Load RunwayML data to DuckDB

RunwayML is a generative media platform that provides an API for programmatic access to its generative video and image models. Everything needed to build a working RunwayML → 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 RunwayML 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 RunwayML 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 RunwayML 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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RunwayML API at a glance

These values come from the RunwayML API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the RunwayML API?

Requests require an API key passed in the headers. The environment variable RUNWAYML_API_SECRET is typically used to store this key.

1. Get your credentials

1. Navigate to the Runway Developer Portal at https://dev.runwayml.com/ and sign in or create an account. 2. Upon logging in, create an organization if you have not already done so. 3. Within your organization dashboard, navigate to the API Keys tab or the Manage tab. 4. Click the New API key button. 5. Enter a descriptive name for the key. 6. Copy the generated key immediately, as it will not be displayed again after you close the pop-up. Store it securely in a password manager or secret management tool.

2. Add them to .dlt/secrets.toml

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[sources.runwayml_source]
RUNWAYML_API_SECRET = "key_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

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 RunwayML data can I load into DuckDB?

These are the RunwayML endpoints dlt can load into DuckDB:

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How do I load only new RunwayML records?

The RunwayML API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "tasks", "endpoint": {
    "path": "v1/tasks",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 RunwayML pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/text_to_image and /v1/tasks/{id} from the RunwayML API into DuckDB:

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

@dlt.source
def runwayml_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dev.runwayml.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "v1/tasks", "data_selector": "data"}},
            {"name": "avatars", "endpoint": {"path": "v1/avatars", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_runwayml_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="runwayml_pipeline",
        destination="duckdb",
        dataset_name="runwayml_data",
    )
    load_info = pipeline.run(runwayml_source())
    print(load_info)


if __name__ == "__main__":
    load_runwayml_to_duckdb()

Run it with python runwayml_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 RunwayML 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("runwayml_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

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SELECT * FROM runwayml_data.tasks LIMIT 10;

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

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How do I deploy the RunwayML 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 RunwayML 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 RunwayML 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