mnemos
Mounts MNEMOS as a tool provider through an optional memory skill, enabling Hermes agents to read/write memories.
Click on "Deploy 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 the
mnemos-enterprisecontainer image on ghcr.io. Development, issues, and merge requests happen on GitLab.
MNEMOS + GRAEAE
MNEMOS v7.0.5 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 AI 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
first-party CHARON portability: MIF/MPF import and export, universal ingest, migrate-in adapters, and STYX encrypted off-fleet backups
KRONOS recall observability
How it is packaged. MNEMOS ships as a small core (
mnemos-core) plus separately installablemnemos.*namespace subsystems (GRAEAE, PANTHEON, KNEMON) and the standalone STIPHOS hive service. CHARON's portability, migration, ingestion, and STYX backup code became first-party core modules on 2026-09-18; only Docling's heavy document-conversion dependencies remain optional throughmnemos-core[docling]. The published container image isghcr.io/ncz-os/mnemos-enterprise— a single multi-arch (amd64 + arm64) manifest with every backend driver (Oracle, MySQL, MariaDB) except Db2, which is amd64-only. Pin an exact version (:7.0.5) 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 AI Database 26ai 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-enterprise:latest
# everything image: core (including CHARON/STYX) + graeae + pantheon + knemon. SQLite by default.
# Point at a real DB with -e MNEMOS_DATABASE_DSN='postgres://…' (or oracle://… thin).pip (compose your own):
None of
mnemos-core,mnemos-graeae,mnemos-pantheon,mnemos-knemon, ormnemos-stiphosare currently published to PyPI.pip install 'mnemos-core[...]'will 404 — the[server]/[full]extras recurse into these names on the public index. Until they're published, install from source. This exact sequence is tested in a clean venv:
# Core (arch-neutral, no openvino):
git clone https://gitlab.com/ncz-os/mnemos && cd mnemos
python -m pip install -e .
# Add-ons, straight from GitLab — core's already-installed version satisfies
# each add-on's own mnemos-core floor, so pip resolves it locally and never
# needs to reach PyPI for mnemos-core itself:
pip install 'git+https://gitlab.com/ncz-os/graeae.git'
pip install 'git+https://gitlab.com/ncz-os/knemon.git'
pip install 'git+https://gitlab.com/ncz-os/pantheon.git' # needs graeae + knemon installed first
mnemos init # scaffold config + token
mnemos serve # start API on :5002
mnemos doctor # verify subsystems
# Set MNEMOS_BASE=http://localhost:5002 and MNEMOS_API_KEY=<token from mnemos init>
# in shell env and any agent config that needs to reach it.
# Hive (STIPHOS) is a SEPARATE service, same pattern:
git clone https://gitlab.com/ncz-os/mnemos-stiphos && cd mnemos-stiphos
pip install -e '.[mcp]' # port 8080Run pip check afterward as a sanity check — a clean install reports "No
broken requirements found." Anything it does report is a real gap; install it.
Edge device (SQLite kernel only): after the core install above, pip install aiosqlite sqlite-vec (the [edge] extra's own two deps — both real, published
packages, unaffected by the above).
Enterprise backends (Oracle AI Database 26ai, IBM Db2 12.1).
mnemos-enterprise is a single multi-arch (amd64 + arm64) image with every
backend driver baked in. The one asymmetry: Db2's driver has no arm64 wheel,
so the Db2 backend is amd64-only — Oracle (thin driver), MySQL, and MariaDB
all work on arm64 too. 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 (see the "pip (compose your own)" section above for the
# add-on install order — enterprise adds the same set plus the driver extra
# on core itself):
git clone https://gitlab.com/ncz-os/mnemos && cd mnemos
python -m pip install -e '.[enterprise]' # or '.[oracle]' / '.[db2]' — core's own extras, real PyPI deps
pip install 'git+https://gitlab.com/ncz-os/graeae.git'
pip install 'git+https://gitlab.com/ncz-os/knemon.git'
pip install 'git+https://gitlab.com/ncz-os/pantheon.git'
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 AI 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 and PostgreSQL share the cross-backend harness
in tests/test_persistence_parity.py; Oracle, Db2, MySQL, and MariaDB are
each covered by their own live suite.
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 | |
Backend parity matrix | docs/BACKEND_PARITY.md — generated by |
Known limitations | |
SQLite / edge profile | |
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 deployed
Maintenance
Related MCP Connectors
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
An MCP memory server. One memory your agents share — across models, devices and apps.
- memnodeOAuthdev.memnode
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
MCP server for building and testing AI agents with multi-model experimentation and insights.
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.331 PyPI3GPL 3.0
- FlicenseNot gradedqualityCmaintenancePersistent 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.39 npm1-