magi-researchers

MAGI Researchers

Three AI models. One synthesis. Zero lost progress.
Multi-model research pipeline for Claude Code — Claude, Gemini, and Codex debate, cross-verify, and synthesize publication-ready artifacts.

         

Why MAGI? • Get Started • Features • Usage • Roadmap • Changelog

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Like the MAGI system in Evangelion — three supercomputers cross-verifying each other — this plugin orchestrates Claude, Gemini, and Codex for rigorous, multi-perspective research.

Why MAGI?

Single-model research has blind spots. One model hallucinates a citation or misses a critical constraint — and nobody catches it.

	Single Model	MAGI (3 Models)
Brainstorming	One perspective	Three independent perspectives
Verification	Self-review (unreliable)	Cross-model peer review
Blind spots	Undetected	Caught by competing models
Output	Raw text	Structured report with consensus & divergence analysis

• Claude (MELCHIOR) — The Scientist. Active third MAGI personality — synthesis, planning, implementation, and original analytical contributions.
• Gemini — The Critic. Creative brainstorming, cross-verification, broad knowledge.
• Codex — The Builder. Feasibility analysis, code review, implementation focus.

Case Study: Damped Oscillator Equation Discovery

We gave all three single models and MAGI the same physics problem: discover an unknown damping function from noisy sensor data. No single model proposed combining classical diagnostics with modern ML — only MAGI's cross-verification caught that gap.

Source	Score	Highlight
MAGI	90	Staged pipeline: rapid diagnostics → symbolic discovery → validation → fallback
Claude	84	Best code coverage — runnable snippets for every approach
Codex	80	Elegant physics-informed neural ODE constraints
Gemini	67	Most accessible for general audience

Experiment details

• Task: Discover $f(\dot{x})$ in $m\ddot{x} + f(\dot{x}) + kx = 0$ from noisy displacement data
• Setup: Identical prompt → 4 sources → anonymized blind evaluation via MAGI
• Evaluation: Two MAGI evaluator personas scored, cross-reviewed, and debated before synthesis
• Limitations: N=1 case study, self-evaluation (MAGI evaluated MAGI), ~7:1 compute ratio
• Full report: examples/damped_oscillator_comparison/evaluation_report.md
• Raw outputs: examples/damped_oscillator_comparison/

Get Started

Prerequisites: Claude Code + Python 3.11+ with uv + Gemini CLI + Codex CLI

1. Install the plugin (inside Claude Code):

/plugin marketplace add Axect/magi-researchers
/plugin install magi-researchers@magi-researchers-marketplace

2. Set up MCP servers (one-time):

claude mcp add -s user gemini-cli -- npx -y gemini-mcp-tool
claude mcp add -s user codex-cli -- npx -y @cexll/codex-mcp-server
claude mcp add -s user context7 -- npx -y @upstash/context7-mcp@latest

3. Run your first research:

/magi-researchers:research "your research topic" --domain physics

MAGI generates cross-verified hypotheses, writes implementation code, renders publication-quality plots, and synthesizes a structured report — all saved to outputs/{topic}/.