Agno vs CrewAI
CrewAI is a quick way to prototype a crew of role-playing agents, and Flows add structure when needed. Agno is infrastructure for running agents in production. The divide appears after the demo, and it concerns isolation: CrewAI keeps memory in local files and separates users by convention. Agno separates them by construction, giving every request its own agent, backed by your database, with approvals and access control built in.
Facts checked against the CrewAI docs on 2026-08-10
Side by side
| CrewAI | Agno | |
|---|---|---|
| What you write | Crews of agents with roles, goals and backstories, plus Flows for structured steps. | Agents, teams and workflows in plain Python. |
| State | A unified memory store on local disk by default (LanceDB), flow state in local SQLite. | Sessions, memory and knowledge in your own database from day one. |
| Crash recovery | Flows can persist state and resume by id. Crews themselves offer checkpoint-based resume of interrupted kickoffs. | Run checkpointing persists each tool batch. continue_run resumes the same run in place after a crash. |
| Long-running work | Kickoffs run inside your process. Managed execution comes with the AMP platform. | A durable job queue ships in the runtime. Jobs survive restarts and deploys, any replica can claim them, and failures land in a dead letter queue. |
| Streaming | A dropped connection ends the stream. | Streams are resumable. Reconnect with the last event index and catch up where you left off. |
| Human in the loop | Tasks can request human input inline. Building an approval flow with roles is left to you. | Pauses persist in your database and wait as long as needed. Admin approvals with scopes are built in. |
| Serving many users | You tag memories with scope paths and source labels in your code. Isolation is a convention. | Every request gets its own agent, so isolation is structural. JWT scopes and user isolation come with AgentOS. |
| Runtime | The AMP platform deploys crews to managed infrastructure. Your own VPC comes with the Enterprise contract. | AgentOS is open source and self-hosted. Your cloud, your database, your auth. |
| Creating one agent | 18.3 ms and 23.6 KiB, measured with the public harness. | 3.2 µs and 5.2 KiB, same harness, same day. |
| License | MIT for the framework. The platform is a hosted product. | Apache 2.0 for the SDK and AgentOS. |
What CrewAI does well
The role, goal and backstory model makes a first crew genuinely quick to build.
Flows give you typed state, routers and resumable runs when you need structure.
Crew Studio lets people build without writing code.
It stands alone. It does not depend on LangChain.
Choose CrewAI when
You are prototyping a role-based crew and want the fastest path to a working demo.
Your team wants a no-code builder and a fully managed deployment path.
You have a single tenant, so files on local disk suffice.
Choose Agno when
You are past the prototype and real users are about to arrive.
You want memory and sessions in a real database, not files beside the process.
You want isolation, approvals and access control without writing conventions for them.
Sources
Every claim on this page was checked against the documentation linked below. The performance figures come from our public harness, and the benchmarks page shows how to run it. If something here has gone stale, tell us and we will correct it.