Metaflow Python API Docs | dltHub

Metaflow API allows accessing past results, data in S3, and triggering flows via events. It ensures backward compatibility. The Client API enables retrieving and manipulating past run results. The REST API base URL is value is deployment-specific; use the METAFLOW_SERVICE_URL of your Metaflow Service (e.g. https://your-metaflow-service.example.com) and Requests to Metaflow Service require authentication as configured for your deployment (e.g., HTTP Basic or token-based auth)..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Metaflow data in under 10 minutes.

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What data can I load from Metaflow?

Here are some of the endpoints you can load from Metaflow:

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How do I authenticate with the Metaflow API?

Authentication is enforced by the Metaflow Service/metadata provider. Configure METAFLOW_SERVICE_URL to point to your service; include the required auth headers (e.g., Authorization: Bearer or HTTP Basic credentials) as dictated by your service deployment.

1. Get your credentials

Obtain credentials from your Metaflow Service administrator or deployment dashboard. There is no centralized Metaflow-hosted dashboard; credentials are provided/managed per-deployment.

2. Add them to .dlt/secrets.toml

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[sources.metaflow_source]
api_token = "your_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Metaflow API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python metaflow_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline metaflow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset metaflow_data
The duckdb destination used duckdb:/metaflow.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline metaflow_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads flows and runs from the Metaflow API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def metaflow_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "value is deployment-specific; use the METAFLOW_SERVICE_URL of your Metaflow Service (e.g. https://your-metaflow-service.example.com)",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "flows", "endpoint": {"path": "flows", "data_selector": "flows"}},
            {"name": "runs", "endpoint": {"path": "flows/{flow_name}/runs", "data_selector": "runs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="metaflow_pipeline",
        destination="duckdb",
        dataset_name="metaflow_data",
    )
    load_info = pipeline.run(metaflow_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("metaflow_pipeline").dataset()
sessions_df = data.flows.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM metaflow_data.flows LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("metaflow_pipeline").dataset()
data.flows.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Metaflow data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack