mnemos
Mounts MNEMOS as a tool provider through an optional memory skill, enabling Hermes agents to read/write memories.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mnemossearch memories about last week's sprint review"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
📍 Canonical source: GitLab
The authoritative source for this project lives on GitLab — always: https://gitlab.com/ncz-os/mnemos
This GitHub repository is retained only to host container images on ghcr.io (
mnemos-core,mnemos,mnemos-enterprise). Development, issues, and merge requests happen on GitLab.
MNEMOS + GRAEAE
MNEMOS v6.1.7 is the memory operating system for serious agentic work. It is not just a place to put bytes: it is a runtime of named subsystems that manage the full lifecycle of agent memory across providers, agents, and time horizons — write, embed, search, compress, version, reason over, audit, federate, export, import, and operate.
What is in the box:
a packaged FastAPI runtime with a CLI-first deployment surface
EPIMONE, the six-backend persistence layer — SQLite + sqlite-vec by default, PostgreSQL + pgvector, Oracle Database 26ai, IBM Db2 12.1, MySQL 9.0 Enterprise/HeatWave, and MariaDB 11.7+. Every backend self-provisions its schema on first connect. See Persistence.
the GRAEAE reasoning bus and PANTHEON unified LLM facade
an operator-audited compression stack
a divergent dream-state pipeline: REPLAY → CLUSTER → CONSOLIDATE → SYNTHESISE → EXTRACT
a GDPR right-to-be-forgotten worker
the PERSEPHONE archival subsystem
KRONOS recall observability
How it is packaged. MNEMOS ships as a small core (
mnemos-core) plus separately installablemnemos.*namespace subsystems (GRAEAE, PANTHEON, KNEMON, CHARON) and the standalone STIPHOS hive service. Container images are published toghcr.io/ncz-os—mnemos-core,mnemos(amd64 + arm64), andmnemos-enterprise(amd64, adds the Oracle/Db2/MySQL drivers). Pin the minor line (:6.1) to keep a fleet on identical code. Install and DSN/driver setup for each backend are in docs/INSTALL.md; the agent-facing contract is in AGENTS.md.
Related MCP server: SyncContext
Quick Start
🚀 Fastest path — the free-backend Quickstart. Two commands to durable, MCP-accessible agent memory on a free database. It's built around IBM Db2 12.1 — the reference deployment for the "mnemos on Db2" IBM TechXchange write-up — but the exact same image and steps run unchanged on Oracle Database 23ai Free, PostgreSQL + pgvector, or MariaDB 11.7+. Pick a backend in
quickstart/docs/BACKENDS.md; we lead with Db2, it works with all of them.
Memory and reasoning runtime for AI agents: persistent search, versioned storage, webhook fanout, and a unified LLM routing bus - all behind a single MCP interface.
1. Agent-driven install
Paste into Claude Code, Cursor, or Codex. The agent runs the install; you confirm. Agents should read AGENTS.md — it has the machine-readable module registry and a deterministic procedure for installing exactly the requested modules on the operator's arch + backend.
The pip package is mnemos-core (the subsystems are separate dists pulled
via extras). mnemos is the published image name, not a pip package.
Turnkey (container, any arch):
docker run -p 5002:5002 -v mnemos-data:/data ghcr.io/ncz-os/mnemos:latest
# everything image: core + graeae + pantheon + knemon + charon. SQLite by default.
# Point at a real DB with -e MNEMOS_DATABASE_DSN='postgres://…' (or oracle://… thin).pip (compose your own):
Install MNEMOS on this machine.
Steps:
1. pip install 'mnemos-core[server]' # everything; arch-neutral (no openvino)
2. mnemos init # scaffold config + token
3. mnemos serve # start API on :5002
4. mnemos doctor # verify subsystems
5. Set MNEMOS_BASE=http://localhost:5002 and MNEMOS_API_KEY=<token from step 2>
in shell env and any agent config that needs to reach it.
Edge device (SQLite kernel only): pip install 'mnemos-core[edge]'
Single subsystem, e.g. reasoning: pip install 'mnemos-core[graeae]'
Hive (STIPHOS) is a SEPARATE service: pip install 'mnemos-stiphos[mcp]' (port 8080)Enterprise backends (Oracle Database 26ai, IBM Db2 12.1).
Turnkey is the amd64-only mnemos-enterprise image (everything + Oracle/Db2/
MySQL drivers baked in). Note: Oracle uses the thin driver, so it also runs on
the plain mnemos image and on arm64 — only Db2 actually requires enterprise.
See docs/INSTALL.md for full driver, DSN, and migration
steps.
# Turnkey (amd64):
docker run --platform linux/amd64 -p 5002:5002 \
-e MNEMOS_DATABASE_DSN='db2://user:pass@host:50000/dbname' \
ghcr.io/ncz-os/mnemos-enterprise:latest
# Or from source:
git clone https://github.com/ncz-os/mnemos && cd mnemos
python -m pip install -e '.[server,enterprise]' # or '.[server,oracle]' / '.[server,db2]'
export MNEMOS_DATABASE_DSN='oracle://user:pass@host:1521/service_name'
# or: MNEMOS_DATABASE_DSN='db2://user:pass@host:50000/dbname'
mnemos install --profile server
mnemos serve --profile server2. Connect an agent via MCP
Add to ~/.claude/mcp_servers.json (Claude Code) or equivalent:
{
"mcpServers": {
"mnemos": {
"command": "mnemos",
"args": ["serve", "mcp-stdio"],
"env": {
"MNEMOS_BASE": "http://<host>:5002",
"MNEMOS_API_KEY": "<token>"
}
}
}
}For HTTP/SSE transport (ChatGPT, remote agents): mnemos serve mcp-http, which listens on :5003 by default.
