Wattage: A token-spend profiler and cost-regression gate for AI agents
Point it at a trace and it tells you exactly where your tokens are being burned and wasted, prices each waste pattern in real dollars, prescribes a fix, and can fail your CI when a change makes your agent measurably more expensive. A real captured agent trace (see provenance) — Wattage catches a stable prompt prefix being re-sent instead of cached, prices the waste, and prescribes the fix. Regenerate this GIF with vhs docs/assets/demo.tape (see the tape file for the exact command).
- ▪Point it at a trace and it tells you exactly where your tokens are being burned and wasted, prices each waste pattern in real dollars, prescribes a fix, and can fail your CI when a change makes your agent measurably more expensive.
- ▪A real captured agent trace (see provenance) — Wattage catches a stable prompt prefix being re-sent instead of cached, prices the waste, and prescribes the fix.
- ▪Regenerate this GIF with vhs docs/assets/demo.tape (see the tape file for the exact command).
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/faizannraza/wattage |
| Publication time | Sun, 26 Jul 2026 23:27:35 +0000 |
| Retrieval time | 2026-07-26T23:38:30.322Z |
| Last seen | 2026-07-26T23:38:30.322Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | UdRYn855PKYP |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
A Kill-A-Watt meter for your AI agents. Point it at a trace and it tells you exactly where your tokens are being burned and wasted, prices each waste pattern in real dollars, prescribes a fix, and can fail your CI when a change makes your agent measurably more expensive. A real captured agent trace (see provenance) — Wattage catches a stable prompt prefix being re-sent instead of cached, prices the waste, and prescribes the fix. Regenerate this GIF with vhs docs/assets/demo.tape (see the tape file for the exact command). Install and run uvx wattage report trace.json No config file, no API key, fully offline — point it at an OTLP JSON trace export and it prices every call and runs every detector.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.