What is LangChain used for

What is LangChain used for is the core inquiry answered by mapping its main roles in modern AI stacks. Teams apply the open-source LangChain framework to build chatbots, assemble Retrieval-Augmented Generation (RAG) pipelines, orchestrate autonomous agents, and connect large language models (LLMs) with private data. LangChain handles prompt templates, memory, tool calling, and vector-store retrieval, so developers focus on product logic instead of glue code. In customer support it powers context-aware assistants that cite knowledge-base articles; in software engineering it enables code copilots that pull docs, run linters, and open pull requests; in analytics it fuels voice or text dashboards that query SQL, summarize results, and draft reports. Because every component—LLM, database, API—is pluggable, LangChain scales from a laptop demo to a distributed microservice. Its granular callbacks, streaming tokens, and guardrails cut latency, boost observability, and enforce compliance, making it the go-to toolkit for shipping reliable, data-grounded AI applications fast.