LangChain vs LangGraph: Agent Frameworks Comparison
Compare LangChain and LangGraph side by side. Both are tools in the Agent Frameworks category.
Quick Comparison
| Category | Agent Frameworks | Agent Frameworks |
| Pricing | Open Source | — |
| Best For | Developers building complex LLM applications who need a comprehensive orchestration framework | — |
| Website | langchain.com | langchain.com |
| Key Features |
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| Use Cases |
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When to Choose LangChain vs LangGraph
- Building RAG applications
- Multi-agent orchestration with LangGraph
- Tool-using conversational agents
How to Choose a Agent Frameworks Tool
Key criteria to evaluate when comparing Agent Frameworks solutions:
About LangChain
LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.
About LangGraph
LangGraph is LangChain's graph-based orchestration framework for building stateful, multi-step AI agents. It models agent workflows as directed graphs with nodes and edges, enabling complex control flow patterns like branching, looping, and human-in-the-loop interactions. LangGraph supports persistent state, streaming, and deployment via LangGraph Cloud.
What is Agent Frameworks?
Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.
Browse all Agent Frameworks tools →