Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developers who keep hitting context limits on large debug traces and codebases, because it turns oversized evidence into smaller but still exact slices instead of approximate paraphrases.
Who should use it
Who should skip it
Skip sleeplesshan/token-router if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
sleeplesshan/token-router is tracked by RepoRadar as a infrastructure tool in the AI Infrastructure section. It was first seen on 2026-06-27 and last updated on 2026-06-27. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, sleeplesshan/token-router is strongest on workflow potential (9.2) and open-source/build quality (8.4) and weakest on momentum (5.0) — a profile worth weighing against your own priorities. 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 sleeplesshan/token-router a composite score of 8.1 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 5.0 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
The routing step depends on a local Ollama model, so validate slice accuracy on your own logs and source files before trusting it in a high-stakes debug loop; The README uses forward-looking model examples such as GPT-5.5 in a non-sponsor context, so treat provider suggestions as illustrative and pin to models that actually exist in your environment; It reduces context cost but still sends the selected raw slices to a downstream reasoning model unless you replace that final step with your own local stack.