Mux - Video Python API Docs | dltHub

Mux is a developer platform for video streaming and video management via REST APIs. The REST API base URL is https://api.mux.com/video/v1 and All requests use HTTP Basic auth with an Access Token ID (username) and Access Token Secret (password)..

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 Mux - Video data in under 10 minutes.

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What data can I load from Mux - Video?

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

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How do I authenticate with the Mux - Video API?

Authenticate requests using HTTP Basic Auth: the Access Token ID is the username and the Access Token Secret is the password; the Authorization header becomes 'Basic base64(TOKEN_ID:TOKEN_SECRET)'.

1. Get your credentials

1. Sign in to the Mux dashboard (https://dashboard.mux.com). 2) Go to Settings → Access Tokens. 3) Click 'Create token' (choose environment and permissions, e.g., Video Read for GETs or Read+Write for create/update). 4) Copy the Access Token ID and Access Token Secret (secret shown only once). 5) Use the ID as username and Secret as password for HTTP Basic auth.

2. Add them to .dlt/secrets.toml

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[sources.mux_video_source]
token_id = "your_mux_token_id"
 token_secret = "your_mux_token_secret"

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 Mux - 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 mux_video_pipeline.py

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

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Pipeline mux_video_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mux_video_data
The duckdb destination used duckdb:/mux_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 mux_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 assets and live_streams from the Mux - 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 mux_video_source(token_id, token_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mux.com/video/v1",
            "auth": {
                "type": "http_basic",
                "token_id (and token_secret)": token_id, token_secret,
            },
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "video/v1/assets", "data_selector": "data"}},
            {"name": "live_streams", "endpoint": {"path": "video/v1/live-streams", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mux_video_pipeline",
        destination="duckdb",
        dataset_name="mux_video_data",
    )
    load_info = pipeline.run(mux_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("mux_video_pipeline").dataset()
sessions_df = data.assets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mux_video_data.assets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mux_video_pipeline").dataset()
data.assets.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 Mux - 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

Ensure you are using the Access Token ID as the HTTP Basic username and the Access Token Secret as the password. If the secret is lost you must create a new Access Token in the dashboard; Mux does not store secrets. API requests must use HTTPS.

Rate limits and 429 responses

Mux enforces rate limits per account. Exceeding the threshold returns HTTP 429. POSTs to Video endpoints are rate‑limited to ~1 RPS sustained; other methods (GET, PUT, DELETE) are limited to ~5 RPS sustained. Implement retry/backoff for 429.

Pagination quirks

List endpoints use page & limit (page starts at 1, default 10, max 100). The Assets list also supports cursor pagination: responses include a next_cursor you can pass as ?cursor=<value> to fetch the next page. Do not assume responses return all records.

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