Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for agent builders who want something more operational than ad hoc prompt poking when they need to test safety, behavior, and regression changes across agent releases.
Who should use it
Who should skip it
Skip responsibleai/ASSERT if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
responsibleai/ASSERT is tracked by RepoRadar as a agent evaluation harness in the Developer Tools section. It was first seen on 2026-06-29 and last updated on 2026-06-29. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. responsibleai/ASSERT leads on workflow potential (9.5) and open-source/build quality (8.4); its lowest signal is momentum (6.0), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned responsibleai/ASSERT a composite score of 8.0 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 4.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
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
The quickstart expects a provider key and an external judge path by default, so keep sensitive prompts and traces off third-party services unless you control the endpoint; Trace-grounded evaluation can capture model inputs, outputs, and metadata, so review what your target integration emits before you run it on production traffic.