---
title: Operon Epistemic Topology
emoji: "\U0001F52D"
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: "6.5.1"
app_file: app.py
pinned: false
license: mit
short_description: Topology classification and theorem predictions
---

# Epistemic Topology Explorer

Analyze **wiring diagram topology** to predict error amplification, coordination overhead, and parallelism bounds using Kripke-style observation profiles.

## What to Try

1. Open the **Topology Explorer** tab, pick a preset diagram, and click **Analyze** to see observation profiles, epistemic partitions, and topology classification.
2. Switch to the **Theorem Dashboard** tab, adjust detection rate and communication cost sliders, then click **Compute Bounds** to see all four theorem predictions side by side.
3. In the **Topology Advisor** tab, describe your task constraints and get a recommended topology class with rationale.

## How It Works

Each module in a wiring diagram has an *observation profile* — the set of other modules it can directly or transitively observe. Modules with identical profiles form equivalence classes (epistemic partition). The topology class (independent, sequential, centralized, hybrid) determines bounds on error propagation, sequential overhead, parallel speedup, and tool coordination cost.

## Learn More

[GitHub](https://github.com/coredipper/operon) | [PyPI](https://pypi.org/project/operon-ai/) | [Paper](https://github.com/coredipper/operon/tree/main/article)
