Comparison Preset
CrewAI is the more prudent choice for an enterprise context due to its superior security posture, showing zero known vulnerabilities against Agno's three, one of which is CRITICAL. While both frameworks have strong bus factor scores of 8/10 and permissive licenses (MIT and Apache-2.0), the vulnerability data presents a clear risk differentiation. CrewAI also explicitly offers enterprise-grade features like role-based access control and team management, which are essential for governed environments. Although Agno is the older project, CrewAI's cleaner security record and built-in enterprise capabilities make it a more defensible choice for long-term stability and risk management.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides an SDK to build agents, teams, and workflows, along with AgentOS for production deployment as a stateless FastAPI backend, and a Control Plane for monitoring. It enables teams to build and run their own AI agent platforms across various cloud providers like AWS, GCP, and Kubernetes.
CrewAI is a framework for building and orchestrating multi-agent systems, providing capabilities for agent design, workflow automation, and process management. It includes features like guardrails, memory, knowledge, and observability. The platform supports enterprise deployments with team management and integration triggers.
Best For
Building and running custom AI agent platforms with production-ready deployment and monitoring.
Designing, orchestrating, and automating multi-agent systems with baked-in guardrails and memory.
Avoid If
You only need a single, simple agent and not a comprehensive agent platform solution.
no data
Strengths
- +Provides an SDK for building agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations.
- +Offers a production-ready, stateless, secure FastAPI backend for running agent platforms.
- +Includes a Control Plane UI for monitoring and managing the deployed agent system.
- +Supports deployment across multiple major cloud providers, including AWS, GCP, Kubernetes, and Docker.
- +Composes agents with tools, memory, knowledge, and structured outputs using Pydantic.
- +Orchestrates start/listen/router steps, manages state, persists execution, and resumes long-running workflows.
- +Defines sequential, hierarchical, or hybrid processes with guardrails, callbacks, and human-in-the-loop triggers.
- +Provides enterprise features like environment management, safe redeployment, monitoring, RBAC, and team management.
- +Integrates with external services such as Gmail, Slack, Salesforce, HubSpot, Outlook, Teams, OneDrive, and Amazon Bedrock Agents.
Weaknesses
- −Requires self-hosting and management of the AgentOS FastAPI backend in the user's cloud, incurring operational overhead and costs.
- −The initial setup process is prescriptive, relying on cloning provider-specific repositories and running 'setup-platform skills'.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
The AgentOS backend is stateless, while the SDK enables building agents and workflows with integrated memory and knowledge capabilities.
Flows manage state, persist execution, and enable resuming long-running workflows.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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