What LangChain is
LangChain is an open-source framework for building applications powered by large language models. On its own, a model API gives you a single function: send a prompt, get text back. Real applications need much more around that call — formatted prompts, conversation memory, the ability to look things up in your own documents, and the ability to call external tools. LangChain packages those concerns into reusable components so you do not rebuild them for every project.
It exists in both Python and JavaScript, and it became popular precisely because it arrived when developers were all writing the same glue code by hand. LangChain gave that glue a shared vocabulary.
The chain abstraction
The framework’s name comes from its central idea: the chain. A chain is a sequence of steps where each step’s output flows into the next. A minimal chain might:
- Fill a prompt template with the user’s input.
- Send it to a model.
- Pass the response through an output parser to get structured data.
Because each piece is a standardised component, you can swap the model, change the prompt, or add a step without rewriting everything. More advanced chains can branch, loop, or run steps in parallel, which is what makes complex applications manageable.
Agents, tools, and memory
Two LangChain features unlock the most powerful patterns:
- Agents and tools. An agent is an LLM that decides what to do instead of following a fixed script. You give it tools — a web search, a calculator, a database lookup, an API call — and it chooses which to use, reads the result, and continues until it can answer. This is the foundation of agentic applications.
- Memory. By default a model has no recollection between calls. LangChain’s memory modules store conversation history (or summaries of it) and inject the relevant parts back into each new prompt, so a chatbot can hold a coherent multi-turn conversation.
RAG and when to choose LangChain
LangChain is also a common way to build retrieval-augmented generation (RAG): it connects to vector databases, retrieves relevant chunks of your documents, and feeds them into the prompt so the model can answer from your data rather than guessing.
That said, LangChain is not always the right tool. If your application simply sends one prompt and returns one answer, the framework adds layers of abstraction that can make code harder to read and debug — a plain API call is clearer. LangChain pays off when you have multiple steps, retrieval, tools, or memory to coordinate. For simple or latency-critical paths, a small hand-written pipeline is often the better engineering decision. The honest rule of thumb: reach for LangChain when the orchestration is the hard part, and skip it when it is not.