Goldcast Python API Docs | dltHub

Goldcast is an all-in-one virtual event platform that provides REST APIs to programmatically create, manage, and retrieve event, agenda, attendee, and related resources. The REST API base URL is https://customapi.goldcast.io and All requests require a Goldcast personal access token (Bearer token) provided in the 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 Goldcast data in under 10 minutes.

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What data can I load from Goldcast?

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

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How do I authenticate with the Goldcast API?

Goldcast uses personal API tokens (personal access tokens). Include header: Authorization: Bearer {TOKEN}. Tokens must be generated in Goldcast Studio (Tokens settings) and are shown only once when created.

1. Get your credentials

1. Login to Goldcast Studio (https://admin.goldcast.io/auth/login). 2) Go to Settings -> Tokens. 3) Click Create New Token, name it and Generate Token. 4) Copy the token value immediately; it is only displayed once. Note: tokens are disabled by default and must be enabled by contacting Goldcast Support if not available in your plan.

2. Add them to .dlt/secrets.toml

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[sources.goldcast_source]
token = "your_goldcast_token_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 Goldcast 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 goldcast_pipeline.py

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

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Pipeline goldcast_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset goldcast_data
The duckdb destination used duckdb:/goldcast.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 goldcast_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 events and event_members from the Goldcast 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 goldcast_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://customapi.goldcast.io",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "event/", "data_selector": "results"}},
            {"name": "event_members", "endpoint": {"path": "event/event-members/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="goldcast_pipeline",
        destination="duckdb",
        dataset_name="goldcast_data",
    )
    load_info = pipeline.run(goldcast_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("goldcast_pipeline").dataset()
sessions_df = data.events.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM goldcast_data.events LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("goldcast_pipeline").dataset()
data.events.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 Goldcast 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 that the Authorization header is present and formatted as Bearer {TOKEN}. Ensure the token was copied when created; tokens are shown only once. If tokens are disabled for your organization, contact Goldcast Support.

Pagination and data selectors

List endpoints return a paginated object containing a results array along with count, next, and previous fields. Use the results key as the data selector when iterating records.

Rate limits and other errors

Goldcast does not publish explicit rate‑limit values. For 429 Too Many Requests, implement exponential back‑off and retry. For 403 Forbidden, check token scope and organization permissions.

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