LangChain

What is LangChain?

Build an app directly on a model API and you'll hit three walls fast: context doesn't fit, the model "forgets" after each turn, and hooking up a database or search is painful. LangChain is the answer — an open-source framework for LLM apps that packages chaining, memory, tools and agents into building blocks you snap together.

Which pain points does it solve?

Chains
It strings together "fetch data → build prompt → call model → post-process" into one pipeline, so you don't write glue code yourself.
Memory
It lets the model remember earlier turns in a conversation instead of starting fresh every time.
Tools & retrieval
It plugs into search engines, databases and APIs, and feeds outside knowledge to the model via RAG.
Agents
It lets the model decide which tool to call next, upgrading it from "answering questions" to "finishing tasks".

Why is it so popular?

It dramatically lowers the barrier to building with LLMs: a beginner can assemble a chatbot with memory, retrieval and tool use in a few lines of code. The ecosystem is rich, too — models, vector stores and tools are mostly plug-and-play.

Who's it for — and what to watch

For prototypes, MVPs and proof-of-concepts, LangChain is the default. But its abstraction layers can get in the way when you tune for production, and some argue it's overkill for simple cases. For finer-grained control, teams often reach for its sibling framework, LangGraph.

Bottom line: LangChain turns LLM apps into Lego — chains, memory, tools and agents you snap together to go from idea to product fast.