RepairShopr Python API Docs | dltHub

RepairShopr is a cloud-based repair shop management platform providing ticketing, CRM, invoicing, inventory and service management via a REST API. The REST API base URL is https://{subdomain}.repairshopr.com/api/v1 and all requests require an API token (api_key) for authentication.

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 RepairShopr data in under 10 minutes.

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What data can I load from RepairShopr?

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

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How do I authenticate with the RepairShopr API?

Authentication is done with an API token (API key) generated in your RepairShopr account. The API key is provided to the API as the api_key query parameter (e.g. ?api_key=APIKEY) when calling endpoints and can be entered in the Swagger docs' Authorize dialog to test calls.

1. Get your credentials

1. Sign in to your RepairShopr account. 2. Go to More → Admin → API → API Tokens (or Admin → API - API Tokens). 3. Click New Token → Custom Permissions. 4. Give the token a Name, optionally set an Expiration, toggle the specific permissions needed, and click Create API Token. 5. Copy and securely store the API key shown (it cannot be retrieved again).

2. Add them to .dlt/secrets.toml

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[sources.repair_shop_resources_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 RepairShopr 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 repair_shop_resources_pipeline.py

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

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Pipeline repair_shop_resources_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset repair_shop_resources_data
The duckdb destination used duckdb:/repair_shop_resources.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 repair_shop_resources_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 contacts and tickets from the RepairShopr 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 repair_shop_resources_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.repairshopr.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts"}},
            {"name": "tickets", "endpoint": {"path": "tickets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="repair_shop_resources_pipeline",
        destination="duckdb",
        dataset_name="repair_shop_resources_data",
    )
    load_info = pipeline.run(repair_shop_resources_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("repair_shop_resources_pipeline").dataset()
sessions_df = data.tickets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM repair_shop_resources_data.tickets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("repair_shop_resources_pipeline").dataset()
data.tickets.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 RepairShopr 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 requests return 401/403: verify you generated an API token under Admin → API → API Tokens and that you are supplying the exact token value. When using the Swagger docs, put your subdomain in the Server variable and use the Authorize dialog to paste the token.

Rate limits

RepairShopr enforces a rate limit of 180 requests per minute per IP address. Implement client-side rate limiting and retries with exponential backoff when receiving 429 responses.

Pagination

Most list endpoints are paginated. Use the page parameter (e.g. ?page=1) to iterate pages. Confirm the pagination fields in responses from your tenant (the live OAS is connected to account data).

Common errors

• 401/403: invalid or missing api_key
• 404: resource not found (bad id or path)
• 422: validation error for POST/PUT
• 429: rate limit exceeded (backoff and retry)
• 500: server error → retry with backoff and contact API support if persistent

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