How does synchronization across machines work?

It uses encrypted Passport bundles and rclone for delta sync across various backends like S3, Google Drive, and MinIO.

What embedding model is used?

The server uses a built-in Qwen3 embedding model implemented via ONNX Runtime.

Is there a limit on memory storage or usage?

No, the server is designed to be unlimited and persistent.

Can I extend or customize the server?

Yes, it supports plugins and multi-provider LLM dispatch for extensibility.

Mnemo MCP

memorysqliteembeddingsyncmcp

Persistent AI memory server with SQLite hybrid search, built-in Qwen3 embeddings, and encrypted rclone sync.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "n24q02m-mnemo-mcp": {
      "command": "uvx",
      "args": [
        "mnemo-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This MCP server provides persistent AI memory using a hybrid search approach combining SQLite FTS5 and semantic embeddings. It includes built-in Qwen3 text embeddings and supports encrypted synchronization across machines via rclone, enabling zero-configuration setup without cloud dependencies or usage limits. Use this server when you need a local, unlimited memory store for AI agents with advanced search and cross-device sync.

Use cases

Store and retrieve AI memory persistently with hybrid search

Synchronize AI memory securely across multiple devices

Embed text using built-in Qwen3 embeddings for semantic search

Implement zero-configuration AI memory without cloud reliance

Manage temporal decay and compression of memory data

Key features

Hybrid search combining SQLite FTS5 and semantic embeddings

Built-in Qwen3 text embedding model

Encrypted sync across machines using rclone with Passport bundles

Temporal decay and importance-based memory archiving

Multi-provider LLM dispatch and plugin support

Open source with no usage limits or cloud dependencies

Capabilities & Tool Schemas (3)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Mnemo MCP.

config

Server status, trigger sync, update settings, pre-download embedding model, authenticate sync provider, manage HTTP setup form lifecycle, passport export/import

help

Full documentation for any tool

config__open_relay

Open the zero-config relay setup form (registered via mcp-core)

Documentation Overview

Mnemo MCP Server

mcp-name: io.github.n24q02m/mnemo-mcp

Persistent AI memory with hybrid search and embedded sync. Open, free, unlimited.

Sister projects from n24q02m (click to expand)

Project	Tagline	Tag
agent-chat-plugin	Peer AI agents chat in a shared folder — no human relay, no orchestrator, wor...	Tooling
better-code-review-graph	Knowledge graph for token-efficient code reviews -- semantic search and call-...	MCP
better-drive	2-way Google Drive sync with .driveignore filter — rclone engine, Windows tray	Tooling
better-email-mcp	IMAP/SMTP email for AI agents -- read, send, organize folders, and manage att...	MCP
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better-workspace-mcp	Google Workspace MCP server (Docs/Drive/Calendar/Gmail/Sheets/Slides/Tasks/Ch...	MCP
claude-plugins	Claude Code plugin marketplace for the n24q02m MCP servers -- install web sea...	Marketplace
imagine-mcp	Image and video understanding + generation for AI agents -- across Gemini, Op...	MCP
jules-task-archiver	Chrome Extension for bulk operations on Jules tasks via batchexecute API -- a...	Tooling
mcp-core	Shared foundation for building MCP servers -- Streamable HTTP transport, OAut...	MCP
mnemo-mcp	Persistent AI memory with hybrid search and embedded sync. Open, free, unlimi...	MCP
fastretrieval	Multi-model retrieval runtime for ONNX/GGUF embeddings and reranking	Library
skret	Secrets without the server.	CLI
tacet	A self-distilling neuro-symbolic cascade that amortises LLM cost across knowl...	Tooling
web-core	Shared web infrastructure package for search, scraping, HTTP security, and st...	Library
wet-mcp	Open-source MCP server for AI agents: web search, content extraction, and lib...	MCP

Features
Status
Documentation
Smithery
Tools
Security
Build from Source
CLI
Remote (HTTP mode)
Deploy to Cloudflare
Trust Model
License

Roadmap (current = Phase 3 / v2.x)

