Load Arkose Labs data in Python using dltHub

Build a Arkose Labs-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Arkose Labs data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def arkose_labs_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://status.arkoselabs.com/",
            {
    "auth": {
                    "type": "bearer",
                    "token": access_token,
                }
},
        },
        "resources": [
            "verify",
            "api.js",
            "edge-api"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='arkose_labs_pipeline',
        destination='duckdb',
        dataset_name='arkose_labs_data', 
    )
    # Load the data
    load_info = pipeline.run(arkose_labs_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from arkose_labs’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Client API: JavaScript integration for bot detection and challenge presentation
  • Verify API: Server-side token verification and session validation
  • Status API: Service health monitoring and availability checks
  • Truth Data API: Real-time analytics and session telemetry
  • Edge API: CDN integration for enhanced performance
  • Batch Data API: Bulk session data processing

You will then debug the Arkose Labs pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with Arkose Labs support.

    dlt init dlthub:arkose_labs duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Arkose Labs API, as specified in @arkose_labs-docs.yaml 
Start with endpoints "verify" and "api.js" and skip incremental loading for now. 
Place the code in arkose_labs_pipeline.py and name the pipeline arkose_labs_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python arkose_labs_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Arkose Labs uses a dual-key authentication system with public/private key pairs generated through the Arkose Labs Command Center. The public key is used client-side for JavaScript API integration, while the private key is strictly for server-side Verify API calls and must never be exposed on client-facing websites. Additionally, access tokens are available for batch operations with 24-hour validity periods.

    To get the appropriate API keys, please visit the original source at https://www.arkoselabs.com/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python arkose_labs_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline arkose_labs load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset arkose_labs_data
The duckdb destination used duckdb:/arkose_labs.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline arkose_labs_pipeline show --dashboard

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("arkose_labs_pipeline").dataset()
# get verify table as Pandas frame
data.verify.df().head()

Running into errors?

Critical implementation considerations include: tokens are single-use only and must be verified server-side before allowing user actions, fail-open strategies should be implemented for service disruptions, GET requests are being deprecated for security reasons with migration to POST required by April 2025, multiple API versions are reaching end-of-life with V4 being the only supported version going forward, and the Arkose API script should only be loaded once per page to avoid duplicate event listeners.

Extra resources:

Next steps