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HATS — AI agents that disagree to make better decisions Don't ask one AI, start running a team. Run meetings, plan work, and stress-test ideas with a team of AI agents that think differently — and challenge each other on purpose. Most AI tools give you one answer. HATS gives you a panel of perspectives. What makes HATS different? HATS is inspired by the Six Thinking Hats framework — but implemented as a real multi-agent system with structured disagreement. Each agent has a role: ⚪ White Hat — facts, gaps, assumptions 🔴 Red Hat — human impact, intuition ⚫ Black Hat — risks, failure modes 🟡 Yellow Hat — upside, opportunity 🟢 Green Hat — creativity, alternatives 🔵 Blue Hat — facilitation, synthesis They don’t just respond — they debate. Why this matters LLMs tend to:
agree with you sound confident even when wrong miss blind spots
HATS fixes that by:
introducing structured conflict forcing multiple perspectives simulating real team dynamics
Example use cases
Run a product planning meeting Stress-test a startup idea Explore trade-offs in architecture decisions -Replace async brainstorming sessions
Watch 6 AI agents plan a startup and argue about it Click for a demo video:
Architecture highlights
Multi-agent orchestration Per-agent model selection: OpenAI / Claude / Gemini Local support via Ollama / LM Studio Token + cost tracking per agent Voice + 3D avatar support
Live Meetings with Faces and Voices When a meeting starts, HATS opens a stage of animated 3D avatars rendered in real time using Three.js. Each agent speaks aloud via Piper TTS with per-agent voice models.
Lip sync is driven by Rhubarb, mapping phonetic audio timing to ARKit visemes so mouths move in sync with speech.
Five meeting types are supported — Standup, Sprint Planning, Retrospective, Review, and Ad Hoc — all schedulable in advance via a built-in calendar or launched instantly on demand. Humans participate directly, taking turns in the conversation. The full transcript can be downloaded as Markdown when the meeting ends. A Kanban Board That Works Itself HATS includes a six-column Kanban board — Backlog, Ready, In Progress, Blocked, Review, Done — with full drag-and-drop. When a ticket moves to In Progress, it is automatically dispatched to the assigned agent as a live task. When a blocker is resolved, dependent tickets are automatically unblocked and advanced. Tickets assigned to the human team lead are highlighted in orange so nothing falls through the cracks. Agents interact with the board through the Kanban MCP server, creating, moving, and commenting on tickets as part of their normal workflow. MCP and Tools Through the Model Context Protocol (MCP), HATS ships integrations across five categories:
Category Tools
Productivity Kanban, Memory (knowledge graph), Slack
Files & Documents Filesystem, Excel, Word, PDF, PowerPoint
Web Brave Search, Puppeteer/Chrome
Databases SQLite, PostgreSQL
Development GitHub
All servers are togglable from the UI and report live connection status and credential requirements. Project-Scoped, Multi-Team Every HATS project is isolated — its own Kanban board, meeting calendar, agent configurations, sources/ and outputs/ file folders, telemetry log, and state snapshot. Projects can be created, switched, and loaded without restarting. A project goal set by the team lead is automatically injected into every agent's system prompt to keep all thinking anchored to the objective.
The Dashboard The HATS web UI is a four-panel dashboard:
Agents — Live status, communication overlay, add/configure agents Active Board — Kanban columns with real-time updates and drag-and-drop Tools — MCP server controls, CLI, file upload/download, tool explorer Backlog & Calendar — Unstarted tickets and weekly/daily/agenda calendar views
A persistent progress bar below the header shows the project goal alongside a three-segment ticket progress indicator (Done / Active / Backlog) that updates live as work moves through the board.
