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AI Tool Comparison
LangGraph vs LangChain
A side-by-side breakdown to help you pick the right tool for your workflow.
LangGraph
Build stateful, multi-step AI agents that loop, branch, and pause for human input — modeled as graphs so you see exactly what your agent does at every step.
Agents
free
LangChain
Assemble LLM-powered apps and agents from composable building blocks, with LangSmith adding tracing, evaluation, and deployment. Platform rebranded — LangGraph Platform is now LangSmith Deployment.
Developer Tools
freemium
| Attribute | LangGraph | LangChain |
|---|---|---|
| Category | Agents | Developer Tools |
| Pricing | free | freemium |
| Pricing Detail | Open source / Free (LangGraph Cloud available) | Free (5K traces) / $39/seat/mo Plus / Enterprise custom |
| Rating | ★ 4.6(3,700 reviews) | ★ 4.4(11,200 reviews) |
Key Features
LangGraph
- Stateful directed graph model for complex multi-step agent workflows
- Human-in-the-loop interrupt support at any graph node
- Parallel node execution for independent agent branches
- Persistent state checkpointing across workflow runs
- Built-in streaming of intermediate steps and reasoning
- LangGraph Cloud for managed deployment with built-in observability
LangChain
- Chains and agents
- Retrieval (RAG) primitives
- Memory and tool integrations
- LangSmith observability
Pros
LangGraph
- •Best framework for agents that need loops, branches, and human checkpoints
- •Graph visualization makes complex agent logic debuggable
- •Tightly integrated with LangChain's 600+ integrations and tools
LangChain
- •Huge integration ecosystem
- •Rapid prototyping
- •Strong community
Cons
LangGraph
- Steeper learning curve than simpler sequential frameworks
- Graph mental model is overkill for straightforward linear pipelines
- LangGraph Cloud adds cost compared to self-hosted options
LangChain
- Abstractions can be heavy
- Frequent API changes