---
title: Cross-LLM Code Review
type: concept
updated: 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

1. Configure multiple providers (OpenAI, Anthropic, DeepSeek, local Ollama)
2. Assign different providers to review roles (defender, attacker, security)
3. Point meeting-room at a project directory with `-w /path/to/project`
4. Agents read code files, debate quality, and produce structured findings

## Scenario

```bash
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.