MAD Studio is an operational thinking console for structured AI deliberation. Configure 2–100 reasoning agents, run them through peer-reviewed debate protocols, and watch them pressure-test ideas one turn at a time.
MAD Studio operationalizes the leading academic frameworks in multi-agent debate, turning them from research notebooks into a production-grade workbench. Every protocol is traceable to a published methodology.
Du, Li, Torralba, Tenenbaum, Mordatch
Foundational result: agents critiquing each other across rounds converge on more factual, better-reasoned answers.
Liang, He, Ma, Zhang, Wang, Hu, Zhang, Lin
Establishes that adversarial multi-agent debate counteracts degeneration of thought and unlocks deeper reasoning.
Feng, Zhao, Lyu, Li, Tu, Wang
Introduces the per-dimension arbiter sweep that powers MAD Studio's truth-seeking verdict scoring.
Chan, Chen, Yu, Lu, Sun, Liu
Demonstrates that multi-agent debate panels evaluate generated text more reliably than single-judge baselines.
Khan, Hughes, Valentine, Ruis, Sachan, Radhakrishnan, Bowman, Perez
Strong empirical evidence that debate makes weaker judges reliably select truthful answers from stronger debaters — the modern, capable-model successor to the original debate-as-alignment thesis.
Wu, Bansal, Zhang, Wu, Li, Zhu, Wang, Saied, Awadallah, Awadalla, Wang
Shows that role-specialized agent groups orchestrated through structured conversation consistently outperform monolithic prompts on complex tasks.
Shinn, Cassano, Berman, Gopinath, Narasimhan, Yao
Verbal self-critique loops iteratively raise agent performance — the direct precedent for Saga's recursive optimization passes.
Madaan, Tandon, Gupta, Hallinan, Gao, Wiegreffe, Alon, et al.
Single-model iterative refinement via self-generated feedback. The minimal version of what multi-agent debate scales up across roles.
Wang, Bai, Liu, Chen, Cardie, Zhang, et al.
Layered multi-LLM collaboration where each layer's agents refine the previous layer's outputs. Open-source MoA reaches 65.1% on AlpacaEval 2.0, beating GPT-4 Omni.
Choi, Zhu, Li, et al.
Pinpoints when multi-agent debate actually beats majority vote: diversity-aware initialization plus calibrated confidence communication. Directly informs MAD Studio's persona and confidence design.
Every primitive a researcher, strategist, or analyst needs to run a structured AI debate at production quality — without rebuilding the scaffolding from scratch.
Open Discussion for brainstorming, Truth-Seeking Debate with a 10-phase M-MAD verdict, and Team Discussion for two-team battle or collaboration modes.
Compose participants from a reusable Worker library. Snapshotted into the conversation so editing a Worker never rewrites historical transcripts.
Five independent arbiter passes — correctness, evidence use, responsiveness, calibration, citation quality — feed a structured verdict the model cannot retroactively rewrite.
Bounded prompts that scale to long-running runs. Full transcripts stay persisted, but each turn is fed a compact, faithful context.
Hidden child runs iteratively refine the parent prompt. Stops on convergence, score threshold, cost cap, or generation limit.
Sweep temperature, repetition, frequency, and presence penalties across hidden child copies. Score transcripts against a validation prompt and expected outcome.
Inject guidance mid-run, optionally targeting the next responding agent. Interventions are first-class transcript inputs.
OpenRouter, LM Studio, and deterministic dummy providers. Configure per-agent fallbacks with server-side prompt caching.
Hard ceilings on every dimension that matters. Sessions self-terminate before they burn budget or time.
Supabase-backed job claims, runtime locks, and transactional mutations. One failed conversation never stalls the others.
Reusable prompt guidance, hidden discussion rules, and snapshotted 2–6 Worker teams. Configure once, run forever.
Get pinged when a run finishes or hits its cost cap. Resend integration when configured, in-app fallback when not.
