Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for operators who want to see what their agent sessions cost and how they behave before scaling a workflow or standardizing on a toolchain.
Who should use it
Who should skip it
Move on from VasiHemanth/tokentelemetry if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
VasiHemanth/tokentelemetry is tracked by RepoRadar as a observability tool in the AI Infrastructure section. It was first seen on 2026-06-28 and last updated on 2026-06-28. The current verdict is 'try now' with a Silver tier and easy setup difficulty. The standout signals for VasiHemanth/tokentelemetry are workflow potential (9.0) and setup ease (8.8), while maturity (5.8) trails — that balance shapes where it fits best. 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 VasiHemanth/tokentelemetry a composite score of 7.9 out of 10, placing it in the Silver 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 2.0 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 evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
It stores prompts, tool traces, and cost logs locally, so review retention and file permissions before pointing it at sensitive agent sessions; The README compares efficiency against unreleased model names like GPT-5.4, so treat the model-comparison marketing as illustrative rather than independently verified; Optional anonymous product-usage stats are enabled by default per the README, so disable that path if you need a stricter local-only evaluation.