API Video Python API Docs | dltHub

api.video is a platform for uploading, hosting, delivering, and analyzing video-on-demand and live streams via a REST API. The REST API base URL is https://ws.api.video and all requests require HTTP Basic Auth using an API key as username..

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 API Video data in under 10 minutes.

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What data can I load from API Video?

Here are some of the endpoints you can load from API Video:

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How do I authenticate with the API Video API?

api.video uses HTTP Basic Authentication where the API key is provided as the username and the password is empty. Include credentials in the Authorization header (Basic base64(api_key:)). Use the sandbox key for sandbox endpoints and production key for production endpoints.

1. Get your credentials

1. Sign in to https://dashboard.api.video
2. Open the Overview or API keys section
3. Copy the API key for the desired environment (Sandbox or Production)
4. Use that key as the HTTP Basic username when calling the API (leave password blank)

2. Add them to .dlt/secrets.toml

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[sources.api_video_source]
api_key = "your_api_video_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 API Video 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 api_video_pipeline.py

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

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Pipeline api_video_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset api_video_data
The duckdb destination used duckdb:/api_video.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 api_video_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 videos and summaries from the API Video 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 api_video_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://ws.api.video",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "videos", "endpoint": {"path": "videos", "data_selector": "data"}},
            {"name": "summaries", "endpoint": {"path": "summaries", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="api_video_pipeline",
        destination="duckdb",
        dataset_name="api_video_data",
    )
    load_info = pipeline.run(api_video_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("api_video_pipeline").dataset()
sessions_df = data.videos.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM api_video_data.videos LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("api_video_pipeline").dataset()
data.videos.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 API Video 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

If you get 401 Unauthorized, verify you are using the correct API key for the chosen environment (production vs sandbox). Use HTTP Basic auth with the API key as the username and an empty password. Confirm the Authorization header is Basic base64(api_key:).

Rate limits (429 Too Many Requests)

api.video enforces per-minute rate limits differing by environment and plan (reads/writes/uploads). Check response headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Retry-After. Implement exponential backoff and retry after the Retry-After value.

Pagination

List endpoints return paginated responses with a top-level pagination object and a data array. Use pagination.links to follow the rel: "next" URL or pass currentPage/pageSize query params to retrieve subsequent pages.

Summary-specific quirks

When generating a summary via POST /summaries without setting origin=auto, the summary sourceStatus will be "missing" and you must PATCH /summaries/{summaryId}/source to provide the source. The summary resource separates metadata (summary object) from content (source object — contains "abstract" and "takeaways").

Common API errors (short list): 400 Bad Request (invalid params), 401 Unauthorized (invalid credentials or wrong environment), 404 Not Found (resource missing), 409 Conflict (resource conflict), 429 Too Many Requests (rate limits), 500+ Server Errors.

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