CRS Credit API Python API Docs | dltHub

CRS Credit API is an independent credit-data platform exposing credit, fraud and compliance data via a REST API. The REST API base URL is https://crscreditapi.com and All requests require an API key provided via an Authorization header (Bearer token)..

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 CRS Credit API data in under 10 minutes.

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What data can I load from CRS Credit API?

Here are some of the endpoints you can load from CRS Credit API:

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How do I authenticate with the CRS Credit API API?

CRS uses token-based authentication. Include your API credential in the Authorization header as 'Authorization: Bearer <API_KEY>'. Some endpoints may also accept an 'x-api-key' header per account config — prefer Authorization Bearer.

1. Get your credentials

1. Sign up or contact CRS via https://crscreditapi.com/contact-us/ or your CRS account representative. 2) After account setup and KYC/compliance vetting, access the developer portal or account dashboard. 3) In the dashboard or developer‑tools section request/generate an API key. 4) Copy the provided key and use it as the Bearer token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.crs_credit_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 CRS Credit API 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 crs_credit_api_pipeline.py

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

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Pipeline crs_credit_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset crs_credit_api_data
The duckdb destination used duckdb:/crs_credit_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 crs_credit_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 credit_reports and consumers from the CRS Credit API 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 crs_credit_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://crscreditapi.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "credit_reports", "endpoint": {"path": "v1/credit-report", "data_selector": "report"}},
            {"name": "consumers", "endpoint": {"path": "v1/consumers", "data_selector": "consumers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="crs_credit_api_pipeline",
        destination="duckdb",
        dataset_name="crs_credit_api_data",
    )
    load_info = pipeline.run(crs_credit_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("crs_credit_api_pipeline").dataset()
sessions_df = data.credit_reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM crs_credit_api_data.credit_reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("crs_credit_api_pipeline").dataset()
data.credit_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 CRS Credit API 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 or 403 Forbidden, verify your API key (Bearer token) is present and not expired. Confirm you are using 'Authorization: Bearer <API_KEY>' and that the token has permissions for the requested endpoint. Contact CRS support to verify account status.

Rate limits and throttling

If you receive 429 Too Many Requests, reduce request rate and implement exponential backoff. Check your account plan for rate limit quotas; contact CRS to request higher limits.

Pagination quirks

Large list endpoints may be paginated. Look for standard pagination fields in responses (e.g., 'page', 'per_page', 'total', 'next_page' or a 'links' object). Use provided cursor or page parameters as described in the OpenAPI examples.

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