Load Kie AI data to DuckDB

Kie AI is an AI generation platform offering REST APIs for creating text, images, video, and other content. Everything needed to build a working Kie AI → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your Kie AI to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Kie AI to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Kie AI API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

Kie AI API at a glance

These values come from the Kie AI API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Kie AI API?

All requests require authentication using an API key provided in an Authorization header with the format 'Bearer <YOUR_API_KEY>'. Additionally, the 'Content-Type: application/json' header must be included with every request.

1. Get your credentials

To obtain your Kie AI API credentials, follow these steps: 1. Sign in to your account at the Kie AI platform dashboard (https://kie.ai). 2. Ensure you have a positive account balance by adding credits in the billing or balance area, as the platform is pay-as-you-go. 3. Navigate to the API Key Management section (accessible via https://kie.ai/api-key or through your dashboard settings). 4. Create a new API key by following the on-screen prompts. 5. Copy your new key immediately, as you will not be able to view it again. Secure it appropriately, as it is a secret credential.

2. Add them to .dlt/secrets.toml

Copy
[sources.kie_ai_source]
kie_api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What Kie AI data can I load into DuckDB?

These are the Kie AI endpoints dlt can load into DuckDB:

---

How do I load only new Kie AI records?

Kie AI exposes updated_at on api/v1/jobs/recordInfo, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "tasks", "endpoint": {
    "path": "api/v1/jobs/recordInfo",
    "data_selector": "data",
    "incremental": {"cursor_path": "updated_at", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated Kie AI pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading jobs/createTask and jobs/recordInfo from the Kie AI API into DuckDB:

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

@dlt.source
def kie_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kie.ai",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "api/v1/jobs/recordInfo", "data_selector": "data"}},
            {"name": "tasks_list", "endpoint": {"path": "api/v1/jobs/list", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kie_ai_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kie_ai_pipeline",
        destination="duckdb",
        dataset_name="kie_ai_data",
    )
    load_info = pipeline.run(kie_ai_source())
    print(load_info)


if __name__ == "__main__":
    load_kie_ai_to_duckdb()

Run it with python kie_ai_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Kie AI data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("kie_ai_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

Copy
SELECT * FROM kie_ai_data.tasks LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the Kie AI to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Kie AI loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Kie AI data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack