🦞 ClawGraph

Interactive Knowledge Graph for ClawMemory with natural language queries

English | ไทย

ClawGraph solves the classic memory system pain point: "I remember it but can't find it"

It transforms ClawMemory into an interactive Knowledge Graph showing relationships between data (project ↔ deadline ↔ client ↔ task ↔ expense from ClawReceipt etc.) + natural language queries (e.g. "Which projects have deadlines this month with Thai clients?")

Automatically builds on ClawSelfImprove (the graph gets smarter with every feedback)

Table of Contents

• Features
• Demo
• How It Works
• Tech Stack
• Installation
• Usage
• Examples
• Contributing
• License

Features

• Local Graph Database: Built on ClawMemory (SQLite + vector embeddings)
• Interactive Visualization: Node-edge graph with zoom, drag, click functionality
• Natural Language Queries: Automatic translation of Thai/English queries to graph queries
• Self-Improving: Learns from user feedback patterns via ClawSelfImprove integration
• Privacy First: 100% local execution, zero cost, no data leaves your machine

Demo

Type Thai query → Instant interactive graph with highlighted relationship paths

How It Works

1. Capture Relationships (from ClawMemory)

• Every event (task, receipt, reminder) → Automatically create nodes + edges
• Example: "Project X" → "deadline March 15" → "client ABC" → "expense 4,200 THB"

2. Build Graph

• Uses NetworkX + optional local Neo4j (or SQLite with recursive CTE)
• Vector embeddings from ClawMemory connect semantic edges (e.g. "similar to previous project")

3. Natural Language Query

• Type: "Summarize projects nearing deadline related to Thai clients"
• LLM translates → Graph query → Retrieve nodes/edges → Display + visual graph

4. Visualize

• Interactive graph (zoom, drag, click nodes)
• Highlight paths (e.g. "client → project → expense")
• Export to PNG or Mermaid code

5. Self-Improve Loop

• Tell it "This is important" or "This is irrelevant" → ClawSelfImprove adjusts edge weights
• Next time prioritizes relevant nodes automatically

Tech Stack (100% Free + Local)

• Core Graph: NetworkX (Python) + PyVis (interactive HTML generation)
• Query Engine: Llama3/Ollama (natural language → graph query translation)
• Storage: Direct integration with ClawMemory (SQLite + Chroma vector DB)
• Dashboard: Extends ClawMemory UI (Next.js) or standalone web view
• Integration: ClawFlow (install), ClawSelfImprove (feedback learning), ClawReceipt (expense edge pull)

Installation

1. Install ClawFlow
2. Run clawflow install openkrab/claw-graph
3. Configure in ClawMemory dashboard

Usage

1. Build Graph: python scripts/build_graph.py
2. Query: Enter natural language in the UI
3. Visualize: View the interactive graph

Examples

See examples/queries-thai.md for sample Thai queries.

Contributing

Contributions welcome! Please see our issues for current tasks.

License

MIT