Kong OpenID Connect Python API Docs | dltHub

Kong OpenID Connect is a Kong Gateway plugin that implements OpenID Connect Relying Party functionality, discovery cache management, and JWKS publication via the Kong Admin API. The REST API base URL is http://{KONG_ADMIN_HOST}:8001 and Admin API access required (no plugin-specific bearer token); secure Admin API in production..

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 Kong OpenID Connect data in under 10 minutes.

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What data can I load from Kong OpenID Connect?

Here are some of the endpoints you can load from Kong OpenID Connect:

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How do I authenticate with the Kong OpenID Connect API?

Requests are made to the Kong Admin API host (default http://{KONG_ADMIN_HOST}:8001). By default the Admin API is accessible on localhost without authentication; in production you must secure it (firewall, Kong RBAC/Admin authentication or an authentication proxy). Include any required Admin API auth headers according to your Kong deployment.

1. Get your credentials

1. Access your Kong admin host (the machine or load-balanced endpoint exposing the Admin API). 2) If your Admin API is secured by an auth mechanism (RBAC, JWT, HTTP basic, or an auth plugin), create the required admin credentials in Kong (or in your identity provider) and note the header/value to use. 3) Ensure network access from the dlt runner to the Admin API host and port (default 8001). 4) Test with: curl -i http://{KONG_ADMIN_HOST}:8001/ (or curl -i -H 'Authorization: Bearer ' http://{KONG_ADMIN_HOST}:8001/ if secured).

2. Add them to .dlt/secrets.toml

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[sources.kong_openid_connect_source]
admin_url = "http://your-kong-admin:8001"
# If your Admin API requires a header credential, include it as: admin_auth_header = "Bearer your_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.

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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 Kong OpenID Connect 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 kong_openid_connect_pipeline.py

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

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Pipeline kong_openid_connect_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kong_openid_connect_data
The duckdb destination used duckdb:/kong_openid_connect.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 kong_openid_connect_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 openid_connect_jwks and discovery_cache from the Kong OpenID Connect 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 kong_openid_connect_source(admin_url=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://{KONG_ADMIN_HOST}:8001",
            "auth": {
                "type": "none",
                "": admin_url,
            },
        },
        "resources": [
            {"name": "openid_connect_jwks", "endpoint": {"path": "openid-connect/jwks", "data_selector": "keys"}},
            {"name": "discovery_cache", "endpoint": {"path": "openid-connect/discovery-cache", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kong_openid_connect_pipeline",
        destination="duckdb",
        dataset_name="kong_openid_connect_data",
    )
    load_info = pipeline.run(kong_openid_connect_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("kong_openid_connect_pipeline").dataset()
sessions_df = data.discovery_cache.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM kong_openid_connect_data.discovery_cache LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("kong_openid_connect_pipeline").dataset()
data.discovery_cache.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 Kong OpenID Connect 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

Admin API authentication failures

If you receive 401 or 403 responses when calling plugin endpoints, your Kong Admin API is secured. Ensure the dlt runner has network access and supplies the required Admin API authentication headers (or uses the management proxy). Test with curl including the same headers.

Missing JWKS or 404 responses

A 404 from /openid-connect/jwks or /openid-connect/discovery-cache/{id} indicates the requested JWKS or cache entry does not exist. Confirm the plugin generated keys (check plugin config using Admin API plugin list for the target service) or list discovery-cache to find valid ids.

Rediscovery and validation errors

If token validation fails due to stale discovery data, the plugin attempts rediscovery. If rediscovery fails (non-2xx) the plugin may fallback to cached data; check plugin logs and the discovery-cache entries. You can clear the discovery cache via DELETE /openid-connect/discovery-cache to force fresh discovery.

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