Freshcaller Python API Docs | dltHub

Freshcaller is a cloud‑based call centre platform that exposes REST APIs to access and manage calls, users, teams, call metrics, recordings and export jobs. The REST API base URL is https://{account_domain}.freshcaller.com/api/v1 and all requests require an X-Api-Auth API key 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 Freshcaller data in under 10 minutes.

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What data can I load from Freshcaller?

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

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How do I authenticate with the Freshcaller API?

Freshcaller uses an API key (access‑token) obtained from a user’s Profile Settings. Include this value in the X‑Api‑Auth request header for all API calls and set Accept: application/json.

1. Get your credentials

1. Sign in to your Freshcaller account at your account domain (https://{account_domain}.freshcaller.com). 2) Click your profile icon (top right) → Profile Settings. 3) Copy the API key shown under "YOUR API KEY". 4) Use this key as the value of the X-Api-Auth header in API requests.

2. Add them to .dlt/secrets.toml

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[sources.freshcaller_source]
api_key = "your_freshcaller_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 Freshcaller 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 freshcaller_pipeline.py

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

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Pipeline freshcaller_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshcaller_data
The duckdb destination used duckdb:/freshcaller.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 freshcaller_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 users and calls from the Freshcaller 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 freshcaller_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{account_domain}.freshcaller.com/api/v1",
            "auth": {
                "type": "http_header",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "api/v1/users"}},
            {"name": "calls", "endpoint": {"path": "api/v1/calls"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshcaller_pipeline",
        destination="duckdb",
        dataset_name="freshcaller_data",
    )
    load_info = pipeline.run(freshcaller_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("freshcaller_pipeline").dataset()
sessions_df = data.calls.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshcaller_data.calls LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshcaller_pipeline").dataset()
data.calls.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 Freshcaller 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 401/403, verify X-Api-Auth header contains the API key from Profile Settings and that you are using the correct account domain (https://{account_domain}.freshcaller.com). Ensure Accept: application/json is set.

Rate limits

Freshcaller enforces plan‑based per‑minute rate limits (Blossom=100, Garden=200, Estate=400). Monitor response headers: X-RateLimit-Total, X-RateLimit-Remaining and back off on 429 responses.

Pagination

List endpoints paginate results; use query params per_page (default 10, max 1000) and page. Responses include a meta object with total_count, total_pages, current. Many list endpoints return results as a top‑level array — iterate pages until exhausted.

Common error responses

Standard HTTP codes returned: 200, 201, 302, 401, 403, 404, 422, 423, 429, 500. Error bodies include {"error_type":..., "message":..., "errors":[...]}. For model validation failures inspect the errors array for attribute‑level problems.

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