State is where agent frameworks diverge most. The state model decides what workflows you can build — and most teams pick a framework without thinking about state until it's too late.
State is the part of an agent framework that's hardest to change later. A framework with the wrong state model for your workflow will fight you at every step, and by the time you notice, you're committed.
The Issue Tree: How Each Handles State
- **CrewAI.** Task-and-role-based; state flows through tasks. Simple, less explicit; loses on complex state.
- **Agno.** Component-based; state in sessions. Flexible, performant; you manage more of it.
- **LangGraph.** Explicit graph state, typed, checkpointable. Most control; most complexity.
- **OpenAI Agents SDK.** Minimal, conversation-state; simple, less explicit.
The Design Question
The design question is what state your workflow needs: a simple linear flow (any framework), a branching flow with shared state (LangGraph or Agno), or a long-running checkpointable workflow (LangGraph). The framework whose state model matches your workflow's shape is the one that won't fight you.
- Linear: any framework.
- Branching, shared: LangGraph or Agno.
- Long-running, checkpointable: LangGraph.
What Breaks When State Is Wrong
- A workflow that can't represent its own state, so it leaks into ad-hoc variables.
- A long-running workflow that can't checkpoint, so it can't resume.
- A state model that can't scale beyond a single session.
How to Choose on State
Choose on state first, before ergonomics. Prototype your workflow's state in two frameworks and see which represents it cleanly. The framework that makes your state explicit and typed is the one that scales; the one that hides it is the one that breaks under complexity.
Conclusion
State is where agent frameworks diverge most. Choose on the state model first — explicit, typed, checkpointable for complex workflows; simple for linear ones — and the framework won't fight you at every step.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Describe your workflow's state to us.
We'll tell you which framework's state model fits. See the framework comparison for the broader choice.
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