Tiledesk Python API Docs | dltHub

Tiledesk is a platform that provides a REST API for building conversational chatbots and managing messages, requests, bots, members, and conversations. The REST API base URL is https://api.tiledesk.com and All requests require the x-project-id header and either an x-auth-token header or a Bearer token for 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 pip install "dlt[workspace]" and start loading Tiledesk data in under 10 minutes.

---

What data can I load from Tiledesk?

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

---

How do I authenticate with the Tiledesk API?

Authentication is performed via HTTP headers: include x-project-id with your project ID and either x-auth-token with your token or an Authorization: Bearer header.

1. Get your credentials

1. Log into the Tiledesk dashboard.
2. Select the project you want to access.
3. Navigate to Project Settings → API Keys.
4. Click Create New Token, give it a name, and generate the token.
5. Copy the generated token; it will be shown only once.
6. Use the token as the value for the x-auth-token header or in the Authorization Bearer header.
7. Note the Project ID shown in the project overview; this is the value for the x-project-id header.

2. Add them to .dlt/secrets.toml

Copy
[sources.tiledesk_requests_source]
project_id = "your_project_id"
token = "your_api_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Tiledesk 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:

Copy
python tiledesk_requests_pipeline.py

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

Copy
Pipeline tiledesk_requests_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tiledesk_requests_data
The duckdb destination used duckdb:/tiledesk_requests.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline tiledesk_requests_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.

---

Python pipeline example

This example loads messages and requests from the Tiledesk API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def tiledesk_requests_source(project_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tiledesk.com",
            "auth": {
                "type": "bearer",
                "token": project_id,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "projects/{projectId}/messages", "data_selector": "messages"}},
            {"name": "requests", "endpoint": {"path": "projects/{projectId}/requests", "data_selector": "requests"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tiledesk_requests_pipeline",
        destination="duckdb",
        dataset_name="tiledesk_requests_data",
    )
    load_info = pipeline.run(tiledesk_requests_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.

---

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):

Copy
import dlt

data = dlt.pipeline("tiledesk_requests_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM tiledesk_requests_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("tiledesk_requests_pipeline").dataset()
data.messages.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Tiledesk 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.

---

Troubleshooting

Authentication Errors

• 401 Unauthorized – Missing or invalid x-project-id or x-auth-token/Bearer token.
• 403 Forbidden – Token does not have permission for the requested project.

Rate Limiting

• 429 Too Many Requests – The API enforces rate limits; back‑off and retry after the Retry-After header.

Pagination

• Endpoints such as /messages and /requests support page and perPage query parameters. Use these to iterate through large result sets.
• The response includes pagination metadata (e.g., total, page, perPage) to help determine when all pages have been retrieved.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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