Appfigures Python API Docs | dltHub

Appfigures is a market intelligence platform that provides app analytics, sales, and revenue data through a REST API. The REST API base URL is https://api.appfigures.com/v2/ and All requests require a client_key and use either a Personal Access Token or OAuth 2.0 Bearer token 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 Appfigures data in under 10 minutes.

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What data can I load from Appfigures?

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

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How do I authenticate with the Appfigures API?

Requests must include a client_key header and an Authorization: Bearer <token> header when using a Personal Access Token or OAuth 2.0.

1. Get your credentials

1. Log in to your Appfigures account.
2. Navigate to Settings → API Clients.
3. Click Create New Client.
4. Provide a name and select the desired permissions.
5. After creation, note the displayed client_key and secret_key.
6. For Personal Access Tokens, click Generate Token on the same page and copy the token.
7. Store the client_key and token securely; they will be used in API requests.

2. Add them to .dlt/secrets.toml

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[sources.appfigures_source]
client_key = "your_client_key_here"
access_token = "your_personal_access_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 Appfigures 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 appfigures_pipeline.py

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

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Pipeline appfigures_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset appfigures_data
The duckdb destination used duckdb:/appfigures.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 appfigures_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 products_search and products_mine from the Appfigures 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 appfigures_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.appfigures.com/v2/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "products_search", "endpoint": {"path": "products/search/{term}"}},
            {"name": "products_mine", "endpoint": {"path": "products/mine"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="appfigures_pipeline",
        destination="duckdb",
        dataset_name="appfigures_data",
    )
    load_info = pipeline.run(appfigures_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("appfigures_pipeline").dataset()
sessions_df = data.products_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM appfigures_data.products_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("appfigures_pipeline").dataset()
data.products_search.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 Appfigures 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 Errors

• 401 Unauthorized – Occurs when the client_key is missing or the Bearer token is invalid. Verify that both headers are present and correctly formatted.
• 403 Forbidden – The token does not have permission for the requested resource. Ensure the API client has the necessary scopes.

Rate Limits

• The API enforces a monthly call quota per account. Exceeding the quota returns a 429 Too Many Requests response with a JSON body containing an error field describing the limit breach.
• Monitor usage via the account dashboard and consider requesting a higher quota if needed.

Pagination

• List endpoints that return large result sets support page and per_page query parameters. Missing or out‑of‑range parameters may result in a 400 Bad Request. Always check the next_page field in the response when present.

General Errors

• 500 Internal Server Error – Indicates a problem on the Appfigures side. Retry after a short delay.
• JSON parsing errors – Ensure the response is not empty and that you are handling top‑level arrays versus objects correctly as described in the endpoint documentation.

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