What is langchain?

LangChain helps you build applications powered by large language models (the technology behind tools like ChatGPT). On its own, a language model can generate text and answer questions, but it's limited to what it learned during training. This library bridges that gap, letting you connect models to your own documents, databases, tools, and APIs so they can do real work, like answering questions about your company's internal wiki or looking up live data before responding. The project is built around the idea of "chains", sequences of steps where each step feeds into the next. A chain might start by pulling relevant text from your documents, then pass that text to a language model to generate an answer. Beyond simple chains, it supports agents: programs where the language model itself decides what to do next based on the situation. For example, an agent could decide to search a database, read the results, then use a calculator tool before composing a final answer. It also handles memory, so an application can remember context across multiple back-and-forth interactions. A founder building a customer support chatbot that draws from product docs would use this. A PM prototyping a tool that summarizes legal contracts could use it. A developer creating an internal assistant that searches a Notion workspace and answers team questions would reach for it. The common thread is anyone who needs a language model to interact with specific data or tools rather than just chatting in isolation. The project covers six areas: managing prompts, building chains, connecting external data sources, creating autonomous agents, maintaining conversation memory, and evaluating model outputs. It's written in Python (with a separate JavaScript version available) and is open source under an MIT license. The team is actively moving some experimental features into a separate package to keep the core library leaner and safer, which means some functionality may shift around over time.