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NeuralMemory

Memory that recalls by following relationships rather than by similarity score — and runs offline, with no embedding model in the loop.

by nhadaututtheky ↗Agent MemoryCommunity

LABSCO SUMMARY

No embedding call, so recall costs nothing per query.

Retrieval here is graph traversal, which means one hop gets you the outage, the JWT expiry that caused it and the review that flagged it — a similarity search would need several queries to assemble the same chain, and an embedding call for each. The size is the thing to plan for: 63 tools is more than most clients want in context, which is why a fresh install registers 10 and leaves the rest behind a tier setting. Start there and widen only when a workflow needs it.

What it is

A local memory server that stores what an agent learns as a graph of neurons and typed synapses, then recalls by spreading activation out from a query instead of ranking by vector distance. Storage is SQLite by default and nothing leaves the machine. The tool surface is deliberately layered: three tools cover the daily loop, and 63 exist for everything else.

What you get

  • The everyday three — nmem_remember to store, nmem_recall to retrieve through spreading activation, nmem_health for a graded health score with the fixes attached
  • Bulk and automatic capture — nmem_remember_batch takes up to 20 at once, nmem_auto extracts memories from a block of text, nmem_todo files a note that expires in 30 days
  • Cross-session project context — nmem_eternal saves it, nmem_recap loads it at session start at three levels of detail, nmem_session tracks the current task
  • Where a fact came from — nmem_provenance traces the origin chain, nmem_source registers the document or API it was learned from
  • A reasoning loop rather than a store: nmem_hypothesize, nmem_evidence with Bayesian confidence updates, nmem_predict, nmem_verify, and nmem_cognitive as the dashboard over all of it
  • Learn from things you already have — nmem_train ingests PDF, DOCX, PPTX, HTML, JSON, XLSX and CSV; nmem_train_db reads a database schema; nmem_index extracts symbols and imports from a codebase; nmem_import migrates from ChromaDB, Mem0, Cognee, Graphiti or LlamaIndex
  • Keep it from rotting — nmem_consolidate runs sleep-like maintenance, nmem_drift finds tags that mean the same thing, nmem_review schedules spaced repetition, nmem_conflicts and nmem_gaps surface contradictions and blind spots
  • Explain the graph — nmem_explain gives the shortest path between two concepts with synapse types and weights, nmem_narrative builds a timeline or causal chain, nmem_visualize renders charts
  • Safety rails — nmem_version snapshots and rolls back the brain, nmem_pin and nmem_reflex protect knowledge from decay and pruning, nmem_sync and nmem_telegram_backup move it off the machine

Requirements

Python 3.11 or later. pip install neural-memory gets the base profile — CLI, MCP over stdio and SQLite — and clients point at nmem-mcp. A fresh install starts on the standard tool tier, which registers 10 tools; set the tool tier in the config to see all 63. Optional extras are separate installs: neural-memory[extract] for document training, [server] for the FastAPI dashboard, [embeddings] for local embedding models. Semantic recall runs on the InfinityDB backend, switched on with storage_backend in ~/.neuralmemory/config.toml; keyword recall over SQLite needs nothing extra. MIT licensed.

Setup effort

One command — pip install neural-memory

See installation options