Empireflippers Python API Docs | dltHub

Empire Flippers is the #1 curated online marketplace for buying and selling established, profitable online businesses; it also provides a Valuation Tool API for automated seller valuations. The REST API base URL is https://api.empireflippers.com/api/v1 and Public Marketplace endpoints require no authentication; the Valuation Tool API requires an X-Empire-Flippers-API-Key header (Referral Partner Toolkit API key)..

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 Empireflippers data in under 10 minutes.

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What data can I load from Empireflippers?

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

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How do I authenticate with the Empireflippers API?

Valuation Tool requests must include the X-Empire-Flippers-API-Key header with your Referral Partner Toolkit API key; Marketplace public endpoints do not require authentication but are rate‑limited.

1. Get your credentials

1. Sign in to your Empire Flippers account.
2. Open your user profile and go to the Referral Partner tab: https://app.empireflippers.com/users/me/referrals
3. Copy the Referral Partner Toolkit API key shown there; rotate if needed.

2. Add them to .dlt/secrets.toml

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[sources.empireflippers_source]
api_key = "your_referral_toolkit_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 Empireflippers 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 empireflippers_pipeline.py

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

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Pipeline empireflippers_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset empireflippers_data
The duckdb destination used duckdb:/empireflippers.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 empireflippers_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 listings and valuation from the Empireflippers 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 empireflippers_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.empireflippers.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "listings", "endpoint": {"path": "listings/list", "data_selector": "data"}},
            {"name": "valuation", "endpoint": {"path": "partner-toolkit/valuation-tool/valuation", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="empireflippers_pipeline",
        destination="duckdb",
        dataset_name="empireflippers_data",
    )
    load_info = pipeline.run(empireflippers_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("empireflippers_pipeline").dataset()
sessions_df = data.listings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM empireflippers_data.listings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("empireflippers_pipeline").dataset()
data.listings.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 Empireflippers 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 X-Empire-Flippers-API-Key header is missing or invalid when calling the Valuation Tool API you will receive 403 with body { "errors": ["Invalid API Key"], "data": null }. Verify the key from your Referral Partner tab and include header: X-Empire-Flippers-API-Key: your-API-key.

Rate limits

Marketplace API: limit requests to 1 request per second. Valuation Tool API: limit to 120 requests per minute. When the rate limit is exceeded you will receive 429 with { "errors": ["You have exceeded the maximum number of attempts. Please try again later."], "data": null }. Implement back‑off and retry logic.

Invalid parameters

Bad parameter formats (e.g., an incorrectly formatted date) return 400 with JSON { "errors": ["business_created_at format should be YYYY-MM-DD"], "data": null }. When debug_mode=true the API returns detailed validation errors for monetization parameters.

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