langchain

# 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 ``` ### Basic LLM usage ```python from langchain_anthropic import ChatAnthropic # Initialize model llm = ChatAnthropic(model="claude-sonnet-4-5-20250929") # Simple completion response = llm.invoke("Explain quantum computing in 2 sentences") print(response.content) ``` ### Create an ag...