Freshsales Python API Docs | dltHub

Freshsales is a CRM platform for managing leads, contacts, deals and sales activities via a REST API. The REST API base URL is https://{domain}.myfreshworks.com/crm/sales/api and Requests accept either an API key (Token) header or OAuth access token (Bearer)..

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 Freshsales data in under 10 minutes.

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What data can I load from Freshsales?

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

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How do I authenticate with the Freshsales API?

The API supports API‑key authentication passed in the Authorization header as "Authorization: Token token=<api_key>". OAuth 2.0 access tokens are sent as "Authorization: Bearer <access_token>".

1. Get your credentials

1. Sign in to your Freshworks CRM account at your org domain (e.g. widgetz.myfreshworks.com). 2) Click your profile picture → Profile Settings → API Settings. 3) Copy the displayed API key. 4) For OAuth apps, create an app in the Freshworks Developer console, note the client_id and client_secret, and follow the OAuth authorization_code flow to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.freshsales_source]
api_key = "your_api_key_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 Freshsales 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 freshsales_pipeline.py

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

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Pipeline freshsales_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshsales_data
The duckdb destination used duckdb:/freshsales.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 freshsales_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 contacts and deals from the Freshsales 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 freshsales_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{domain}.myfreshworks.com/crm/sales/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts"}},
            {"name": "deals", "endpoint": {"path": "deals", "data_selector": "deals"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshsales_pipeline",
        destination="duckdb",
        dataset_name="freshsales_data",
    )
    load_info = pipeline.run(freshsales_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("freshsales_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshsales_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshsales_pipeline").dataset()
data.contacts.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 Freshsales 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

Ensure the Authorization header is present. For API‑key auth provide: Authorization: Token token=<api_key>. A 401 response indicates a missing or incorrect header.

Rate limits

Freshsales enforces a per‑account limit of 1000 requests per hour. Exceeding this returns HTTP 429 (Too many requests).

Pagination

List endpoints are paginated. Use the page query parameter (starting at 1) and per_page to control page size (default per_page = 25). Some list endpoints include a top‑level key containing the records (e.g. "contacts", "deals", "filters").

Common error responses

The API returns standard HTTP status codes. Error bodies follow:

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{ "errors": { "code": "<status>", "message": "<description>" } }

Examples: 400 (validation), 401 (auth), 403 (access denied), 404 (not found), 429 (rate limit).

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