Phase	Version	Status	Highlights
Phase 1	v1.x	Shipped	Typed `memory(action="capture")` (6 context_types + dedup) -- RRF (k=60) hybrid fusion + cross-encoder rerank + temporal decay -- importance x recency archive policy + restore -- Alembic migrations -- multi-provider LLM dispatch -- plugin trinity (recall-context + memory-commit skills, SessionStart + opt-in PostToolUse hooks)
Phase 2	v1.x+1	Shipped	LLM-driven compression of older memories + Passport sync (encrypted import/export bundle for cross-machine bootstrap) -- AES-256-GCM + Argon2id, S3 / R2 / B2 / MinIO + GDrive backends, delta-sync with LWW per row
Phase 3	v2.0.0	Shipped (BREAKING)	Temporal knowledge graph -- bitemporal `valid_from` / `valid_to` columns -- entity resolution via embedding KNN -- `entity_search` / `entity_graph` / `history` actions -- KG-aware passport bundle sections -- `KG_AUTO_ENABLED` opt-in auto-extract on capture

Features

Hybrid retrieval -- FTS5 + vector search (sqlite-vec locally, Vectorize on Cloudflare), fused via Reciprocal Rank Fusion (k=60), then re-ranked by a configurable rerank chain (RERANK_MODELS, order = litellm fallback; empty -> Fastretrieval's local Qwen3 reranker) with temporal decay and importance boost
Typed capture -- memory(action="capture") with 6 context_types (conversation/fact/preference/skill/task/decision), embedding-based dedup, and a configurable LLM chain (LLM_MODELS, order = litellm fallback)
Knowledge graph -- Automatic entity extraction and relation tracking; top results boosted by graph proximity
Importance scoring + archive policy -- LLM-scored 0.0-1.0 importance; soft-archive when recency_factor * (1 - importance) > 1.0; restore action available
Auto-archive trigger -- Background sweep every Nth capture (default 100) -- no cron required
STM-to-LTM consolidation -- LLM summarization of related memories in a category
Duplicate detection -- Warns before adding semantically similar memories
Zero config -- Fastretrieval's built-in local registry resolves Qwen3 ONNX embedding + reranking, no API keys needed. Optional cloud providers (Jina AI, Gemini, OpenAI, Cohere)
Multi-machine sync -- JSONL-based merge sync via Google Drive (bundled Desktop OAuth public client)
Plugin trinity -- Ships /recall-context + /memory-commit skills and SessionStart + opt-in PostToolUse hooks (see docs/ARCHITECTURE.md)
Proactive memory -- Tool descriptions and skills guide AI to save preferences, decisions, facts at the right moment
LLM compression -- Per-turn compression via the multi-provider dispatcher targets ~3x token reduction at >=0.9 fact retention; graceful skip when no provider configured (see docs/compression.md)
Encrypted passport sync -- AES-256-GCM bundles + Argon2id KDF, S3 (R2 / B2 / MinIO) and Google Drive backends, delta-sync with last-write-wins per row (see docs/passport.md). Bootstrap via the passport-bootstrap skill.
Temporal knowledge graph -- Bitemporal columns (valid_from / valid_to / superseded_by) on every memory + entity-resolution dedup (embedding KNN at default 0.85 cosine threshold) + audit trail (memory_audit table with prev/new state hashes) + new actions (entity_search / entity_graph / history) + opt-in KG_AUTO_ENABLED auto-extract on capture. BREAKING for clients that called memory.get expecting historical-inclusive results: pass as_of for time-travel; default now filters to current-state (valid_to IS NULL).

Comparison vs. peers

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

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npm downloads

Package downloads in the last 30 days.

Last commit

27d ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

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Frequently Asked Questions about Mnemo MCP

No, it operates fully locally with no cloud dependencies.

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Technical Specs & Signals

CategoryKnowledge & Memory

PricingFree

60Quality signal: Good · 60/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools27/30

Adoption & activity8/15

Community engagement0/10

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Scanned 23d ago via OSV.dev · mnemo-mcp (PyPI)

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