Show HN: Tokentoll, a CI gate for LLM API cost regressions

Tokentoll, a new CI gate for LLM API costs, statically analyzes Python, JavaScript, and TypeScript code in pull requests to detect cost regressions before deployment. The tool scores each PR against a configurable policy and posts a PASS/WARN/FAIL verdict directly on the pull request, with the option to fail the workflow when policy violations occur. This prevents expensive model swaps or excessive API calls from being merged into production code.

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. Jwrede/tokentoll-demo https://github.com/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 https://github.com/Jwrede/tokentoll-demo/pull/1 . New call site, well within budget. Verdict: PASS, workflow green. PR 2: switch supportbot to gpt-4o https://github.com/Jwrede/tokentoll-demo/pull/2 . A model swap that trips two policy rules. Verdict: FAIL, workflow red. Open each PR's conversation tab to see the verdict comment tokentoll actually posts. When a PR violates your policy, tokentoll comments with a verdict and a blocking-findings list, then exits non-zero so the check fails. Example: tokentoll verdict: FAIL Blocking findings 2 : - src/agent.py:42 - per-call cost grew 15.0x threshold 5x - total monthly delta +$812.00 exceeds budget $250.00 Required action: revert the regression, raise the threshold in .tokentoll.yml , or add an exemption. When the PR is clean, the verdict is PASS and the comment shows only the cost delta table. When no policy is configured, tokentoll posts an informational delta comment with no verdict. Add .github/workflows/tokentoll.yml : name: tokentoll on: pull request: paths: - " .py" - " .ts" - " .tsx" - " .js" - " .jsx" permissions: contents: read pull-requests: write jobs: cost-gate: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: Jwrede/tokentoll@v0.7.0 with: fail-on-policy-violation: true Then add .tokentoll.yml to your repo root: budgets: max monthly delta usd: 250 max callsite monthly usd: 100 max relative increase: 5.0 policies: block unknown models: true fail on policy violation: true Future PRs receive a verdict comment. PRs that exceed the thresholds fail the workflow. For SHA-pinned installs and minimal-permissions setups, see docs/github-action.md /Jwrede/tokentoll/blob/main/docs/github-action.md . For the full policy schema, see docs/policy.md /Jwrede/tokentoll/blob/main/docs/policy.md . For the security posture, see docs/security.md /Jwrede/tokentoll/blob/main/docs/security.md . Python | SDK | Patterns | |---|---| | OpenAI | chat.completions.create , responses.create | | Anthropic | messages.create , messages.stream | | Google GenAI | models.generate content | | LiteLLM | completion , acompletion | | LangChain | ChatOpenAI , ChatAnthropic , init chat model | | Zhipu AI | ZhipuAiClient , ZhipuAI GLM models | JavaScript / TypeScript parsed via tree-sitter, handles .js , .jsx , .ts , .tsx | SDK | Patterns | |---|---| | OpenAI Node SDK | client.chat.completions.create , client.responses.create , client.embeddings.create | | Anthropic SDK | client.messages.create , client.messages.stream | | Vercel AI SDK | generateText , streamText , generateObject , streamObject , embed , embedMany | | LangChain.js | new ChatOpenAI , new ChatAnthropic , new ChatGoogleGenerativeAI , ... | | OpenAI-compatible | same shape as OpenAI Node SDK, picked up automatically | The policy block in .tokentoll.yml controls when a PR fails: | Rule | Trigger | |---|---| budgets.max monthly delta usd | total estimated monthly delta exceeds the threshold | budgets.max callsite monthly usd | any new or changed call site exceeds the threshold | budgets.max relative increase | per-call cost for any modified call site grows by more than this multiplier | policies.block unknown models | any new or modified call site uses an unpriced or unresolved model | policies.fail on policy violation | tokentoll diff exits 1 on FAIL CI gate behavior | Each rule is independent. Leave a field unset to disable that rule. Full reference in docs/policy.md /Jwrede/tokentoll/blob/main/docs/policy.md . pip install tokentoll Scan