Chargebee Time Machines Python API Docs | dltHub

Chargebee Time Machines is a simulation feature that lets you simulate billing events by time-traveling a test site to run renewals, dunning, webhooks, and other billing behaviors. The REST API base URL is https://{site}.chargebee.com/api/v2 and all requests require HTTP Basic auth with site API key as 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 Chargebee Time Machines data in under 10 minutes.

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What data can I load from Chargebee Time Machines?

Here are some of the endpoints you can load from Chargebee Time Machines:

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How do I authenticate with the Chargebee Time Machines API?

Chargebee API uses HTTP Basic authentication: use your site API key as the username and a blank password (or any value). Include standard headers like User-Agent and Accept: application/json. Example curl uses -u {site_api_key}: to send basic auth.

1. Get your credentials

1. Log in to your Chargebee dashboard for your test site. 2) Go to Settings > Configure Chargebee > API Keys (or Developers > API Keys). 3) Create or view an existing API key for the test site. 4) Use the key as the HTTP Basic auth username; password can be blank. 5) Ensure Time Machine is enabled under Settings > Configure Chargebee > Time Machine for API use.

2. Add them to .dlt/secrets.toml

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[sources.chargebee_time_machines_source]
api_key = "your_site_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 Chargebee Time Machines 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 chargebee_time_machines_pipeline.py

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

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Pipeline chargebee_time_machines_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chargebee_time_machines_data
The duckdb destination used duckdb:/chargebee_time_machines.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 chargebee_time_machines_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 time_machines and time_machine from the Chargebee Time Machines 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 chargebee_time_machines_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{site}.chargebee.com/api/v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "time_machines", "endpoint": {"path": "time_machines"}},
            {"name": "time_machine", "endpoint": {"path": "time_machines/delorean", "data_selector": "time_machine"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chargebee_time_machines_pipeline",
        destination="duckdb",
        dataset_name="chargebee_time_machines_data",
    )
    load_info = pipeline.run(chargebee_time_machines_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("chargebee_time_machines_pipeline").dataset()
sessions_df = data.time_machine.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chargebee_time_machines_data.time_machine LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chargebee_time_machines_pipeline").dataset()
data.time_machine.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 Chargebee Time Machines 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

Ensure you are using HTTP Basic auth with the site API key as the username and an empty password. 401 responses indicate invalid or revoked API key. Verify you are using the test site API key for test endpoints and the correct site subdomain in the base URL.

Time Machine not enabled or EAP

If Time Machine isn't enabled for the site you will not be able to use these endpoints. Enable Time Machine in the dashboard under Settings > Configure Chargebee > Time Machine. Some Time Machine features may be in Early Access; check docs and site settings.

Asynchronous operations & status polling

Operations like travel_forward and start_afresh are asynchronous. Check the time_machine.time_travel_status field (values: not_enabled, in_progress, succeeded, failed) on subsequent GET /time_machines/delorean requests to determine completion and inspect failure_code/failure_reason/error_json on failure.

Rate limits and TLS

Chargebee enforces rate limits and may return 429 on excessive calls. Also ensure TLS certificates are up to date per Chargebee notices (DigiCert G2 rotation) to avoid SSL errors.

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