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CrewAI

CrewAI Inc.

79·Strong

Overall Trust Score

Role-playing multi-agent framework for orchestrating collaborative autonomous agents. Agents work together as a crew with defined roles, goals, and backstories to tackle complex tasks through delegation and collaboration.

multi-agent
collaborative
open-source
Version: 1.2.1
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

81
task completion accuracy
83
Methodology
Based on multi-agent coordination testing
Evidence
CrewAI Documentation
Task completion depends on agent collaboration and delegation
Date: 2024-10-20
Confidence: mediumLast verified: 2025-11-09
tool use reliability
82
Methodology
Tool integration testing
Evidence
CrewAI Tools
Supports LangChain tools and custom tool creation
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
multi step planning
85
Methodology
Complex task testing
Evidence
CrewAI Process Types
Sequential and hierarchical processes for complex task decomposition
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
memory persistence
78
Methodology
Memory system evaluation
Evidence
CrewAI Memory
Short-term, long-term, and entity memory support
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
error recovery
76
Methodology
Error handling testing
Evidence
Community Reports
Basic error handling, agent delegation can help recovery
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
agent collaboration
88
Methodology
Multi-agent coordination testing
Evidence
CrewAI Core Concepts
Purpose-built for multi-agent collaboration and delegation
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09

Security

74
tool sandboxing
68
Methodology
Security architecture review
Evidence
CrewAI Architecture
No built-in sandboxing, relies on tool implementation
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
access control
72
Methodology
Access control assessment
Evidence
Self-Hosted Framework
Access control implementation is developer's responsibility
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
prompt injection defense
75
Methodology
Injection attack testing
Evidence
Agent Role System
Role-based constraints provide some protection
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
data isolation
78
Methodology
Data architecture review
Evidence
Crew Isolation
Crews operate independently with separate contexts
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source transparency
92
Methodology
Source code review
Evidence
CrewAI GitHub
Open source MIT license, 20k+ stars, active community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

80
data retention
85
Methodology
Privacy architecture review
Evidence
Self-Hosted Architecture
Full control over data retention when self-hosted
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr compliance
82
Methodology
Compliance capabilities assessment
Evidence
Open Source Framework
GDPR compliance possible with proper configuration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
third party data sharing
73
Methodology
Data flow analysis
Evidence
LLM Integration
Data sent to configured LLM provider (OpenAI default)
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment option
90
Methodology
Deployment options assessment
Evidence
Local LLM Support
Supports local LLMs via Ollama and LM Studio
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

83
documentation quality
85
Methodology
Documentation completeness review
Evidence
CrewAI Docs
Good documentation with examples and tutorials
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
execution traceability
78
Methodology
Logging capabilities assessment
Evidence
Logging Features
Basic logging of agent interactions and task execution
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
decision explainability
80
Methodology
Explainability features assessment
Evidence
Agent Roles
Agent roles and goals provide context for decisions
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source code
92
Methodology
Open source assessment
Evidence
GitHub Repository
MIT licensed, 20k+ stars, very active development
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
community activity
88
Methodology
Community engagement analysis
Evidence
GitHub Activity
Very active community with frequent updates
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Operational Excellence

79
ease of integration
85
Methodology
Integration complexity assessment
Evidence
CrewAI Quickstart
Simple API with intuitive role-based design
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
scalability
75
Methodology
Scalability testing
Evidence
Community Discussions
Scalability depends on infrastructure and LLM rate limits
Date: 2024-09-15
Confidence: mediumLast verified: 2025-11-09
cost predictability
88
Methodology
Pricing model analysis
Evidence
Open Source Pricing
Free framework, costs only from LLM API calls
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring capabilities
72
Methodology
Monitoring features assessment
Evidence
Built-in Features
Basic logging, requires external tools for comprehensive monitoring
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
76
Methodology
Production readiness assessment
Evidence
Framework Maturity
Rapidly evolving framework, some API changes between versions
Date: 2024-10-20
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Intuitive role-playing paradigm makes agent design natural
  • •Excellent for complex tasks requiring specialized expertise
  • •Strong multi-agent collaboration and delegation capabilities
  • •Open source with very active community and rapid development
  • •Easy to get started with simple, clean API
  • •Good integration with LangChain tools ecosystem

⚠️ Limitations

  • •Can be expensive with multiple agents making LLM calls
  • •Agent coordination overhead can increase latency
  • •Rapidly evolving API may require code updates
  • •Limited built-in security and sandboxing features
  • •Monitoring and observability require external tools
  • •Performance can be unpredictable with complex crews

📊 Metadata

license: MIT
supported models:
0: OpenAI GPT-4
1: Anthropic Claude
2: Local LLMs via Ollama
3: Azure OpenAI
programming languages:
0: Python
deployment type: Self-hosted
tool support:
0: LangChain tools
1: Custom tools
2: Built-in tools
github stars: 39900+
first release: 2023
pricing: Free (MIT license) - Costs only from LLM API calls
python requirement: Python >=3.10 <3.14
adoption: Powers 1.4B+ agentic automations globally

Use Case Ratings

customer support

84

Multiple specialized agents can handle different support aspects

code generation

86

Separate agents for coding, reviewing, and testing works well

research assistant

90

Excellent for multi-agent research teams (researcher, analyst, writer)

data analysis

83

Good for collaborative data analysis with specialized roles

content creation

92

Ideal for content teams (writer, editor, SEO specialist)

education

81

Multi-agent teaching team concept works for complex topics

healthcare

75

Requires significant security hardening for healthcare use

financial analysis

77

Self-hosted option suitable but needs compliance features

legal compliance

82

Multiple specialized legal agents can analyze different aspects

creative writing

94

Outstanding for creative teams with different perspectives