Kairos Face Recognition Python API Docs | dltHub

Kairos Face Recognition is a REST API platform for face detection, recognition, verification, enrollment and gallery management. The REST API base URL is https://api.kairos.com and all requests require app_id and app_key sent as HTTP headers.

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 Kairos Face Recognition data in under 10 minutes.

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What data can I load from Kairos Face Recognition?

Here are some of the endpoints you can load from Kairos Face Recognition:

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How do I authenticate with the Kairos Face Recognition API?

Set headers app_id and app_key on every request. Optionally set store_image: "false" to avoid storing submitted images.

1. Get your credentials

1. Sign up or log in at https://developer.kairos.com (or https://www.kairos.com/docs/getting-started-with-kairos-face-recognition). 2) Open the dashboard / API keys section. 3) Create a new application/api key to obtain app_id and app_key. 4) Use those values in request headers app_id and app_key.

2. Add them to .dlt/secrets.toml

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[sources.kairos_face_recognition_source]
app_id = "your_app_id_here"
app_key = "your_app_key_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 Kairos Face Recognition 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 kairos_face_recognition_pipeline.py

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

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Pipeline kairos_face_recognition_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kairos_face_recognition_data
The duckdb destination used duckdb:/kairos_face_recognition.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 kairos_face_recognition_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 detect and recognize from the Kairos Face Recognition 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 kairos_face_recognition_source(app_key_pair=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kairos.com",
            "auth": {
                "type": "api_key",
                "app_key": app_key_pair,
            },
        },
        "resources": [
            {"name": "detect", "endpoint": {"path": "detect", "data_selector": "images"}},
            {"name": "recognize", "endpoint": {"path": "recognize", "data_selector": "images"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kairos_face_recognition_pipeline",
        destination="duckdb",
        dataset_name="kairos_face_recognition_data",
    )
    load_info = pipeline.run(kairos_face_recognition_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("kairos_face_recognition_pipeline").dataset()
sessions_df = data.detect.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM kairos_face_recognition_data.detect LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("kairos_face_recognition_pipeline").dataset()
data.detect.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 Kairos Face Recognition 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 omit or use invalid app_id/app_key the API will return errors; ensure both headers are present. Use dashboard to rotate/regenerate keys.

Common API error responses

Many API error responses are returned with HTTP 200 and an Errors array, e.g. {"Errors":[{"Message":"no faces found in the image","ErrCode":5002}]} or {"Errors":[{"Message":"gallery name not found","ErrCode":5004}]}. Also some endpoints return {"status_code":..., "status_message":...} for processing states. Check ErrCode mapping in docs.

Rate limits & best practices

Kairos recommends minimizing image storage (set store_image: "false" header) and using selectors (e.g. liveness) only when needed. Follow SDK/examples in docs for batching and retries. No public numeric rate limits are documented; contact Kairos for production limits.

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