CrewAI vs Agno
CrewAI models work as crews of role-based agents running tasks sequentially or under a manager, plus Flows for deterministic steps. Agno offers agents, teams with a leader in coordinate, route, broadcast or tasks mode, and workflows, served by its AgentOS runtime with your own database. Both are open-source Python with paid vendor platforms.
Facts side by side
| Fact | CrewAI | Agno |
|---|---|---|
| Type | Framework | Framework |
| Languages / SDKs | Python | Python |
| License | MIT | Apache-2.0 |
| Pricing model | Open core | Open core |
| Orchestration pattern | Crew / roles | Supervisor |
| GitHub stars | 59,217 (as of 2026-09-30) | 42,396 (as of 2026-09-30) |
| GitHub forks | 8,613 | 6,046 |
| Last push | 2026-09-29 | 2026-09-30 |
| Latest release | 1.15.23 | v3.0.11 |
| Repository | crewAIInc/crewAI | agno-agi/agno |
| Website | crewai.com | www.agno.com |
| Documentation | docs.crewai.com | docs.agno.com |
| Last verified | 2026-09-30 | 2026-09-30 |
| MCP support | Yes (checked 2026-09-30) | Yes (checked 2026-09-30) |
| A2A support | Yes (checked 2026-09-30) | Yes (checked 2026-09-30) |
| AG-UI support | Partial (checked 2026-09-30) | Yes (checked 2026-09-30) |
| Install verified | 2026-09-30 | 2026-09-30 |
Choose CrewAI if
- You think in roles, goals and tasks and want sequential or hierarchical processes defined for you.
- You want event-driven Flows with
@human_feedbackrouting in the same package. - You want checkpointing that lets a failed crew or flow resume or fork.
Choose Agno if
- You want a runtime (AgentOS) that serves agents as a FastAPI app with sessions, memory, traces and approvals stored in your own database.
- You want A2A and AG-UI interfaces built into the runtime; CrewAI's AG-UI support goes through an adapter maintained in the AG-UI repository.
- You want to switch team behavior (coordinate, route, broadcast, tasks) without rewriting member agents.
Migration notes
A CrewAI agent (role, goal, backstory, tools) maps to an Agno Agent with instructions and tools. A hierarchical crew maps to an Agno Team in coordinate mode; a sequential crew maps to a Workflow with sequential steps; CrewAI Flows map to Agno workflows with condition and router steps. Human approval differs: CrewAI uses human_input=True or @human_feedback; Agno pauses a run on @tool(requires_confirmation=True) and resumes with continue_run(), with paused runs persisted. Both send telemetry by default (OTEL_SDK_DISABLED=true for CrewAI, AGNO_TELEMETRY=false for Agno). Agno's hosted control plane is free for local runtimes only; live runtimes start at the Pro plan. Agno v2 users must run the v3 database migration first.
FAQ
Which is lighter to self-host?
Both run as plain Python. Agno additionally documents AgentOS as a self-hosted FastAPI runtime with storage in your database; CrewAI's production deployment and webhook-based human input are documented as AMP features.
Do both support MCP, A2A and AG-UI?
MCP and A2A: yes for both, with evidence on each tool page. AG-UI: yes for Agno (AgentOS interface), partial for CrewAI (adapter package maintained in the AG-UI repository).
What do the paid plans cover?
CrewAI sells AMP for deployment and management. Agno's pricing page lists a free control plane for local runtimes, a Pro plan for live runtimes and custom Enterprise pricing.