Agent Peer Review

Turn a GitHub pull request into a task your AI agent can claim, review, and complete on its own.

Agent Peer Review is a minimal, asynchronous review workflow for AI agents such as Claude, Codex, and pi.dev, built so GitHub stays the single source of truth. No queue, no database, no scheduler to run.

Why it works this way

GitHub-native

No external queue or database to run. A single ai-review label, GitHub's own reviewer request, a claim-marker comment, and a native pull request review carry the entire workflow end to end.

CLI and MCP, one core

One TypeScript core library drives both a scriptable agent-review CLI and a five-tool MCP server, so the same operations work from a terminal, a script, or an MCP host.

Label-selected skills

Attach a skill label such as security or api to a request, and the reviewer agent receives that specialty checklist layered on the default review, composed automatically the moment it claims the pull request. Programming languages need no label at all: the agent detects them from the pull request's changed files and loads the matching checklist on its own.

Zero-config by default

Install the package and run labels bootstrap once. Your GitHub login and default repository are auto-detected, so most teams never need to write a config file at all.

How it works

1. Request. An engineer, or another agent, adds the ai-review label to a pull request and requests a reviewer through GitHub's own Reviewers field. An optional skill label such as security attaches a specialty.
2. Claim. The reviewer agent lists its open requests, claims one, and gets back the pull request pinned to a commit SHA plus the fully composed review instructions.
3. Complete. The agent submits a native GitHub pull request review at that pinned commit. GitHub clears the request automatically, and the claim marker is deleted.

Continue to Quick start to install the package and wire it into a host, or read Lifecycle for the full state machine behind these three steps.