LA
Editor pick
Freemium
Advanced
Hard
LangGraph
Build stateful agent flows with branching and checkpoints.
Use LangGraph when an agent needs state, approvals, or retryable steps.
Quick take
Best for
Teams building complex workflows
Check if this matches what you need right now.
Cost and setup
Hard · Freemium
Look at price and setup together.
Use cases
Agent products with approval steps
Actions
Key details
Pricing
Freemium
Skill level
Advanced
Setup
Hard
If your workflow is already clear, keep this on your shortlist.
What it does
Best for workflows that cannot stay linear from start to finish.
LangGraph is designed for more explicit control than a simple chain can offer. It works well when you need state, review steps, recoverable execution, or multi-step agent logic.
Who it is for
Teams building complex workflows
Agent products with approval steps
Builders who need recoverable state
- Multi-step assistants
- Supervised agent execution
- Escalation and review loops
Feature highlights
- Graph-based control flow
- Stateful execution
- Good for human-in-the-loop systems
Pros
- Excellent for advanced orchestration
- Makes state explicit
- Supports robust production patterns
Considerations
- Requires more architecture thinking
- Overkill for simple single-step assistants
Integrations and ecosystem
LangChain
OpenAI
Anthropic
Postgres
Onboarding steps
- 1Design the graph states
- 2Define transitions and checkpoints
- 3Connect model calls and tools
Getting started
- Model the workflow first
- Add state persistence early
- Test the failure and resume paths
Screenshots
FAQ
Is LangGraph only for agents?
It is best known for agents, but the graph model works for any stateful AI workflow.
Do I need LangChain to use LangGraph?
No. Many teams use them together, but LangGraph can stand on its own.
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