AI AGENT FRAMEWORKS
Head-to-Head Comparison

AutoGen vs MetaGPT

Quick answer: AutoGen leads this head-to-head in 2026, scoring 94 on the Hyland Score against MetaGPT's 90. It scores higher on the Hyland Score (94 vs 90). MetaGPT 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

MetaGPT

DeepWisdom
90HYLAND SCORE

Multi-agent framework that simulates a software company with product managers, architects, and engineers coll…

Try MetaGPT

Spec-by-spec

SPEC
AUTOGEN
METAGPT
Hyland Score
94
90
Category
Multi-Agent
Multi-Agent
Pricing
Open Source
Open Source
Open source
Yes
Yes
Self-hostable
Yes
Yes
Languages
Python
Python
GitHub stars
32K
45K
Open source
Yes
Yes
Self-hostable
Yes
Yes
Free tier
Yes
Yes

AutoGen: strengths & weaknesses

  • Higher Hyland Score (94 vs 90)
  • Open Source pricing lowers the cost of getting started

MetaGPT: strengths & weaknesses

  • Open Source pricing lowers the cost of getting started
  • Lower Hyland Score (90 vs 94)
The Verdict

Choose AutoGen for most teams

AutoGen comes out ahead because it scores higher on the Hyland Score (94 vs 90).

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

AutoGen vs MetaGPT: FAQ

Which is better, AutoGen or MetaGPT?

Based on the Hyland Score, AutoGen leads with 94 vs 90, because it scores higher on the Hyland Score (94 vs 90). However, MetaGPT 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 MetaGPT free to use?

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

Can I use AutoGen and MetaGPT together?

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

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