Chatgpt Kg

Builds a personal knowledge graph from selected ChatGPT conversations (stored in Neo4j) and exposes it to Claude Code as an on-demand MCP search tool.

Setup

1. Install Neo4j Desktop, create a local database, note its password.
2. python -m venv .venv && source .venv/bin/activate && pip install -e ".[dev]"
3. cp config.example.toml config.toml and edit as needed.
4. export KG_NEO4J_PASSWORD=...

Usage

• Drop a ChatGPT export (conversations.json or its zip) into inbox/.
• kg ingest — build/update the graph.
• kg query "resume" — search from the terminal.
• kg stats — counts.
• kg rebuild — wipe and rebuild.

Claude Code integration

1. Ensure Neo4j is running and the graph is built (kg ingest).
2. Keep .mcp.json in the project root (it points Claude Code at the server's Python and module). The server reads the Neo4j password from .env, so no secret goes in .mcp.json.
3. In Claude Code, run /mcp to confirm the chatgpt-kg server is connected.
4. Ask a question about prior ChatGPT work; Claude calls search_knowledge_graph when relevant.