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An open-source AI tools directory with clear use cases, visible source freshness, and maintenance evidence for anyone deciding what to try.

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Rankings use public ecosystem signals. Treat them as a starting point, then confirm license, setup, safety, and fit with the upstream project.
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Project comparison

Awesome MCP ZH vs transformers

Compare adoption, momentum, maintenance health, and project basics before choosing which tool to evaluate deeper.

Awesome MCP ZH

MCP 资源精选, MCP指南,Claude MCP,MCP Servers, MCP Clients

Best matched with other framework tools.

7.6K
Stars
+513
Weekly
56/100
Health
transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Best matched with other framework tools.

166.9K
Stars
+220
Weekly
89/100
Health

Quick Take

Adoption

transformers has the larger GitHub footprint with 166.9K stars.

Momentum

Awesome MCP ZH is currently growing faster at +513 stars this week.

Maintenance

transformers has the stronger automated maintenance signal at 89/100. This is not a security or fit verdict.

Side-by-side evidence

Use these signals to narrow your choice, then confirm setup, license, and fit upstream.

SignalAwesome MCP ZHtransformers
Evidence statusBasic listing· 2026-10-04 09:17 UTCVerified snapshot· 2026-10-04 09:19 UTC
GitHub stars7.6K166.9K
Weekly growth+513+220
Health scoreWatch56/100Strong89/100
Contributors633.9K
Commits per week3.564.0
Open issues762.4K
LanguageN/APython
LicenseMITApache-2.0
Last commit2026-07-03 22:06 UTC2026-10-04 01:58 UTC
Last releaseN/Av5.8.0

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