TestRail Python API Docs | dltHub

TestRail is a test case management tool that provides an HTTP‑based REST API for interacting with test cases, runs, and results. The REST API base URL is https://<your_domain>.testrail.io/index.php?/api/v2 and All requests require an API key for authentication, passed as the api_key query parameter or via HTTP Basic Auth..

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 TestRail data in under 10 minutes.

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What data can I load from TestRail?

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

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How do I authenticate with the TestRail API?

Include the API key as the api_key query parameter on every request, or use HTTP Basic Auth with the API key as the password.

1. Get your credentials

1. Log in to your TestRail instance.
2. Click your user avatar in the top‑right corner and select My Settings.
3. Choose the API Keys tab.
4. Click Add Key, give it a name, and click Generate.
5. Copy the generated key; it will be shown only once.
6. Store the key securely and use it as the api_key in dlt configurations.

2. Add them to .dlt/secrets.toml

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[sources.test_rail_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 TestRail 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 test_rail_pipeline.py

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

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Pipeline test_rail_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset test_rail_data
The duckdb destination used duckdb:/test_rail.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 test_rail_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 cases and runs from the TestRail 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 test_rail_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your_domain>.testrail.io/index.php?/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "cases", "endpoint": {"path": "get_cases", "data_selector": "cases"}},
            {"name": "runs", "endpoint": {"path": "get_runs", "data_selector": "runs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="test_rail_pipeline",
        destination="duckdb",
        dataset_name="test_rail_data",
    )
    load_info = pipeline.run(test_rail_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("test_rail_pipeline").dataset()
sessions_df = data.cases.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM test_rail_data.cases LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("test_rail_pipeline").dataset()
data.cases.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 TestRail 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 the supplied api_key is missing or invalid, TestRail returns a 401 Unauthorized response. Verify that the key is correct and included either as the api_key query parameter or via HTTP Basic Auth.

Rate limiting / request limits

TestRail imposes a default limit of 250 records per page for bulk GET endpoints. Use the limit and offset query parameters to paginate through larger result sets.

Pagination quirks

When navigating pages, the response includes _links with next and prev URLs. Ensure you follow the next link until it becomes null to retrieve all records.

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