Cachet Python API Docs | dltHub

Cachet is an open-source, self-hosted status page system that provides a RESTful JSON API to programmatically read and manage status page resources. The REST API base URL is https://v3.cachethq.io/api and all requests require an API token passed in a request header.

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 Cachet data in under 10 minutes.

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What data can I load from Cachet?

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

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How do I authenticate with the Cachet API?

Cachet uses an API token for authentication. Include your token in each request using the X-Cachet-Token header (for example: X-Cachet-Token: your_token_here).

1. Get your credentials

1. Log in to your Cachet dashboard (self-hosted instance or demo at https://v3.cachethq.io/). 2) Open your user profile / account settings. 3) Create or view API tokens (API / Integrations / Personal access tokens) and copy a token. 4) Use that token as the value of the X-Cachet-Token header in API requests.

2. Add them to .dlt/secrets.toml

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[sources.cachet_source]
api_key = "your_cachet_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 Cachet 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 cachet_pipeline.py

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

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Pipeline cachet_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cachet_data
The duckdb destination used duckdb:/cachet.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 cachet_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 incidents and components from the Cachet 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 cachet_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v3.cachethq.io/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "incidents", "endpoint": {"path": "incidents", "data_selector": "data"}},
            {"name": "components", "endpoint": {"path": "components", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cachet_pipeline",
        destination="duckdb",
        dataset_name="cachet_data",
    )
    load_info = pipeline.run(cachet_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("cachet_pipeline").dataset()
sessions_df = data.incidents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cachet_data.incidents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cachet_pipeline").dataset()
data.incidents.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 Cachet 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 responses, verify you included the API token in the X-Cachet-Token header and that the token is valid and not expired. Ensure you are calling the correct base URL for your Cachet instance (for the demo use https://v3.cachethq.io/api).

Pagination

Most list endpoints are paginated and return a top-level "meta" and "links" object alongside the "data" array. Use the query parameter page to request additional pages (e.g., ?page=2). Check "meta.per_page" and "meta.current_page" to drive iteration.

Rate limits and common errors

Cachet instances may enforce rate limits or return 429 responses. If you encounter 4xx errors, inspect the response body for details. Server errors (5xx) indicate problems with the Cachet instance.

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