kkanbu

A taste oracle that learns who you are — your preferences, values, and reasoning patterns — then speaks on your behalf.

kkanbu is an MCP server that builds a persistent knowledge graph of your identity. When an AI agent needs to know something about you, kkanbu answers grounded in what it actually knows — not guesses.

kkanbu was born as the user-proxy for Ouroboros, a specification-first AI development workflow: during Ouroboros's Socratic interviews, kkanbu answers on the user's behalf so autonomous runs never block on human availability — and when it isn't confident enough to speak for you, it flags the question for your later review instead of guessing. It works standalone with any MCP client, but that flag-and-continue loop is what it was designed around.

How It Works

AI Agent ←──MCP──→ kkanbu ←──→ Knowledge Graph (SQLite)

kkanbu stores knowledge as a graph of nodes (preferences, values, patterns, reasoning) connected by edges (supports, contradicts, derives_from). Each node tracks what you believe and why. When kkanbu isn't confident, it flags the question for later review rather than guessing.

Installation

Requires Python 3.11+ and an Anthropic API key.

With Claude Code (recommended)

claude mcp add --scope user kkanbu -- uvx kkanbu

This installs kkanbu from PyPI and registers it as an MCP server. The --scope user flag makes it available across all sessions.

From source

git clone https://github.com/Jaeha0526/kkanbu.git
cd kkanbu
pip install -e .

# Optional: JS-rendered webpage ingestion (SPAs, React sites)
pip install -e ".[js]"

# Register with Claude Code
claude mcp add --scope user kkanbu -- uv run --directory /path/to/kkanbu kkanbu

Manual MCP config

Add to your .mcp.json:

{
  "mcpServers": {
    "kkanbu": {
      "command": "uvx",
      "args": ["kkanbu"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Usage

Create a profile and log in

kkanbu_of_who(action="create", username="myname")
kkanbu_of_who(action="login", username="myname")

Each user gets an isolated knowledge graph at ~/.kkanbu/profiles/<username>/knowledge.db.

Teach it about yourself

kkanbu_learn(
  insight="I value simplicity in design — clean, minimal interfaces over feature-packed ones",
  why="Visual noise stresses me out"
)

Or ingest existing material — codebases, documents, or webpages:

kkanbu_ingest(path="/path/to/my-project")
kkanbu_ingest(path="https://my-portfolio.com")

Let it ask you questions

kkanbu can proactively find gaps in what it knows and ask deep questions:

kkanbu_curious()
# → "When you feel most like yourself, what are you usually doing?"

kkanbu_curious(session_id="abc123", user_response="When I'm building something...")
# Drills deeper into the same thread

Answer on your behalf

kkanbu_answer(question="What kind of work environment do you thrive in?")
# → Voice-faithful answer grounded in the knowledge graph,
#   with confidence scores and automatic flagging when uncertain

Correct mistakes

kkanbu_correct(
  original="prefers quiet isolation",
  correction="prefers collaborative spaces",
  why="Does best thinking when bouncing ideas off people"
)

Reflect on the graph

kkanbu_reflect()
# → Finds contradictions, tensions, and blind spots
#   Stores findings as flags for review

Visualize

kkanbu_visualize()
# → Opens interactive D3.js graph at http://localhost:7742

Tools

Configuration

Development

pip install -e ".[dev]"
pytest
ruff check src/

License

MIT