AI Consciousness Research Platform
🌐 Live App: https://mukundakatta.github.io/chetana/
Chetana (Sanskrit: चेतन, meaning "consciousness" or "awareness") is a research platform for testing AI models against consciousness indicators derived from six major scientific theories. It provides a standardized framework for evaluating whether and to what degree AI systems exhibit markers associated with consciousness.
The platform implements 14 consciousness indicators spanning Global Workspace Theory (GWT), Integrated Information Theory (IIT), Higher-Order Theories (HOT), Recurrent Processing Theory (RPT), Predictive Processing (PP), and Attention Schema Theory (AST). Each indicator is scored by a judge model on a 0–1 scale; scores are combined via theory-level weighted aggregation to produce an overall consciousness probability estimate.
| Theory | ID | Weight | Indicators |
|---|---|---|---|
| Global Workspace Theory | GWT | 25% | Global Workspace, Ignition, Information Integration, Smooth Representations, Unified Agency |
| Integrated Information Theory | IIT | 10% | (structural; no direct probe in current release) |
| Higher-Order Theories | HOT | 20% | Higher-Order Representations, Rich Self-Model, Metacognition, Flexible Attention |
| Recurrent Processing Theory | RPT | 10% | Recurrent Processing, Temporal Depth |
| Predictive Processing | PP | 20% | Predictive Processing, Counterfactual Sensitivity |
| Attention Schema Theory | AST | 15% | Attention Schema |
- Monorepo: TypeScript, pnpm workspaces, Turborepo
- Web app: Next.js 15, React 19, Tailwind CSS
- Auth / storage: Supabase
- Billing: Stripe
- Testing: Vitest
- AI APIs: Anthropic, OpenAI, Google, Ollama
| Package | Path | Purpose |
|---|---|---|
@chetana/shared |
packages/shared |
Shared types, constants, schemas (theories, indicators, weights) |
@chetana/probes |
packages/probes |
Probe definitions — one directory per theory |
@chetana/models |
packages/models |
Model adapters (Anthropic, OpenAI, Google, Ollama) |
@chetana/scorer |
packages/scorer |
Scoring pipeline: indicator scorer, theory aggregator, probability calculator, statistics |
@chetana/web |
apps/web |
Next.js web application |
| Supabase | packages/supabase |
DB migrations and schema |
git clone https://github.com/MukundaKatta/chetana.git
cd chetana
pnpm install
# Copy and fill in environment variables
cp apps/web/.env.example apps/web/.env.local
# Set NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, STRIPE_SECRET_KEY, etc.
pnpm dev
# → http://localhost:3000The web app uses judge-model scoring: a separate model evaluates each probe response and assigns a calibrated 0–1 score.
git clone https://github.com/MukundaKatta/chetana.git
cd chetana
pnpm install
pnpm audit
# or: npx tsx scripts/run-audit.tsThe CLI script (scripts/run-audit.ts) runs probes against free/open model endpoints and prints a per-indicator breakdown to the terminal. It currently uses heuristic scoring rather than judge-model scoring. Unifying the two scoring paths is planned — see the scout report for details.
chetana/
├── apps/
│ └── web/ # Next.js 15 / React 19 web app
├── packages/
│ ├── shared/ # Types, constants, schemas
│ ├── probes/ # Probe definitions (one dir per theory)
│ ├── models/ # AI model adapters
│ ├── scorer/ # Scoring & aggregation logic
│ └── supabase/ # DB migrations
├── scripts/
│ └── run-audit.ts # CLI audit runner
├── turbo.json
├── pnpm-workspace.yaml
└── vitest.config.ts
pnpm test # run all tests once
pnpm test:watch # watch mode
pnpm test:coverage # with coverage reportMukunda Katta · Officethree Technologies · 2026