LangChain is an AI development framework designed for builders who want to turn large language models into real products. It gives you the components needed to connect models to data, tools, memory systems and structured workflows so you can move beyond simple chat and create full applications that reason, retrieve knowledge and take action.
At the core of LangChain is a modular stack. It includes chains for sequencing model calls, agents for dynamic decision making, document loaders for pulling data from files or APIs, retrievers and vector stores for semantic search and tool interfaces for actions like web requests, database queries or code execution. This structure makes it possible to combine models with real data and logic in a controlled, testable way.
LangChain works with every major model provider, from OpenAI and Anthropic to Mistral, Google and open-weight models. Developers can choose the model that fits their use case, switch providers at any time and integrate custom tools or data sources as needed. Its companion platform LangSmith adds observability, evaluation and dataset management, helping teams debug prompts, test agent behaviour and refine performance before deployment.
Companies use LangChain to build internal copilots, search systems, customer support agents, document analyzers, research assistants and agentic applications that require planning and reliable tool execution. It provides the foundation for apps that must handle retrieval, multi-step reasoning and integration with enterprise systems.
LangChain is used by AI startups, enterprise engineering teams and independent developers who need a flexible and production-ready foundation for LLM applications. It gives you the structure and control to build high-quality AI workflows that scale from prototype to full deployment.
