Checkr Python API Docs | dltHub

Checkr is a RESTful background screening API that enables programmatic candidate creation, invitation/apply flows, report ordering and retrieval, and webhook-driven status updates. The REST API base URL is https://api.checkr.com/v1 and All requests use HTTP Basic auth: use your Secret API key as the username and an empty password..

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 Checkr data in under 10 minutes.

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What data can I load from Checkr?

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

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How do I authenticate with the Checkr API?

Authenticate using HTTP Basic auth with your Checkr Secret API key as the username and a blank password. Send requests over HTTPS; examples in the docs use curl with -u <API_KEY>: .

1. Get your credentials

1. Log in to Checkr Dashboard; 2) Go to Account Settings > Developer Settings; 3) In the 'API keys' section click 'Create Key' and choose 'Secret'; 4) Copy the Secret API key and store it securely. For staging, request a staging account and use the staging API key and host (see docs).

2. Add them to .dlt/secrets.toml

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[sources.checkr_source]
api_key = "sk_live_your_secret_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 Checkr 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 checkr_pipeline.py

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

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Pipeline checkr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset checkr_data
The duckdb destination used duckdb:/checkr.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 checkr_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 candidates and reports from the Checkr 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 checkr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.checkr.com/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "candidates", "endpoint": {"path": "candidates", "data_selector": "data"}},
            {"name": "reports", "endpoint": {"path": "reports"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="checkr_pipeline",
        destination="duckdb",
        dataset_name="checkr_data",
    )
    load_info = pipeline.run(checkr_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("checkr_pipeline").dataset()
sessions_df = data.reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM checkr_data.reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("checkr_pipeline").dataset()
data.reports.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 Checkr 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 Unauthorized: verify you are using HTTP Basic auth with the Secret API key as the username and an empty password. Ensure the key is active and you are calling the correct environment (production vs staging). Example curl: curl -u <API_KEY>: https://api.checkr.com/v1/reports/

Rate limiting

Checkr enforces a per-account rate limit (docs reference 600–1200 requests/minute historically). When you exceed the limit you will get HTTP 429 Too Many Requests and X-Ratelimit-Reset header indicating reset time. Implement backoff and respect X-Ratelimit-Remaining and X-Ratelimit-Reset headers.

Pagination

List endpoints are paginated. Use page and per_page query params (default per_page=25). List responses include pagination metadata (example: object: "list", next_href, previous_href, count) and the records array under the data key.

Webhook handling

Webhook payloads use an event envelope: {"id", "object":"event", "type", "created_at", "data":{"object":{}}}. If include_object is enabled the full related object is included in data.object. Webhook URLs must be HTTPS in production; Basic Auth credentials may be embedded in the webhook URL if desired (URL-escaped).

Common error responses

400 Bad Request — malformed payload or missing required fields. 401 Unauthorized — auth credentials missing/invalid. 403 Forbidden — account lacks permission. 404 Not Found — resource does not exist. 429 Too Many Requests — rate limit exceeded. Error details are returned in the body per docs' Error codes section.

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