Load Fal-ai data to DuckDB

Fal.ai is a platform for accessing and deploying state-of-the-art AI models for generation tasks. Everything needed to build a working Fal-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.

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Build your Fal-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 Fal-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 Fal-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.

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Fal-ai API at a glance

These values come from the Fal-ai API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Fal-ai API?

All requests require an 'Authorization' header. The value must be formatted as 'Key ' followed by your API key (e.g., 'Authorization: Key YOUR_API_KEY').

1. Get your credentials

1. Navigate to the fal.ai dashboard at https://fal.ai/dashboard/keys. 2. If you are working within a team, ensure the correct team account is selected in the top-left corner of the dashboard before proceeding. 3. Click the Create Key button. 4. Assign a descriptive name to your key for easier management. 5. Select the appropriate scope: choose API for standard model inference, or ADMIN if you require access to CLI operations, serverless deployment, or admin-scoped platform APIs. 6. Copy the generated key immediately, as it will not be visible again once the page is closed.

2. Add them to .dlt/secrets.toml

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[sources.fal_ai_source]
fal_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.

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What Fal-ai data can I load into DuckDB?

These are the Fal-ai endpoints dlt can load into DuckDB:

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How do I load only new Fal-ai records?

Fal-ai exposes cursor on models, 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.

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{"name": "models", "endpoint": {
    "path": "models",
    "data_selector": "models",
    "incremental": {"cursor_path": "cursor", "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.

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What does the generated Fal-ai pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/models and /v1/serverless/metrics from the Fal-ai API into DuckDB:

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

@dlt.source
def fal_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fal.ai/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "models", "data_selector": "models"}},
            {"name": "billing_events", "endpoint": {"path": "models/billing-events", "data_selector": "billing_events"}}
        ],
    }
    yield from rest_api_resources(config)


def load_fal_ai_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fal_ai_pipeline",
        destination="duckdb",
        dataset_name="fal_ai_data",
    )
    load_info = pipeline.run(fal_ai_source())
    print(load_info)


if __name__ == "__main__":
    load_fal_ai_to_duckdb()

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

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How do I query Fal-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):

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import dlt

data = dlt.pipeline("fal_ai_pipeline").dataset()
df = data.models.df()
print(df.head())

SQL:

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SELECT * FROM fal_ai_data.models LIMIT 10;

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

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How do I deploy the Fal-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 Fal-ai loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Fal-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.

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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