Community
91/100
Community is based on recent contributor activity. Git.Top recorded 7 contributors in the current metrics window.
Weight 16%Project Score
Why jina-ai/vectordb has a 65/100 Git.Top Score, 58/100 Agent Score, and 5/100 Quality Score.
Community
Community is based on recent contributor activity. Git.Top recorded 7 contributors in the current metrics window.
Weight 16%Maintenance
Maintenance combines repository activity, recent commits, release cadence, and issue-response signals.
Weight 20%Documentation
The project has an agent-readable summary with enough context for recommendation workflows.
Weight 16%Stability
Stability considers release cadence, issue load, recent pushes, and maturity signals.
Weight 16%Adoption
Adoption is normalized from ecosystem attention, stars, forks, and integration-like signals.
Weight 16%Agent Readability
Agent readability measures structured summaries, use cases, tradeoffs, dependencies, alternatives, and deployment clarity.
Weight 16%Adoption Guidance
Strongest Dimension
Agent-readable documentation signals are strong enough for recommendation workflows.
Weakest Dimension
Maintenance reflects repository activity, commits, releases, issue-response signals, and recent push freshness.
Risk Flags
Agent Score
Agent Score is optimized for agent use: documentation, maintenance, deployment clarity, popularity, and community activity.
Quality Score
Quality score is separate from Agent Score. It weights star movement, commits, releases, contributors, and issue response.
Why This Score
jina-ai/vectordb scores 65/100 overall. The lowest dimension is maintenance at 5/100, which is the first place to inspect before adopting it.
Data Confidence
Scores are useful for ranking, but production recommendations should cite source metadata and signal confidence.
Score Confidence
Score evidence is usable for comparison, but agents should cite the review notes before making a strong recommendation.
Formula