langchain
The agent engineering platform.
What is it?
What it is
LangChain is a framework for building applications powered by large language models. It provides chains, agents, retrievers, and integrations with vector stores, tools, and model providers.
Why it exists
LLM apps need composable primitives for prompts, memory, retrieval, and tool use. LangChain standardizes these patterns across Python and JavaScript.
Who should use it
Developers building RAG pipelines, chatbots, agents, or any LLM-powered application in Python or TypeScript.
Who should avoid it
Teams wanting a single deployable product out of the box. LangChain is a library, not an end-user app.
How it works
A quick walkthrough in plain English
How a LangChain app answers questions
Step 1 of 4
Someone asks a question
A user types in your chatbot or app sends a query.
Features
Advantages
- Largest LLM app ecosystem
- Integrations with OpenAI, Anthropic, Ollama, and more
- Active development and community
- LangGraph for stateful agent workflows
Disadvantages
- API surface changes frequently
- Can be over-abstracted for simple use cases
- Steep learning curve for agents and LCEL
Installation
docker
# Use within your app Dockerfile FROM python:3.12-slim RUN pip install langchain langchain-community
native
pip install langchain langchain-openai # or npm install langchain @langchain/openai
FAQ
LangChain vs LangGraph?
LangChain is for composing LLM components. LangGraph adds cyclic stateful graphs for complex agent workflows.
Does LangChain work with local models?
Yes. Integrations include Ollama, llama.cpp, vLLM, and Hugging Face.
Is LangChain production-ready?
Yes, with LangSmith for tracing and evaluation. Many companies run LangChain in production.
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