Agno vs CrewAI
Side-by-side comparison of two agent options that often come up together when people are choosing between self-hosted frameworks, managed assistants, and extensible AI tooling.
Open source40k stars
Agno
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
Open source50k stars
CrewAI
Role-playing multi-agent framework where AI agents collaborate as a crew to complete complex tasks
Category
Agno
CrewAI
Tagline
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
Role-playing multi-agent framework where AI agents collaborate as a crew to complete complex tasks
Deployment
Self-hosted / Agno Cloud
Self-hosted
Pricing
Open source and free to self-host. Agno Cloud available for managed deployments.
Free and open source. Requires your own LLM API key.
Channels
Web, api, CLI
api
Open source
Yes
Yes
Privacy
Self-hosted deployments keep data on your infrastructure.
Data processed by your chosen LLM provider. No CrewAI cloud dependency.
Agno pros
- Extremely fast — benchmarks show 3x LangGraph speed.
- Native multi-agent team support built-in.
- Strong memory architecture with multiple storage backends.
CrewAI pros
- Role-based agent design makes complex workflows intuitive to reason about.
- Strong community and active development.
- Integrates with LangChain tools ecosystem.
Agno cons
- Developer-focused — no visual builder.
- Ecosystem smaller than LangChain/LangGraph.
- Requires Python knowledge.
CrewAI cons
- Multi-agent coordination adds latency and API cost.
- Debugging agent interactions can be opaque.
- Less suited for single-agent personal assistant use cases.
Agno gotchas
- Formerly called Phidata — old docs may use old name.
- Start with single agent before multi-agent orchestration.
CrewAI gotchas
- Token costs multiply quickly with multi-agent crews — set budget limits.
- Agent loops can occur if goal specification is ambiguous.
Not sure which one fits you?
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