⚡ Knowledge Master

Your codebase's memory. A local knowledge graph that gives AI agents real understanding of your architecture — not just text search.

What you get

Capability	Description
🔍 Semantic Search	Find code, docs, emails by meaning — not just keywords
🕸️ Knowledge Graph	Relationships between services, people, repos, technologies
💥 Blast Radius	"What breaks if I change X?" — instant dependency analysis
📏 Convention Enforcement	Detects your team's patterns and enforces them
🤖 MCP Server	Plugs directly into AI agents (Claude, Cursor, Kiro)
🖥️ Web UI	Visual graph, search, file browser
🔒 100% Local	Nothing leaves your machine. Ever.

How it works

Your repos + docs + emails
        ↓ index
  ┌─────────────────────────┐
  │   Knowledge Graph        │
  │   (FalkorDB)            │
  │                         │
  │   Repo → Tech           │
  │   Repo → Service        │
  │   Person → Document     │
  │   Chunk + Embedding     │
  └─────────────────────────┘
        ↓ query
  AI Agent gets precise,
  grounded answers with
  full context

Performance

Metric	Value
Search latency	~100ms (with re-ranking)
Token savings	60-80% fewer tokens for codebase questions
Accuracy	~85-90% precision (vs ~50% without RAG)
Indexing speed	~100 files/minute
Storage overhead	~3x raw data size
RAM usage (idle)	~400MB total

Get started

pip install knowledge-master
km start
km index ~/your-project
km search "how does auth work"

→ Full installation guide