chatgpt-kg
Provides a knowledge graph search tool built on Neo4j, enabling AI agents to query personal knowledge graphs from ChatGPT conversations stored in Neo4j.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@chatgpt-kgsearch my past conversations about microservices"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Install Neo4j Desktop, create a local database, note its password.
python -m venv .venv && source .venv/bin/activate && pip install -e ".[dev]"cp config.example.toml config.tomland edit as needed.export KG_NEO4J_PASSWORD=...
Related MCP server: Graph-Memory
Usage
Drop a ChatGPT export (
conversations.jsonor its zip) intoinbox/.kg ingest— build/update the graph.kg query "resume"— search from the terminal.kg stats— counts.kg rebuild— wipe and rebuild.
Claude Code integration
Ensure Neo4j is running and the graph is built (
kg ingest).Keep
.mcp.jsonin 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.In Claude Code, run
/mcpto confirm thechatgpt-kgserver is connected.Ask a question about prior ChatGPT work; Claude calls
search_knowledge_graphwhen relevant.
This server cannot be deployed
Maintenance
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