Criteo retail media Python API Docs | dltHub

Criteo Retail Media API is a REST API that enables programmatic management and reporting of retail media advertising campaigns, catalogs, audiences and analytics. The REST API base URL is https://api.criteo.com and All requests require an OAuth2 Bearer access token..

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 Criteo retail media data in under 10 minutes.

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What data can I load from Criteo retail media?

Here are some of the endpoints you can load from Criteo retail media:

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How do I authenticate with the Criteo retail media API?

The API uses OAuth2 client‑credentials flow. Obtain an access token from https://api.criteo.com/oauth2/token and include it in every request as Authorization: Bearer <access_token>.

1. Get your credentials

1. Log in to the Criteo Developer Portal.
2. Navigate to My Apps and click Create New App.
3. Choose Retail Media as the service and select Client Credentials (or Authorization Code) as the authentication method.
4. Activate the required scopes such as Campaigns, Catalogs, Audiences, etc.
5. After the app is created, copy the client_id and client_secret displayed on the app details page.
6. Use these values to request an access token from https://api.criteo.com/oauth2/token with grant_type=client_credentials.

2. Add them to .dlt/secrets.toml

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[sources.criteo_retail_media_source]
client_id = "your_client_id"
client_secret = "your_client_secret"

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 Criteo retail media 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 criteo_retail_media_pipeline.py

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

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Pipeline criteo_retail_media_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset criteo_retail_media_data
The duckdb destination used duckdb:/criteo_retail_media.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 criteo_retail_media_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 line_items from the Criteo retail media 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 criteo_retail_media_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.criteo.com",
            "auth": {
                "type": "oauth2_client_credentials",
                "access_token": client_id,
            },
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "retail/2025-07/campaigns", "data_selector": "items"}},
            {"name": "line_items", "endpoint": {"path": "retail/2025-07/line-items", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM criteo_retail_media_data.campaigns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("criteo_retail_media_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 Criteo retail media 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 receive a 401 Unauthorized response, the access token is missing, invalid, or expired. Regenerate the token via the /oauth2/token endpoint and ensure the Authorization: Bearer <access_token> header is present.

Rate limits

When the API returns a 429 Too Many Requests status, pause requests and implement exponential back‑off. The response headers include X-RateLimit-Limit and X-RateLimit-Reset to help manage quotas.

Pagination

List endpoints support pagination via query parameters such as page and page_size (or limit/offset depending on the endpoint). The response contains metadata fields like total, page, and page_size. Continue fetching subsequent pages until all records are retrieved.

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