LangChain

Framework for building applications powered by language models.

What it does

LangChain provides a common set of abstractions — prompts, chains, memory, tool-calling, retrieval — for composing LLM calls into larger applications, so you're not gluing together bespoke plumbing for every new model provider or vector store.

Why people use it

• Broad integration surface: dozens of LLM providers, vector stores, and tools behind one API
• Building blocks (chains, agents, retrieval) cover most common LLM-app patterns out of the box
• Large ecosystem and community, so examples exist for almost any use case

Pros

• Fast to prototype an LLM-backed feature
• Actively maintained, frequent releases
• Works with nearly any model provider, not locked to one vendor

Cons

• Abstraction layers can make it harder to see (or control) exactly what prompt gets sent
• API has changed significantly across major versions, so older tutorials can mislead
• For a single, well-understood use case, calling a provider's SDK directly is often simpler

Related Grove

AI