Zoho CRM Python API Docs | dltHub

Zoho CRM is a cloud customer relationship management platform exposing REST APIs to manage CRM records and metadata. The REST API base URL is https://www.zohoapis.com/crm/v2 and All requests require OAuth2 Bearer access tokens..

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 CRM data in under 10 minutes.

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What data can I load from Zoho CRM?

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

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How do I authenticate with the Zoho CRM API?

Use OAuth2 to obtain access and refresh tokens via Zoho Accounts and include an "Authorization: Bearer <access_token>" header in each request.

1. Get your credentials

1. Log in to the Zoho API Console and create a new client.
2. Note the generated client_id and client_secret.
3. Build the authorization URL: https://accounts.zoho.com/oauth/v2/auth?client_id=<client_id>&scope=ZohoCRM.modules.ALL&response_type=code&redirect_uri=<redirect_uri>.
4. Open the URL, grant access, and capture the authorization code.
5. Exchange the code for tokens via POST to https://accounts.zoho.com/oauth/v2/token with client_id, client_secret, redirect_uri, code, and grant_type=authorization_code.
6. Store the returned access_token and refresh_token for API calls; refresh the access token when it expires using the refresh_token.

2. Add them to .dlt/secrets.toml

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

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 CRM 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_crm_pipeline.py

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

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Pipeline zoho_crm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zoho_crm_data
The duckdb destination used duckdb:/zoho_crm.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_crm_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 leads and contacts from the Zoho CRM 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_crm_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.zohoapis.com/crm/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "crm/v2/Leads", "data_selector": "data"}},
            {"name": "contacts", "endpoint": {"path": "crm/v2/Contacts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoho_crm_pipeline",
        destination="duckdb",
        dataset_name="zoho_crm_data",
    )
    load_info = pipeline.run(zoho_crm_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_crm_pipeline").dataset()
sessions_df = data.leads.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zoho_crm_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zoho_crm_pipeline").dataset()
data.leads.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 CRM 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)

• Cause: Missing, expired, or invalid access token.
• Remedy: Regenerate the access token using the refresh token or repeat the OAuth2 grant flow.

Rate limits (429)

• Cause: Exceeding the allowed number of requests per minute.
• Header: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset.
• Remedy: Honor the Retry-After header or implement exponential backoff.

Invalid parameters (400)

• Cause: Incorrect query syntax, unsupported fields, or malformed IDs.
• Remedy: Check the error code and message in the response body and adjust the request accordingly.

Pagination

• Zoho returns an info object with more_records (boolean) and page number.
• Use the page query parameter and continue fetching while info.more_records is true.

Token expiry

• Access tokens are short‑lived; when a 401 with invalid_token occurs, use the stored refresh token to obtain a new access token via the token 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