🧭 The Chart Room

Four panels. Four perspectives. One truth.

A multi-model perspective library and web app — route a single query to four different model perspectives, analyze consensus and divergence.

Panel	Navigator	What They See
🎣 Top-left	Fisherman	Ecological detail, bottom structure, local patterns. What's actually there.
⛵ Top-right	Sailor	Systemic flows, weather patterns, navigational structure. How things move.
📸 Bottom-left	Tourist	Surface features, landmarks, narrative salience. What's obvious and memorable.
🏝️ Bottom-right	Native	Negative space, absence patterns, what others don't see. What's missing.

Each chart is a different attention configuration — a different way of allocating the same fixed cognitive budget (γ + H = C). The overlap is consensus. The divergence is discovery. The negative space is where the future is.

Library Usage

from chartroom import ChartRoom

room = ChartRoom()
result = room.navigate("What is consciousness?")

print(result["fisherman"]["text"])   # Ecological detail
print(result["sailor"]["text"])      # Systemic flows
print(result["tourist"]["text"])     # Surface features  
print(result["native"]["text"])      # Negative space

print(result["consensus"]["level"])   # consensus | complementary | contradiction
print(result["divergence"]["points"]) # Unique observations per chart

Without an API key, ChartRoom runs in mock mode — useful for testing and demos.

Single Chart

room = ChartRoom()
result = room.chart("native", "What's missing from this design?")

Async (parallel API calls)

import asyncio
from chartroom import ChartRoom

async def main():
    room = ChartRoom(api_key="sk-...")
    result = await room.navigate_async("complex query here")

asyncio.run(main())

Web App

pip install chartroom[server]
python app.py
# Open http://localhost:5000

Installation

pip install chartroom          # library only
pip install chartroom[server]  # library + Flask web app
pip install chartroom[dev]     # library + test tools

Configuration

Set via environment variables:

Variable	Default	Description
OPENAI_API_KEY	—	API key for model provider
OPENAI_API_BASE	https://api.deepinfra.com/v1/openai	Base URL
FISHERMAN_MODEL	deepseek-ai/DeepSeek-V4-Flash	Fisherman chart model
SAILOR_MODEL	ByteDance/Seed-2.0-pro	Sailor chart model
TOURIST_MODEL	deepreinforce-ai/Ornith-1.0-35B	Tourist chart model
NATIVE_MODEL	moonshotai/Kimi-K2.7-Code	Native chart model

How It Works

Each chart gets a different system prompt that configures its attention — what it looks for, what it values, how it allocates its token budget. This is chart-system theory applied: same territory, different charts, different distortions.

The consensus analysis finds where charts agree (shared concepts above a frequency threshold). The divergence analysis finds where charts diverge (unique observations only one chart noticed). Together, they map the cognitive terrain.

License

MIT

Related

• chart-system — polyformal navigation framework
• skénna — negative-space navigation