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📍 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 installable mnemos.* namespace subsystems (GRAEAE, PANTHEON, KNEMON, CHARON) and the standalone STIPHOS hive service. Container images are published to ghcr.io/ncz-osmnemos-core, mnemos (amd64 + arm64), and mnemos-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 server

2. 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

search_memories

Semantic + filtered search across the memory store

create_memory

Write a new memory with category, tags, and content

get_memory

Fetch a memory by ID

kg_search

Query the knowledge-graph triple store

kronos_anomalies

Surface recall anomalies and memory health signals

list_deletions

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

integrations/claude-code/ - copy hooks + set MNEMOS_BASE

ZeroClaw

Zeroclaw agent reads/writes memories via MCP

integrations/zeroclaw/ + mnemos serve mcp-stdio in zeroclaw config

OpenClaw

OpenClaw gateway routes memory ops through MCP

integrations/openclaw/ + MCP server entry in openclaw.json

Hermes

Optional memory skill mounts MNEMOS as a tool provider

integrations/hermes/optional-skills/memory/mnemos/

Webhooks (any)

Push memory.created, memory.updated, memory.deleted, consultation.completed events to any HTTPS endpoint

POST /api/webhooks/register with {"url": "...", "events": [...]}

Cursor / Cline / Continue.dev / Zed / Aider

Any MCP-capable IDE connects via stdio or HTTP transport

See docs/connectors/


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

vec0 virtual table

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 INMEMORY NEIGHBOR GRAPH

Also JSON Duality and TDE. Thin driver, so it runs on the standard mnemos image and on arm64.

IBM Db2

DiskANN

Hot paths emit native Db2 SQL — VECTOR_DISTANCE(..., EUCLIDEAN) with FETCH APPROX FIRST, engaging the DiskANN index on the user-facing query path. The default dialect (MNEMOS_DB2_DIALECT=compat) still translates inherited Oracle-shaped SQL at the cursor layer; set MNEMOS_DB2_DIALECT=native for the pass-through backend. amd64 only.

MySQL 9.0+

VECTOR_DISTANCE

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+

VEC_DISTANCE_COSINE + HNSW VECTOR INDEX

The strongest MySQL-family option, and available in the free Community edition. Embeddings live in a memory_embeddings join table. Its vector engine is newer than pgvector's and correspondingly less battle-tested.

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

docs/INSTALL.md

Specification

docs/SPECIFICATION.md

System requirements

SYSTEM_REQUIREMENTS.md

Memory architecture

docs/MEMORY_ARCHITECTURE.md

Compression

docs/COMPRESSION.md

GRAEAE reasoning

docs/GRAEAE_FEATURES.md

PANTHEON provider facade

docs/PANTHEON.md

KRONOS observability

docs/KRONOS.md

Audit chain

docs/AUDIT_CHAIN.md

Portability format (MIF 1.0)

docs/MEMORY_EXPORT_FORMAT.md

Scaling

docs/SCALING.md

Operations

docs/OPERATIONS.md

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.

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