Chargify Python API Docs | dltHub

Chargify is a subscription billing and revenue management platform providing a RESTful API. The REST API base URL is https://{subdomain}.chargify.com and All requests use HTTP Basic Authentication with an API key (or Direct API ID/password/secret for v2)..

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 Chargify data in under 10 minutes.

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What data can I load from Chargify?

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

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How do I authenticate with the Chargify API?

Authentication is performed via the Authorization header using HTTP Basic Auth. The API key is supplied as the username and any non‑empty value (commonly "x") as the password. For Direct API calls, include api_id, api_password, and api_secret as query parameters or headers.

1. Get your credentials

1. Log in to your Chargify dashboard.
2. Navigate to Settings → API Keys.
3. Click Create API Key to generate a key for the legacy API (used as the username in Basic Auth).
4. For Direct API v2, go to Settings → Direct API.
5. Note the API ID, API Password, and API Secret displayed on the page; copy them for use in your integration.

2. Add them to .dlt/secrets.toml

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[sources.chargify_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 Chargify 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 chargify_pipeline.py

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

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Pipeline chargify_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chargify_data
The duckdb destination used duckdb:/chargify.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 chargify_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 subscriptions and customers from the Chargify 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 chargify_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.chargify.com",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "subscriptions", "endpoint": {"path": "subscriptions.json", "data_selector": "subscriptions"}},
            {"name": "customers", "endpoint": {"path": "customers.json", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chargify_pipeline",
        destination="duckdb",
        dataset_name="chargify_data",
    )
    load_info = pipeline.run(chargify_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("chargify_pipeline").dataset()
sessions_df = data.subscriptions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chargify_data.subscriptions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chargify_pipeline").dataset()
data.subscriptions.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 Chargify 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 – Indicates missing or incorrect API credentials. Verify that the API key (or Direct API credentials) are correct and included in the Basic Auth header.

Rate Limiting

• 429 Too Many Requests – Chargify enforces rate limits per account. Implement exponential back‑off and respect the Retry-After header before retrying.

Pagination

• Chargify uses page and per_page query parameters. Retrieve subsequent pages by incrementing page until the response array is empty.

Webhook Signature Verification

• Webhooks include the header X-Chargify-Webhook-Signature-Hmac-Sha-256. Compute the HMAC‑SHA‑256 of the raw request body using your shared secret and compare it to this header to ensure authenticity.

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