Cypress Python API Docs | dltHub

Cypress Cloud Data Extract API is an enterprise reporting API that returns test and run analytics and raw test‑result data from Cypress Cloud. The REST API base URL is https://cloud.cypress.io/enterprise-reporting/report and All requests require an organization API key passed as the token query parameter..

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 Cypress data in under 10 minutes.

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What data can I load from Cypress?

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

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How do I authenticate with the Cypress API?

The Data Extract API authenticates using an API key (Enterprise API key) supplied as the token query parameter (e.g., token=YOUR-API-KEY). No special headers are required.

1. Get your credentials

1. Sign in to Cypress Cloud (https://cloud.cypress.io). 2) Open your organization settings and navigate to Integrations → Enterprise API (or Data Extract API). 3) Copy the displayed API key (token) for your organization. 4) Store the key securely, e.g., in secrets.toml, and use it as the token query parameter in API requests.

2. Add them to .dlt/secrets.toml

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[sources.cypress_configuration_api_source]
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 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 Cypress 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 cypress_configuration_api_pipeline.py

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

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Pipeline cypress_configuration_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cypress_configuration_api_data
The duckdb destination used duckdb:/cypress_configuration_api.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 cypress_configuration_api_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 project-list and spec-details from the Cypress 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 cypress_configuration_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloud.cypress.io/enterprise-reporting/report",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "project_list", "endpoint": {"path": "enterprise-reporting/report?report_id=project-list"}},
            {"name": "spec_details", "endpoint": {"path": "enterprise-reporting/report?report_id=spec-details"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cypress_configuration_api_pipeline",
        destination="duckdb",
        dataset_name="cypress_configuration_api_data",
    )
    load_info = pipeline.run(cypress_configuration_api_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("cypress_configuration_api_pipeline").dataset()
sessions_df = data.spec_details.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cypress_configuration_api_data.spec_details LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cypress_configuration_api_pipeline").dataset()
data.spec_details.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 Cypress 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 or 403 responses, verify that you are using the correct Enterprise API key and that it is passed as the token query parameter. Ensure the organization has access to the Data Extract API.

Export format & payload size limits

The API supports export_format=csv,json,xlsx. Certain reports (e.g., spec-details, test-details, failed-test-details) cap results at 500,000 records per request. Split large date ranges into smaller intervals to stay under the limit.

Data latency

Aggregated reports are refreshed daily at midnight UTC. Individual result‑level data becomes available roughly 30 minutes after a run completes.

Common errors

• 401/403 – missing or invalid API key.
• 400 – required parameters such as start_date are missing.
• 429 – rate limit exceeded; back off and retry.
• Large payload – request exceeds the 500,000 record limit; reduce the date range.

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