io.github.n24q02m/mnemo-mcp MCP Server

Mnemo MCP Server

Renamed (2026-09-13): repo is now mnemo — CLI-first (mnemo command). PyPI package stays mnemo-mcp; MCP server remains a secondary surface.

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

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

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<!-- BEGIN: AUTO-GENERATED-CROSS-PROMO --> <details> <summary><strong>Sister projects from n24q02m</strong> (click to expand)</summary>

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
better-godot-mcp	Composite MCP server for Godot Engine -- 17 composite tools for AI-assisted g...	MCP
better-notion-mcp	Markdown-first Notion for AI agents -- pages, databases, blocks, and comments...	MCP
better-semantic-release	Drop-in python-semantic-release fork with built-in release-safety guards (orp...	Tooling
better-telegram-mcp	Telegram for AI agents -- messages, chats, media, and contacts across both bo...	MCP
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

</details> <!-- END: AUTO-GENERATED-CROSS-PROMO -->

Table of contents

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

<a href="https://glama.ai/mcp/servers/n24q02m/mnemo-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/n24q02m/mnemo-mcp/badge" alt="Mnemo MCP server" /> </a>

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).

Quick install

# Method 1 (default): plugin install via Claude Code
/plugin marketplace add n24q02m/claude-plugins
/plugin install mnemo-mcp@n24q02m-plugins

# Method 2 (CLI): direct uvx invocation
claude mcp add mnemo -- uvx mnemo-mcp

# Method 3 (remote): point a client at an HTTP deployment
claude mcp add --transport http mnemo https://<your-host>/mcp

Install matrix (stdio unless noted; see the Setup page for full steps):

Client	Install
Claude Code (plugin)	/plugin marketplace add n24q02m/claude-plugins then /plugin install mnemo-mcp@n24q02m-plugins
Claude Code (stdio)	claude mcp add mnemo -- uvx mnemo-mcp
Codex	register stdio command uvx mnemo-mcp under mcp_servers in ~/.codex/config.toml
Gemini CLI	add the mcpServers JSON below to ~/.gemini/settings.json
Cursor / Windsurf	add the mcpServers JSON below via the client's MCP settings (mcp.json)
Any client (HTTP self-host)	point the client at https://<your-host>/mcp (Streamable HTTP, OAuth-gated)

Example stdio config (zero-config local defaults):

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

Comparison vs. peers

Feature	mnemo-mcp	Mem0	Letta	OpenMemory
Hybrid retrieval (FTS + vec)	yes (FTS5 + RRF; sqlite-vec local / Vectorize on Cloudflare)	yes	partial	yes
Cross-encoder rerank chain	yes (Fastretrieval Qwen3 local + Jina + Cohere)	partial (Cohere only)	no	no
Temporal decay scoring	yes (exp half-life)	no	no	no
Importance boost in rank	yes (LLM 0.0-1.0)	no	no	no
Soft-archive + restore policy	yes (importance x recency)	no	no	no
Self-hostable (single SQLite file)	yes (zero ext deps)	partial (cloud-first)	yes (Postgres)	yes (Postgres + Qdrant)
Multi-provider LLM dispatch	yes (LLM_MODELS chain, any litellm provider)	partial	yes	partial
Plugin trinity (skills + hooks)	yes (recall-context + memory-commit)	n/a	n/a	n/a
Multi-machine sync	yes (GDrive bundled OAuth)	yes (cloud)	n/a	n/a
E2E-encrypted passport sync	yes (AES-256-GCM + Argon2id, S3 + GDrive)	no	no	no
LLM compression on capture	yes (multi-provider, ~3x at >=0.90 retention)	no	no	no
Backend-pluggable sync architecture	yes (S3 / R2 / B2 / MinIO + GDrive)	no	no	no
Bitemporal valid_from / valid_to queries	yes (as_of time-travel)	no	partial (events only)	no
Entity resolution via embedding KNN	yes (cosine threshold tunable)	no	no	no
Audit trail with state hashes	yes (memory_audit table)	no	no	no

Status

2026-05-02 -- Architecture stabilization update

Past months saw significant churn around credential handling and the daemon-bridge auto-spawn pattern. This caused multi-process races, browser tab spam, and inconsistent setup UX across plugins. The architecture is now stable: 2 clean modes (stdio + HTTP), no daemon-bridge layer, no auto-spawn from stdio.

Apologies for the instability period. If you encountered issues with prior versions, please update to the latest release and follow the current setup docs -- most prior workarounds are no longer needed.

