Fetchapp Python API Docs | dltHub

FetchApp is a hosted service for selling, storing, and delivering downloadable products (digital files) with a REST API to manage accounts, products/files, orders, downloads and related resources. The REST API base URL is https://{your_account}.fetchapp.com/api/v2 and all requests require HTTP Basic authentication (API key and 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 Fetchapp data in under 10 minutes.

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What data can I load from Fetchapp?

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

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How do I authenticate with the Fetchapp API?

The API uses HTTP Basic auth where the username is your api_key and the password is your api_token. Send an Authorization header with the Base64 encoded "api_key:api_token" (Authorization: Basic ) or embed credentials in the URL (http://api_key:api_token@{your_account}.fetchapp.com/api/...). Endpoints historically return XML; set Content-Type: application/xml when posting.

1. Get your credentials

1. Log into your FetchApp account (your subdomain, e.g. warner.fetchapp.com). 2) In account/settings locate API credentials (api_key and api_token) or use the "new_token" endpoint to generate a token. 3) Copy api_key and api_token for use in Basic auth (username=api_key, password=api_token). 4) If you need a replacement token, call GET /api/v2/new_token (authenticated) to rotate the token.

2. Add them to .dlt/secrets.toml

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[sources.fetchapp_source]
api_key = "your_api_key_here"
api_token = "your_api_token_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 Fetchapp 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 fetchapp_pipeline.py

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

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Pipeline fetchapp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fetchapp_data
The duckdb destination used duckdb:/fetchapp.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 fetchapp_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 orders and products from the Fetchapp 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 fetchapp_source(api_key, api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_account}.fetchapp.com/api/v2",
            "auth": {
                "type": "http_basic",
                "api_token": api_key, api_token,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "api/v2/orders", "data_selector": "orders"}},
            {"name": "products", "endpoint": {"path": "api/v2/products", "data_selector": "products"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fetchapp_pipeline",
        destination="duckdb",
        dataset_name="fetchapp_data",
    )
    load_info = pipeline.run(fetchapp_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("fetchapp_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fetchapp_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fetchapp_pipeline").dataset()
data.orders.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 Fetchapp 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 401/403, verify you are using HTTP Basic auth with username=api_key and password=api_token. Ensure the Authorization header is "Basic <base64(api_key:api_token)>" or use credentials in the URL. Tokens can be rotated via /api/v2/new_token; if rotated, update stored secret.

Response format (XML) and data selectors

FetchApp API historically returns XML (application/xml) where lists are wrapped in plural elements (e.g., , , , <products/items>) and individual resources are singular elements (, , , ). Use the plural element name as the data selector for the list of records. For per-account v3 swagger, responses may be JSON—inspect your account-specific swagger UI at https://{your_handle}.fetchapp.com/api/swaggerui#/ to confirm the exact JSON root keys.

Pagination

Many list endpoints accept per_page and page query parameters (e.g., ?per_page=25&page=2). If using the XML API, iterate pages until no records are returned. No global rate-limit headers documented; implement retry/backoff on 429/5xx responses.

Common errors and handling

• 401 Unauthorized: invalid/rotated credentials. Refresh token or check base64 encoding.
• 404 Not Found: incorrect subdomain, path, or resource identifier (sku or order id).
• 422/400: invalid payload for create/update operations. Validate XML structure.
• 429 / 5xx: transient; retry with exponential backoff.

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