Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
That matters because an agent that eventually gets the answer can still be wasteful, brittle, or impossible to support in real workflows. Tooling-aware evaluation is more useful to builders than another abstract benchmark win.
Who should use it
Who should skip it
Move on from Is it agentic enough? Benchmarking open models on your own tooling if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
Is it agentic enough? Benchmarking open models on your own tooling is tracked by RepoRadar as a research project in the Radar section. First seen 2026-06-19; the source record was last checked on 2026-06-19. The current verdict is 'watch' with a Gold tier and advanced setup difficulty. Is it agentic enough? Benchmarking open models on your own tooling leads on workflow potential (9.1) and practical usefulness (8.0); its lowest signal is setup ease (4.2), so factor that in before investing setup time. This page summarizes the public evidence on the linked source page and states where additional review is still needed.
How this item is evaluated
The Is it agentic enough? Benchmarking open models on your own tooling record combines a 8.3/10 composite score with separate popularity (71.0), risk (none), and setup (advanced) signals. See the scoring methodology for the current weights and evidence definitions.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
No inherent user-impacting risk is flagged from the captured evidence.