Related plugins from the same author:

• wet-mcp -- Web search + content extraction
• imagine-mcp -- Image/video understanding + generation
• better-notion-mcp -- Notion API
• better-email-mcp -- Email management
• better-telegram-mcp -- Telegram
• better-godot-mcp -- Godot Engine
• better-code-review-graph -- Code review knowledge graph

All plugins share the same architecture -- install once, learn pattern transfers.

Documentation

Full docs at mcp.n24q02m.com/servers/mnemo-mcp/setup/:

• Setup -- install methods for Claude Code, Codex, Gemini CLI, Cursor, Windsurf, mcp.json
• Modes overview -- stdio / local-relay / remote-relay / remote-oauth
• Multi-user setup -- per-JWT-sub credential model

Install with AI agent -- paste this to your AI coding agent:

Install MCP server mnemo-mcp following the steps at https://raw.githubusercontent.com/n24q02m/claude-plugins/main/plugins/mnemo-mcp/setup-with-agent.md

Smithery

mnemo-mcp is packaged for Smithery -- install or run it straight from the registry. It starts over stdio via uvx mnemo-mcp with no configuration required to launch; credentials are configured at runtime through the server's own config flow (see Documentation). The published start command lives in smithery.yaml.

Tools

15 MCP tools, 17 memory actions. The memory surface is exposed both as 11 specialized single-purpose tools and a deprecated legacy memory dispatcher (same actions), plus config, help, and config__open_relay:

Tool	Actions	Description
add_memory, search_memory, list_memories, update_memory, delete_memory, export_memories, import_memories, memory_stats, restore_memory, archived_memories, consolidate_memories	(one action each)	Specialized single-purpose memory tools -- the recommended surface
memory (legacy dispatcher, DEPRECATED -- use the granular tools above instead; will be removed in a future release)	add, capture, search, list, update, delete, export, import, stats, restore, archived, archive_now, consolidate, compress, entity_search, entity_graph, history	Core CRUD + typed capture (6 context_types) + hybrid search (RRF + rerank + temporal decay) + import/export + soft-archive + restore + on-demand archive sweep + LLM consolidation + LLM compression + temporal KG (entity search / graph / history)
config	status, sync, set, warmup, setup_sync, setup_status, setup_start, setup_skip, setup_reset, setup_complete, setup_relay, sync_now, export_passport, import_passport	Server status, trigger sync, update settings, pre-download embedding model, authenticate sync provider, manage HTTP setup form lifecycle, passport export/import
help	topic="memory" or topic="config"	Full documentation for any tool
config__open_relay	(HTTP relay mode)	Open the zero-config relay setup form (registered via mcp-core)

Plugin trinity (Claude Code marketplace install):

Component	Trigger	Purpose
mnemo:recall-context skill	session start, before significant decisions, "what do I know about X?"	Pulls cwd / topic-relevant memories with context_type filtering
mnemo:memory-commit skill	"remember this" / "save this" / "ghi nho" / "luu lai"	Typed manual capture with context_type decision tree
mnemo:knowledge-audit skill	periodic / "audit memory"	Find duplicates, contradictions, stale entries; consolidate
mnemo:session-handoff skill	end of session	Capture decisions / preferences / corrections / conventions / open questions
mnemo:temporal-query skill	"as of" / "back in" / "history of" / "what did I think then"	Point-in-time snapshots via action="as_of" and version-chain tracing via superseded_by
SessionStart hook	every session init	Non-blocking nudge to invoke recall-context
PostToolUse hook (opt-in)	CAPTURE_AUTO_ENABLED=true	Hint memory-commit after Write/Edit of CLAUDE.md / AGENTS.md / ARCHITECTURE.md / docs/*.md

MCP Resources

URI	Description
mnemo://stats	Database statistics and server status

MCP Prompts

Prompt	Parameters	Description
save_summary	summary	Generate prompt to save a conversation summary as memory
recall_context	topic	Generate prompt to recall relevant memories about a topic

Security

• Graceful fallbacks -- Cloud → Local embedding, no cross-mode fallback
• Sync token security -- OAuth tokens stored at ~/.mnemo-mcp/tokens/ with 600 permissions
• Input validation -- Sync provider, folder, remote validated against allowlists
• Error sanitization -- No credentials in error messages

Build from Source

git clone https://github.com/n24q02m/mnemo.git
cd mnemo-mcp
uv sync
uv run mnemo-mcp

CLI

The package ships two distinct console scripts:

• mnemo -- CLI-first memory surface (primary for scripts/agents; it never starts a server): capture, recall, reflect, fetch, and the standing-* family operate directly on a SQLite memory DB. mnemo-pilot is a legacy alias of the same entry point.
• mnemo-mcp -- the MCP server plus one-shot operator subcommands. A bare invocation (or any ---prefixed flag) starts the server; a leading subcommand runs an action and exits.

