Welcome to the GraphDone documentation! This directory contains comprehensive guides, API references, and deployment information for working with GraphDone.
- GraphQL schema and resolvers
- REST endpoints
- WebSocket subscriptions
- Authentication and authorization
- User Stories — the backlog that drives development - Every feature starts here; every story maps to tests
- Systems Reference - What's shipped and where it lives — every subsystem mapped to its code, tests, and story
- Interaction Model — the friction-free contract - UX constitution: modes, exits, click budgets
- Progressive Streaming design - ADAPT-4: scale to huge graphs on slow links
- Testing & Refinement Plan - The never-done loop; current cycle's verification debt
- AI Agents Quickstart - 5-minute MCP/GraphQL setup for agent teammates
- Getting Started - Setup and first steps
- Architecture Overview - System design and technical decisions
- Testing Guide - E2E testing with robust authentication system
- SQLite Deployment Modes - Local dev vs Docker authentication storage
- User Flows - How teams actually use GraphDone
Start here: Simple AI Agent Reality Check - What we're actually building
Implementation Guides:
- Simple AI Agent Reality Check - 🎯 THE PLAN: Smart chia pet with Ollama
- AI Agents Technical Spec - 📚 Complete technical implementation (advanced)
- Agent Planning Scenarios - 🎪 Interactive planning examples (future)
CRITICAL FOR RELEASE: TLS Implementation Plan - Required before production
Security Documentation:
- TLS Implementation Plan - 🚨 MUST READ: HTTPS, SSL certificates, secrets management
- Production Security Checklist - Pre-launch security validation
- Docker setup
- Kubernetes manifests
- Cloud provider guides
- Production considerations (see Security section above for TLS)
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Setup Development Environment
./tools/setup.sh
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Start Development Servers
./tools/run.sh
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Run Tests
./tools/test.sh
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Build for Production
./tools/build.sh --production
- Graph-native collaboration - Work flows through natural dependencies
- Spherical priority model - Ideas migrate from periphery to center
- Democratic prioritization - Community validation guides resource allocation
- Human-AI coordination - Smart chia pets that help with planning (see AI docs above)
For teams who think differently 🌐