LangChain is an open-source framework designed to simplify the development of applications powered by large language models (LLMs). Created by Harrison Chase in late 2022, it provides a modular, composable set of tools and abstractions for connecting language models to external data sources, APIs, and workflows. LangChain has quickly become one of the most popular frameworks in the AI application development ecosystem, with solid support for both Python and JavaScript/TypeScript.
At the heart of LangChain is the concept of "chains," sequences of operations that combine LLM calls with other processing steps. For example, a chain might retrieve relevant documents from a vector database, format them into a prompt, send the prompt to an LLM, and then parse the structured output. LangChain provides ready-made components for each of these steps, including document loaders, text splitters, embedding models, vector stores, prompt templates, output parsers, and memory modules that maintain conversation context.
LangChain supports several key patterns for building AI applications:
- Retrieval-Augmented Generation (RAG): Ground LLM responses in your own data by retrieving relevant documents before generating answers.
- Agents: Give LLMs the ability to use tools (search engines, calculators, APIs) and make decisions about which actions to take.
- Structured output: Parse LLM responses into typed data structures for reliable downstream processing.
- Multi-step workflows: Orchestrate complex sequences of LLM calls, conditional logic, and external integrations.
For teams building AI-powered software, LangChain cuts down the boilerplate required to integrate LLMs into production applications. Its companion tools, LangSmith for tracing and evaluation, and LangGraph for stateful multi-agent workflows, round out a comprehensive development platform for rapidly building and iterating on AI features like intelligent document processing and conversational assistants.