CLI-first memory surface (mnemo; every subcommand takes --db <path>, prints a JSON envelope, and exits with a taxonomy-mapped code):

uvx --from mnemo-mcp mnemo recall --db ./mem.db "package naming" --k 3   # try without a persistent install

mnemo capture --db ./mem.db "keep PyPI name mnemo-mcp; repo is mnemo" --tags decision --category decision
mnemo recall --db ./mem.db "release ladder" --k 5        # search a subject's memories
mnemo reflect --db ./mem.db "why keep the alias?" --k 5  # bounded cited reflect over retrieval
mnemo fetch --db ./mem.db <memory_id>                    # fetch one memory by id
mnemo standing-refresh --db ./mem.db onboarding "how do releases cut?" --k 5   # materialize a standing page
mnemo standing-read --db ./mem.db onboarding             # cheap read with staleness info

Server operator CLI (mnemo-mcp):

mnemo-mcp                       # start the stdio server (default transport)
mnemo-mcp --http                # start the Streamable HTTP server
                                # (also via MCP_TRANSPORT=http or TRANSPORT_MODE=http)

mnemo-mcp auth google           # authorize Google Drive sync via OAuth
mnemo-mcp auth google --client-id <ID> --client-secret <SECRET>   # bring-your-own OAuth client
mnemo-mcp logout                # clear the local Google Drive sync token
mnemo-mcp warmup                # pre-download Fastretrieval-managed local embedding + rerank models

mnemo-mcp config status         # report whether stored config exists
mnemo-mcp config delete --yes   # delete the stored (encrypted) config
mnemo-mcp relay status          # show the active browser-setup relay session
mnemo-mcp relay open            # open the relay setup form in a browser
mnemo-mcp relay reset           # clear relay session state
mnemo-mcp doctor                # environment diagnostics (Python, backend, store, mode)

Subcommand	Purpose
auth <provider>	Authorize a sync credential provider (currently google); --client-id / --client-secret supply a bring-your-own OAuth client
warmup	Pre-download the Fastretrieval-managed local Qwen3 ONNX embedding + rerank models so first use works offline
config status | config delete [--yes]	Inspect or remove the stored encrypted configuration
relay status | relay open | relay reset	Inspect, open, or clear the zero-config browser setup session
doctor	Report Python version, credential backend, store dir, config, relay session, and storage mode

Remote (HTTP mode)

Deployed over HTTP, mnemo speaks Streamable HTTP transport and is OAuth-gated. Point any MCP client that supports remote HTTP + OAuth at https://<your-host>/mcp and authenticate on first connect; each authenticated user gets an isolated per-user credential store (see Trust Model). To stand up an instance, see Deploy to Cloudflare.

Public OCI image publication is discontinued. Existing historical registry tags remain untouched; new container deployments build from source or use the Cloudflare-managed registry.

Deploy to Cloudflare

Run your own mnemo instance serverless on Cloudflare (Containers + D1 + Vectorize + KV).

Paused 2026-09-13 (maintained instance only): the CF deploy token was removed from the account as off-manifest (process violation), so the CD deploy-cf job no-ops behind the CF_DEPLOY_ENABLED repo variable. The maintained instance freezes at its last deployed release until a token is re-established via the documented process and the variable is set to true. Self-hosting on your own account (below) is unaffected.

Prerequisites: a Cloudflare account on the Workers Paid plan — required for Containers, D1, and Vectorize (the Cloudflare free tier does not include them) — and the wrangler CLI.

1. git clone https://github.com/n24q02m/mnemo && cd mnemo-mcp
2. wrangler login
3. Provision the storage bindings mnemo uses -- the memories database, the embedding index, and the encrypted credential store:

   wrangler d1 create mnemo-memories
   wrangler vectorize create mnemo-memory-vectors-1536 --dimensions 1536 --metric cosine
   wrangler kv namespace create mnemo-kv
   

   Paste the returned D1 database ID and KV namespace ID into wrangler.jsonc (the Vectorize index binds by name, so no ID is needed), then create the memories schema (tables, indexes, and the FTS5 full-text index) in the database you just made:

   wrangler d1 migrations apply mnemo-memories --remote
   

   The SQL lives in migrations/0001_init.sql, and the D1 binding in wrangler.jsonc points at that folder via migrations_dir: "migrations". Full-text search uses FTS5, which D1 ships; vector similarity is served by Vectorize rather than by an in-database extension, because D1 cannot load one.
4. Build the HTTP container from this checkout and push it to your Cloudflare managed registry (CF Containers cannot pull from external registries directly), then set <YOUR_ACCOUNT_ID> in wrangler.jsonc:

   docker build --target http -t mnemo-mcp:local .
   wrangler containers push mnemo-mcp:local
   # set image to registry.cloudflare.com/<YOUR_ACCOUNT_ID>/mnemo-mcp:local
   