Full REST API and a first-class Model Context Protocol server. Spin up sessions, inject human turns, stream transcripts, and run entire experiments — programmatically, from anywhere.
Every UI action is mirrored as a documented endpoint with stable contracts, idempotency keys, and webhook callbacks for long-running runs.
Drop MAD Studio into Claude Desktop, Cursor, or any MCP-compatible client. Orchestrate full debates as a callable tool — your agent of agents.
{
"mcpServers": {
"mad-studio": {
"command": "npx",
"args": ["-y", "@mad-studio/mcp"]
}
}
}Saga and Experiments run hidden recursive debates until the agents stop surprising each other. Surface insights no model — not GPT 5.5, not Opus 4.7 — would give you from a single shot.
Saga spawns zero-turn child sessions, scores each transcript against your rubric, applies the best optimizer suggestion, and re-runs. It stops only when the score curve flattens — or when the answer changes everything you thought you knew.
Experiments fan out hidden child runs across temperature, repetition, frequency, and presence sweeps. Score every transcript against a validation prompt. Promote the configuration that beats the field. The answer you would have hand-written, rewritten by the search.
Beyond what one prompt can reach. Both Saga and Experiments surface answers no human or single model would have arrived at unaided.
The MAD Studio Evaluation Matrix scores every agent, turn, and run across twelve rigorous dimensions — from logical consistency to steelman quality to calibration. The single number you ship is the single number you can prove.
| Agent | Logical | Evidence | Rebuttal | Steelman | Calibration | Novelty | Signal-to-noise | Citation | Position | Concession | Falsifiability | Decision-readiness |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Debater A | 92 | 78 | 88 | 71 | 84 | 66 | 81 | 90 | 58 | 73 | 87 | 79 |
| Debater B | 85 | 91 | 76 | 82 | 70 | 88 | 74 | 83 | 65 | 80 | 72 | 86 |
| Arbiter | 94 | 89 | 95 | 90 | 93 | 71 | 92 | 96 | 84 | 91 | 88 | 95 |
Every factual claim is extracted, cross-checked against your evidence pack, the public web, and the agent's own earlier turns. Hallucinated citations, drifted positions, false precision, and unsupported assertions all push the needle. Bullshit gets flagged before it becomes a quote.
Define your own dimensions, weights, and scoring guidance. The arbiter pipeline picks them up without re-architecting the protocol.
Pairwise debate outcomes update per-agent, per-topic ELO ratings. Find which Worker is actually best at adversarial law versus product strategy.
Compare two runs side by side. Spot the dimension that shifted when you tweaked the prompt. Export to CSV, JSON, or paste straight into a report.
Every score is traceable to the turn that earned it. Click any cell to jump to the supporting transcript span and the arbiter rationale.
MAD Studio is the only platform on the web that delivers peer-reviewed multi-agent debate as a daily-driver tool. From war rooms to lab notebooks to weekend curiosity — the same engine, the same rigor.
Stage opposition agents that hammer your platform with the strongest rebuttals from across the spectrum. Find weak claims before journalists do.
Run hypotheses through a panel of skeptical agents with persona-specific priors. Capture structured rebuttals, citations needed, and open questions.
Run two competing campaign angles as Teams. Battle mode surfaces critique; collaboration mode synthesizes the best of both into a single brief.
Configure prosecutor and defense agents over a shared evidence pack. Get a structured claims ledger and a five-dimension verdict scorecard.
Pressure-test roadmap decisions, pricing models, and launch plans against agents seeded with competitor personas, customer archetypes, and risk lenses.
Make critical thinking visible. Students follow turn-by-turn argument structure, evidence handling, and the arbiter's dimension-level rationale.
Before publication, simulate the most aggressive defense your subject could mount. Identify the holes that will get raised — and patch them.
Pick a spicy topic. Pick six agents. Hit start. Watch them go. Export the transcript. No wrong answers — that's what the arbiter is for.
Quick answers to the questions teams ask before they run their first MAD Studio session.
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