Agno vs CrewAI
A side-by-side comparison of Agno and CrewAI, two Orchestration tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Agno | CrewAI |
|---|---|---|
| Category | Orchestration | Orchestration |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Open core |
| Deployment | — | — |
| Platforms | API, CLI | API, CLI |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Agno | crewAIInc |
| Capabilities (differs) |
|
|
The honest brief
Agno
Built for speed and scale — agents instantiate near-instantly with low memory, and ship to production via the bundled AgentOS FastAPI runtime.
- Fast agent instantiation, low memory use
- Multi-modal agents and agent teams
- Bundled AgentOS production runtime
- Model- and provider-agnostic
- Younger than LangChain and LlamaIndex
- Rapid changes since the Phidata rename
- Smaller community and ecosystem
CrewAI
Models work as a crew of role-typed agents that delegate to each other, built standalone rather than on LangChain.
- Role-based multi-agent model
- Independent of LangChain
- Model-agnostic
- Good for research pipelines
- Opinionated structure
- Less flexible than graph frameworks
- Debugging multi-agent runs is hard
When to pick which
CrewAI leans on Agent framework as a headline capability; Agno treats it as secondary.
- Agent framework (primary capability)
They also differ on:
- Pricing
- FREE · FREEMIUM
- License
- Open source · Open core
Their capability lists differ in recorded depth — compare the full lists above before deciding.