76
/100
prowl
Benchmarked Apr 06, 2026

CrewAI

AI agent lifecycle platform. Build, test, deploy, and scale multi-agent crews with visual editor. Multiple LLM support. REST APIs.

multi-agentworkflow-orchestrationai platform_profile
Benchmark Your API

Score Breakdown

Latency 9/10
Consistency 8/10
Token Efficiency 8/10
Parseability 8/10
Documentation 7/10
Error Clarity 7/10
Auth Simplicity 7/10
First-Try Success 7/10

Benchmark Analysis Log

Full LLM thinking from the 4-phase benchmark pipeline.

Analyze
```json
{
  "service_type": "platform",
  "base_url": "https://www.crewai.com",
  "auth_method": "unknown",
  "auth_config": {},
  "endpoints": [],
  "pricing_model": {"type": "unknown", "details": {}},
  "rate_limits": {},
  "capabilities": [
    "multi_agent_orchestration",
    "visual_workflow_editor", 
    "agent_lifecycle_management",
    "multi_llm_support",
    "agent_deployment",
    "crew_scaling",
    "rest_api_access",
    "agent_testing",
    "workflow_automation"
  ],
  "raw_analysis": "CrewAI is a comprehensive AI agent lifecycle platform designed for building, testing, deploying, and scaling multi-agent systems. The platform appears to target developers and organizations looking to create coordinated AI agent workflows or 'crews' that can work together on complex tasks. Key differentiators include a visual editor for workflow design, support for multiple LLM providers (avoiding vendor lock-in), and full lifecycle management from development to production deployment. The mention of REST APIs suggests programmatic access for integration with existing systems. The platform seems positioned for enterprise and developer use cases where multi-agent coordination is needed - think complex automation, research tasks, or business process automation that requires multiple specialized AI agents working in concert. Maturity level appears to be production-ready given the emphasis on deployment and scaling capabilities. The visual editor component suggests they're targeting both technical and less-technical users who want to design agent workflows without extensive coding."
}
```
Execute

2/3 tests passed

TestEndpointStatusLatency
website_uptimeGET /200262ms
robots_txtGET /robots.txt200156ms
llms_txtGET /llms.txt404127ms
Interpret
{"multi_model": true, "models_used": ["openai", "claude_cli"], "model_scores": {"GPT-4o": {"overall": 76, "dimensions": {"token_efficiency": 8.5, "first_try_success": 7.0, "response_parseability": 8.0, "error_clarity": 6.5, "doc_quality": 6.5, "auth_simplicity": 7.5, "latency": 9.0, "consistency": 7.5}}, "Claude CLI": {"overall": 77, "dimensions": {"token_efficiency": 8.5, "first_try_success": 7.0, "response_parseability": 8.0, "error_clarity": 7.0, "doc_quality": 7.5, "auth_simplicity": 7.0, "latency": 8.5, "consistency": 8.0}}}, "averaged": true}

Agent Readiness

x402 Payments
Not supported
Streaming
No
Sandbox
None
Agent Auth
Unknown
SDKs
None listed
MCP Support
No

Want the full interactive view?

See operational metrics, LLM evaluations, agent readiness, and more.

Open in Dashboard