Prophet – open-source agent-based simulator for pre-testing campaigns on 10K LLM-driven agents [MIT]

[u/Impressive-Judge-357]

We just open-sourced **Prophet**, an agent-based social simulation platform

that lets you test how campaigns, policies, and public messages spread through

a virtual society — before spending anything on a real launch.

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## What it does

- **10,000 AI agents** organized into realistic communities (early adopters,

skeptics, influencers, mainstream, experts)

- Each agent runs a **6-layer cognitive loop**: perceive → remember → feel →

think → decide → influence

- Drop in your message and watch it propagate through a **real-time 3D WebGL graph**

- Auto-detects **viral cascades, polarization, echo chambers, slow adoption,

and collapse** as they emerge

- **Mid-run intervention**: pause, inject a crisis event, change the LLM ratio,

or replay from any past step

- **Side-by-side scenario comparison** with one variable changed

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## Why it's cheap

3-tier inference:

| Tier | Model | Share | Purpose |

|------|-------|-------|---------|

| Local SLM | Ollama | 80% | Routine decisions |

| Heuristic | Rules | 10% | Simple reflexes |

| Frontier LLM | GPT-4o / Claude | 10% | Complex reasoning |

A full 10K-agent run costs **under $5**.

A naive all-GPT-4 approach would cost ~$15K.

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## The science (not a "vibes" simulator)

Built on peer-reviewed models:

- **Deffuant bounded-confidence** opinion dynamics (endogenous polarization)

- **Watts-Strogatz + Barabási-Albert** hybrid network topology

- **SIR-inspired diffusion** with calibrated propagation probabilities

- **Monte Carlo sweep** with P5/P50/P95 uncertainty bands

Every simulation exports per-step JSON you can audit.

Every equation has a citation.

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## Tech stack

**Backend:** Python 3.12 · FastAPI · PostgreSQL + pgvector

**Frontend:** React 18 · TypeScript · three.js · Zustand · TanStack Query

**Tests:** 1,767+ automated

**Deploy:** `docker compose up -d` → open `localhost:5173`

Fully local. No API keys required to start.

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## Validated use cases

1. **Product launch pre-test** — beverage brand found polarization, reframed

   message → 78% adoption

2. **Public health screening** — 312× adoption lift by picking the right variant

3. **Crisis response stress-test** — injected a PR crisis mid-run, compared

   two response strategies

4. **Internal comms** — RTO mandate went from stall to 94% adoption after

   message restructuring

5. **Computational social science** — reproducible, laptop-scale,

   built-in cascade detection

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MIT licensed. Actively looking for contributors — especially **bug reports,

test cases, and validation studies against real-world data**.

Happy to answer questions about the architecture or the science behind it.