current directory for LLM API calls and their costs tokentoll scan . Show cost impact of your last commit tokentoll diff HEAD~1 Compare two refs and fail on policy violation tokentoll diff main..HEAD --fail-on-policy-violation Subcommands: tokentoll scan PATH... --format table|json|markdown --calls-per-month N --config PATH tokentoll diff REF --base REF --head REF --format table|json|markdown|github-comment --config PATH --fail-on-policy-violation tokentoll update refresh bundled pricing data from LiteLLM .tokentoll.yml lives in the repo root and is auto-discovered. Beyond the policy block: Per-SDK defaults for dynamic runtime-resolved model names default models: openai: gpt-4o-mini anthropic: claude-haiku-3-20240307 Assumed monthly call volume per call site used for dollar estimates calls per month: 5000 Skip cost estimation for dynamic models entirely. Default false: dynamic calls are priced against the per-SDK default. skip dynamic models: false Default excludes tests/, examples/, docs/, cookbook/, benchmarks/, evals/, scripts/, notebooks/ are applied automatically. Opt out with: use default excludes: false Additional excludes prefix or glob exclude: - " test.py" - vendor/ Per-path overrides longest prefix match overrides: - path: src/agents/ default model: gpt-4o calls per month: 10000 - path: src/azure/ skip dynamic models: true Resolution order for dynamic model defaults: default models per-SDK default model generic built-in SDK defaults. tokentoll requires no API keys, sends no telemetry, and runs entirely inside your CI environment. Pricing data ships with the package and updates from LiteLLM on demand. For the recommended permission set, SHA pinning, and fork PR risk, see docs/security.md /Jwrede/tokentoll/blob/main/docs/security.md . tokentoll ships an MCP Model Context Protocol server so Claude Code and other MCP hosts can check the cost impact of LLM code changes from inside an agent conversation: pip install tokentoll mcp claude mcp add --transport stdio tokentoll -- tokentoll-mcp Two tools are exposed: scan estimate costs across a path and diff compare two refs . Both return JSON. Source code .py, .ts, .tsx, .js, .jsx | v +----------------+ +------------------+ | AST scanners |-- | SDK detectors | | ast Python + | | OpenAI, Anthropic| | tree-sitter | | Google, LiteLLM, | | JS/TS | | LangChain, Zhipu,| +----------------+ | Vercel AI SDK | +------------------+ | v +------------------+ | Pricing engine | | 2200+ models | +------------------+ | v +------------------+ | Diff engine | | old vs new | +------------------+ | v +------------------+ | Policy evaluator | | PASS/WARN/FAIL | +------------------+ | v +------------------+ | PR comment / CLI | | output | +------------------+ A multi-pass constant propagation engine resolves model names through variable assignments, os.getenv / process.env.X fallbacks, function defaults, class attributes, constructor arguments, dict and object literals, kwargs unpacking, and Vercel AI SDK provider wrappers openai "gpt-4o" , so real-world code with indirection still produces useful estimates. Pricing is bundled and works offline. To refresh from LiteLLM: tokentoll update Coverage: 300+ models across OpenAI, Anthropic, Google, AWS Bedrock, Azure, and more, plus 2200+ entries from LiteLLM's combined catalog. - Static analysis only. Models loaded from databases or remote config cannot be resolved; tokentoll falls back to the configured per-SDK default and marks the call site as default . - Token estimates use a characters/4 heuristic unless tiktoken https://github.com/openai/tiktoken is installed pip install tokentoll tiktoken . - Monthly estimates assume uniform call volume per call site. Override per-project with calls per month or per-path with overrides . - JS/TS resolution is same-file only. Importing a model name from another module produces a dynamic call site rather than a resolved value. v0.9 : Public demo repo with a known-failing PR, gpt-researcher case study, expanded adoption section Future : Context-aware call frequency inference FastAPI routes versus scripts versus loops ; cross-file import resolution for JS/TS MIT