World ID Python API Docs | dltHub

World ID is an anonymous proof-of-humanity service that lets apps verify users are unique humans without sharing personal identity data. The REST API base URL is https://developer.worldcoin.org/api and Requests use the app_id in the path (no bearer token required for public verify endpoint); some developer APIs use developer portal 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 World ID data in under 10 minutes.

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What data can I load from World ID?

Here are some of the endpoints you can load from World ID:

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How do I authenticate with the World ID API?

Verification endpoints accept your app identifier (app_id or rp_id) as a path parameter; no Authorization header is required for the public /verify endpoints shown in docs. Other developer portal endpoints may require developer credentials via the Developer Portal.

1. Get your credentials

1. Sign in or create an account at the World developer portal (https://developer.worldcoin.org or https://world.org developer pages). 2) Create a World ID application in the Developer Portal; note the provided app_id (or rp_id for v4). 3) Use that app_id when calling verification endpoints (included in the path).

2. Add them to .dlt/secrets.toml

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[sources.world_id_source]
app_id = "your_app_id_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 World ID 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 world_id_pipeline.py

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

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Pipeline world_id_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset world_id_data
The duckdb destination used duckdb:/world_id.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 world_id_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 verify and verify_v4 from the World ID 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 world_id_source(app_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://developer.worldcoin.org/api",
            "auth": {
                "type": "api_key",
                "app_id": app_id,
            },
        },
        "resources": [
            {"name": "verify", "endpoint": {"path": "v2/verify/{app_id}"}},
            {"name": "verify_v4", "endpoint": {"path": "api/v4/verify/{rp_id}", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="world_id_pipeline",
        destination="duckdb",
        dataset_name="world_id_data",
    )
    load_info = pipeline.run(world_id_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("world_id_pipeline").dataset()
sessions_df = data.verify.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM world_id_data.verify LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("world_id_pipeline").dataset()
data.verify.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 World ID 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 get 404 when calling /v2/verify/{app_id}, confirm you are using the correct app_id (or rp_id for v4) from the Developer Portal; the verify endpoint uses the app id in the path rather than an Authorization header.

Verification errors and payload validation

Verification returns HTTP 200 on success with success=true; 400 or 404 for validation or app-not-found errors. Error responses include fields: success=false, code, detail, and optionally results with per-proof failure information.

Rate limiting and developer portal access

Public verify endpoints are documented without explicit rate limits; if you need higher throughput or access to additional management endpoints, use the Developer Portal and contact support for rate limits or API keys.

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