Multi-Engine Code Review with Codex + Gemini + Claude: Principles, Patterns, and Implementation

TL;DR AI models rationalize their own code when reviewing it. Using three different CLIs for independent review effectively catches blind spots -- this post covers the design philosophy and practical workflow patterns behind the approach.

#claude-code #gemini-cli #codex-cli #code-review #agentic-workflow #multi-model

Table of Contents

Why One Model Isn’t Enough
Five Major Patterns
Role Assignment Patterns
Implementation: Three-Engine Review Pipeline
Overall Takeaway
References

🌏 中文版

Earlier this year, Codex CLI launched publicly and Gemini CLI went open source. The AI coding tool landscape shifted from “one IDE plugin” to “multiple CLI tools collaborating with each other.” An interesting pattern emerged in the community: instead of having AI write more code, people started having different AIs review each other’s output.

This post compiles the most common multi-engine workflows in the community today, and explains why “having the same model review its own code” is fundamentally problematic.

Why One Model Isn’t Enough

The author of Lad MCP Server (an AI consulting firm) put it clearly in their README:

“LLMs generate text token by token. When an agent makes a questionable design choice early on, every subsequent token tries to justify and reinforce that mistake to maintain cohesion. The agent effectively gaslights itself.”

This is the “bad token” problem. A model generates a problematic piece of code, but because every subsequent token needs to maintain semantic coherence, it keeps reinforcing that wrong direction instead of questioning it. Having the same model review its own output essentially means asking it to read its own chain of reasoning — it sees “reasonable” because that’s how it reasoned in the first place.

Only a different model, one without that token history, can truly “challenge” rather than “rationalize.”

The multi-model-review project makes a similar point:

“A different model reading the same spec and diff has: different training data (catches issues the builder’s model is blind to), different calibration on severity (less confirmation bias), different house style.”

Five Major Patterns

Pattern 1: Drafter + Reviewer Separation

The most common approach. Claude Code handles planning and implementation; Codex handles the final code review and application.

Using Synapse as an example:

init → plan → Gate (human confirmation)
     → Claude/Gemini generates diff draft
     → Codex reviews and rewrites to production quality
     → verify (lint/typecheck/test)
     → Claude/Gemini reviews the final diff

External models only generate drafts and never directly touch your files. Codex serves as the gatekeeper for execution.

Pattern 2: Consensus Gate (Majority Vote)

Three models review in parallel; consensus must be reached before proceeding. Cerberus implements this pattern as a Claude Code plugin:

Any output from Claude Code
    → Sent in parallel to Codex + Gemini + Claude Opus
    → Majority vote
    → Pass: session auto-approved
    → Fail: revision requested, up to 3 rounds

Supports --mode fast|smart|max, where max mode uses the deepest reasoning level for high-risk code changes.

Pattern 3: Markdown as the Interface Between Models

Models don’t communicate directly; instead, they exchange information via markdown files on disk. The multi-model-review workflow:

# Claude packages all review-relevant information
/multi-model-review:review-package

# You manually send this package to another model
gemini --file .cross-review/packages/<timestamp>/review-package.md \
  > .cross-review/packages/<timestamp>/review-report.md

# Claude reads the report and addresses issues one by one
/multi-model-review:apply-review

The advantage of this pattern is that it requires zero integration between models. You can send the same package to both Codex and Gemini, then compare the two reports.

Pattern 4: Orchestration Daemon

A persistent service monitors GitHub issues and dispatches to the appropriate CLI based on labels:

GitHub comment: /review
    → daemon reads label: agent:codex
    → Starts Codex CLI review session
    → Automatically triggers review after implementation

ghiagor supports /plan, /implement, /review, /loopfix, and other commands, letting you control which AI does what directly through GitHub slash commands.

Pattern 5: MCP Server Wrapping Multiple Engines

Lad MCP Server packages dual-engine review into two MCP tools (system_design_review, code_review), usable by any MCP-compatible client. It defaults to calling two different models simultaneously via OpenRouter (kimi-k2 + minimax), claiming a 15-20% improvement in AI-generated code quality.

Role Assignment Patterns

After reviewing over a dozen projects, each model shows clear tendencies in what it’s trusted to do:

Model	Common Roles
Claude Code	Architecture planning, risk analysis, security, applying fixes
Gemini CLI	Frontend/UX review, accessibility, breadth scanning, documentation
Codex CLI	API feasibility, code execution gatekeeping, DX checks
Cursor Agent	File structure, module navigation, project layout

These aren’t hard rules, but they reflect the community’s real-world experience: Gemini is more sensitive to UI-related issues, Codex is stricter about executability and API design, and Claude goes deeper on architecture and security.

Implementation: Three-Engine Review Pipeline

Based on the patterns above, here’s my current code review workflow:

Step 1: Determine the Base Branch

BASE=$(gh pr view --json baseRefName -q .baseRefName 2>/dev/null \
  || git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null \
    | sed 's|refs/remotes/origin/||' \
  || echo "main")

Step 2: Codex Review

codex review \
  "Focus on repository code only. Check: logic errors, security issues, performance, architecture consistency." \
  -c 'model_reasoning_effort="high"' \
  --enable web_search_cached

Codex’s review subcommand has its own diff parsing and P1/P2 issue classification. --enable web_search_cached lets it look up documentation.

Step 3: Gemini Review

git diff "$BASE"...HEAD | gemini -p "Review this diff.
Report issues as a table: | Severity | File | Issue | Suggestion |
Severity: High (bug/security), Medium (perf/maintainability), Low (style).
Be direct. No compliments." \
  --approval-mode yolo

Step 4: Claude Haiku Review

git diff "$BASE"...HEAD | claude -p "Review this diff.
Same table format. Same severity levels.
Be direct. No compliments." \
  --model claude-haiku-4-5-20251001

Haiku runs in headless mode — fast with low token cost, making it ideal as a third independent opinion.

Step 5: Cross-Model Analysis

After all three engines complete, compare findings:

All three report the same issue → High priority, must fix
Two report it → Strongly recommend verifying
Only one reports it → Likely a model-specific preference, let a human decide

Overall Takeaway

The core assumption behind multi-engine review is that different AI models have different training data, different style preferences, and different blind spots. Having them independently review the same code and then comparing divergences is currently the lowest-cost way to improve the credibility of AI code review.

The downside is time cost — running three CLIs sequentially can take 5-10 minutes. If the diff is large, Codex’s review alone might need 3-5 minutes.

For now, this approach works best as a comprehensive quality gate before pushing, rather than something you run after every small change.