Deployment Guide

Containerized deployment of the MultiMind API. The image is a multi-stage build on python:3.11-slim: the builder stage wheels the package, the runtime stage installs the wheel plus the gateway and rag extras. The process serves create_platform_app: one Swagger UI at /docs for chat, sessions, compliance checks, governance, the guard proxy, and RAG. No docs, tests, or examples are included. RAG pulls FAISS, Chroma, and sentence-transformers, so the image carries a CPU-only torch; GPU libraries are left out on purpose.

Pull the published image

Every merge to develop and every release publishes the image to GitHub Container Registry for linux/amd64 and linux/arm64 (the Docker image workflow in .github/workflows/docker.yml). No login is needed.

docker pull ghcr.io/multimindlab/multimind-sdk:latest
docker run -d --name multimind -p 8000:8000 --env-file .env ghcr.io/multimindlab/multimind-sdk:latest
curl http://127.0.0.1:8000/health

Tag	Built from
`latest`	the most recent release tag (`v1.0.0` and later)
`1.0.0`, `1.0`	that release
`develop`	the current `develop` branch, after the smoke test passes
`sha-<short>`	one specific commit on `develop`

The workflow can also be run by hand from the Actions tab (Docker image, “Run workflow”); untick “Push” for a build-only run, or add an extra tag such as rc1. Pull requests that touch the Dockerfile or the package build the image and run the smoke test without pushing. docker compose up uses the published image; run docker compose build to build from the checkout instead.

Build locally

docker build -t multimind-sdk:latest .

Optional: bake extra dependency groups into the image (see pyproject.toml for available extras):

docker build --build-arg MULTIMIND_EXTRAS=gateway,rag -t multimind-sdk:rag .

Run

docker run -d --name multimind -p 8000:8000 \
  -e OPENAI_API_KEY=... \
  -e ANTHROPIC_API_KEY=... \
  multimind-sdk:latest

All provider keys are optional; set only the ones you use: OPENAI_API_KEY, ANTHROPIC_API_KEY, GROQ_API_KEY, MISTRAL_API_KEY, GEMINI_API_KEY, DEEPSEEK_API_KEY, HUGGINGFACE_API_KEY. Other settings: DEFAULT_MODEL (default openai), OLLAMA_API_BASE, OLLAMA_MODEL_NAME, LOG_LEVEL, MULTIMIND_PORT (default 8000). Or pass an env file instead of individual -e flags:

docker run -d --name multimind -p 8000:8000 --env-file .env multimind-sdk:latest

Docker Compose

The gateway reads a .env file at the repo root automatically (optional).

docker compose up -d               # API on :8000 and dashboard on :8501
docker compose --profile local up -d          # plus ollama (local models)
docker compose --profile vector-store up -d   # plus chroma

The API and the dashboard share the multimind_governance volume (/data): audit log, cost log, and guardrails.json. POST /v1/guardrails/reload applies that file to the running proxy. Set API_KEYS when the API should require an X-API-Key header. /health, /ready, and /docs stay open. Compose allows the dashboard origin (http://localhost:8501) through MULTIMIND_CORS_ORIGINS so the browser can call the API.

Fully local, no API keys

DEFAULT_MODEL=ollama docker compose --profile local up -d
docker compose exec ollama ollama pull mistral
curl -X POST http://localhost:8000/v1/chat \
  -H 'Content-Type: application/json' \
  -d '{"model": "ollama", "messages": [{"role": "user", "content": "Hello"}]}'

The gateway is preconfigured with OLLAMA_API_BASE=http://ollama:11434, so the two containers talk over the compose network. Ollama models persist in the ollama_models volume.

Health checks

GET /health — liveness (no auth, no provider keys needed). Used by the image HEALTHCHECK and the compose healthcheck.
GET /ready — readiness probe.

curl http://localhost:8000/health
docker inspect --format '{{.State.Health.Status}}' multimind

The healthcheck uses Python’s urllib, so no curl is required inside the image.

Running the CLI in the container

docker run --rm -it --env-file .env multimind-sdk:latest multimind chat start -m openai

The CLI works torch-free: only the convert command (dispatched before the Click group, so not listed in multimind --help) needs torch, and it fails with a clear install hint in the lean image. For model conversion inside a container, build with the finetune extra (large image, several GB):

docker build --build-arg MULTIMIND_EXTRAS=gateway,finetune -t multimind-sdk:cli .
docker run --rm -it --env-file .env multimind-sdk:cli multimind convert --help

Production notes

The container runs as non-root user multimind (uid 10001).

Read-only root filesystem is supported. Chat sessions are written under /app, so mount it as a tmpfs owned by the app user:

docker run -d --read-only --tmpfs /app:uid=10001,gid=10001 \
  -p 8000:8000 --env-file .env multimind-sdk:latest

Set resource limits, e.g. docker run --memory 1g --cpus 2 ..., or in compose:

services:
  gateway:
    deploy:
      resources:
        limits:
          memory: 1g
          cpus: "2"

Never bake secrets into the image; .env is excluded by .dockerignore. Inject keys at runtime via env vars or your orchestrator’s secret store.
CORS is off unless MULTIMIND_CORS_ORIGINS (comma-separated origins) is set; compose sets it to the dashboard origin only. MULTIMIND_ALLOWED_ORIGINS is accepted as a legacy alias.
Chat sessions in compose persist in the multimind_sessions volume; drop the volume mount if you want stateless containers.

Start

Guides

REST API

Reference

Pull the published image

Build locally

Run

Docker Compose

Fully local, no API keys

Health checks

Running the CLI in the container

Production notes

​Pull the published image

​Build locally

​Run

​Docker Compose

​Fully local, no API keys

​Health checks

​Running the CLI in the container

​Production notes

Pull the published image

Build locally

Run

Docker Compose

Fully local, no API keys

Health checks

Running the CLI in the container

Production notes