Osinix MCS Engine

Most AI systems generate answers. Osinix MCS challenges them.

MCS puts multiple AI models, cloud and local, into one structured deliberation.

Osinix MCS: most AI systems generate answers, MCS challenges them

MCS Enterprise: the same engine on your own servers, under your control.

What is MCS

An engine that makes your AI models work together.

MCS Engine is software that makes several AI models work on one problem together. Instead of trusting a single model's answer, MCS runs a structured deliberation: the models propose, challenge each other, defend their reasoning, and converge on an answer that survived scrutiny. Runs on your Linux or macOS machine. MCS is not a model and does not host models. It is the engine that turns the models you already have into a team.

Read the full introduction

Multiple AI modelsDifferent strengths, shared purpose
→
Structured deliberationAnalyze, challenge, improve together
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Continuous refinementRounds continue until the answer is strong
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Better resultsWell reasoned, thoroughly evaluated
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You decideInsights you can trust
Why MCS

MCS does not sell more models.
It delivers challenged, defensible answers when being wrong is expensive.

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A meeting, not a survey

Other tools collect independent answers and pick one. In MCS, participants see each other's reasoning across multiple rounds and must defend or revise their positions.

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Your machine, your rules

Mix cloud providers with local models running on your own hardware. Your data goes only where you send it.

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Reasoning you can inspect

Every session produces a full record of who proposed what, who challenged it, and why the conclusion held. Not a verdict, but a defensible position.

AI models forget. Osinix MCS remembers. Sessions persist and continue.
A real session

See it run, start to finish.

This is MCS running a legal strategy deliberation: a wrongful termination case, with the case brief attached as a reference file. Three models from three providers: Claude moderating, GPT-4o and Grok arguing opposing positions. No pipelines to build, no code to write, nothing to compile: you configure the panel in your browser and press start. These are unedited screenshots. Click any of them to enlarge. Every package ships with nine sessions like this one, ready to run.

Starting the MCS engine from a terminal with one command

1. Start the engine

One command in a terminal. MCS starts its web server and opens the interface in your browser.

Building the panel: moderator, participants, roles, and the case brief

2. Build the panel

Claude as moderator, GPT-4o and Grok with opposing roles, each with its own instructions, and the case brief attached as a reference file.

The deliberation running live, models challenging each other

3. Watch them deliberate

Live, turn by turn: the models argue the case, cite the brief, and challenge each other's numbers.

The session folder on disk: transcript, turns, summaries, and the produced memo

4. Everything lands on disk

Full transcript, per-turn files, summaries, and the memo the panel produced, all as ordinary files in the session folder.

The final verdict with the produced strategy memo and session statistics

5. The verdict

A client-ready strategy memo, produced as a real file. This session: nine turns, two rounds, 3 minutes 10 seconds.

Your models, your files.

OpenAI, Anthropic, Google, and local models through any OpenAI compatible server, all in the same deliberation, at the same table. No vendor lock-in, ever. MCS participants collaborate on images, documents, audio, source code, and data. Real files, passed between models during the discussion, so every decision gets richer context.

MCS models collaborate using images, documents, audio, source code and data
The MCS SDK

Deliberation, from your own code.

The SDK is a C API and a Python package that let your own programs start an MCS deliberation, watch its progress, and collect the results. The SDK is included with Pro Max and Enterprise, and available as a $15/mo add on for Basic and Pro.

import time
import mcs

mcs.init()  # finds your engine and license on its own

with mcs.start("mcs.conf", "session.conf") as session:
    while not session.is_done():
        time.sleep(2)

    print("status:", session.status())
    for name in session.output_files():
        print("produced:", name)