langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill langchain --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain - Build LLM Applications with Agents & RAG The most popular framework for building LLM-powered applications. ## When to use LangChain **Use LangChain when:** - Building agents with tool calling and reasoning (ReAct pattern) - Implementing RAG (retrieval-augmented generation) pipelines - Need to swap LLM providers easily (OpenAI, Anthropic, Google) - Creating chatbots with conversation memory - Rapid prototyping of LLM applications - Production deployments with LangSmith observability **Metrics**: - **119,000+ GitHub stars** - **272,000+ repositories** use LangChain - **500+ integrations** (models, vector stores, tools) - **3,800+ contributors** **Use alternatives instead**: - **LlamaIndex**: RAG-focused, better for document Q&A - **LangGraph**: Complex stateful workflows, more control - **Haystack**: Production search pipelines - **Semantic Kernel**: Microsoft ecosystem ## Quick start ### Installation ```bash # Core library (Python 3.10+) pip install -U langchain # With OpenAI pip install langchain-openai # With Anthropic pip install langchain-anthropic # Common extras pip install langchain-community # 500+ integrations pip install langchain-chroma # Vector store ``` ##
- When to use LangChain
- Quick start
- Installation
- Basic LLM usage
- Create an agent (ReAct pattern)
- Core concepts
- 1. Models - LLM abstraction
- 2. Chains - Sequential operations
- 3. Agents - Tool-using reasoning
- 4. Memory - Conversation history
- RAG (Retrieval-Augmented Generation)
- Basic RAG pipeline
- Conversational RAG with memory
- Advanced agent patterns
Core library (Python 3.10+) pip install -U langchain With OpenAI pip install langchain-openai With Anthropic pip install langchain-anthropic Common extras pip install langchain-community # 500+ integrations pip install langchain-chroma # Vector store
What does the langchain skill do?
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill langchain --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.