koios/docs/tools/mnemosyne.md

Mnemosyne

Multimodal personal knowledge base — text, images, and graph-structured content.

• MCP server name: mnemosyne (runs in the lab; FastMCP at /mcp on its own host)
• Prompt snippet: prompts/tools/mnemosyne.md
• Project repo: /home/robert/git/mnemosyne (full README, architecture docs)

What It Is

Mnemosyne is "the memory of everything you know" — a content-type-aware multimodal knowledge management system built on Neo4j vectors and Qwen3-VL embeddings. Unlike a generic vector store, Mnemosyne knows what kind of thing a document is (a novel, a textbook, an album, a journal entry, a business proposal) and adjusts chunking, embedding, and retrieval accordingly.

It is a retrieval engine, not a synthesis engine. It returns ranked chunks plus metadata; the calling agent does its own synthesis. Architecturally this is intentional — letting the LLM see chunks and pivot mid-search beats pre-digesting answers server-side.

What It's Good For

• Searching the user's personal knowledge base across libraries (fiction, nonfiction, technical, music, film, art, journal, business, finance)
• Multimodal queries — find a book cover, an album sleeve, a screenshot, alongside text
• "Did I read something about X" / "what did I write about Y on what date"
• Pulling source material the user has actually curated, rather than guessing from training data
• Following graph relationships (Author → Book → Topic; Artist → Album → Track)

What It's Not Good For

• General web knowledge — that's Argos
• Anything not already in the KB — Mnemosyne only knows what's been ingested
• Synthesis or "give me the answer" — Mnemosyne returns chunks; the calling agent synthesizes
• Real-time information (status, news) — content is ingested, not live

MCP Tools Exposed

Tool	Purpose
search	Hybrid search (vector + graph + full-text), re-ranked
get_chunk	Retrieve the full text of a chunk by ID
list_libraries	What libraries exist (fiction, technical, etc.)
list_collections	Collections within a library
list_items	Items within a collection
get_health	Service health probe

Known Gotchas

• It's retrieval, not answers. A search call returns chunks; the agent then has to read them and form the answer. Don't expect Mnemosyne to "tell you" something.
• Library type matters. Searching the fiction library for technical content returns nothing useful. Use list_libraries first if uncertain.
• Citations should be preserved. Mnemosyne returns chunk IDs and source metadata — when synthesizing, cite back to the chunk so the user can verify and trace.
• Empty results may mean the index isn't ready. If setup_neo4j_indexes hasn't been run for a given environment, vector search returns empty results and the app logs a readiness warning. Surface that, don't silently confabulate.