AutoReview Crew turns code review into a visible multi-agent workflow. A human mentions the Lead Reviewer in a Band room, and the Lead coordinates a full pull-request review through Band: it delegates to Correctness and Security specialists, waits for the current round of reports, recruits a Test Reviewer when coverage is required, aggregates every finding, and escalates risky changes to the human owner. The project targets Track 2: Multi-Agent Software Development. It covers automated PR review, merge preparation, test coverage, cross-model code review, and human-in-the-loop escalation. Band is not a wrapper around the workflow; it is the collaboration layer. @mentions route the work, the shared room carries context between agents, Band participant tools let the Lead recruit a specialist mid-review, and room events provide an audit trail of every tool call and decision. The demo run completes in about 51 seconds. The Security Reviewer, running Qwen2.5-72B on Featherless AI, flags a hardcoded Stripe API key, SQL injection, and missing input validation. The Correctness Reviewer, running through AI/ML API, flags division by zero and a mutable default argument. The Lead then recruits the Test Reviewer through Band, receives concrete pytest coverage, and only then sends its final ESCALATE_TO_HUMAN verdict. The crew is cross-model and cross-provider by design: AI/ML API powers the Lead, Correctness, and Test agents, while Featherless AI powers the open-source Security Reviewer. The result is not one chatbot reviewing code, but a team of specialists that coordinate, recruit help when they need it, and know when to hand a risky change back to a human. MIT licensed.
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