Camio Python API Docs | dltHub

Camio is a cloud video monitoring platform and API for accessing cameras, recorded content, Camio devices and events. The REST API base URL is https://camio.com/api and All requests require an OAuth access token (GET via access_token parameter; other methods via Authorization: token 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 Camio data in under 10 minutes.

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What data can I load from Camio?

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

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How do I authenticate with the Camio API?

Camio uses OAuth tokens. For GET requests the token can be supplied as an access_token URL parameter; for POST/PUT/PATCH include an Authorization: token YOUR_TOKEN header.

1. Get your credentials

1. Sign in to your Camio account at https://camio.com
2. Navigate to Settings → Integrations (or open /settings/integrations).
3. Click the Generate button to create a development OAuth token (or register an app if applicable).
4. Copy the generated token; use it as the access_token query parameter for GET requests or as the Authorization: token <token> header for other methods.

2. Add them to .dlt/secrets.toml

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[sources.camio_source]
access_token = "your_oauth_token_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 Camio 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 camio_pipeline.py

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

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Pipeline camio_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset camio_data
The duckdb destination used duckdb:/camio.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 camio_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 cameras and camios from the Camio 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 camio_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://camio.com/api",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "cameras", "endpoint": {"path": "users/me/cameras", "data_selector": "cameras"}},
            {"name": "camios", "endpoint": {"path": "users/:token/camios", "data_selector": "camios"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="camio_pipeline",
        destination="duckdb",
        dataset_name="camio_data",
    )
    load_info = pipeline.run(camio_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("camio_pipeline").dataset()
sessions_df = data.cameras.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM camio_data.cameras LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("camio_pipeline").dataset()
data.cameras.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 Camio 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 receive 401 Unauthorized, verify you are using a valid OAuth token. GET endpoints accept ?access_token=TOKEN; POST/PUT/PATCH require Authorization: token TOKEN. Ensure the token is not expired and contains no extra characters.

Rate limiting

Responses with status 429 indicate rate limiting. Apply exponential backoff and retry after the period suggested in the Retry-After header.

Common error responses

Camio returns JSON error bodies such as { "message": "Invalid request" } together with standard HTTP status codes: 400, 401, 404, 429, 500. Inspect the message field for details.

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