README · 076f0077f641

Emergent — Multi-Model AI Hub

Chat with GPT, Claude, Gemini, Grok, DeepSeek, and Perplexity in parallel. Compare, synthesize, and chain their responses — all in one interface.

Try it live

emergentapp.interdependentway.org

What is this?

Emergent is a multi-model AI hub that lets you send one prompt to multiple LLMs simultaneously and work with their responses together. Instead of copy-pasting between chat tabs, you get a single interface with color-coded, side-by-side or stacked responses from every model you care about.

Beyond simple fan-out, Emergent supports structured interaction patterns — synthesis (feed multiple responses into one model for analysis), shared rooms (models that see and respond to each other), daisy chains (A→B→C sequential pipelines), council mode, and roleplay scenarios. The EDCM engine analyzes conversation transcripts across six cognitive metrics and surfaces actionable insights.

Interaction Patterns

Self-hosting

Backend (FastAPI + MongoDB)

cd backend
pip install -r requirements.txt

# Required env vars
export MONGO_URI="mongodb://localhost:27017"
export JWT_SECRET="your-secret"
export STRIPE_SECRET_KEY="sk_..."        # optional: for payments
export STRIPE_WEBHOOK_SECRET="whsec_..." # optional: for webhooks

uvicorn server:app --reload

Frontend (React)

cd frontend
npm install
npm start

The frontend expects the backend at http://localhost:8000 by default.

aimmh-lib — the open-source core

The orchestration patterns are extracted into a standalone, zero-dependency Python library.

pip install aimmh-lib

Functional API

import asyncio
from aimmh_lib import fan_out

async def call_model(model_id: str, messages: list[dict]) -> str:
    # plug in any model backend here
    return f"Response from {model_id}"

async def main():
    results = await fan_out(
        call_fn=call_model,
        model_ids=["gpt-4o", "claude-sonnet-4-6", "gemini-2.0-flash"],
        messages=[{"role": "user", "content": "What is the best programming language?"}],
    )
    for r in results:
        print(f"{r.model_id}: {r.content}")

asyncio.run(main())

Instantiation API

Bind a backend once and call any pattern as a method:

from aimmh_lib import MultiModelHub

hub = MultiModelHub(call_model)

results = await hub.fan_out(["gpt-4o", "claude-sonnet-4-6"], messages)
results = await hub.daisy_chain(["gpt-4o", "claude-sonnet-4-6", "gemini-2.0-flash"], "Explain gravity")
results = await hub.council(["gpt-4o", "claude-sonnet-4-6"], "What is consciousness?")

Stateful single-model conversations

from aimmh_lib import ModelInstance

gpt = ModelInstance(call_model, "gpt-4o", system_context="You are a Socratic tutor.")
r1 = await gpt.send("What is entropy?")
r2 = await gpt.send("Give me an example.")  # history carries over automatically
print(gpt.history)
gpt.clear()  # reset to fresh state

All six patterns available: fan_out, daisy_chain, room_all, room_synthesized, council, roleplay.

PyPI →

Tech Stack

Backend: FastAPI · Motor (async MongoDB) · asyncio · Stripe · Google OAuth · JWT

Frontend: React · Tailwind CSS · Shadcn UI · React Router

Library: Pure Python 3.11+ · zero runtime dependencies

Repository Structure

aimmh_lib/   # pip install aimmh-lib — zero-dep async orchestration library
backend/     # FastAPI service (auth, multi-model chat, payments, EDCM)
frontend/    # React UI

License

This project is licensed under the Mozilla Public License 2.0 (MPL-2.0). See LICENSE. Relicensed from MIT to MPL-2.0 — weak copyleft: embed anywhere, but changes to these files must be published. (Earlier history: AGPL-3.0-or-later + commercial, then MIT.)

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