LangChain
Definition
LangChain is a framework for building LLM applications: chains, agents, tools, and RAG pipelines. It abstracts providers, prompts, and retrieval for quick prototyping and production.
It complements LlamaIndex (which emphasizes data and indexing); LangChain emphasizes composable chains and agent loops. Use it when you need RAG, agents with tools, or multi-step prompt workflows with minimal glue code.
How it works
You compose components: LLM (OpenAI, Anthropic, local, etc.), prompts, retrievers (vector stores, embeddings), and tools (APIs, search, code). Chains wire them in sequence (e.g. prompt → LLM → parser). Agents add a loop: LLM decides which tool to call, you execute it and append the result, repeat until the LLM returns a final answer. LangSmith provides tracing and evaluation. Integrations cover many vector databases, document loaders, and tool APIs. Start with a template (e.g. RAG, agent) and swap or add components as needed.
Use cases
LangChain is used to assemble LLM apps quickly: RAG, agents, and workflows with minimal boilerplate.
- Building RAG pipelines and document Q&A applications
- Implementing agents with tools (search, APIs, code)
- Rapid prototyping of LLM workflows with minimal boilerplate