
Build, test, and deploy reliable AI agents
LangChain is an open-source framework and engineering platform for building AI-powered applications. It provides standardized, model-agnostic interfaces for connecting LLMs with data sources and APIs, enabling developers to create sophisticated agents, RAG pipelines, and multi-step workflows with composable components.
Open-source Python and JavaScript libraries with composable components for building LLM applications
Framework for building stateful, multi-agent AI applications with full control over agent behavior and orchestration
Observability and evaluation platform for tracing, debugging, testing, and monitoring LLM applications in production
Built-in support for Retrieval-Augmented Generation with document loaders, vector stores, and retrieval chains
Standardized interface supporting OpenAI, Anthropic, Google, and dozens of other LLM providers
Advanced conversation memory and state management for building context-aware AI agents
Connect LLMs to external tools, APIs, databases, and custom functions through a unified interface
Build sophisticated chatbots and virtual assistants with memory, tool use, and multi-step reasoning capabilities
Create retrieval-augmented generation systems that ground LLM responses in your organization's proprietary data
Develop AI copilots for internal workflows like code review, document analysis, and customer support automation
Orchestrate multiple AI agents working together on complex tasks using LangGraph's stateful workflow engine
Best overall RAG framework — the most comprehensive toolkit for teams building complex, production-grade knowledge base systems with custom retrieval pipelines
The reference framework — broadest ecosystem and fastest prototyping, but its abstraction complexity drives teams toward specialized alternatives for production use
AI-assisted evaluations, regression testing, and dataset management for quality assurance
Deploy agents with LangSmith's managed deployment service for production-ready applications
Automate document summarization, extraction, and analysis across large volumes of unstructured data

The open-source AI-native vector database for search and retrieval