Freshteam Python API Docs | dltHub

Freshteam is a cloud HR platform providing employee, recruitment, onboarding, time‑off and organisation data via a REST API. The REST API base URL is https://{company}.freshteam.com/api and All requests require a Bearer token for authentication..

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 Freshteam data in under 10 minutes.

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What data can I load from Freshteam?

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

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How do I authenticate with the Freshteam API?

Freshteam uses an API access token placed in the Authorization header as: Authorization: Bearer <API_KEY>. Also include Accept: application/json.

1. Get your credentials

1. Sign in to your Freshteam account (yourcompany.freshteam.com). 2) Click the profile icon and open “API Settings”. 3) Create or copy the access token shown as “Your API Key”. 4) Use that token in the Authorization header as Bearer for API calls.

2. Add them to .dlt/secrets.toml

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[sources.freshteam_source]
api_key = "your_freshteam_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 Freshteam 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 freshteam_pipeline.py

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

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Pipeline freshteam_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshteam_data
The duckdb destination used duckdb:/freshteam.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 freshteam_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 employees and job_postings from the Freshteam 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 freshteam_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{company}.freshteam.com/api",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "api/employees"}},
            {"name": "job_postings", "endpoint": {"path": "api/job_postings"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshteam_pipeline",
        destination="duckdb",
        dataset_name="freshteam_data",
    )
    load_info = pipeline.run(freshteam_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("freshteam_pipeline").dataset()
sessions_df = data.employees.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshteam_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshteam_pipeline").dataset()
data.employees.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 Freshteam 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 get 401 Unauthenticated or error code invalid_credentials, verify the Authorization header is exactly: Authorization: Bearer <API_KEY>. Ensure the token copied from API Settings matches the Freshteam subdomain you are calling. Include Accept: application/json.

Rate limits

Freshteam enforces per‑minute rate limits depending on plan (Trial 10/min; Growth/Pro up to 50 /min; Enterprise up to 100 /min). Watch response headers x‑ratelimit‑total, x‑ratelimit‑remaining and x‑ratelimit‑used‑currentrequest. On 429 Too Many Requests, back off and retry after the delay indicated.

Pagination

Listing endpoints are paginated with query parameters page (default 1) and a default page size of 50. Pagination metadata is returned in headers total-objects, total-pages and a link header with the next‑page URL. Use updated_since where available (e.g., employees) for incremental loads.

Common error responses

Freshteam returns standard HTTP status codes and a JSON errors array with objects {code, message, field}. Typical codes include invalid_credentials (401), forbidden (403), not_found (404), invalid_value (400), duplicate_value (400), method_not_allowed (405), invalid_accept_header (406), invalid_content_type (415), internal_server_error (500).

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