Cloud bot Python API Docs | dltHub

Automation 360 Control Room (Cloud) API is a REST API that lets you authenticate, list and manage bots (automations), deploy/trigger bots, and manage executions and related Control Room objects. The REST API base URL is https://<your-control-room-domain> and All requests require a JWT token returned by the Authentication API and passed in X-Authorization 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 Cloud bot data in under 10 minutes.

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What data can I load from Cloud bot?

Here are some of the endpoints you can load from Cloud bot:

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How do I authenticate with the Cloud bot API?

Obtain a JWT by POSTing credentials (username + password or apiKey) to the Authentication API at {control_room_url}/v1/authentication. Include the returned token in subsequent requests in the X-Authorization header (X-Authorization: ). Content-Type: application/json.

1. Get your credentials

1. Log into Automation 360 Control Room as an admin or user with permission to generate API keys.
2. If using API key flow, create/generate an API key in the Control Room (assign role permission 'Generate API-key' if needed).
3. Use either username/password or username+apiKey in the authentication POST body to /v1/authentication to receive a JWT.
4. Save the JWT for use in X-Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.cloud_bot_source]
username = "your_username"
api_key = "your_api_key"

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 Cloud bot 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 cloud_bot_pipeline.py

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

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Pipeline cloud_bot_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cloud_bot_data
The duckdb destination used duckdb:/cloud_bot.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 cloud_bot_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 automations and automations/{id} from the Cloud bot 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 cloud_bot_source(management_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-control-room-domain>",
            "auth": {
                "type": "bearer",
                "token": management_token,
            },
        },
        "resources": [
            {"name": "automations", "endpoint": {"path": "v3/automations", "data_selector": "automations"}},
            {"name": "executions", "endpoint": {"path": "v3/automations/executions", "data_selector": "executions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cloud_bot_pipeline",
        destination="duckdb",
        dataset_name="cloud_bot_data",
    )
    load_info = pipeline.run(cloud_bot_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("cloud_bot_pipeline").dataset()
sessions_df = data.automations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cloud_bot_data.automations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cloud_bot_pipeline").dataset()
data.automations.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 Cloud bot 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 authentication returns 401: verify POST to /v1/authentication body includes correct username and password or apiKey. Ensure user has permission to generate API keys if using apiKey. The response contains error code and message (400/401).

Missing or invalid X-Authorization header

All protected endpoints require X-Authorization with the JWT returned by /v1/authentication. A missing or expired token returns 401; refresh by re-authenticating.

Bot deployment and execution errors

Bot Deploy (/v3/automations/deploy) can return 400 (bad request), 401 (auth), 404 (resource not found), or 500 (server). Verify fileId (bot id) and runAsUserIds exist and the bot is in a public workspace with required permissions and that target Bot Runner devices are available.

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