HATS is built on Node.js with a TypeScript backend, Three.js avatar rendering, and a plain HTML/CSS/JS frontend requiring no build step. Installation Setup scripts The scripts folder contains two scripts: setup.bat for Windows and setup.sh fro linux/MacOs. These perform some of the tasks below. Prerequisites Node.js Node.js 20+ is required. npm install API Keys Copy .env.example to .env and fill in at least one LLM provider key: cp .env.example .env
Variable Description
ANTHROPIC_API_KEY Anthropic Claude (default provider)
OPENAI_API_KEY OpenAI GPT models
GEMINI_API_KEY Google Gemini models
BRAVE_API_KEY Brave Search (optional, for web search tool)
Piper TTS (optional — required for avatar speech) Piper runs in one of two modes. Server mode (recommended) keeps voice models loaded in memory and supports one voice per agent. Subprocess mode is a simpler fallback using a single voice loaded on demand. Download one or more voice models (.onnx + .onnx.json) from the Piper voices page and place them in piper_voices/. Server mode (recommended) Requires the piper-tts Python package. At startup the app automatically spawns one Flask server per voice model on consecutive ports — you do not need to start anything manually.
pip install piper-tts PIPER_VOICES_DIR=piper_voices # directory containing .onnx files PIPER_SERVER_PORT_START=5100 # first port; each voice gets the next port PYTHON_BIN=python # python executable (default: "python") Each agent can be assigned its own voice from a dropdown in the UI. If a previously selected voice is no longer present at startup the agent falls back to the first available voice. Subprocess mode (fallback) Uses the native Piper binary. The model is loaded from disk on every sentence, which adds ~0.5 s latency per sentence.
Download the Piper release for your platform from the Piper releases page. Extract to piper/ in the project root (so the binary is at piper/piper.exe on Windows).
PIPER_BIN=piper/piper.exe # path to native piper binary PIPER_MODEL=piper_voices/en_GB-cori-high.onnx The companion .onnx.json file must sit next to the .onnx file. If neither PIPER_VOICES_DIR nor PIPER_MODEL is set, avatar speech is silently disabled and agents respond text-only. Rhubarb Lip Sync (optional — required for avatar lip sync) Rhubarb Lip Sync extracts mouth-shape timing from audio to drive avatar visemes. Required alongside Piper TTS if you want lip-synced avatars.
Download a Rhubarb release for your platform from the Rhubarb releases page. Set RHUBARB_BIN in .env to the path of the executable (defaults to rhubarb):
RHUBARB_BIN=rhubarb/rhubarb The phonetic recognizer is used by default — no additional dependencies (PocketSphinx, etc.) are required. Quick start # 1. Clone and install git clone <repo-url> cd HATS npm install
# 2. Configure cp .env.example .env # Edit .env and add at least one LLM provider API key
# 3. Run npm start The web UI is available at http://localhost:3001.
Alternatively, run the setup script which checks all dependencies: # macOS / Linux / Git Bash on Windows bash scripts/setup.sh
# Windows Command Prompt scripts\setup.bat Project structure src/ agent/ Agent execution loop and state machine api/ HTTP + SSE + WebSocket server (port 3001) hats/ Six Thinking Hat definitions and directives human/ CLI interface for terminal interaction mcp/ MCP tool servers (kanban, filesystem, etc.) orchestrator/ Team coordination, meetings, task dispatch prompt/ System prompt generation providers/ LLM adapters (Anthropic, OpenAI, Gemini, Ollama, LM Studio) speech/ TTS + lip-sync pipeline (Piper + Rhubarb) store/ Event log, telemetry, and snapshot storage tools/ Tool schema definitions available to agents util/ Shared utilities (logger, etc.) webui/ Web UI — HTML/CSS/JS + Three.js avatar renderer avatars/ GLB avatar models and avatars.json catalogue projects/ Per-project runtime data (kanban, meetings, agent state) scripts/ Setup scripts (setup.sh, setup.bat) tools/ Developer utilities (glb-viewer.mjs) rhubarb/ Install rhubarb in here piper_voices/ Install voices for piper tts in here Development npm test # run tests (vitest) npm run test:watch # watch mode npm run build # compile TypeScript to dist/ No build step is required to run the app — npm start uses tsx to run TypeScript directly.