Picsart Python API Docs | dltHub

Picsart is a platform offering image, video, and generative AI tools via REST APIs. The REST API base URL is https://api.picsart.io and All requests require an API key passed in the X-Picsart-API-Key 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 Picsart data in under 10 minutes.

---

What data can I load from Picsart?

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

---

How do I authenticate with the Picsart API?

Include the header X-Picsart-API-Key: <your_api_key> in every request.

1. Get your credentials

1. Register for a Picsart account on https://picsart.io and log in.
2. After logging in, a default application is created automatically.
3. Open the application card in the dashboard.
4. Click the copy icon next to the API Key to copy it, or view the full key on the app details page.
5. Store the copied key securely for use in the X-Picsart-API-Key header.

2. Add them to .dlt/secrets.toml

Copy
[sources.picsart_source]
api_key = "your_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Picsart 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:

Copy
python picsart_pipeline.py

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

Copy
Pipeline picsart_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset picsart_data
The duckdb destination used duckdb:/picsart.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline picsart_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.

---

Python pipeline example

This example loads balance and image_search from the Picsart API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def picsart_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.picsart.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "balance", "endpoint": {"path": "tools/1.0/balance"}},
            {"name": "image_search", "endpoint": {"path": "image-tools/search", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="picsart_pipeline",
        destination="duckdb",
        dataset_name="picsart_data",
    )
    load_info = pipeline.run(picsart_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.

---

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):

Copy
import dlt

data = dlt.pipeline("picsart_pipeline").dataset()
sessions_df = data.balance.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM picsart_data.balance LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("picsart_pipeline").dataset()
data.balance.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Picsart 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.

---

Troubleshooting

Authentication Errors

• 401 Unauthorized – Occurs when the X-Picsart-API-Key header is missing or the key is invalid.
• 406 Not Acceptable – Returned if the Accept: application/json header is not set.

Rate Limiting

• The API enforces request limits per minute. Exceeding the limit returns a 429 Too Many Requests response. Implement exponential back‑off and respect the Retry-After header.

Pagination

• List endpoints use page and page_size query parameters. The response includes next_page when additional data is available. Ensure to iterate until next_page is absent.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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