LangChain overview

LangChain overview is the high-level snapshot of an open-source framework that lets developers chain large language models (LLMs) with data sources, tools, and logic to build production-grade AI applications. Core modules include Loaders (ingest PDFs, SQL, web pages), Splitters & Embeddings (turn text into vectors), Vector Stores (Chroma, Milvus, Elasticsearch) for similarity search, LLM Wrappers (GPT-4, Claude, Gemini) with a unified API, Chains for sequential prompts, Agents & Tools for autonomous decision-making, Memory for chat context, and Callbacks for streaming, tracing, and cost tracking. These components share interchangeable interfaces, so teams can swap models, databases, or prompts by editing a single line of Python or TypeScript. Out of the box, LangChain supports Retrieval-Augmented Generation (RAG), multi-agent workflows, and function calling, while integrations with FastAPI, Streamlit, and AWS Lambda simplify deployment. Weekly releases, MIT licensing, and a vibrant GitHub community accelerate innovation, making LangChain the de-facto toolkit for data-grounded chatbots, copilots, and autonomous agents.