Greenhouse Python API Docs | dltHub

Greenhouse is a recruiting platform offering the Harvest REST API to programmatically access recruiting data (jobs, candidates, applications, offers, users, etc.). The REST API base URL is https://harvest.greenhouse.io/v1 and All requests require HTTP Basic auth where the username is the Harvest API token and the password is blank..

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 Greenhouse data in under 10 minutes.

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What data can I load from Greenhouse?

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

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How do I authenticate with the Greenhouse API?

Harvest uses HTTP Basic authentication over HTTPS. Use your Harvest API token as the username and an empty password; include an Authorization: Basic <base64(token:)> header on every request.

1. Get your credentials

1. Log into Greenhouse as a user with Developer permission "Can manage ALL organization's API Credentials".
2. Go to Configure → Dev Center → API Credential Management.
3. Create a new Harvest API Key; assign only the endpoint permissions required.
4. Copy the generated API token (treat it as a secret).

2. Add them to .dlt/secrets.toml

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[sources.greenhouse_harvest_source]
api_token = "your_harvest_api_token_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 Greenhouse 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 greenhouse_harvest_pipeline.py

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

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Pipeline greenhouse_harvest_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset greenhouse_harvest_data
The duckdb destination used duckdb:/greenhouse_harvest.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 greenhouse_harvest_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 candidates and jobs from the Greenhouse 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 greenhouse_harvest_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://harvest.greenhouse.io/v1",
            "auth": {
                "type": "http_basic",
                "username": api_token,
            },
        },
        "resources": [
            {"name": "candidates", "endpoint": {"path": "v1/candidates"}},
            {"name": "jobs", "endpoint": {"path": "v1/jobs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="greenhouse_harvest_pipeline",
        destination="duckdb",
        dataset_name="greenhouse_harvest_data",
    )
    load_info = pipeline.run(greenhouse_harvest_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("greenhouse_harvest_pipeline").dataset()
sessions_df = data.candidates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM greenhouse_harvest_data.candidates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("greenhouse_harvest_pipeline").dataset()
data.candidates.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 Greenhouse 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 Authorization header is Basic base64(:). Requests over HTTP (not HTTPS) will return 403. Invalid or unauthorized tokens return 401 or 403. Verify the token has permission for the endpoint in Dev Center.

Rate limiting / throttling

Harvest applies rate limits; when exceeded the API returns 429. Respect the Link pagination headers and avoid excessive parallel requests.

Pagination

Most list endpoints are paginated and return an RFC-5988 Link header with rel="next" (and possibly rel="last"). Follow the Link header to retrieve subsequent pages. Some endpoints use a newer next-only paging approach — consult the endpoint's docs.

Common API errors

• 401 Unauthorized — invalid or missing API token.
• 403 Forbidden — request over HTTP or insufficient permissions.
• 404 Not Found — resource not found.
• 422 Unprocessable Entity — invalid parameters.
• 429 Too Many Requests — rate limit exceeded.
• 500 Server Error — transient server issue.

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