inFlow Inventory Python API Docs | dltHub

inFlow Inventory API is a REST API that provides access to inventory management data, including products, customers, vendors, sales orders, and purchase orders. The REST API base URL is https://cloudapi.inflowinventory.com and All requests require an API key sent as an Authorization Bearer token 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 inFlow Inventory data in under 10 minutes.

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What data can I load from inFlow Inventory?

Here are some of the endpoints you can load from inFlow Inventory:

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How do I authenticate with the inFlow Inventory API?

Authentication is done by providing an API key in the 'Authorization' header with the 'Bearer' scheme. The API key is generated from the inFlow Inventory integration page.

1. Get your credentials

1. Ensure you have an active inFlow Inventory subscription and the API add-on enabled.
2. Go to inFlow's Integration page.
3. Click 'Add new API key' and give it a name.
4. Copy the generated API key and your 'companyId'. The API key can only be copied once.

2. Add them to .dlt/secrets.toml

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[sources.inflow_inventory_source]
api_key = "your_api_key_here"
company_id = "your_company_id_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 inFlow Inventory 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 inflow_inventory_pipeline.py

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

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Pipeline inflow_inventory_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset inflow_inventory_data
The duckdb destination used duckdb:/inflow_inventory.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 inflow_inventory_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 products and sales_orders from the inFlow Inventory 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 inflow_inventory_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloudapi.inflowinventory.com",
            "auth": {
                "type": "bearer",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "{companyId}/products"}},
            {"name": "sales_orders", "endpoint": {"path": "{companyId}/sales-orders"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="inflow_inventory_pipeline",
        destination="duckdb",
        dataset_name="inflow_inventory_data",
    )
    load_info = pipeline.run(inflow_inventory_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("inflow_inventory_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM inflow_inventory_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("inflow_inventory_pipeline").dataset()
data.products.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 inFlow Inventory 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 encounter a 401 Unauthorized error, it indicates that your API key is either missing or invalid. Ensure that you have correctly generated and provided your API key in the 'Authorization' header with the 'Bearer' scheme.

Insufficient Permissions

A 403 Forbidden error suggests that your API key does not have the necessary permissions to access the requested resource. Verify the permissions associated with your API key in the inFlow Inventory integration settings.

Rate Limiting

The API may return a 429 Too Many Requests error if you exceed the allowed request rate. Implement exponential backoff or reduce the frequency of your API calls to avoid hitting rate limits.

Invalid Filters

A 400 Bad Request error can occur if you provide invalid filters or parameters in your API request. Refer to the API documentation for the correct syntax and available filtering options for each endpoint.

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