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- README: remove autogen/crewai references, add v2 architecture diagram, code-review scenario, cross-LLM config examples, roadmap - wiki/overview: event-driven architecture, Pydantic v2, roadmap - wiki/index: add entities/roles, concepts/code-review, packages/pydantic - wiki/log: Phase 1 completion, code-review decision - New: entities/roles.md, concepts/code-review.md, packages/pydantic.md Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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title, type, updated
| title | type | updated |
|---|---|---|
| Cross-LLM Code Review | concept | 2026-05-04 |
Cross-LLM Code Review
Use meeting-room to run multi-agent code review where different LLM providers cross-check each other's work.
How it works
- Configure multiple providers (OpenAI, Anthropic, DeepSeek, local Ollama)
- Assign different providers to review roles (defender, attacker, security)
- Point meeting-room at a project directory with
-w /path/to/project - Agents read code files, debate quality, and produce structured findings
Scenario
meeting-room -s code-review.md -w /path/to/project
Why cross-LLM?
Different models have different blind spots. GPT-4o may catch type errors that Claude misses, while DeepSeek may spot performance issues neither other model flagged. Cross-LLM review is the "second pair of eyes" principle applied to AI code review.
Role separation
- defender — explains design decisions, pushes back on unjustified criticism
- attacker — hunts bugs, SOLID violations, dead code, edge cases
- security — checks injection, auth gaps, CVEs in dependencies
- moderator — categorizes findings (CRITICAL / IMPORTANT / MINOR / APPROVED)
Future (Phase 3)
Boss role will allow injecting questions mid-review. CSO (Claude Code) will be able to join the review via REST API.