ApiDog CORS Proxy Python API Docs | dltHub

ApiDog CORS Proxy is a service that helps users overcome Cross-Origin Resource Sharing (CORS) issues by proxying requests, offering cloud-hosted, browser extension, and self-hosted agent options. The REST API base URL is https://web-proxy.apidog.com/api/v1 and Authentication for the ApiDog CORS Proxy is not based on traditional API keys or bearer tokens for the cloud service; access to the self-hosted agent is managed through network configuration and environment variables..

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 ApiDog CORS Proxy data in under 10 minutes.

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What data can I load from ApiDog CORS Proxy?

Here are some of the endpoints you can load from ApiDog CORS Proxy:

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How do I authenticate with the ApiDog CORS Proxy API?

The ApiDog CORS Proxy does not appear to use a direct API authentication mechanism such as API keys or bearer tokens. For the self-hosted agent, access is managed through network configurations and environment variables.

1. Get your credentials

For the ApiDog Cloud Agent, no explicit API credentials are required as it's managed by Apidog. For the self-hosted Request Proxy Agent, deploy it via Docker, then add its host information (default port 9159) to your team resources within the Apidog UI.

2. Add them to .dlt/secrets.toml

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[sources.apidog_cors_proxy_source]
No specific `key=value` lines for authentication are described in the provided documentation for the ApiDog CORS Proxy.

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 ApiDog CORS Proxy 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 apidog_cors_proxy_pipeline.py

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

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Pipeline apidog_cors_proxy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apidog_cors_proxy_data
The duckdb destination used duckdb:/apidog_cors_proxy.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 apidog_cors_proxy_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 The ApiDog CORS Proxy does not expose traditional REST API endpoints for data retrieval; its primary function is to proxy requests through a single endpoint like /request. from the ApiDog CORS Proxy 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 apidog_cors_proxy_source(No specific authentication parameter is described in the provided documentation for the ApiDog CORS Proxy.=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://web-proxy.apidog.com/api/v1",
            "auth": {
                "type": "No explicit API authentication type (e.g., bearer, api_key) is described for the ApiDog CORS Proxy in the provided documentation.",
                "No specific authentication token key is described in the provided documentation for the ApiDog CORS Proxy.": No specific authentication parameter is described in the provided documentation for the ApiDog CORS Proxy.,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apidog_cors_proxy_pipeline",
        destination="duckdb",
        dataset_name="apidog_cors_proxy_data",
    )
    load_info = pipeline.run(apidog_cors_proxy_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("apidog_cors_proxy_pipeline").dataset()
sessions_df = data.The ApiDog CORS Proxy does not expose traditional REST API endpoints for data retrieval; its primary function is to proxy requests through a single endpoint like `request`..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM apidog_cors_proxy_data.The ApiDog CORS Proxy does not expose traditional REST API endpoints for data retrieval; its primary function is to proxy requests through a single endpoint like `request`. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("apidog_cors_proxy_pipeline").dataset()
data.The ApiDog CORS Proxy does not expose traditional REST API endpoints for data retrieval; its primary function is to proxy requests through a single endpoint like `request`..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 ApiDog CORS Proxy 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

Cloud Agent Limitations

The Cloud Agent cannot access endpoints located on internal networks or private environments. If you need to proxy requests to such endpoints, you must use a self-hosted Request Proxy Agent.

Self-hosted Agent Connection Issues

When adding a self-hosted Request Proxy Agent to your team resources in Apidog, any connection failures will be displayed within the Apidog user interface. Ensure the agent is running correctly and network settings (like host and port 9159) are properly configured for Apidog to connect.

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