LangChain
By LangChain, Inc.
LangChain is an open-source framework that provides standardized building blocks—prompts, chains, memory, retrievers, and agents—for developing applications powered by large language models (LLMs).
Definition
LangChain is an open-source framework that provides standardized building blocks—prompts, chains, memory, retrievers, and agents—for developing applications powered by large language models (LLMs).
Overview
LangChain emerged in late 2022 as developers building on top of models like GPT-3 kept re-implementing the same plumbing: prompt templates, output parsers, and ways to chain multiple calls together. Harrison Chase open-sourced the project to formalize those patterns, and it quickly became one of the most widely adopted frameworks for LLM application development, with core libraries available in Python and JavaScript/TypeScript. At its core, LangChain models an application as a sequence of composable steps: a prompt template renders user input into text, that text is sent to a model such as one from OpenAI or Anthropic, and the response is parsed into structured output. "Chains" link these steps together, "retrievers" pull relevant context from a vector store such as Weaviate to ground answers in external data, and "agents" let a model decide which tools to call and in what order to accomplish a task. The ecosystem has since split into complementary projects: LangGraph for building stateful, controllable agent workflows, and LangSmith for tracing, evaluating, and debugging LLM calls in production. Because it's model-agnostic, LangChain pairs well with open-weight models like Llama or Mistral AI's models as easily as with hosted APIs, and it's frequently taught alongside SkillVeris's AI Agents & Agentic Workflows course and covered conceptually in AI Agents Explained: How They Actually Work.
Key Features
- Composable "chains" that link prompts, model calls, and output parsers into multi-step pipelines
- Built-in retrievers and vector store integrations for retrieval-augmented generation (RAG)
- Agent framework that lets models choose which tools to call and in what sequence
- Memory modules for maintaining conversational context across turns
- Model-agnostic design supporting OpenAI, Anthropic, open-weight models, and self-hosted LLMs
- LangGraph extension for building stateful, graph-based multi-agent workflows
- LangSmith for tracing, debugging, and evaluating LLM application runs in production
- Large library of pre-built integrations for document loaders, APIs, and databases
Use Cases
Frequently Asked Questions
From the Blog
LangChain for Beginners: Build Your First LLM App
LangChain is a framework that connects language models to prompts, your data, and tools so you can build real LLM apps fast. Here is how to start today.
Read More AI & TechnologyWhat Are AI Agents Frameworks: LangChain vs LlamaIndex
AI agent frameworks orchestrate LLM tool use, memory, and control flow. LangChain excels at general agent workflows; LlamaIndex specializes in data-heavy retrieval.
Read More AI & TechnologyAI Agents Explained: How They Actually Work
AI agents are transforming what software can do autonomously — from booking travel to writing and running code. This guide explains the agent loop, tool use, memory systems, and how frameworks like LangChain, CrewAI, and OpenAI Assistants implement them.
Read More