Helpdesk Python API Docs | dltHub

HelpDesk is a REST API for managing help desk tickets and related resources. The REST API base URL is https://api.helpdesk.com and All requests require an Authorization header using Basic auth (account ID and token) or an OAuth 2 Bearer token..

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 Helpdesk data in under 10 minutes.

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What data can I load from Helpdesk?

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

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How do I authenticate with the Helpdesk API?

Clients must send an Authorization header. Use Basic authentication with the account ID as the username and the generated personal token as the password, or supply a Bearer token obtained via OAuth 2.

1. Get your credentials

1. Log into your HelpDesk account dashboard.
2. Navigate to Settings → API & Integrations.
3. Click Generate New Token.
4. Copy the token value and note your account ID displayed on the profile page.
5. Store the token securely; it will be used as the password in Basic auth (account ID as username).

2. Add them to .dlt/secrets.toml

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[sources.helpdesk_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 Helpdesk 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 helpdesk_pipeline.py

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

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Pipeline helpdesk_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset helpdesk_data
The duckdb destination used duckdb:/helpdesk.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 helpdesk_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 tickets and views from the Helpdesk 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 helpdesk_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.helpdesk.com",
            "auth": {
                "type": "http_basic",
                "password": api_key,
            },
        },
        "resources": [
            {"name": "tickets", "endpoint": {"path": "v1/tickets"}},
            {"name": "views", "endpoint": {"path": "v1/views"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="helpdesk_pipeline",
        destination="duckdb",
        dataset_name="helpdesk_data",
    )
    load_info = pipeline.run(helpdesk_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("helpdesk_pipeline").dataset()
sessions_df = data.tickets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM helpdesk_data.tickets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("helpdesk_pipeline").dataset()
data.tickets.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 Helpdesk 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 – Verify that the Authorization header is correctly formed. For Basic auth, ensure the base64‑encoded account_id:token string is accurate. For OAuth, confirm the Bearer token is still valid.
• 403 Forbidden – The token may lack required scopes; regenerate a token with appropriate permissions.

Rate Limiting

• 429 Too Many Requests – The API enforces a request quota. Respect the Retry-After header and implement exponential backoff before retrying.

Pagination Issues

• The tickets endpoint uses cursor‑based pagination. Each response includes a next_cursor field. Continue requesting /v1/tickets?cursor=<next_cursor> until the cursor is empty. Missing or malformed cursors will result in a 400 error.

General API Errors

• 400 Bad Request – Check query parameters for correct syntax and required fields.
• 500 Internal Server Error – Retry after a short delay; if the problem persists, contact HelpDesk support.

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