Open GoPro Python API Docs | dltHub

Open GoPro is an open source interface specification and SDK that allows controlling GoPro cameras via BLE and HTTP (Wi‑Fi/USB), including capture, media listing, settings, and status. The REST API base URL is http://<camera_ip>:8080 and Local camera HTTP API requires no authentication; COHN mode uses COHN credentials appended as headers..

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 Open GoPro data in under 10 minutes.

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What data can I load from Open GoPro?

Here are some of the endpoints you can load from Open GoPro:

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How do I authenticate with the Open GoPro API?

Direct connections to the camera’s local HTTP API (camera Wi‑Fi or USB) typically do not require authentication. For COHN mode, the SDK provisions COHN credentials and automatically appends COHN headers to each request.

1. Get your credentials

1. Install the Open GoPro Python SDK.
2. Instantiate a WirelessGoPro or WiredGoPro object.
3. Follow the COHN provisioning steps shown by the SDK (the SDK will create and store COHN credentials on the device).
4. The SDK automatically adds the required COHN headers to subsequent HTTP requests.
5. For local Wi‑Fi/USB connections, no credentials are needed.

2. Add them to .dlt/secrets.toml

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[sources.open_gopro_source]
cohn_token = "your_cohn_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 Open GoPro 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 open_gopro_pipeline.py

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

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Pipeline open_gopro_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset open_gopro_data
The duckdb destination used duckdb:/open_gopro.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 open_gopro_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 version and media_list from the Open GoPro 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 open_gopro_source(cohn_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<camera_ip>:8080",
            "auth": {
                "type": "none / cohn",
                "cohn_token": cohn_token,
            },
        },
        "resources": [
            {"name": "version", "endpoint": {"path": "gopro/version", "data_selector": "version"}},
            {"name": "media_list", "endpoint": {"path": "gopro/media/list"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_gopro_pipeline",
        destination="duckdb",
        dataset_name="open_gopro_data",
    )
    load_info = pipeline.run(open_gopro_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("open_gopro_pipeline").dataset()
sessions_df = data.media_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM open_gopro_data.media_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("open_gopro_pipeline").dataset()
data.media_list.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 Open GoPro 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

Direct local HTTP access normally does not require authentication; if requests receive 401/403 when using COHN, re‑provision COHN credentials via the SDK and ensure COHN headers are present.

Unsupported API version

The SDK supports Open GoPro API version 2.0 only. Connecting to cameras with a different version will raise an InvalidOpenGoProVersion error—check /gopro/version first.

Connectivity and discovery

BLE discovery and Wi‑Fi provisioning can fail if the camera is not in pairing or AP mode. Ensure the camera is discoverable and on the same network before attempting BLE or Wi‑Fi connections.

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