Key MCP tools the agent gets:
Tool | What it does |
| Semantic + filtered search across the memory store |
| Write a new memory with category, tags, and content |
| Fetch a memory by ID |
| Query the knowledge-graph triple store |
| Surface recall anomalies and memory health signals |
| List soft-deleted memories pending hard deletion |
3. Webhooks + integrations
Integration | What connects | How |
Claude Code | Hooks fire on session-start, prompt-submit, stop - auto-log to MNEMOS |
|
ZeroClaw | Zeroclaw agent reads/writes memories via MCP |
|
OpenClaw | OpenClaw gateway routes memory ops through MCP |
|
Hermes | Optional memory skill mounts MNEMOS as a tool provider |
|
Webhooks (any) | Push |
|
Cursor / Cline / Continue.dev / Zed / Aider | Any MCP-capable IDE connects via stdio or HTTP transport | See |
Full documentation: docs/
Architecture
MNEMOS is a packaged FastAPI service with a single mnemos CLI for
installation, serving, MCP transport, and operational checks. Agents connect
through MCP stdio, MCP HTTP/SSE, REST, or OpenAI-compatible SDKs, while the
runtime routes memory, reasoning, session, webhook, federation, portability,
and observability work through the mnemos/ package. GRAEAE handles
multi-provider reasoning and model routing; MOIRAI handles operator-audited
compression through APOLLO and ARTEMIS.
Persistence
The backend is chosen at runtime by DSN scheme, never by rebuild. Six are
implemented, in mnemos/persistence/:
Backend | Vector support | Notes |
SQLite + sqlite-vec |
| Default. Edge and development installs; no server to run. |
PostgreSQL + pgvector | HNSW | Recommended for vector and semantic workloads — the most mature and predictable option, with broad managed-service support. |
Oracle Database 26ai | HNSW | Also JSON Duality and TDE. Thin driver, so it runs on the standard |
IBM Db2 | DiskANN | Hot paths emit native Db2 SQL — |
MySQL 9.0+ |
| For the managed-cloud MySQL audience (RDS and Aurora MySQL, HeatWave). Note that the vector functions ship only in MySQL Enterprise/HeatWave, not Community. |
MariaDB 11.7+ |
| The strongest MySQL-family option, and available in the free Community edition. Embeddings live in a |
Every backend satisfies the same PersistenceBackend protocol set
(mnemos/persistence/base.py) and self-provisions its schema idempotently on
backend.open(), DSN-aware, with the dimension taken from
MNEMOS_EMBEDDING_DIM. SQLite, PostgreSQL, Oracle, and Db2 share the
cross-backend harness in tests/test_persistence_parity.py; MySQL and MariaDB
are covered by their own live suites.
Documentation
Topic | File |
Installation | |
Specification | |
System requirements | |
Memory architecture | |
Compression | |
GRAEAE reasoning | |
PANTHEON provider facade | |
KRONOS observability | |
Audit chain | |
Portability format (MIF 1.0) | |
Scaling | |
Operations | |
Benchmark harness | scripts/bench_v4.py — cross-backend vector-search harness (PG / Oracle / Db2 / SQLite). Results published post-GA. |
License
MNEMOS is licensed under the Apache License, Version 2.0. See LICENSE for the full text.
Build infrastructure & partners
Continuous integration and package distribution for this project are generously supported by our open-source infrastructure partners:
GitLab — canonical source hosting and CI pipelines (format / lint / test gates), via the GitLab for Open Source program.
Buildkite — CI/CD orchestration with hosted macOS and Linux agents, and our APT package registry host (
packages.buildkite.com/ncz-os/ncz), via the Buildkite Open Source program.
Thank you to both for backing open-source software.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceA production-ready MCP server that enables multiple AI agents to collaborate through a shared, concurrency-safe memory space. It supports advanced search, full CRUD operations, and automatic backups to facilitate asynchronous communication between agents.MIT
- AlicenseAqualityDmaintenanceMCP server that provides a shared semantic memory layer for AI coding agents, enabling teams to store, search, and sync context, decisions, and knowledge across projects with project-based isolation and multi-backend support.141MIT
- AlicenseNot gradedqualityCmaintenanceUniversal MCP server providing adaptive semantic memory for AI agents, supporting document ingestion, semantic search, chat persistence, cross-project linking, and cloud storage overflow.3GPL 3.0
- AlicenseNot gradedqualityBmaintenancePersistent memory MCP server for AI agents that stores, recalls, and searches conversation history, key-value context, and long-term entries across sessions with semantic search and FIFO queues.751Unlicense - libtelnet variant
Related MCP Connectors
An MCP memory server. One memory your agents share — across models, devices and apps.
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Cloud-hosted MCP server for durable AI memory
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ncz-os/mnemos'
If you have feedback or need assistance with the MCP directory API, please join our Discord server