Overview
LangChain is a framework for building LLM-powered applications. It provides composable components for models, embeddings, vector stores, retrievers, and tools, enabling fast development of RAG, agent orchestration, and other LLM applications.
Key Features
- Extensive integrations: adapters for many model providers, vector stores, and retrievers.
- Composable architecture: abstract interfaces for models, chains, and agents to make swapping components easy.
- Ecosystem tools: complementary products like LangSmith and LangGraph for evaluation, orchestration, and observability.
Use Cases
- Build retrieval-augmented generation (RAG) systems for knowledge-driven Q&A.
- Integrate LLMs with external systems for data augmentation and automation.
- Develop controllable agents that perform multi-step reasoning and orchestration.
Technical Characteristics
- Language focus: primarily Python, with a parallel JS/TS ecosystem (LangChain.js).
- Extensible deployment: plugin-based integrations and compatibility with multiple vector databases and model providers.
- Large community: extensive examples, tutorials, and enterprise integrations; very active development and maintenance (100k+ GitHub stars).