LangChain
Python and JavaScript framework for building LLM applications — chains, agents, memory, retrieval, and tool use.
Definition
LangChain is the most widely adopted framework for assembling LLM pipelines. Its core abstractions are LLMs, prompts, chains (composed sequences), agents (tool-using loops), memory (conversation state), and retrievers (over vector stores). LangChain's MultiServerMCPClient adds MCP support. It is often paired with LangGraph for explicit stateful orchestration and LangSmith for tracing.
When to use
See also
- LangGraph — LangChain's stateful-graph framework for building agents as explicit nodes, edges, and conditional branches.
- agent — An LLM-driven program that plans, calls tools, observes results, and loops until it reaches a goal.
- MCP — Anthropic's open standard for connecting LLMs to external tools and data sources over JSON-RPC.
Mentioned in
- Best AI Agent Framework for Node.js Developers in 2026
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- Fix LangChain Output Parsing Errors (handle_parsing_errors=True)
- Fix LangChain's 'authorizedCollections is not supported' on Firestore
- How to Deploy an AI Agent to Production with Express and Docker
- How to Fix LangChain AgentExecutor Not Calling Tools
- How to Handle Errors in Custom LangChain Tools (with Middleware)
- LangChain vs LangGraph: when to use each for AI agents
- LlamaIndex vs LangChain: which framework for RAG and agents?
- n8n AI Agent Nodes Break After Google's Gemini Interactions API Migration: How to Fix It