Hyperbrowser Python API Docs | dltHub

Hyperbrowser is a cloud browser platform for running automated browser sessions at scale using Puppeteer, Playwright, or SDKs. The REST API base URL is https://api.hyperbrowser.ai and all requests require an 'x-api-key' 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 add "dlt[hub]" and start loading Hyperbrowser data in under 10 minutes.

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What data can I load from Hyperbrowser?

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

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How do I authenticate with the Hyperbrowser API?

Requests must include an 'x-api-key' HTTP header containing the user's API key.

1. Get your credentials

To obtain your Hyperbrowser API credentials, navigate to the Hyperbrowser dashboard at https://app.hyperbrowser.ai/. Sign in to your account, then locate the 'Settings' or 'API Keys' section (often directly accessible via the quickstart or settings menu). From there, you can generate a new API key and copy it to your clipboard.

2. Add them to .dlt/secrets.toml

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[sources.hyperbrowser_source]
hyperbrowser_api_key = "your_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Hyperbrowser 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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uv run python hyperbrowser_pipeline.py

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

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Pipeline hyperbrowser_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hyperbrowser_data
The duckdb destination used duckdb:/hyperbrowser.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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uv run dlthub 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 /api/session and /api/task from the Hyperbrowser 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 hyperbrowser_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.hyperbrowser.ai",
            "auth": {"type": "api_key", "api_key": api_key, "name": "x-api-key", "location": "header"},
        },
        "resources": [
            {"name": "sessions", "endpoint": {"path": "api/sessions", "data_selector": "sessions"}},
            {"name": "crawl_job_results", "endpoint": {"path": "api/web/crawl/{id}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hyperbrowser_pipeline",
        destination="duckdb",
        dataset_name="hyperbrowser_data",
    )
    load_info = pipeline.run(hyperbrowser_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("hyperbrowser_pipeline").dataset()
sessions_df = data.sessions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hyperbrowser_data.sessions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hyperbrowser_pipeline").dataset()
data.sessions.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 Hyperbrowser 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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