Load GreytHR data to DuckDB

greytHR is a cloud HRMS platform offering REST APIs to access HR data including employees, attendance, leave, payroll, and documents. Everything needed to build a working GreytHR → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your GreytHR to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from GreytHR to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the GreytHR API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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GreytHR API at a glance

These values come from the GreytHR API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the GreytHR API?

Authentication requires an OAuth2 flow to obtain an access token, which is then passed in the ACCESS-TOKEN header. Additionally, requests must include the x-greythr-domain header specifying the customer's company domain.

1. Get your credentials

1. Log in to your greytHR admin portal as an administrator. 2. Navigate to Settings > My Account > API Users (or My Account > API Users depending on your version). 3. Click Create API User. 4. Enter a Username and a Description for the user. 5. Select the required roles (e.g., Employee API, Salary API) from the Select Roles dropdown/checklist. 6. Click Next. 7. Copy the generated Username and Password immediately, as the password will not be shown again for security reasons. Click Done to save.

2. Add them to .dlt/secrets.toml

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[sources.greythr_source]
access_token = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What GreytHR data can I load into DuckDB?

These are the GreytHR endpoints dlt can load into DuckDB:

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How do I load only new GreytHR records?

The GreytHR API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "employee_list", "endpoint": {
    "path": "employee/v2/employees?page={page}&size={size}",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated GreytHR pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading oauth2/client-token and employees from the GreytHR API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def greythr_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.greythr.com",
            "auth": {"type": "api_key", "api_key": access_token, "name": "ACCESS-TOKEN", "location": "header"},
        },
        "resources": [
            {"name": "employee_list", "endpoint": {"path": "employee/v2/employees?page={page}&size={size}", "data_selector": "content"}},
            {"name": "users_list", "endpoint": {"path": "user/v1/users?page={page}&size={size}", "data_selector": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def load_greythr_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="greythr_pipeline",
        destination="duckdb",
        dataset_name="greythr_data",
    )
    load_info = pipeline.run(greythr_source())
    print(load_info)


if __name__ == "__main__":
    load_greythr_to_duckdb()

Run it with python greythr_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query GreytHR data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("greythr_pipeline").dataset()
df = data.employee_list.df()
print(df.head())

SQL:

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SELECT * FROM greythr_data.employee_list LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the GreytHR to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw GreytHR loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load GreytHR data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack