Ezo Inventory Python API Docs | dltHub

EZOfficeInventory is an asset and inventory management platform that exposes a REST API to programmatically access and manage assets, inventory, stock, locations, users and related resources. The REST API base URL is https://<SUBDOMAIN>.ezofficeinventory.com and all requests require an Access Token sent in the token HTTP 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 Ezo Inventory data in under 10 minutes.

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What data can I load from Ezo Inventory?

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

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How do I authenticate with the Ezo Inventory API?

The API uses a per‑account Access Token (company token) placed in an HTTP header named token for every request. All requests must use HTTPS.

1. Get your credentials

1. Log in as the account owner. 2) Go to Settings → Integrations / Add Ons → API Integration (or Settings → Enable API). 3) Enable the API integration and generate the Access Token (company token). 4) Copy and store the token securely; use it in the token header for requests.

2. Add them to .dlt/secrets.toml

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[sources.ezo_inventory_source]
company_token = "your_company_token_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 Ezo 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 ezo_inventory_pipeline.py

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

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Pipeline ezo_inventory_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ezo_inventory_data
The duckdb destination used duckdb:/ezo_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 ezo_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 assets and inventory from the Ezo 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 ezo_inventory_source(company_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<SUBDOMAIN>.ezofficeinventory.com",
            "auth": {
                "type": "api_key",
                "api_key": company_token,
            },
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "assets.api", "data_selector": "assets"}},
            {"name": "inventory", "endpoint": {"path": "inventory.api", "data_selector": "volatile_assets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ezo_inventory_pipeline",
        destination="duckdb",
        dataset_name="ezo_inventory_data",
    )
    load_info = pipeline.run(ezo_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("ezo_inventory_pipeline").dataset()
sessions_df = data.assets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ezo_inventory_data.assets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ezo_inventory_pipeline").dataset()
data.assets.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 Ezo 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 receive 401 Unauthorized or 403 Forbidden responses, verify that the account API integration is enabled and that you are sending the Access Token in the HTTP header named token. The token is generated in Settings → Integrations/API Integration and must be used as: -H "token:<COMPANY_TOKEN>".

Rate limiting / Fair‑use

The documentation states a Fair Use limit of 60 requests per minute. If you see 429 responses, back off and implement exponential retry with jitter and ensure you page results rather than requesting large ranges in tight loops.

Pagination quirks

Most list endpoints are paginated (assets and inventory default to 25 items per page). Use the page query parameter (page=<PAGE_NUM>) to iterate pages. Some history endpoints return only 5 items per page; responses also include total pages — read those fields to know when to stop.

Common error responses

• 400 Bad Request: malformed parameters (missing required query/body fields).
• 401 Unauthorized / 403 Forbidden: invalid or missing token, or API integration not enabled.
• 404 Not Found: resource id does not exist.
• 429 Too Many Requests: rate limit exceeded.
• 500/502/503: server errors; retry with backoff.

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