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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>
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
---
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title: Cross-LLM Code Review
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type: concept
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updated: 2026-05-04
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---
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# Cross-LLM Code Review
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Use meeting-room to run multi-agent code review where different LLM providers cross-check each other's work.
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## How it works
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1. Configure multiple providers (OpenAI, Anthropic, DeepSeek, local Ollama)
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2. Assign different providers to review roles (defender, attacker, security)
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3. Point meeting-room at a project directory with `-w /path/to/project`
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4. Agents read code files, debate quality, and produce structured findings
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## Scenario
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```bash
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meeting-room -s code-review.md -w /path/to/project
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```
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## Why cross-LLM?
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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.
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## Role separation
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- **defender** — explains design decisions, pushes back on unjustified criticism
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- **attacker** — hunts bugs, SOLID violations, dead code, edge cases
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- **security** — checks injection, auth gaps, CVEs in dependencies
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- **moderator** — categorizes findings (CRITICAL / IMPORTANT / MINOR / APPROVED)
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## Future (Phase 3)
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Boss role will allow injecting questions mid-review. CSO (Claude Code) will be able to join the review via REST API. |