Freepik Python API Docs | dltHub

Freepik is an API platform that provides AI-powered image upscaling, editing, generation, and related media services. The REST API base URL is https://api.freepik.com/v1 and all requests require an x-freepik-api-key header (API key) for 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 Freepik data in under 10 minutes.

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What data can I load from Freepik?

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

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How do I authenticate with the Freepik API?

Authentication is performed using an API key sent in the x-freepik-api-key HTTP header on every request. Also include Content-Type: application/json and Accept: application/json where appropriate.

1. Get your credentials

1. Sign in or create a Freepik Developers account at https://www.freepik.com/api or the dashboard at https://www.freepik.com/developers/dashboard. 2) In the Developers Dashboard request or generate an API key (x-freepik-api-key). 3) Copy the key and store it securely; use it in requests as the x-freepik-api-key header.

2. Add them to .dlt/secrets.toml

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[sources.freepik_image_upscaler_source]
api_key = "your_freepik_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 Freepik 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 freepik_image_upscaler_pipeline.py

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

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Pipeline freepik_image_upscaler_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freepik_image_upscaler_data
The duckdb destination used duckdb:/freepik_image_upscaler.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 freepik_image_upscaler_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 image_upscaler_tasks and image_upscaler_task from the Freepik 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 freepik_image_upscaler_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.freepik.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "image_upscaler_task", "endpoint": {"path": "v1/ai/image-upscaler/{task_id}", "data_selector": "data"}},
            {"name": "image_upscaler_tasks", "endpoint": {"path": "v1/ai/image-upscaler", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freepik_image_upscaler_pipeline",
        destination="duckdb",
        dataset_name="freepik_image_upscaler_data",
    )
    load_info = pipeline.run(freepik_image_upscaler_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("freepik_image_upscaler_pipeline").dataset()
sessions_df = data.image_upscaler_task.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freepik_image_upscaler_data.image_upscaler_task LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freepik_image_upscaler_pipeline").dataset()
data.image_upscaler_task.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 Freepik 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 401/403 responses, ensure the x-freepik-api-key header is present and contains a valid API key from your Freepik Developers dashboard. Confirm there are no extra spaces or missing characters.

Rate limiting and errors

The Freepik docs reference rate limiting on the developer pages. If you receive 429 responses, back off and retry with exponential backoff. Check the response body for error details.

Asynchronous tasks and webhooks

Upscaler endpoints are asynchronous: POST returns a task_id and initial status in the "data" object. Use GET /v1/ai/image-upscaler/{task_id} to poll status or provide webhook_url in the POST to receive completion notifications. The webhook payload mirrors the GET response (without the data field in some webhook payloads).

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