Get all experiments

Search experiments with optional filtering and sorting.

Args: filter_string: Filter query, e.g. "name LIKE 'btc%'" or "tags.team = 'ml'". Supports name, creation_time, last_update_time, tags.. order_by: List of sort clauses, e.g. ["last_update_time DESC", "name ASC"]. max_results: Maximum number of experiments to return (default 100).

Examples: search_experiments(filter_string="name LIKE 'btc%'") search_experiments(order_by=["last_update_time DESC"])

Get experiment details by name (more convenient than ID)

Get all unique metric names used across all runs in an experiment

Get all unique parameter names used across all runs in an experiment

Get all unique tag keys used across all runs in an experiment

Get runs for a specific experiment with full details.

Each run contains full metrics, params, and tags — keep limit small (3-10) to avoid flooding context. Use offset to paginate.

Args: experiment_id: The experiment ID limit: Maximum number of runs to return. Keep small — each run is large. offset: Number of runs to skip order_by: List of sort clauses, e.g. ['metrics.rmse DESC', 'params.lr ASC']. Use backticks for special characters: ['metrics.trading/loss DESC']

Examples: get_runs("1", limit=5) get_runs("1", order_by=["metrics.accuracy DESC"])

Get detailed information about a specific run. Run data can be large — avoid fetching many runs at once.

Get the parent run of a nested run. Returns None if the run has no parent.

Args: run_id: The child run ID to find the parent for.

Query runs using MLflow's filter syntax with optional sorting.

Runs can be large. Use wise limits to avoid flooding context.

Args: experiment_id: The experiment ID query: MLflow filter string (e.g., 'metrics.accuracy > 0.9') limit: Maximum number of runs to return offset: Number of runs to skip order_by: List of sort clauses, e.g. ['metrics.rmse DESC', 'params.lr ASC']. Use backticks for special characters: ['metrics.trading/loss DESC']

Examples: query_runs("1", "metrics.accuracy > 0", order_by=["metrics.accuracy DESC"]) query_runs("1", "", order_by=["metrics.f1/score DESC"])

List artifacts for a specific run. Use 'path' to browse into directories (e.g., 'configs')

Download and return the local path to a specific artifact

Get all metrics for a specific run with their latest values

Get the full history of a specific metric for a run

Get the best run by a specific metric (e.g., highest accuracy, lowest loss). Works with metrics containing special characters like '/' (e.g., 'trading/total_profit')

Compare runs side-by-side with full metrics and params. Runs can be large — keep the list short.

List all registered models in the model registry

Get all versions of a registered model

Get specific model version details (metrics, stage, run_id)

Get full details of a registered model including all versions and aliases. Can be large for models with many versions.

Args: name: Name of the registered model.

Get a model version by its alias (e.g. 'champion', 'production').

Args: name: Name of the registered model. alias: The alias assigned to the version, e.g. 'champion'.

Get latest model versions for each stage (e.g. 'Staging', 'Production').

Args: name: Name of the registered model. stages: List of stages to filter by, e.g. ['Production', 'Staging']. If None, returns latest version for all stages.

Find runs with specific tags (e.g., {'team': 'nlp', 'production': 'true'}). Runs can be large — use wise limits.

Read and return artifact content (for text/json files)

Search for logged models across one or more experiments. Results can be large — use wise limits.

Args: experiment_ids: List of experiment IDs to search in (at least one required). filter_string: SQL-like filter, e.g. 'metrics.accuracy > 0.9' or "tags.release = 'v1.0'". Multiple conditions use AND only (OR not supported). max_results: Maximum number of models to return (default 5). datasets: Filter by datasets the model was evaluated on. Each dict must include 'name' (str) and 'digest' (str), e.g. [{'name': 'val', 'digest': 'abc123'}]. order_by: List of sort clauses, each a dict with 'field_name' (str) and 'ascending' (bool), e.g. [{'field_name': 'metrics.accuracy', 'ascending': False}].

Examples: search_logged_models(["1"], filter_string="metrics.accuracy > 0.9") search_logged_models(["1", "2"], order_by=[{"field_name": "metrics.f1", "ascending": False}])

Get detailed information about a specific logged model by its ID.

Args: model_id: The logged model ID (obtained from search_logged_models results).

Register a model into the model registry. Creates the registered model if it doesn't exist.

Args: model_name: Name for the registered model. model_uri: URI of the model to register. Supports: - LoggedModel: 'models:/m-abc123' - Run artifact: 'runs:/run_id/artifact_path' tags: Optional dict of tags to set on the model version.

Examples: register_model("btc-classifier", "models:/m-abc123") register_model("btc-classifier", "runs:/abc123/model", tags={"framework": "lightgbm"})

Set a tag on a registered model (e.g. problem_type, team, framework).

Args: name: Name of the registered model. key: Tag key, e.g. 'problem_type', 'team', 'framework'. value: Tag value.

Assign an alias to a specific model version (e.g. promote best model to 'champion').

Args: name: Name of the registered model. alias: Alias to assign, e.g. 'champion', 'production', 'baseline'. version: Model version number to assign the alias to.

Examples: set_model_alias("lightgbm", "champion", "3")

Set a tag on a run (e.g. annotate best model, flag for review).

Args: run_id: The run ID to tag. key: Tag key, e.g. 'best_model', 'reviewed_by'. value: Tag value.

Set a tag on an experiment.

Args: experiment_id: The experiment ID to tag. key: Tag key, e.g. 'team', 'status'. value: Tag value.

Update the description of a model version.

Args: name: Name of the registered model. version: Model version number. description: New description text.

Transition a model version to a new stage (Staging, Production, Archived).

Deprecated since MLflow 2.9. Prefer aliases (set_model_alias) and copy_model_version for MLflow 3+ workflows. Use this only when working with legacy stage-based deployments.

Args: name: Name of the registered model. version: Model version number. stage: Target stage: 'Staging', 'Production', or 'Archived'. archive_existing: If True, archive existing versions in the target stage.

Promote a model version to another registered model (MLflow 3 promotion pattern). Creates the destination model if it doesn't exist.

Args: src_model_name: Source registered model name. src_version: Source model version number. dst_model_name: Destination registered model name, e.g. 'my-model-prod'.

Examples: copy_model_version("my-model-dev", "3", "my-model-prod")

Remove an alias from a registered model (e.g. revoke 'champion'). The alias is permanently removed; the model version itself is not affected.

Args: name: Name of the registered model. alias: Alias to remove, e.g. 'champion', 'production'.

Delete a specific model version from the registry. Irreversible — the version and its metadata cannot be recovered.

Args: name: Name of the registered model. version: Version number to delete.

Delete an entire registered model and all its versions. Irreversible — all versions, aliases, and tags are permanently removed.

Args: name: Name of the registered model to delete.

Delete a run. Moves it to the 'deleted' lifecycle stage — not shown in UI or queries, but recoverable via the MLflow API.

Args: run_id: The run ID to delete.

Delete an experiment and all its runs. Moves to the 'deleted' lifecycle stage — not shown in UI or queries, but recoverable via the MLflow API.

Args: experiment_id: The experiment ID to delete.

Check MLflow server health and connectivity