Castr Python API Docs | dltHub

Castr is a live and on-demand video streaming platform providing REST APIs to create, manage, and play live streams. The REST API base URL is https://api.castr.com/v2 and API uses HTTP Basic authentication with an Access ID and Secret provided as a Base64‑encoded Authorization header..

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 Castr data in under 10 minutes.

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What data can I load from Castr?

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

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How do I authenticate with the Castr API?

Authentication is performed using HTTP Basic auth: supply the Access ID as the username and Secret Key as the password; include as Authorization: Basic <base64(access_id:secret_key)> and Accept: application/json.

1. Get your credentials

1. Log into the Castr dashboard. 2) Open Settings → API. 3) Click "Create API Token" to generate an Access ID and Secret Key. 4) Save the Secret Key immediately; it is not stored by Castr.

2. Add them to .dlt/secrets.toml

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[sources.castr_source]
api_key = "YOUR_ACCESS_ID:YOUR_SECRET_KEY"

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 Castr 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 castr_pipeline.py

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

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Pipeline castr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset castr_data
The duckdb destination used duckdb:/castr.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 castr_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 live_streams and live_stream from the Castr 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 castr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.castr.com/v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "live_streams", "endpoint": {"path": "v2/live_streams"}},
            {"name": "live_stream", "endpoint": {"path": "v2/live_streams/{stream_id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="castr_pipeline",
        destination="duckdb",
        dataset_name="castr_data",
    )
    load_info = pipeline.run(castr_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("castr_pipeline").dataset()
sessions_df = data.live_streams.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM castr_data.live_streams LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("castr_pipeline").dataset()
data.live_streams.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 Castr 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 a request returns 401 Unauthorized, verify that the Authorization: Basic <base64(access_id:secret)> header is correctly formed and that the Access ID / Secret Key are valid. The secret is only shown once when generated, so ensure it was saved.

Pagination and empty list

GET endpoints support page and limit query parameters. An empty array indicates that the requested page contains no records; try lowering the page number or increasing the limit.

Rate limits and errors

The API uses standard HTTP status codes. For 429 Too Many Requests, implement exponential backoff before retrying. For other 4xx/5xx responses, inspect the response body for an error description provided by the service.

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