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Deployment

Python Version

mnemory requires Python 3.11–3.13. Python 3.14 is not yet supported.

The fastembed dependency (required for BM25 hybrid search) uses a Rust extension (py_rust_stemmers) that segfaults on Python 3.14 due to a PyO3/C API incompatibility. This is a known upstream issue (qdrant/fastembed#576). Python 3.13 is recommended for production. The Docker image uses python:3.13-slim.

Installation Methods

Using uvx (recommended)

uvx mnemory

mnemory starts on http://localhost:8050/mcp, stores data in ~/.mnemory/.

Using pip

pip install mnemory
mnemory

Using Docker

export OPENAI_API_KEY=sk-your-key
docker-compose up -d

Production Setup

For production, use remote Qdrant for vectors and S3/MinIO for artifacts:

docker run -d \
  -p 8050:8050 \
  -e OPENAI_API_KEY=sk-your-key \
  -e LLM_BASE_URL=https://your-litellm-proxy/v1 \
  -e LLM_MODEL=gpt-5.4-mini \
  -e QDRANT_HOST=qdrant.example.com \
  -e ARTIFACT_BACKEND=s3 \
  -e S3_ENDPOINT=http://minio.example.com:9000 \
  -e S3_ACCESS_KEY=admin \
  -e S3_SECRET_KEY=secret \
  -e MCP_API_KEYS='{"your-api-key": "your-username"}' \
  -v mnemory-data:/data \
  genunix/mnemory:latest

Docker Build

# Build for linux/amd64 (for Kubernetes deployment)
docker buildx build --platform linux/amd64 -t genunix/mnemory:latest .

# Push
docker push genunix/mnemory:latest

Authentication

mnemory can be deployed either as a standalone service (API keys) or behind Cognis (JWT service auth).

Single API Key

For simple setups, set a single shared API key:

MCP_API_KEY=your-secret-key

All clients must include Authorization: Bearer your-secret-key in requests. No user binding — user_id must come from headers or tool parameters.

Multi-User API Keys (MCP_API_KEYS)

Map API keys to user IDs so the LLM doesn't need to pass user_id in every tool call:

MCP_API_KEYS='{"mnm-key-for-filip": "filip", "mnm-shared-service-key": "*"}'
  • "key": "username" — authenticates AND binds user_id=username to the session
  • "key": "*" — authenticates only (wildcard), user_id must come from identity headers or tool parameter

See Configuration for full identity resolution details.

Cognis JWT Service Auth

When Cognis is the client, configure Mnemory to trust Cognis-issued ES256 JWTs:

export MNEMORY_JWT_PUBLIC_KEY=/path/to/cognis-public.pem
# or
export MNEMORY_JWKS_URL=https://cognis.example.com/.well-known/jwks.json

Mnemory expects:

  • Authorization: Bearer <jwt>
  • iss="cognis"
  • aud includes "mnemory"
  • sub = user identity
  • optional agent_id claim (falls back to X-Agent-Id header)

API keys remain supported for backward compatibility with standalone clients.

Kubernetes

Health Probes

Set MGMT_PORT to serve unauthenticated /health and /metrics on a separate port:

MGMT_PORT=9090          # /health and /metrics on port 9090, no auth
MGMT_HOST=127.0.0.1     # Optional: bind management to localhost only

This is the recommended setup for Kubernetes liveness/readiness probes and Prometheus scraping. The main port continues to expose authenticated /health and /metrics for service-to-service checks such as Cognis provider health. See Configuration for details.

Session Persistence

By default, mnemory persists sessions to a local SQLite database (~/.mnemory/sessions.db). For clustered deployments with multiple replicas, use Redis:

SESSION_BACKEND=redis
REDIS_URL=redis://redis.example.com:6379/0

Redis is an optional dependency — install with pip install mnemory[redis]. If REDIS_URL is set and SESSION_BACKEND is not explicitly set, Redis is auto-selected.

Sessions use a write-through cache (in-memory + backend) with lazy loading on cache miss. Losing a session is harmless — the client gets a fresh one on next recall.

Stateless HTTP

mnemory runs in stateless HTTP mode (stateless_http=True) for Kubernetes compatibility. No sticky sessions or WebSocket connections required.

Monitoring

mnemory exposes Prometheus metrics at /metrics. See Monitoring for available metrics, Prometheus scrape config, and a pre-built Grafana dashboard.