Multi-Agent PR Reviewer
A working multi-agent application, not a demo. The browser fetches the PR + file diffs from the GitHub public API, then fans out four parallel streaming LLM calls — each with a tightly scoped system prompt focused on a single review dimension (correctness · security · style · tests). Cards stream in independently as the model produces tokens. Once all four finish, a lead-reviewer prompt receives the diff plus all four reviews and emits a structured verdict (LGTM / LGTM_WITH_NITS / NEEDS_CHANGES / BLOCK) plus top severity-graded issues. Reuses the existing OpenRouter streaming route, the per-IP rate limiter, and the same UI patterns as the rest of AI Labs. Lives at /pr-review on the portfolio.
Frontend
Backend
Database
Tools
- • Most 'AI code review' tools are single-call wrappers — one prompt does everything, blurring critique across dimensions
- • GitHub diffs can be enormous; sending the whole thing to a model wastes context and money
- • Multi-step agent flows tend to feel slow and opaque if the UI doesn't show the work happening
- • Four parallel specialised reviewer agents, each with a single-dimension system prompt — cleaner critique, smaller per-call context, half the latency of a sequential pipeline
- • Server-side PR fetch ranks files by total changes and keeps the top 10 with per-file diff truncation; total context stays well under 50 KB
- • Live streaming cards per reviewer + a final structured-verdict card so the visitor sees parallel orchestration happening in real time
Working tool that reviews real public PRs in seconds
Demonstrates real multi-agent orchestration — not a sandbox simulation
Reuses /api/demo/generate, the shared rate limiter, and the AI Labs UI vocabulary
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