LangChain

What It Does

LangChain provides abstractions and tooling for building applications powered by large language models. It connects LLMs to external data sources, enables multi-step reasoning through chains, and supports autonomous agent behavior with tool use.

Key Features

LangChain offers document loaders for 100+ sources, text splitters, embedding integrations, vector store connections, retrieval chains (RAG), conversational memory, agent frameworks with tool use, LangSmith for observability, and LangGraph for complex agent workflows.

When To Use It

Use LangChain when building RAG applications, chatbots with memory, autonomous agents, or any application requiring LLM orchestration with external data. It excels when you need to chain multiple LLM calls with tools and retrieval.

Alternatives

LlamaIndex specializes in data indexing and retrieval. CrewAI focuses on multi-agent collaboration. Semantic Kernel targets enterprise .NET integration. For simple LLM calls, direct API usage may be simpler without LangChain’s abstractions.