MongoDB Backend for MLflow Registered Models

[WARNING] This project is under active development and has not reached a stable release. APIs, storage formats, and configuration may change at any time without notice. It is not intended for production use. See LICENSE for the full disclaimer of warranties.

An MLflow plugin that stores registered models, versions, aliases, tags, and related metadata in MongoDB. MLflow tracking data, such as experiments and runs, remains in a separate backend store.

Installation

Python 3.10 or newer, MLflow 3.5 or newer within the 3.x series, and a MongoDB deployment are required.

python -m pip install "mlflow-mongodb>=0.1.0.dev0"

Running MLflow

The tracking store and registered-model store are configured independently. For example, the following configuration keeps experiments and runs in SQLite and stores registered models and their versions in MongoDB:

export TRACKING_STORE_URI="sqlite:///tracking.db"
export REGISTRY_STORE_URI="mongodb://localhost:27017/mlflow_registry"
export MLFLOW_BIND_HOST="127.0.0.1"
export MLFLOW_BIND_PORT="5000"

mlflow server \
  --backend-store-uri "${TRACKING_STORE_URI}" \
  --registry-store-uri "${REGISTRY_STORE_URI}" \
  --host "${MLFLOW_BIND_HOST}" \
  --port "${MLFLOW_BIND_PORT}"

Open http://127.0.0.1:5000 to use the MLflow UI. Clients can connect to the server with:

export MLFLOW_TRACKING_URI="http://127.0.0.1:5000"

The MongoDB URI must include the database name; mlflow_registry is the database name in the example above. Authentication, replica-set, TLS, and other connection options can be provided using standard MongoDB URI syntax. For example:

mongodb://username:password@mongo.example.com:27017/mlflow_registry?authSource=admin

When using the MongoDB tracking backend, MLflow 3.5 clients upload full trace payloads to MLflow's existing artifact storage. Configure a reachable experiment artifact location, such as S3 or an MLflow artifact proxy. The MongoDB store returns the trace artifact URI; it does not implement its own artifact provider or upload the payload itself.

On MLflow 3.5 servers, trace retrieval uses artifacts while spans are also persisted in MongoDB. On MLflow 3.6 and newer servers, modern clients use MongoDB-backed trace retrieval. A 3.5 client cannot read a trace written only to MongoDB by a modern client unless an artifact payload also exists. Cross-version reads of that kind are not guaranteed.

Aggregated trace metric queries require MLflow 3.9 or newer; available metrics and dimensions depend on the installed MLflow version. Older versions preserve native MLflow spans without newer OTel translations, and derive cost summaries only when MLflow provides its cost aggregator. These limitations do not disable run metric logging.

Documentation

New to MLflow? Start with the MLflow Tracking Quickstart. The following guides provide additional context for using this plugin:

• Model Registry Tutorial — register and manage model versions.
• Backend Stores — understand where MLflow stores tracking data.
• Tracking Server Configuration — configure tracking and registry stores independently.
• MongoDB Connection Strings — configure authentication, TLS, replica sets, and other URI options.

Versioning

This project follows Semantic Versioning (MAJOR.MINOR.PATCH), same as MLflow itself. It's currently on major version 0, meaning the API isn't yet stable and may change between releases. main always carries a .devN version (e.g. 0.1.0.dev0) to signal it's unreleased and ahead of the latest published version.

Contributor setup

For development, clone the repository and install it in editable mode:

git clone https://github.com/mongodb-developer/mlflow-mongodb.git
cd mlflow-mongodb

python -m venv .venv
source .venv/bin/activate
python -m pip install -e .

If you downloaded a source archive instead, extract it and run the venv and pip commands from the extracted repository root. The final . in pip install -e . means “install the project in the current directory,” and -e keeps the installation linked to that source checkout.

Install the development dependencies and Git hooks with:

python -m pip install -e ".[dev]"
pre-commit install --install-hooks

Run all formatting, linting, and repository checks with:

pre-commit run --all-files

Functional tests

The functional tests use a real MongoDB 8.0 or newer server. Install the project in editable mode with its development dependencies before running them:

python -m pip install -e ".[dev]"

Set MONGODB_URI to a dedicated test database. The database name must contain test as a distinct hyphen- or underscore-separated segment; for example:

export MONGODB_URI="mongodb://localhost:27017/mlflow_functional_test"
python -m pytest tests/functional

Authentication, replica-set, TLS, and other standard MongoDB URI options can be included in MONGODB_URI. Use a replica set when running tests that exercise MongoDB transactions.

The functional suite deletes documents from its application collections after every test so their indexes can be reused. At the end of the test session, it drops the database selected by MONGODB_URI. Never point MONGODB_URI at a database containing data that must be preserved.