memory_storeLABSCO SUMMARY
It filters what gets remembered, not just what gets returned.
Most memory servers store whatever they are handed. This one scores every candidate first, and the scoring is pattern matching and statistics with no model call, so it costs nothing per write. The other engineering worth knowing is defensive: a circuit breaker sits in front of the FAISS and embedding path, and search degrades to BM25 rather than failing when embeddings are unavailable.
What it is
A Python memory server that writes to local JSONL with an optional FAISS index. Search fuses keyword and semantic scoring; every candidate memory is first scored on four dimensions by a purely algorithmic filter, with no model call involved.
What you get
- A memory persisted with
importancefrom 1 to 4, tags and a confidence value, scored on the way in —memory_store - Hybrid search combining BM25 at 40%, FAISS at 50%, importance at 5% and recency at 5%, falling back to keyword-only when embeddings are unavailable —
memory_recall - A context window assembled to a token budget —
memory_context - Confidence adjusted, a memory removed, and system statistics —
memory_update_confidence,memory_delete,memory_stats - The boundary score computed on demand, with acceptance rate and trigger statistics —
bnd_check,bnd_stats - Pattern extraction across high-quality memories —
deduce - Exact-match deduplication: an identical memory updates importance and confidence instead of storing a duplicate
Requirements
No account and no key. pip install mindcore-memory, then run mindcore-memory, or launch the mindcore_memory.server module under python with MINDCORE_MEMORY_PATH pointing at your store. Semantic search is an optional extra — pip install mindcore-memory[semantic] — and without it search runs BM25-only; the [encrypt] extra adds Fernet encryption at rest. No PostgreSQL, Redis or external service is needed. The package is mindcore-memory 0.1.15.
Setup effort
One command — pip install mindcore-memory
USE CASES
Keeping context across sessions
Store the decisions and preferences that matter and recall them at the start of the next conversation.
Filling a context window on a budget
Build the context to a token limit rather than pasting everything you have stored.
Keeping noise out of memory
Check the boundary score before storing, and read the acceptance statistics to see what is being rejected.
Running without a vector service
Install the base package and let search work on BM25 alone until you want semantics.
9 tools
Listed in project docs. Your client may show a different list.
memory_recallmemory_contextmemory_update_confidencememory_deletememory_statsbnd_checkbnd_statsdeduceBEFORE YOU INSTALL
What to know first
Tools you get
9 tools named in the project's documentation.
Requires
python on PATH
Where it runs
All 3 documented clients run the server on your machine.
INSTALLATION
Connect your client
Server setup
pip install mindcore-memoryMCP Clients
Claude Desktop, Cursor, Cline
{
"mcpServers": {
"mindcore-memory": {
"command": "python",
"args": [
"-m",
"mindcore_memory.server"
],
"env": {
"MINDCORE_MEMORY_PATH": "~/.mindcore/memory"
}
}
}
}