LangChain is a popular open-source framework for building applications powered by language models. It provides ready-made building blocks and abstractions for the common pieces of an LLM application — prompts, model calls, memory, tools, agents, and crucially, retrieval — so developers can assemble complex pipelines without wiring everything from scratch.
For vector search, LangChain offers a standard interface to many vector databases, so you can swap between them with minimal code changes, along with utilities for loading documents, chunking them, generating embeddings, and constructing retrieval-augmented generation pipelines. This makes it a common starting point for building RAG systems and agents that use a vector database as their knowledge or memory layer.
The benefit is speed of development and a large ecosystem of integrations and examples; the trade-off is an added layer of abstraction that can obscure what is happening underneath and may be more than a simple application needs. LangChain is one of several such frameworks — LlamaIndex is another — and teams often use it for prototyping and standardising how their application talks to models and vector stores.