Load MLflow data to DuckDB

MLflow provides a REST API for managing experiments, runs, artifacts, models, and authentication configuration. Everything needed to build a working MLflow → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your MLflow to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from MLflow to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the MLflow API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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MLflow API at a glance

These values come from the MLflow API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MLflow API?

MLflow supports multiple authentication methods including HTTP Basic Authentication (username/password) or Bearer Token authentication via the 'Authorization' header. Alternatively, credentials can be managed via environment variables like MLFLOW_TRACKING_TOKEN or MLFLOW_TRACKING_USERNAME/MLFLOW_TRACKING_PASSWORD.

1. Get your credentials

MLflow typically uses Basic Authentication or API tokens depending on the deployment. 1. If using basic authentication, you do not need an 'API key' from the dashboard; instead, provide the username and password via environment variables MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD. 2. Alternatively, you can store these in ~/.mlflow/credentials in INI format. 3. If using managed platforms (like Databricks), follow the platform-specific instructions to generate a Personal Access Token (PAT) from the user settings or experiment UI. 4. If interacting with LLM-specific features (formerly AI Gateway), credentials are managed under 'LLM Connections' in the UI settings (e.g., /#/settings).

2. Add them to .dlt/secrets.toml

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[sources.mlflow_source]
mlflow_tracking_username = "your_username"
mlflow_tracking_password = "your_password"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What MLflow data can I load into DuckDB?

These are the MLflow endpoints dlt can load into DuckDB:

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How do I load only new MLflow records?

MLflow exposes page_token on 2.0/mlflow/experiments/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "experiments", "endpoint": {
    "path": "2.0/mlflow/experiments/search",
    "data_selector": "experiments",
    "incremental": {"cursor_path": "page_token", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated MLflow pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading 2.0/mlflow/experiments/search and 2.0/mlflow/runs/search from the MLflow API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def mlflow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<host>:<port>/api/2.0",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "experiments", "endpoint": {"path": "2.0/mlflow/experiments/search", "data_selector": "experiments"}},
            {"name": "runs", "endpoint": {"path": "2.0/mlflow/runs/search", "data_selector": "runs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mlflow_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mlflow_pipeline",
        destination="duckdb",
        dataset_name="mlflow_data",
    )
    load_info = pipeline.run(mlflow_source())
    print(load_info)


if __name__ == "__main__":
    load_mlflow_to_duckdb()

Run it with python mlflow_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query MLflow data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("mlflow_pipeline").dataset()
df = data.experiments.df()
print(df.head())

SQL:

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SELECT * FROM mlflow_data.experiments LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the MLflow to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw MLflow loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load MLflow data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack