FrameworkAI and agent development
langchain
Combine interchangeable models, data sources, and tools in LLM applications.
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- Updated Jul 10, 2026
MIT
Combine interchangeable models, data sources, and tools in LLM applications.
LangChain provides common interfaces and modular components for building agents and other LLM-powered applications. Developers can connect models, embeddings, vector stores, retrievers, and third-party tools while swapping providers without rebuilding the whole application. The core framework works on its own; LangGraph handles lower-level orchestration, while LangSmith products support debugging, evaluation, and deployment workflows.
Resource types
Use cases
Capabilities
Trace, evaluate, monitor, and manage LLM, agent, and ML workflows.
Build and run Python agents with graph-based workflows
Build type-safe Python agents with tools, structured outputs, and provider choice
Audience
Public GitHub facts last synced Jul 10, 2026.
Optimizes AI agents with almost no code changes across frameworks and training methods.