5. Set <YOUR_PUBLIC_URL> (e.g. https://mnemo.example.com) and <YOUR_WORKER_DOMAIN> (e.g. mnemo.example.com) in wrangler.jsonc, then set the secrets:

   wrangler secret put CREDENTIAL_SECRET              # per-user vault key (encrypts the cf-kv credential store)
   wrangler secret put MCP_RELAY_PASSWORD             # shared password gating the browser setup form
   wrangler secret put MCP_DCR_SERVER_SECRET          # required once PUBLIC_URL is set (multi-user, per-JWT-sub)
   

6. wrangler deploy and complete setup in the browser relay form at your Worker domain. Save each subject's models, endpoints and provider keys there, not in Worker environment variables. The managed route uses Minimax-free completion and paid Cohere embedding/reranking through Cloudflare AI Gateway -- obtain the required budget authorization before exercising the paid tiers (Provider Spend Gate); see the per-task configuration. Storage maps to Cloudflare via MCP_STORAGE_BACKEND=cf-kv (credentials / tokens, encrypted), MEMORY_DB_BACKEND=cf-d1 (the memories database + FTS5 full-text; unset or sqlite keeps the local SQLite file at DB_PATH), and Vectorize (embeddings, cosine). Cloud embedding, reranking and completion resolve per authenticated subject. Remote startup does not probe shared provider credentials or download local Fastretrieval models. Missing subject configuration never selects a process-wide provider/model fallback.

Authority & Sync Boundary

On Cloudflare deployments, Cloudflare D1 + Vectorize + KV is the sole production authority:

• D1 (MEMORY_DB_BACKEND=cf-d1): Authoritative storage for memory rows, metadata, bitemporal valid ranges, and FTS5 search.
• Vectorize (MCP_VECTORIZE_IDX): Dense vector index for semantic similarity search.
• KV (MCP_STORAGE_BACKEND=cf-kv): Encrypted per-user credential and session store.
• Sync boundary: MEMORY_DB_BACKEND=cf-d1 disables Google Drive OAuth and all external sync paths even if SYNC_ENABLED is toggled on or stale S3/Google settings remain. SYNC_ENABLED=false independently disables sync on non-CF deployments.
• Local & self-host bootstrap: Local stdio (~/.mnemo-mcp/memories.db) and self-hosted instances retain optional passport sync (Google Drive Device Code OAuth or S3/R2/B2) for workstation migration.

Deployment (maintained instance)

Every tagged release deploys automatically: the CD deploy-cf job checks out the released tag, builds the http-slim image, pushes it to the Cloudflare-managed registry as immutable :<release-tag>, deploys the Worker, and gates on a canary health check -- a release is live at exactly its own version. A beta dispatch redeploys the beta; a stable dispatch is maintainer-gated. Manual wrangler deploy against the maintained instance is not permitted: it would break the release-tag ↔ live-image correspondence. Self-hosting on your own Cloudflare account (the button above) is unaffected.

Trust Model

This plugin implements TC-Local (machine-bound, single trust principal). The mode/storage/encryption breakdown below is the full classification.

Mode	Storage	Encryption	Who can read your data?
stdio (default)	~/.mnemo-mcp/config.json	AES-GCM, machine-bound key	Only your OS user (file perm 0600)
HTTP self-host	Same as stdio	Same	Only you (admin = user)
HTTP multi-user remote (PUBLIC_URL)	Per-JWT-sub credential store	AES-GCM	Only the authenticated user (per-sub isolation)

Workspace username (HTTP setup form)

The browser setup form has an optional workspace username field. Entering the same username always lands you in the same per-sub bucket, so your credentials and memories stay reachable across a re-authorization and across devices, instead of being tied to the one-off subject minted for each /authorize round-trip. Leaving it blank keeps the previous per-authorize behaviour.

Trust boundary: when the form is gated by a shared MCP_RELAY_PASSWORD, the username is a partition key, not a secret -- anyone who knows that password can type any username and reach that bucket. That is fine for a trusted group; an untrusted multi-tenant deployment needs a per-user secret or delegated OAuth instead.

One-time migration: existing users must re-enter their credentials once after this change. Nothing is deleted; credentials stored under the old random subject are simply no longer addressed.

License

Apache-2.0 -- See LICENSE.