Freshdesk Python API Docs | dltHub

Freshdesk is a cloud-based customer support/helpdesk platform providing ticketing, contacts/companies, knowledge base, and related support APIs. The REST API base URL is https://{your_domain}.freshdesk.com/api/v2 and All requests use HTTP Basic auth with your API key as username..

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 Freshdesk data in under 10 minutes.

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What data can I load from Freshdesk?

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

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How do I authenticate with the Freshdesk API?

Use your Freshdesk personal API key as the username and any dummy password (or X) as the password in HTTP Basic auth; include Content-Type: application/json. Example curl: curl -u YOUR_API_KEY:X -H "Content-Type: application/json" https://your_domain.freshdesk.com/api/v2/tickets

1. Get your credentials

1. Sign in to your Freshdesk portal. 2. Click your profile picture (top-right) and open Profile settings. 3. Copy the API key displayed under API key section. 4. Use that key as the username in HTTP Basic auth for API calls.

2. Add them to .dlt/secrets.toml

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[sources.freshdesk_support_source]
api_key = "your_freshdesk_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 Freshdesk 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 freshdesk_support_pipeline.py

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

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Pipeline freshdesk_support_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshdesk_support_data
The duckdb destination used duckdb:/freshdesk_support.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 freshdesk_support_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 contacts from the Freshdesk 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 freshdesk_support_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_domain}.freshdesk.com/api/v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "tickets", "endpoint": {"path": "tickets"}},
            {"name": "contacts", "endpoint": {"path": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM freshdesk_support_data.tickets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshdesk_support_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 Freshdesk 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 Unauthorized, verify you are using HTTP Basic auth with your API key as username. Ensure the API key from Profile Settings is correct and that requests use HTTPS. Freshdesk may require Base64 encoding when sending Authorization header manually; using curl -u handles this.

Rate limits and 429

Freshdesk enforces account-level rate limits (varies by plan). Responses include X-RateLimit-Remaining and Retry-After on 429. Back off and retry after the specified seconds.

Pagination quirks

List endpoints are paginated with page and per_page (default 30, max 100). Use the Link response header (rel="next") to discover the next page instead of assuming continuous pages; avoid deep pagination beyond page 500.

Common API error responses

400 - validation/client error (missing or invalid fields) 401 - authentication failure (invalid API key) 403 - access denied (insufficient privileges) 404 - resource not found 405 - method not allowed 409 - conflict (duplicate/invalid state) 415 - unsupported content type (only application/json) 429 - rate limit exceeded 500 - server error

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