it
One vocabulary across providers
The same concepts — models, prompts, retrievers, tools — apply whether you are on one provider or another. That keeps your application portable and cuts the cost of switching later.
It handles the glue
Chunking documents, embedding them, retrieving the relevant pieces, feeding them into a prompt: the framework provides those pieces. Without it you write that pipeline yourself, every time.
Agents and tool use
LangChain includes the building blocks for models that call functions and take multi-step actions. If your application needs to do things rather than only answer questions, this is the layer that makes it tractable.
A large ecosystem to draw on
The integration catalogue is unusually broad, so the model or database you already use probably has a supported connector, saving you from writing an adapter.
How it scores
These are our editorial ratings, not benchmark results. We score on how the project behaves in practice — how long it takes to get running, how good the documentation is, how responsive the maintainers are, and how well it holds up over time. The licence and star count above are read from GitHub and are a snapshot.
Pros and cons
Strengths
- Broad integrations across model providers, vector stores and tools
- Consistent abstractions that make swapping components easier
- Strong support for agents, retrieval and tool calling
- Very large community, with examples and third-party material everywhere
- Actively maintained with a published release cadence
Trade-offs
- Steep learning curve — the abstractions take time to understand
- Frequent changes between versions can break existing code
- Overkill for simple single-call use cases, where it adds ceremony
- Debugging a chain of abstractions is harder than reading plain code
How to get it
One-line install
Prefer the terminal? Copy a command and you are done.
pip install langchain
Source and documentation
At a glance
| Category | AI Frameworks |
|---|---|
| Licence | MIT |
| Written in | Python |
| GitHub stars | 146,055 |
| Repository | langchain-ai/langchain |
Licence and star count were read from the GitHub API when this page was generated and are a snapshot. Check the repository for the current figure and the authoritative licence text.
Other AI Frameworks tools
We are not affiliated with langchain or its maintainers. We list it because it is genuinely useful and genuinely free. If you plan to build a commercial product on top of it, read the licence on the repository first.