Graphthulhu MCP Server Gives AI Agents Knowledge Graph Memory for Logseq/Obsidian

Graphthulhu is an MCP server that gives AI agents full read-write access to a Logseq or Obsidian vault. Instead of embedding text chunks into vectors, the agent writes structured pages with properties and [[links]] between them, creating a knowledge graph as memory.

How It Works

The system stores agent memory as plain markdown files on disk. Every page has type (project/decision/research/lesson/intel), status, and created/updated timestamps. The agent writes after learning, links related pages, and follows property standards. During periodic heartbeats, the agent reviews recent daily notes and promotes important content to the graph.

Results After One Month

• 404 pages created
• 1,451 cross-references between pages
• Projects link to decisions, which link to research, which link to lessons learned
• The memory becomes a web of connected knowledge that grows denser over time

Problems with Vector Memory Addressed

The author identifies three issues with typical vector-based memory systems:

• Single-angle retrieval: Search queries must match the angle the memory was stored at. "Fitbit auth failure" and "browser cookie issue" might be the same memory, but vectors won't connect them unless you search for both.
• No structure: Everything is stored as embeddings with equal weight. A core preference and a one-off event look the same to the retrieval system.
• No relationships: Knowing fact A and fact B exist is useless if you can't see that A caused B.

Knowledge Graph Advantages

• Multi-hook retrieval: Every [[link]] is a retrieval path. Search for "OpenChaos" and you get the project page, then follow links to find governance crises, competitive analysis, and academic research.
• Types are native: The graph knows structurally that a preference and an event are different things. No learned decay rates needed.
• Persistence: Agent crashes, session resets, model swaps - the knowledge persists. No database, no embeddings to recompute, no vector store to maintain. Back it up with git for versioned memory.

Technical Details

• Single Go binary
• 37 MCP tools
• Works with both Logseq and Obsidian backends
• Open source on GitHub

Tradeoffs and Future Plans

The approach requires more upfront structure than "just embed everything." The agent needs discipline to write after learning, always link related pages, and follow property standards. The author is trading convenience for depth.

Future plans include adding RAG on top of the graph: embed page contents for fuzzy semantic search to find the entry point, then use graph traversal to pull in everything connected to it. This follows Microsoft's GraphRAG paper pattern of semantic search for discovery and graph links for context expansion.