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Haustorium12MCP SERVER · OFFICIAL PROJECT

Memory v2

Local persistent memory for a coding assistant — ranked by how often each memory is used, decaying what stops being used.

by Haustorium12 ↗Agent Memory✓ Official

LABSCO SUMMARY

Memory that forgets on purpose, and can show why it kept something.

Instead of an append-only note file, each memory carries an activation score built from how often and how recently it was retrieved; the score falls off over time and the decay sweep eventually archives what nothing touches, while anything marked protected is exempt. Embeddings, fact extraction and compression all run against a local Ollama, so there is no key to manage and nothing leaves the machine.

What it is

A local memory server backed by a single SQLite file holding the rows, a keyword index and vector embeddings side by side. Retrieval fuses keyword and vector search, then reranks with an activation score borrowed from cognitive science. A separate knowledge graph adds multi-hop discovery the flat index cannot do. Nothing leaves the machine: embedding and language-model work all run against a local Ollama.

What you get

  • add_memory, get, update, forget — store with novelty checking and credential scanning, read, revise, or archive to a graveyard; memories marked protected cannot be forgotten.
  • search — hybrid keyword and vector retrieval, fused and reranked; the everyday entry point.
  • keyword_search — pure keyword matching, for exact terms, file names and error codes.
  • graph_search — Personalized PageRank traversal that reaches related concepts through the graph rather than through wording.
  • graph_stats_tool — node and edge counts, type distribution, top entities, graph density.
  • stats, list_recent, list_topics — active, archived and protected counts; recent memories; topic clusters drawn from the graph's communities.
  • reindex — incremental re-indexing of a markdown vault, skipping files whose hash did not change.
  • extract_from_conversation — a two-pass pipeline that pulls facts out of text, then decides add, update, delete or nothing for each.
  • compact_text — compresses text while pulling protected content out first and verifying the summary stayed faithful.
  • decay_sweep — background maintenance that updates importance, promotes and demotes layers, and archives dead memories.
  • agent_sync and check_conflicts — changelog-style sync between agents with consumer offsets, and the list of unresolved conflicts between them.
  • check_integrity — verifies vault files against a stored manifest and reports modified, new and missing ones.

Requirements

Python 3.9 or newer, the package from PyPI, and a SQLite file you nominate on the command line — the whole store is that one file. The real prerequisite is a running Ollama with the nomic-embed-text embedding model pulled (qwen2.5:3b too, if you want fact extraction and compaction): there is no API key because there is no hosted service, and the cost of that is that Ollama has to be up. The server pre-warms the embedding model at startup, so the first query does not pay the cold start.

Setup effort

Follow the published steps — pip install memory-v2-hx

See installation options