Side-by-side comparison
AutoGen vs CrewAI
vs
Side-by-side comparison based on our agenticness evaluation framework
At a glance
Quick Facts
| Feature | AutoGen | CrewAI |
|---|---|---|
| Category | Multi-Agent Orchestration, Agent Frameworks & Orchestration | Multi-Agent Orchestration, Agent Frameworks & Orchestration |
| Deployment | Self-hosted | Hybrid (cloud + self-hosted) |
| Autonomy Level | Semi-autonomous | Semi-autonomous |
| Model Support | Multi-model | Single model |
| Open Source | Yes | Yes |
| MCP Support | Yes | -- |
| Team Support | Small team | Enterprise |
| Pricing Model | Free / open source | Freemium |
| Interface | api, gui, cli | gui, web, api |
36-point evaluation
Agenticness
14/36
Adaptive Collaborator
AutoGen
11/36
Guided Assistant
CrewAI
Dimension Breakdown (0-4 each)
Action Capability
AutoGen
3
CrewAI
2
Autonomy
AutoGen
2
CrewAI
1
Planning
AutoGen
2
CrewAI
2
Adaptation
AutoGen
1
CrewAI
0
State & Memory
AutoGen
1
CrewAI
0
Reliability
AutoGen
0
CrewAI
1
Interoperability
AutoGen
2
CrewAI
1
Safety
AutoGen
1
CrewAI
2
Scores from our agenticness evaluation framework. Higher is more autonomous.
Features & Use Cases
AutoGen
Features
- Builds multi-agent AI applications
- Provides a low-level Core API for message passing and event-driven agents
- Includes AgentChat for higher-level multi-agent patterns
- Supports extensions for model clients and tools
- Can connect to MCP servers for external tool use
- Works with OpenAI models in the quickstart examples
- Includes AutoGen Studio for no-code workflow prototyping
- Supports browser-based workflows through Playwright MCP
Use Cases
- Developing custom multi-agent assistants for internal workflows
- Prototyping agent workflows without writing code in AutoGen Studio
- Building tool-using assistants that can browse the web through MCP
- Orchestrating expert sub-agents for tasks like math, research, or domain-specific reasoning
- Extending existing applications with agent behavior and external integrations
CrewAI
Features
- Visual editor for building agentic workflows
- AI copilot for workflow creation
- Integrated tools and triggers
- Workflow execution limits by plan
- Cloud SaaS deployment
- Self-hosted deployment via Kubernetes and VPC for Enterprise
- SSO for Enterprise
- Secret manager integration for Enterprise
Use Cases
- Teams building production AI agent workflows with a visual interface
- Organizations that want to deploy agents in a managed cloud environment
- Enterprises that need self-hosted agent infrastructure on private cloud or on-prem systems
- Developers who want to prototype an agent workflow and later scale it for production
Pricing
AutoGen
- **Free / open source** — full functionality available at no cost.
CrewAI
- **Free (Basic):** Free tier with a visual editor, AI copilot, integrated tools and triggers, and 50 workflow executions per month.
- **Professional ($25/month):** Includes everything in Basic, plus 1 additional seat, 100 workflow executions per month, and support via the community forum.
- **Enterprise:** Custom pricing. Includes SaaS or self-hosted deployment via Kubernetes and VPC, SOC2, SSO, secret manager integration, PII detection and masking, dedicated support, uptime SLAs, Slack or Teams support channels, and forward-deployed engineers.
Analysis
Our Verdict
In practice, pick AutoGen when you need maximum flexibility to build custom multi-agent systems in Python—especially if you want MCP-based external tool integrations and browser automation via Playwright MCP, and you’re comfortable self-hosting the orchestration logic. Pick CrewAI when you want a more turnkey, production-oriented agent workflow platform with a visual workflow editor, AI copilot-assisted building, and a clear path to deploying/operating at scale through cloud or enterprise self-hosting (Kubernetes/VPC) with enterprise controls like SSO, secret management, and PII masking.
Choose AutoGen if...
- +Choose AutoGen if you want a developer-first, code-based multi-agent framework where you can orchestrate agent-to-agent interaction using its core runtime and the higher-level AgentChat API to rapidly implement custom agent workflows.
- +Choose AutoGen if your workflows need tool use and integrations beyond “built-in tools,” since it supports connecting to MCP servers for external tool access (including browser workflows via Playwright MCP) and works with model clients like the OpenAI integrations shown in the quickstart examples.
- +Choose AutoGen if you want flexible “semi-autonomous” agent behavior tailored to your application (e.g., decomposing tasks into specialist sub-agents) and you’re comfortable self-hosting with Python 3.10+ and pip installs rather than using a managed platform.
Choose CrewAI if...
- +Choose CrewAI if your team values a visual editor and lifecycle platform features—AI copilot for building workflows, integrated tools/triggers, and clear plan-based workflow execution limits—so you can iterate quickly and then operate workflows more like production systems.
- +Choose CrewAI if you want managed or enterprise-ready deployment out of the box: it offers cloud SaaS and, for Enterprise, self-hosted deployment via Kubernetes and VPC plus operational/security features like SSO, secret manager integration, and PII detection/masking.
- +Choose CrewAI if you’re planning to scale from prototype to production with governance and support expectations (SSO, uptime SLAs, dedicated support, and enterprise support channels), and you prefer this platform’s “workflow executions” model over building orchestration logic directly in code.