AI AGENT FRAMEWORKS
Head-to-Head Comparison

AutoGen vs CrewAI

Quick answer: AutoGen leads this head-to-head in 2026, scoring 94 on the Hyland Score against CrewAI's 89. It scores higher on the Hyland Score (94 vs 89). CrewAI remains a strong alternative — the gap is narrow, so evaluate both against your workload.

HIGHER SCORE

AutoGen

Microsoft
94HYLAND SCORE

Microsoft multi-agent conversation framework enabling complex LLM applications through agent collaboration.

Try AutoGen

CrewAI

CrewAI Inc
89HYLAND SCORE

Role-based multi-agent framework where agents have specific roles, goals, and backstories.

Try CrewAI

Spec-by-spec

SPEC
AUTOGEN
CREWAI
Hyland Score
94
89
Category
Multi-Agent
Multi-Agent
Pricing
Open Source
Open Source
Open source
Yes
Yes
Self-hostable
Yes
Yes
Languages
Python
Python
GitHub stars
32K
24K
Open source
Yes
Yes
Self-hostable
Yes
Yes
Free tier
Yes
Yes

AutoGen: strengths & weaknesses

  • Higher Hyland Score (94 vs 89)
  • Open Source pricing lowers the cost of getting started

CrewAI: strengths & weaknesses

  • Open Source pricing lowers the cost of getting started
  • Lower Hyland Score (89 vs 94)
The Verdict

Choose AutoGen for most teams

AutoGen comes out ahead because it scores higher on the Hyland Score (94 vs 89).

CrewAI remains a strong alternative — the gap is narrow, so evaluate both against your workload.

AutoGen vs CrewAI: FAQ

Which is better, AutoGen or CrewAI?

Based on the Hyland Score, AutoGen leads with 94 vs 89, because it scores higher on the Hyland Score (94 vs 89). However, CrewAI remains a strong alternative — the gap is narrow, so evaluate both against your workload.

Is AutoGen free to use?

AutoGen uses a Open Source pricing model. It is open source. It can be self-hosted.

Is CrewAI free to use?

CrewAI uses a Open Source pricing model. It is open source. It can be self-hosted.

Can I use AutoGen and CrewAI together?

Yes — many teams combine them. AutoGen strengths: python, coding, research. CrewAI strengths: python, research, automation. Evaluate both against your actual workload before standardizing on one.

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