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
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}/.