This is an early release preview. You may encounter bugs.
← Python Genai

How Python Genai scored an A.

A way.space signal summary built from public evidence. It is not a user rating, and not a safety certification. How scores are calculated.

01 / Breakdown

Derived from GitHub, not voted.

A
89/100

GitHub credibility score

  • code

    84/100
    • ✓ Contributors & bus-factor: 14 recent contributors
    • ✓ Commit recency: last commit 0d ago
    • ✓ Commit cadence: 215 commits in 90 days
    • ✓ Issue close ratio: 77% of issues closed
    • ✓ Release cadence: 70 releases in 12 months
    • ✓ Stars (log-damped): 4,035 stars (log-damped)
    • – Star authenticity: star-velocity check unavailable — GitHub no longer publishes stargazer timestamps for this repo
    • – Dependent repos: not measurable — no package listing on a registry we can query
  • identity

    80/100
    • ✓ Organisation: Built by Google · notable organization (verified: Org profile shows a Verified badge for opensource.google and google.com; profile website https://opensource.google/, description "Google <3 Open Source", location United States of America. Mapped orgs google-gemini (no badge; links ai.google.dev/gemini-api/docs, description "Get started building with the Gemini API") and googleapis (no badge; links googleapis.github.io, description "Clients for Google APIs and tools that help produce them.").)
  • audits

    Not measured
    • – Third-party scans: not measurable — no registry listings for this tool
  • freshness

    100/100
    • ✓ Last shipped: last commit 0d ago — fast-churn frameworks are expected to ship about every 60d
  • community

    36/100
    • – Recommends: no recommends from gate-passing builders yet
    • ✓ Hacker News mentions: 2 Hacker News stories pointing at this tool
    • – Signal depth: sparse data — this is an early reading from very few data points

Identity and Community did not lower this score. Identity and Community can only ever add. They did not raise the average for Python Genai, so the score shown is the one it would have without them. The components are still listed above, with their real values.

Components that could not be measured are left out of the average entirely, and their weight is redistributed, never scored as zero. Identity and Community are left out the same way when they would not have raised the score. How scores are calculated.