Wachete Python API Docs | dltHub

Wachete is a web‑monitoring platform that provides a REST API for creating and managing monitors, retrieving their data/history, and handling notifications. The REST API base URL is https://api.wachete.com and All requests require a bearer token obtained via POST /thirdparty/v1/user/apilogin..

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 Wachete data in under 10 minutes.

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What data can I load from Wachete?

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

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How do I authenticate with the Wachete API?

Authentication is performed by POSTing userId and apiKey to /thirdparty/v1/user/apilogin to obtain a bearer token, which must be included as "Authorization: bearer " in all subsequent calls.

1. Get your credentials

1. Sign in to your Wachete account on the web UI. 2) Open your user profile (account settings) to locate your userId and apiKey. 3) Send a POST request to https://api.wachete.com/thirdparty/v1/user/apilogin with JSON { "userId": "", "apiKey": "" }. 4) Copy the "token" value from the response and use it as the bearer token in the Authorization header for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.wachete_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 Wachete 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 wachete_pipeline.py

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

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Pipeline wachete_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wachete_data
The duckdb destination used duckdb:/wachete.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 wachete_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 data_list and task from the Wachete 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 wachete_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wachete.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "task", "endpoint": {"path": "thirdparty/v1/task"}},
            {"name": "data_list", "endpoint": {"path": "thirdparty/v1/data/list/{id}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wachete_pipeline",
        destination="duckdb",
        dataset_name="wachete_data",
    )
    load_info = pipeline.run(wachete_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("wachete_pipeline").dataset()
sessions_df = data.data_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wachete_data.data_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wachete_pipeline").dataset()
data.data_list.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 Wachete 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 POST /thirdparty/v1/user/apilogin returns an error or no token, verify that you are using the correct userId and apiKey from your profile and that the JSON payload matches { "userId": ..., "apiKey": ... }. Ensure the received token is sent in the header as "Authorization: bearer ".

Pagination / continuationToken

Endpoints that return large histories (e.g., GET /thirdparty/v1/data/list/{id}) may include a continuationToken query parameter. Use this token in subsequent requests to retrieve the next batch of records.

Rate limits and quotas

The documentation does not publish explicit limits. If a 429 response is received, implement exponential back‑off and retry. Contact support (info@wachete.com) for account‑specific limits.

Common errors

• 401 Unauthorized – missing or invalid bearer token.
• 400 Bad Request – invalid parameters such as malformed dates.
• 404 Not Found – unknown task/folder/notification identifier.
• 500 Server Error – transient server issue; retry later.

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