Magic Hour Python API Docs | dltHub

Magic Hour is an AI media generation platform for video, image and audio, exposed as a public REST API. The REST API base URL is https://api.magichour.ai/v1 and all requests require a Bearer token.

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 add "dlt[hub]" and start loading Magic Hour data in under 10 minutes.

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What data can I load from Magic Hour?

Here are some of the endpoints you can load from Magic Hour:

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How do I authenticate with the Magic Hour API?

All requests require the API key to be passed as a Bearer token in the Authorization header, formatted as 'Authorization: Bearer <your_api_key>'.

1. Get your credentials

To obtain your API credentials: 1. Navigate to the Magic Hour Developer Hub at https://magichour.ai/developer. 2. Sign in to your account. 3. Ensure you are on the API Keys tab. 4. Click the "Create New API Key" (or "Create Key") button. 5. Provide a descriptive name for your key and select the necessary permissions. 6. Click "Create Key". IMPORTANT: Copy the API key immediately upon generation, as it will only be displayed once. If lost, you must generate a new one.

2. Add them to .dlt/secrets.toml

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[sources.magic_hour_source]
MAGIC_HOUR_API_KEY = "your_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Magic Hour 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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uv run python magic_hour_pipeline.py

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

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Pipeline magic_hour_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset magic_hour_data
The duckdb destination used duckdb:/magic_hour.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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uv run dlthub 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 v1/ai-image-generator and v1/ai-video-editor from the Magic Hour 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 magic_hour_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.magichour.ai/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "requests", "endpoint": {"path": "v1/requests", "data_selector": "requests"}},
            {"name": "video_projects", "endpoint": {"path": "v1/video-projects/{id}", "data_selector": "video_projects"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="magic_hour_pipeline",
        destination="duckdb",
        dataset_name="magic_hour_data",
    )
    load_info = pipeline.run(magic_hour_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("magic_hour_pipeline").dataset()
sessions_df = data.requests.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM magic_hour_data.requests LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("magic_hour_pipeline").dataset()
data.requests.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 Magic Hour 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 harness:

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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