Show HN: Tokentoll, a CI gate for LLM API cost regressions
Tokentoll is a continuous integration (CI) tool designed to prevent cost regressions in large language model (LLM) API usage. It analyzes code in Python, JavaScript, and TypeScript, providing a verdict on pull requests based on user-defined policies. The tool can automatically fail workflows that violate these policies, ensuring that cost increases are managed before merging changes.
- ▪Tokentoll analyzes LLM API calls in code to enforce cost policies.
- ▪It provides PASS/WARN/FAIL verdicts on pull requests based on compliance with set budgets.
- ▪The tool can automatically block merges that exceed defined cost thresholds.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,864 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Jwrede/tokentoll |
| Publication time | Sat, 30 May 2026 12:41:53 +0000 |
| Retrieval time | 2026-05-30T12:49:36.360Z |
| Last seen | 2026-05-30T12:49:36.360Z |
| 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 | S6oJ4fd70zDu |
| 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
tokentoll Prevent LLM cost regressions before production. tokentoll is a CI gate for LLM cost. It statically analyzes Python, JavaScript, and TypeScript for LLM API calls, scores every pull request against a policy you control, and posts a PASS/WARN/FAIL verdict directly on the PR. Optionally, it fails the workflow when the policy is violated, so cost regressions cannot be merged. Live demo Jwrede/tokentoll-demo is a small polyglot LLM app (Python + TypeScript) wired up to the tokentoll cost gate. Two PRs are already open against it: PR #1: Add Anthropic Haiku translation helper. New call site, well within budget. Verdict: PASS, workflow green. PR #2: switch supportbot to gpt-4o. A model swap that trips two policy rules. Verdict: FAIL, workflow red.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.