Kevel Python API Docs | dltHub

Kevel is a suite of REST APIs for building and managing ad‑serving, reporting, inventory, forecasting, and user‑profile functionality for custom ad platforms. The REST API base URL is https://api.kevel.co/v1 and All requests that require auth use an API key sent in the X-Adzerk-ApiKey / X-Kevel-ApiKey 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 Kevel data in under 10 minutes.

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What data can I load from Kevel?

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

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How do I authenticate with the Kevel API?

Kevel uses API keys passed in the request header named X-Adzerk-ApiKey or X-Kevel-ApiKey. All requests must use TLS.

1. Get your credentials

1. Sign in to the Kevel dashboard.
2. Navigate to the Network Settings or API Keys section (often under Account > API Keys).
3. Copy the displayed API key.
4. Store the key securely and use it in the X-Adzerk-ApiKey or X-Kevel-ApiKey request header.
5. Optionally add the key to your dlt secrets file: api_key = "...".

2. Add them to .dlt/secrets.toml

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[sources.kevel_source]
api_key = "your_kevel_api_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 Kevel 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 kevel_pipeline.py

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

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Pipeline kevel_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kevel_data
The duckdb destination used duckdb:/kevel.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 kevel_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 campaigns and decisions from the Kevel 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 kevel_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kevel.co/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "v1/campaign", "data_selector": "items"}},
            {"name": "decisions", "endpoint": {"path": "api/v2 (hosted on e-<networkId>.adzerk.net)", "data_selector": "decisions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kevel_pipeline",
        destination="duckdb",
        dataset_name="kevel_data",
    )
    load_info = pipeline.run(kevel_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("kevel_pipeline").dataset()
sessions_df = data.campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM kevel_data.campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("kevel_pipeline").dataset()
data.campaigns.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 Kevel 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

Requests missing or using an invalid API key return HTTP 401. Ensure the X-Adzerk-ApiKey or X-Kevel-ApiKey header is present and correct, and always use HTTPS.

Pagination and list selectors

Most list endpoints return a paginated object with an items array containing records; pagination fields such as totalCount, page, and pageSize are provided. Use those fields to iterate through pages.

Decision API quirks

The Decision API is served on a per‑network host (e.g., https://e-<networkId>.adzerk.net/api/v2). Its response is structured under a top‑level decisions object keyed by placement divName. Authentication may be optional for some networks unless explicitly enabled.

Rate limits and error responses

Kevel returns HTTP 401 for auth errors, 403 for insufficient permissions, and 400 for malformed payloads. Rate limiting is enforced per account; the status page (https://kevel.statuspage.io) provides outage information and recommended back‑off strategies.

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