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KOS — Knowledge Operating System

Knowledge-to-Action System — Beyond Second Brain. AI reads knowledge, connects it, plans, and executes.

Pipeline

flowchart LR
    MD["📝 Markdown"]
    EMB["🧠 Embedding"]
    VEC["🗄️ pgvector"]
    GRAPH["🔗 Knowledge Graph"]
    MEM["💭 Agent Memory"]
    TASK["📋 Task Generation"]
    EXEC["⚡ Execution"]

    MD --> EMB --> VEC --> GRAPH --> MEM --> TASK --> EXEC
    EXEC -.->|feedback loop| MD
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Repository Structure

knowledge-os/
├── src/                 # Application code (Next.js + TypeScript)
├── knowledge/           # Markdown knowledge base (vault)
│   ├── concepts/        # Abstract ideas and domains
│   ├── projects/        # Active and archived projects
│   ├── people/          # Individuals and collaborators
│   ├── organizations/   # Companies and communities
│   └── technologies/    # Tools, frameworks, platforms
├── agents/              # AI agent definitions and tools
├── prompts/             # System prompts for agents
├── graph/               # Knowledge graph schema and pipeline
├── architecture/        # Architecture Decision Records (ADRs)
├── tasks/               # Task and milestone tracking
├── database/            # PostgreSQL migrations and schema
├── docs/                # Design documents and roadmap
└── .github/             # CI/CD workflows

Stack

Layer Technology
Frontend Next.js (App Router), TypeScript, Tailwind, shadcn/ui
Backend Supabase (PostgreSQL + Auth + Storage + Realtime)
API tRPC (end-to-end typesafe)
Vector Search pgvector (hybrid full-text + vector)
Knowledge Graph Neo4j (entity-relationship store)
AI OpenAI / Anthropic (LLM gateway)

Getting Started

npm install
cp .env.example .env.local   # Fill in your credentials
npm run dev

Database Setup

Run database/001_initial_schema.sql in your Supabase SQL Editor.

Graph Setup (Optional)

For knowledge graph features, deploy Neo4j (AuraDB or Docker) and configure the connection.

Knowledge-to-Action Flow

  1. Capture → Write in knowledge/ using Markdown
  2. Link → Use [[Wiki Links]] to connect concepts
  3. Embed → Content is vectorized and stored in pgvector
  4. Graph → Entities and relationships extracted into Neo4j
  5. Reason → AI agents query the graph for context
  6. Plan → Agents generate tasks from knowledge gaps
  7. Execute → Tasks flow into project management

Deployment

npm run build
vercel --prod

Set all environment variables from .env.example in Vercel dashboard.

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