**An AI PR reviewer built to eliminate review nois…
An AI PR reviewer built to eliminate review noise — not add to it.
Most AI reviewers re-review the whole PR on every push and repeat themselves until the team mutes them. pr-sage is designed around the opposite goal: say each thing once, follow your team's rules, and stay silent when there is nothing new to say.
🔇 Zero duplicate comments. Findings carry content fingerprints — a line shift won't make the same comment appear twice, and re-runs post nothing when nothing changed.
✅ Finding lifecycle. Follow-up reviews report which findings remain unresolved and which were fixed.
⏩ Incremental by default. After the first review, only the commits you pushed since get reviewed. Less noise, fewer tokens.
📏 Your rules, not generic advice. .pr-sage.json instructions plus automatic CLAUDE.md / CONTRIBUTING.md injection make reviews follow team conventions.
🚦 A quality gate, not just commentary. --fail-on critical blocks merges; --event auto approves clean PRs and requests changes on real problems.
🖥️ Reviews before the PR exists. pr-sage local reviews your git diff pre-push — no server, no PR, no GitHub token.
🔐 Your keys, your data path. No server, nothing stored; code goes only to the provider you choose — Claude, OpenAI, or Gemini — or never leaves your machine at all with a self-hosted OpenAI-compatible endpoint (Ollama, vLLM, LM Studio). See SECURITY.md .
Ships as a CLI, a GitHub Action, and a TypeScript library.
The fastest path — an interactive wizard that writes your config and the GitHub Action workflow, and tells you exactly which secret to register:
npx pr-sage init npx pr-sage doctor
Or by hand (CLI):
export GITHUB_TOKEN=ghp_... export ANTHROPIC_API_KEY=sk-ant-... npx pr-sage review --repo owner/name --pr 123
Preview without posting anything:
npx pr-sage review --repo owner/name --pr 123 --dry-run
Review your local changes before pushing (no PR, no GitHub token needed):
npx pr-sage local --base main # diff vs main npx pr-sage local --staged --fail-on critical # gate staged changes
Review in Korean with a different provider:
export OPENAI_API_KEY=sk-... npx pr-sage review --repo owner/name --pr 123 --provider openai --locale Korean
Option Default Description
-p, --pr <number> (required) Pull request number
-r, --repo <owner/name> $GITHUB_REPOSITORY Target repository
--provider <name> anthropic anthropic | openai | gemini
-m, --model <id> provider default Model id ( claude-opus-4-8 , gpt-5 , gemini-flash-latest )
--locale <lang> English Language for the review output; auto detects it from the PR title/body
--paths <globs> — Only review files matching these comma-separated globs (monorepo scoping)
--max-tokens <n> — Cost guard: stop launching new batches once this many tokens are spent
--force — Review even draft, WIP-titled, or skip-review -labeled PRs (skipped by default)
--exclude <patterns> — Comma-separated globs or substrings to skip (added to defaults: lockfiles, dist/ , build/ , …)
--min-severity <sev> — Drop findings below this severity (e.g. suggestion hides nitpicks)
--fail-on <sev> — Exit 1 if any finding is at or above this severity — use as a CI quality gate
--context <mode> patch full sends complete file contents to the model for better accuracy (more tokens)
--event <mode> comment auto approves clean PRs and requests changes on critical findings (falls back to comment on your own PRs)
--verify off Second model pass that rejects unconfirmed findings
--verify-provider <name> same provider Use a separate provider for verification
--verify-model <id> provider default Use a separate verification model
--verify-failure <mode> abort abort , keep , or drop when verification fails
--output <format> text json or sarif for machine-readable results
--fail-on-incomplete off Fail when filtering, missing patches, or the token budget leaves part of the change unreviewed
--check-run off Publish findings as GitHub Check Run annotations
--no-dedupe — Repost findings already commented by a previous pr-sage review (dedup is on by default)
--no-incremental — Always review the full PR diff instead of only commits since the last pr-sage review
--batch-chars <n> 80000 Max diff characters per model request; larger PRs are reviewed in batches
--config <path> .pr-sage.json Config file path
--dry-run — Print the review to stdout instead of posting
Required environment variables: GITHUB_TOKEN (with pull_requests: write ), plus the API key for your provider ( ANTHROPIC_API_KEY , OPENAI_API_KEY , or GEMINI_API_KEY ).
On repeat runs (e.g. new commits pushed to the PR), pr-sage reviews only the commits pushed since its last review (incremental mode), skips findings it has already commented, and posts nothing when there is nothing new — no duplicate-comment spam, no wasted tokens. If your repo has a CLAUDE.md or CONTRIBUTING.md , it is automatically injected as review context (disable with "repoContext": false ). GitHub Enterprise works out of the box via $GITHUB_API_URL or the githubApiUrl config field.
Each run prints its token usage to stderr ( LLM usage: N call(s), X input / Y output tokens ) so cost stays visible. Every summary also reports review coverage. Partial reviews never auto-approve a PR. Use --fail-on-incomplete when incomplete coverage must fail the CI quality gate.
Point the OpenAI provider at any OpenAI-compatible server and private code never leaves your machine — no API key required:
ollama pull qwen2.5-coder:14b
OPENAI_BASE_URL=http://localhost:11434/v1
npx pr-sage review --repo owner/repo --pr 123 --provider openai --model qwen2.5-coder:14b
Works the same with vLLM, LM Studio, or any gateway that speaks the OpenAI chat completions API. For GitHub Actions, init --provider self-hosted generates a workflow for a self-hosted runner; localhost must refer to that runner, not a GitHub-hosted VM.
Put a .pr-sage.json in the directory you run from (CLI flags override it):
{ "provider" : " anthropic " , "locale" : " auto " , "exclude" : [ " src/generated/** " , " /*.snap " ], "paths" : [ " packages/web/ " ], "minSeverity" : " suggestion " , "failOn" : " critical " , "context" : " full " , "maxTokensPerRun" : 200000 , "failOnIncomplete" : true , "skipLabels" : [ " skip-review " ], "verify" : true , "verifyProvider" : " gemini " , "verifyModel" : " gemini-flash-latest " , "verifyFailure" : " abort " , "checkRun" : true , "pathRules" : [ { "paths" : [ " packages/api/** " ], "instructions" : " Check public API backward compatibility. " , "minSeverity" : " suggestion " , "failOn" : " warning " } ], "instructions" : " We use Result<T, E> for error handling — flag thrown exceptions in domain code. Prefer early returns over nested conditionals. " }
instructions is injected into the review prompt — use it for team conventions the reviewer should enforce.
name : AI Review on : pull_request : types : [opened, synchronize] permissions : contents : read pull-requests : write checks : write concurrency : group : pr-sage-${{ github.event.pull_request.number }} cancel-in-progress : true jobs : review : runs-on : ubuntu-latest steps :
To upload SARIF, add security-events: write and set sarif: "true" . The Action also exposes first-class paths , max-tokens , verify-provider , verify-model , verify-failure , and openai-base-url inputs.
The base-SHA checkout is deliberate: configuration and repositor…
本条由桃子采集流水线（启发式模式）自动整理，原文见文末信源。
