This page gives you a quick, practical overview of LangChain—the popular
LLM framework for building apps that chain models, tools, and memory.
You’ll see where it fits in an AI stack and how to start using it right away.
In short, LangChain helps developers connect prompts, functions, and external systems into reliable workflows.
Moreover, it supports agents that choose tools dynamically, document loaders for ingestion, and retrieval
for context-aware responses. Because of that design, teams can move from prototype to production faster.
If you plan to ship a full RAG workflow, connect your chains to a vector store.
For example, see our guide on
Vector Stores for RAG with LangChain.
In addition, the official docs explain deployment and best practices in detail.
Scroll to the flashcards for a compact refresher. Then, use the links at the end to explore examples and templates.
To get started, build a small chain, add memory once you need context, and then integrate retrieval for accuracy.
Next, containerise your service and expose it with LangServe. Finally, monitor latency and token usage before scaling.
Explore our hands-on guide:
Vector Stores for RAG with LangChain.
For official tutorials and API references, visit the
LangChain documentation.