---
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: Pattern advice, topology, and theorem predictions
---

# Epistemic Topology Explorer

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

If you want the shortest path to something practical, start with the **Pattern Advisor** tab. It translates task constraints into a recommendation like `single_worker_with_reviewer` or `specialist_swarm` before you ever need to look at partitions or theorem cards.

## What to Try

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

## 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. The Pattern Advisor turns that same analysis into a more direct recommendation for engineers who care about what to try next, not just why the result holds.

## Learn More

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