LangChain is a framework for building LLM-powered applications with agents, tools, and retrieval (rag) workflows.

Build LLM apps faster with agents, tool calling, retrieval, and workflow orchestration.

LangChain is built for developers who want more than a chatbox. It enables structured AI workflows where an LLM can call tools, query knowledge sources, follow multi-step plans, and generate grounded responses. Whether you’re building an internal knowledge assistant, customer support automation, data-aware copilots, or agent-driven task execution, LangChain provides the components and patterns to connect models to your stack and keep outputs consistent and controllable.

Core Features & Capabilities

Ideal for developers and AI teams building production LLM applications such as knowledge assistants, customer support copilots, workflow automation agents, and RAG systems that need reliable connections to real data and tools.

• build agents that call tools and APIs safely
• rag pipelines that ground answers in your documents and data
• workflow orchestration for multi-step reasoning and actions
• integrations with llms, vector stores, databases, and apps
• logging and evaluation-ready patterns for production use

Why Builders Choose LangChain

Start with a simple chain for your use case (summarise, classify, extract), then add retrieval by connecting your documents or database. Next, introduce tools (APIs, search, actions) and build an agent for multi-step tasks. Add logging and evaluation early, and iterate with real user queries to tune prompts, retrieval settings, and tool behaviors.

“LangChain gives us the building blocks to go from LLM prototype to real agent workflows connected to data and tools.”

Getting Started with LangChain

By combining agent orchestration, retrieval, and integrations into a repeatable framework, LangChain helps teams build AI applications that are more actionable, data-aware, and production-ready.