Local-first AI memory with knowledge graphs and hybrid search. 17+ AI tools via MCP. Free.
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Superlocalmemory MCP Server is an MCP server that local-first AI memory with knowledge graphs and hybrid search. 17+ AI tools via MCP. Free. Its tool list has not been published yet over stdio, requires no API key, and scores 94/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"superlocalmemory": {
"args": [
"mcp"
],
"command": "slm"
}
}
}Are you the author?
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Every major AI memory system — Mem0, Zep, Letta, EverMemOS — sends your data to cloud LLMs for core operations. That means latency on every query, cost on every interaction, and after August 2, 2026, a compliance problem under the EU AI Act.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'superlocalmemory' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked superlocalmemory against OSV.dev.
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Rent an LLM — but own the memory, for your company and for your industry.
The governed memory layer for AI agents: local-first, auditable, and built for the compliance obligations teams now actually carry.
Models are interchangeable and rented by the token. What your agents remember is
yours — it is your customers' data, your retention obligations, and your audit trail. SLM
keeps that layer on infrastructure you control, with multi-workspace isolation, role-based
access, and GDPR + EU AI Act governance controls built in.
The boundary. SuperLocalMemory starts with a local runtime; provider-backed enrichment, cloud backup, connectors, and proxy use are explicit choices. Different products solve different boundaries. Published benchmark evidence carried into V4 comes from the published V3 research architecture; it is not a claim of a newly rerun V4 package benchmark.
How to check that, rather than believe it. Every reliability
guarantee here is stated as a falsifiable invariant, tested under an adversarial condition with a
negative control, and shipped with the harness that regenerates the evidence:
python benchmark/run_all.py --trials 200 --output-dir results/. What each experiment
does not exercise is stated too.
v4.1.1 — one control plane: SLM-Mesh peer coordination · multi-scope memory (personal / shared / global) · profiles · Cache · Compress · 7-layer retrieval · code graph · Entity Explorer · skill evolution · Modes A/B/C · GDPR retention & audit chain · bounded loops — across CLI, MCP, dashboard, the Claude plugin, the Codex add-on, and documented IDE integrations.
Proxy: slm wrap claude · MCP: add slm_compress to your config · Skill: zero-config
Four public arXiv preprints · V4: arXiv:2608.08253 · companion archive: Zenodo 21853302 (DOI 10.5281/zenodo.21853302) · prior preprints: 2603.02240 · 2603.14588 · 2604.04514.