AppFollow Python API Docs | dltHub

AppFollow is a platform to monitor app reviews, ratings, rankings, keywords and ASO data across stores and provide API access to that data. The REST API base URL is https://api.appfollow.io and All requests use HTTP Basic auth with an API secret as the username..

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 AppFollow data in under 10 minutes.

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What data can I load from AppFollow?

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

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How do I authenticate with the AppFollow API?

Authenticate using HTTP Basic authentication: provide your API secret as the username and an empty password (or a trailing colon in curl). Include the credentials in every request.

1. Get your credentials

1. Log in to your AppFollow workspace. 2) Navigate to Integrations (or Settings) → API Dashboard / API Access Methods. 3) Create or copy the API secret for the workspace or service you need. 4) Use the secret as the HTTP Basic username when making API calls.

2. Add them to .dlt/secrets.toml

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[sources.appfollow_migrations_source]
api_secret = "your_api_secret_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 AppFollow 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 appfollow_migrations_pipeline.py

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

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Pipeline appfollow_migrations_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset appfollow_migrations_data
The duckdb destination used duckdb:/appfollow_migrations.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 appfollow_migrations_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 apps and reviews from the AppFollow 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 appfollow_migrations_source(api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.appfollow.io",
            "auth": {
                "type": "http_basic",
                "api_secret": api_secret,
            },
        },
        "resources": [
            {"name": "apps", "endpoint": {"path": "apps"}},
            {"name": "reviews", "endpoint": {"path": "reviews"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="appfollow_migrations_pipeline",
        destination="duckdb",
        dataset_name="appfollow_migrations_data",
    )
    load_info = pipeline.run(appfollow_migrations_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("appfollow_migrations_pipeline").dataset()
sessions_df = data.reviews.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM appfollow_migrations_data.reviews LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("appfollow_migrations_pipeline").dataset()
data.reviews.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 AppFollow 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 or an error saying unauthorized, confirm you're using HTTP Basic auth with the API secret as the username (empty password). Verify the API secret on Integrations → API Dashboard and that the token belongs to the workspace/app scope requested.

Rate limits and request costs

Some endpoints are metered (documentation notes e.g., Ratings History costs credits). Monitor API dashboard usage and handle 4xx responses; the docs mention per-request credit costs for some methods. If you hit usage limits, contact AppFollow support or purchase additional credits.

Pagination and page limits

Review endpoints support page and per-page behavior: free accounts can retrieve only the first page; premium accounts have larger page limits (up to 100 reviews per page). Use page parameter (and handle missing page availability on free accounts).

Error responses and HTTP status semantics

The docs state server errors are returned in JSON; some errors may return HTTP 200 with an error payload (docs warn that errors are returned in JSON and status 200 except 502/504). Handle responses with error fields in the JSON body as well as non‑2xx HTTP statuses.

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