LangChain
A Python framework for building AI agents and apps powered by language models.
- Stars
- 149k
- Forks
- 25k
- Licence
- MIT
- Language
- Python
- Last update
- 4 days ago
- Type
- Framework
What it is
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
- Call a chat model from a single line of Python using init_chat_model
- Connect a model to external tools, data sources, or vector stores through its integrations
- Build agents that use tools and components from the LangChain ecosystem
- Add monitoring and debugging to an LLM app through LangSmith integration
Great for
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Not ideal for
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
How to get it
- 1
Install with a package manager
You'll need: Python · uv (package manager)
Add LangChain to your project
uv add langchain - 2
Run it
- Install the package with uv add langchain
- Set up credentials for your chosen model provider
- Run the quickstart: from langchain.chat_models import init_chat_model, then model = init_chat_model("openai:gpt-5.5")
- Call model.invoke("Hello, world!") to get a result
- 3
Get the most out of it
- Use LangGraph for controllable, lower-level agent workflows, as the README suggests
- Try Deep Agents for agents with built-in planning, subagents, and file system use
- Connect LangSmith for agent evals, observability, and debugging
- Explore the integrations catalog to add model providers, tools, and vector stores
- Work through the free LangChain Academy courses
Downloads
Strengths
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Trade-offs
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| langchain (this) | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| llama_index | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| semantic-kernel | 28.7k | Library | intermediate | 4 days ago | Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents |
| crewAI | 60.4k | Library | intermediate | 4 days ago | Python developers prototyping multi-agent workflows |
| langgraph | 44.3k | Library | intermediate | 4 days ago | Python developers building multi-step AI agents that must survive failures or long waits |
| Prompt-Engineering-Guide | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.