Zoho Inventory Python API Docs | dltHub

Zoho Inventory is an online inventory management REST API that lets you programmatically manage organizations, items, contacts, sales orders, packages and other inventory resources. The REST API base URL is https://www.zohoapis.com/inventory/v1 and all requests require an OAuth 2.0 access token in the Authorization header (Zoho-oauthtoken).

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 Zoho Inventory data in under 10 minutes.

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What data can I load from Zoho Inventory?

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

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How do I authenticate with the Zoho Inventory API?

Zoho Inventory uses OAuth 2.0 for authentication. Obtain OAuth client credentials in Zoho Developer Console, generate access and refresh tokens, and send the access token in the Authorization header as: Authorization: Zoho-oauthtoken {access_token} (Zoho also supports domain-specific endpoints).

1. Get your credentials

1. Sign in to Zoho Developer Console / Zoho API Console. 2) Create a new client (Server-based/Confidential) and note the client_id and client_secret. 3) Use the OAuth2 authorization code flow to obtain an authorization code and exchange it for access and refresh tokens. 4) Store access_token in your dlt secrets and refresh using the refresh_token when expired.

2. Add them to .dlt/secrets.toml

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[sources.zoho_inventory_source]
access_token = "your_oauth_access_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 Zoho 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 zoho_inventory_pipeline.py

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

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Pipeline zoho_inventory_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zoho_inventory_data
The duckdb destination used duckdb:/zoho_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 zoho_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 items and packages from the Zoho 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 zoho_inventory_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.zohoapis.com/inventory/v1",
            "auth": {
                "type": "bearer (OAuth 2.0)",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "items", "endpoint": {"path": "items", "data_selector": "items"}},
            {"name": "packages", "endpoint": {"path": "packages", "data_selector": "packages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoho_inventory_pipeline",
        destination="duckdb",
        dataset_name="zoho_inventory_data",
    )
    load_info = pipeline.run(zoho_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("zoho_inventory_pipeline").dataset()
sessions_df = data.items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zoho_inventory_data.items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zoho_inventory_pipeline").dataset()
data.items.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 Zoho 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

• 401 Unauthorized: invalid/expired access token. Refresh the token with the refresh_token or re-authenticate. Ensure Authorization header format: Authorization: Zoho-oauthtoken {access_token}.

Rate limits

• Zoho enforces API call limits per organization/data center. If rate-limited, API returns 429. Implement retry/backoff and monitor call usage per the API docs.

Pagination

• List endpoints are paginated. Use 'page' and 'per_page' (or per_page/page) query parameters as documented; responses include pagination metadata. Iterate pages to retrieve full datasets.

Resource-specific quirks

• Many GET/{id} endpoints return a top-level object with a singular key (e.g., "item" or "package") rather than an array. Use the documented data selectors above.

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