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Canonical Knowledge Structure

A universal, representation-independent foundation for verifiable AI knowledge.

CKS is an open ecosystem that gives LLMs a canonical knowledge backbone. Every piece of information must be explicitly structured, validated against formal constraints, and traceable to its origin. This eliminates hallucinations and makes AI-generated knowledge auditable.

How It Works

Your LLM (Claude Desktop, etc.)
        │
        ▼
    cks-mcp ─── Model Context Protocol server
        │
        ▼
 cks-runtime ─── Sessions, transactions, version history
        │
        ▼
   cks-core ─── Immutable semantic engine

Key Capabilities

  • Eliminate citation hallucinations – mechanically detect references to non-existent sources.
  • Ensure verification integrity – cryptographic signing guarantees that source checks actually happened.
  • Full audit trail – every operation is captured in an immutable version history.
  • Time-travel debugging – list versions, compare them, and safely roll back to any previous state.
  • LLM-friendly API – native MCP server with 7 tools, fully compatible with Claude Desktop and other MCP clients.

Projects

Project Description Status
cks-core Semantic engine – immutable knowledge objects, validation, evolution Stable v1.7.0
cks-runtime Operational environment – sessions, transactions, versioning, events Stable v1.0.1
cks-mcp MCP server – exposes CKS to LLMs Stable v1.0.4

Get Started in 5 Minutes

pip install cks-core cks-runtime cks-mcp

Then connect to Claude Desktop – see the Quick Start guide.

Why CKS?

Today, the same knowledge exists in many incompatible forms – documents, databases, JSON, source code, AI prompts. CKS separates knowledge itself from every representation. Representations may change, but canonical knowledge remains the same.