Orchestrating role-playing autonomous AI agents for collaborative task execution
Key Features
Multi-Agent
Single Agent
Human-in-the-Loop
Streaming
Async Support
Type Safe
Short-Term Memory
Long-Term Memory
Shared Memory
Plugin System
Custom Tools
MCP Protocol
A2A Protocol
Code Execution
Web Browsing
File System Access
Sandboxing
Guardrails
Structured Output
DAG Workflows
Visual Builder
CLI
API Server
Self-Hosted
Cloud Hosted
Community Feedback
Strengths
- Intuitive role-based abstraction
- Minimal code for multi-agent pipelines
- Best starting framework for multi-agent
- Mature ecosystem
Weaknesses
- Common case trivial but uncommon harder
- Lacks native file persistence
- Less flexible than LangGraph for complex flows
CrewAI Details
| Organization | CrewAI Inc |
| Organization Type | Company |
| Funding | Series a |
| Category | Framework |
| Subcategory | Multi agent |
| Deployment | Hybrid |
| Primary Language | Python |
| Runtime | Python 3.10+ |
| License | MIT |
| Commercial Use | Unrestricted |
| Install Command | pip install crewai |
| GitHub Stars | 47,090 |
| GitHub Forks | 6,365 |
| Release Cadence | Biweekly |
| Maturity | Stable |
| Pricing Model | Open core |
| Free Tier | MIT-licensed core is free |
| Self-Hosted Free | Yes |
| Cost Model | free + LLM costs |
| Community Size | Very large |
| Community Activity | Very active |
| Sentiment | Very positive |
| GPU Required | No |
| Confidence | High |
| Research Date | 2026-03-24 |
| LLM Providers | OpenAI, Anthropic, Google, Mistral, Ollama, local models |
| API Keys Required | LLM provider API key |
Use Cases
- Role-based multi-agent pipelines
- Collaborative task execution
- Content creation workflows
- Research and analysis automation
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When to Use
Best for: Teams starting with multi-agent systems who want role-based abstractions
Avoid when: Highly custom agent communication patterns are needed
Original data from HuggingFace, OpenCompass and various public git repos.
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Release v20260324