Shotstack Python API Docs | dltHub

Shotstack is a cloud video, image and audio editing service offering REST APIs to programmatically create, ingest, host and serve media assets. The REST API base URL is https://api.shotstack.io/{api_group}/{version} and all requests require an x-api-key header (Developer API key).

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 Shotstack data in under 10 minutes.

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What data can I load from Shotstack?

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

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How do I authenticate with the Shotstack API?

Shotstack uses API keys passed in the x-api-key HTTP header on all requests. Also include Accept: application/json. The same API key is used across Edit, Serve, Ingest and Create APIs.

1. Get your credentials

1. Sign up or sign in at https://shotstack.io. 2) Open the dashboard / account or API keys section (Request API Keys guide). 3) Create or copy your Developer API Key. 4) Use the key as the value of the x-api-key header in requests.

2. Add them to .dlt/secrets.toml

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[sources.shotstack_source]
api_key = "your_shotstack_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 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 Shotstack 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 shotstack_pipeline.py

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

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Pipeline shotstack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset shotstack_data
The duckdb destination used duckdb:/shotstack.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 shotstack_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 renders and assets from the Shotstack 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 shotstack_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.shotstack.io/{api_group}/{version}",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "renders", "endpoint": {"path": "edit/{version}/render/{id}", "data_selector": "response"}},
            {"name": "assets", "endpoint": {"path": "serve/{version}/assets/{id}", "data_selector": "response"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="shotstack_pipeline",
        destination="duckdb",
        dataset_name="shotstack_data",
    )
    load_info = pipeline.run(shotstack_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("shotstack_pipeline").dataset()
sessions_df = data.renders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM shotstack_data.renders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("shotstack_pipeline").dataset()
data.renders.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 Shotstack 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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Troubleshooting

Authentication failures

Ensure the x-api-key header contains a valid Developer API Key. 401/403 errors indicate missing or invalid api key.

Rate limits and throttling

The docs do not list a fixed public rate limit; if you receive 429 responses slow request rate and implement exponential backoff.

Pagination and array selectors

List endpoints return an envelope with success, message and response. The arrays are nested under response (e.g. response.templates, response.sources, response.assets). When parsing list responses use the response.<resource_plural> key.

Not found / invalid id

404 responses indicate the id does not exist; confirm you are using a render id vs asset id (they differ).

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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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