Load InvenTree data to DuckDB

InvenTree is an open source inventory management system that provides a REST API for accessing and manipulating inventory data. Everything needed to build a working InvenTree → 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 InvenTree 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 InvenTree 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 InvenTree 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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InvenTree API at a glance

These values come from the InvenTree API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the InvenTree API?

Token authentication is performed by sending an Authorization header with the value format 'Token <token_value>'. The API also supports basic authentication using username and password credentials.

1. Get your credentials

To obtain your API credentials for InvenTree, you must use token-based authentication. Navigate to your InvenTree instance in a web browser, log in with your user account, and visit your user settings or profile page where API tokens are managed. If you do not see a generated token, you can often generate one via the API by sending a GET request to /api/user/me/token/ using your username and password via Basic Authentication headers; the server will return your API token. For production environments, consult your administrator if you lack the necessary permissions to manage tokens.

2. Add them to .dlt/secrets.toml

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[sources.inventree_source]
url = "https://your-inventree-instance.com/api"
token = "your_inventree_token_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 InvenTree data can I load into DuckDB?

These are the InvenTree endpoints dlt can load into DuckDB:

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How do I load only new InvenTree records?

The InvenTree API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "part", "endpoint": {
    "path": "api/part/",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 InvenTree pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading parts and stock from the InvenTree API into DuckDB:

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

@dlt.source
def inventree_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL depends on the specific InvenTree instance (e.g., http://127.0.0.1:8000).",
            "auth": {"type": "api_key", "api_key": token, "name": "Authorization", "location": "header"},
        },
        "resources": [
            {"name": "part", "endpoint": {"path": "api/part/", "data_selector": "results"}},
            {"name": "stock_item", "endpoint": {"path": "api/stock/item/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_inventree_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="inventree_pipeline",
        destination="duckdb",
        dataset_name="inventree_data",
    )
    load_info = pipeline.run(inventree_source())
    print(load_info)


if __name__ == "__main__":
    load_inventree_to_duckdb()

Run it with python inventree_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 InvenTree 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("inventree_pipeline").dataset()
df = data.part.df()
print(df.head())

SQL:

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SELECT * FROM inventree_data.part LIMIT 10;

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

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How do I deploy the InvenTree 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 InvenTree 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 InvenTree 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