Jitbit Python API Docs | dltHub

Jitbit Helpdesk is a ticketing system that provides a REST API for managing support tickets, users, and categories. The REST API base URL is https://<your_helpdesk_domain>/api and All requests require an 'Authorization' header using either Basic or Bearer token authentication..

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 add "dlt[hub]" and start loading Jitbit data in under 10 minutes.

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What data can I load from Jitbit?

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

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How do I authenticate with the Jitbit API?

The API supports both Basic authentication and Bearer token authentication. For Basic auth, pass the 'Authorization' header as 'Basic ' followed by the base64-encoded 'username

' string. For token auth, pass the 'Authorization' header as 'Bearer '.

1. Get your credentials

To authenticate with the Jitbit Helpdesk REST API, you can use either Basic Authentication (username and password) or Token-based Authentication. For Token-based Authentication: 1. Log in to your Jitbit Helpdesk web interface. 2. Navigate to https://<your_helpdesk_domain>/User/Token (or click your avatar in the top right and select 'API Token' if available). 3. Copy the token value provided. If you prefer Basic Authentication, use your standard Jitbit username (for on-premise installations, include the domain, e.g., 'DOMAIN\Username') and password. Note that changing your password will invalidate existing API tokens.

2. Add them to .dlt/secrets.toml

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[sources.jitbit_source]
api_username = "your_username"
api_password = "your_password"
# or for token-based auth
user_token = "your_user_token"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Jitbit 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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uv run python jitbit_pipeline.py

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

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Pipeline jitbit_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset jitbit_data
The duckdb destination used duckdb:/jitbit.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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uv run dlthub 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 Ticket from the Jitbit 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 jitbit_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your_helpdesk_domain>/api",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "tickets", "endpoint": {"path": "Tickets"}},
            {"name": "users", "endpoint": {"path": "Users"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM jitbit_data.tickets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("jitbit_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 Jitbit 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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