Evite Python API Docs | dltHub

Evite is an online invitation and event management service for creating and sending invitations via email, text, and social platforms. The REST API base URL is `` and .

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 Evite data in under 10 minutes.

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What data can I load from Evite?

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

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How do I authenticate with the Evite API?

No public API or authentication mechanism is documented by Evite; integrations would require contacting Evite directly.

1. Get your credentials

Contact Evite support or partnerships team; no public developer dashboard is available.

2. Add them to .dlt/secrets.toml

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[sources.evite_source]

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 Evite 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 evite_pipeline.py

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

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Pipeline evite_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset evite_data
The duckdb destination used duckdb:/evite.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 evite_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 from the Evite 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 evite_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="evite_pipeline",
        destination="duckdb",
        dataset_name="evite_data",
    )
    load_info = pipeline.run(evite_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("evite_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM evite_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("evite_pipeline").dataset()
data..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 Evite 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

No Public API Available

Evite does not provide a publicly documented REST API. Attempting to call any endpoint will result in HTTP 404 or connection errors because the URLs are not defined.

Recommendation: Reach out to Evite's partnership or support team to inquire about private integrations or use authorized SDKs if available. If you must obtain data programmatically, consider web scraping with respect to Evite’s terms of service, and implement robust error handling for HTML changes and anti‑scraping measures.

Common Errors (When Using Unofficial Scrapers)

• HTTP 403 Forbidden – Likely due to missing cookies or anti‑bot protections.
• HTTP 429 Too Many Requests – Rate limiting on the public web site; add delays and exponential backoff.
• Captcha challenges – Requires manual intervention or captcha‑solving services.

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