Zoho Python API Docs | dltHub

Zoho is a suite of cloud applications and APIs exposing product-specific REST APIs (e.g., Cliq, WorkDrive, Creator, FSM) for programmatic access to collaboration, files, apps and field service data. The REST API base URL is Product-specific; common examples: - Zoho Cliq: https://cliq.zoho.com/api/v2 - Zoho WorkDrive: https://workdrive.zoho.com/api/v1 - Zoho Creator: https://creator.zoho.com/api/v2.1 - Zoho FSM: https://www.zohoapis.com/fsm (product docs use regioned zohoapis domains per account) and All requests require OAuth2 access tokens (access + optional refresh token)..

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 data in under 10 minutes.

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What data can I load from Zoho?

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

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How do I authenticate with the Zoho API?

Zoho APIs use OAuth 2.0 (authorization code / refresh token flows). API calls require an access token passed in request headers (Cliq uses header Authorization: Zoho-oauthtoken ; WorkDrive/Creator/most Zoho products use Bearer access tokens via Authorization: Zoho-oauthtoken or Authorization: Bearer depending on product).

1. Get your credentials

1. Open Zoho API Console (https://api-console.zoho.com/) and create a new client (choose Web-based or Self-client as appropriate).
2. Register redirect URI and obtain Client ID and Client Secret.
3. Generate a grant/authorization code by directing user to https://accounts.zoho.com/oauth/v2/auth with required scopes.
4. Exchange code at https://accounts.zoho.com/oauth/v2/token to receive access_token and refresh_token.
5. Use access_token in Authorization header; refresh tokens to renew expired access tokens via the /oauth/v2/token endpoint.

2. Add them to .dlt/secrets.toml

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[sources.zoho_instance_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
refresh_token = "your_refresh_token"
access_token = "your_access_token"

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 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_instance_pipeline.py

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

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Pipeline zoho_instance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zoho_instance_data
The duckdb destination used duckdb:/zoho_instance.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_instance_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 channels and files from the Zoho 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_instance_source(oauth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Product-specific; common examples: - Zoho Cliq: https://cliq.zoho.com/api/v2 - Zoho WorkDrive: https://workdrive.zoho.com/api/v1 - Zoho Creator: https://creator.zoho.com/api/v2.1 - Zoho FSM: https://www.zohoapis.com/fsm (product docs use regioned zohoapis domains per account)",
            "auth": {
                "type": "bearer",
                "token": oauth_token,
            },
        },
        "resources": [
            {"name": "channels", "endpoint": {"path": "api/v2/channels", "data_selector": "channels"}},
            {"name": "files", "endpoint": {"path": "api/v1/files", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoho_instance_pipeline",
        destination="duckdb",
        dataset_name="zoho_instance_data",
    )
    load_info = pipeline.run(zoho_instance_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_instance_pipeline").dataset()
sessions_df = data.channels.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zoho_instance_data.channels LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zoho_instance_pipeline").dataset()
data.channels.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 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

Zoho APIs return 401 Unauthorized when access tokens are invalid or expired. Ensure Authorization header is present (Cliq: Authorization: Zoho-oauthtoken ; others: Authorization: Bearer or Zoho-oauthtoken) and refresh token flow is implemented to renew tokens.

Rate limits

Products impose per-minute and daily rate limits (example: Cliq quota 30 requests per minute per user for certain APIs; FSM lists daily/org and per-user/min window limits). Handle 429 Too Many Requests and implement exponential backoff and retry.

Pagination quirks

WorkDrive follows JSON:API with "data" arrays and links/meta for pagination. Cliq uses next_token/start_token/next_token/sync_token fields in responses; tokens may be single-use and expire, so implement token-based pagination and fallback to page/limit when available.

Common API errors (summary): 400 Bad Request, 401 Unauthorized, 403 Forbidden, 404 Not Found, 405 Method Not Allowed, 429 Too Many Requests, 500 Server Error.

Notes: Zoho is a multi-product platform; base URLs and exact header schemes can be product- and region-specific. For production-ready dlt integration, map product-specific base_url and exact data selectors from the product's OpenAPI/endpoint docs (e.g., Cliq endpoints return arrays under "channels" or "data"; WorkDrive returns records under "data" per JSON:API).

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