LangChain
FreeThe open-source framework for building agents and LLM-powered applications.
LangChain is an MIT-licensed open-source framework that provides pre-built agent architectures and 1000+ integrations for any model or tool, enabling developers to build and deploy LLM-powered applications and agents without vendor lock-in. It runs on LangGraph's durable runtime with built-in persistence, checkpointing, and human-in-the-loop support.
What is it
LangChain is an open-source framework designed to help developers build applications powered by large language models. It provides a modular set of composable components and pre-built agent architectures, with 1000+ integrations for models, tools, and databases.
What it can do
It offers components for managing prompts, chaining multiple model calls into reusable workflows, and creating autonomous agents using proven patterns like ReAct. LangChain simplifies building RAG-based systems, multi-step automation, and production-grade AI applications that can swap models without rewriting code.
Who is it for
It is a mission-critical tool for AI software engineers, data scientists, and developers who want to build production-grade, context-aware AI applications that move beyond simple chat interfaces into complex, multi-step automated systems.
Key Features
Pre-built Agent Architecture
Build agents in minutes with templates for common use cases. `create_agent` provides a proven ReAct pattern on LangGraph's durable runtime, giving agents built-in persistence, rewind, checkpointing, and human-in-the-loop support out of the box.
1000+ Model & Tool Integrations
Swap models, tools, and databases without rewriting your application. With 1000+ integrations covering OpenAI, Anthropic, Google Gemini, LLaMA, and more, you can future-proof your stack as AI advances with no vendor lock-in.
Composable Chain Workflows
Chain together interoperable components and third-party integrations to simplify AI application development. Extend agent behavior through middleware without rewriting core logic — add human-in-the-loop approval, compress long conversations, or remove sensitive data with simple, composable hooks.
RAG & Data Connectors
Connect LLMs directly to proprietary data sources including PDFs, databases, and APIs using document loaders, text splitters, embeddings, and vector stores. Build retrieval agents that decide whether to retrieve context or respond directly, ensuring AI outputs are grounded in verified information.
Use Cases
Building production AI agents
Software engineers use LangChain's pre-built architectures and templates to rapidly develop deployable AI agents with tool calling, structured outputs, and complex reasoning capabilities, iterating from prototype to production without switching frameworks.
RAG-based knowledge systems
Enterprise developers construct retrieval-augmented generation systems that connect internal documents, databases, and APIs to LLMs. LangChain's data connectors ensure AI outputs are grounded in verified proprietary information, critical for maintaining accuracy in business contexts.
Multi-step workflow automation
Teams use LangGraph to orchestrate deterministic logic alongside agentic behavior, creating workflows with sequential steps, conditional branches, loops, and parallel execution. Each node can be a simple function, an LLM call, or an entire agent with tools.
LLM application prototyping
Data scientists and AI engineers prototype LLM applications using LangChain's modular components at the right abstraction level — from high-level chains for quick starts to low-level components for fine-grained control — then scale confidently to production.
Pricing plans
Frequently Asked Questions
Discussion
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