Culture amp Python API Docs | dltHub

Culture Amp is a RESTful platform for accessing organisational people data (employees, surveys, responses, demographics, reviews) for reporting and integrations. The REST API base URL is https://api.cultureamp.com/v1 and All requests require an OAuth 2.0 Bearer token obtained via Client Credentials flow..

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 Culture amp data in under 10 minutes.

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What data can I load from Culture amp?

Here are some of the endpoints you can load from Culture amp:

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How do I authenticate with the Culture amp API?

Obtain a Bearer token via OAuth2 Client Credentials (client_id & client_secret) and include it in the Authorization: Bearer <access_token> header on every request.

1. Get your credentials

1. Log in to Culture Amp as an Administrator.
2. Navigate to Settings ► Account ► API.
3. Click “Create new API credential”.
4. Record the generated client_id and client_secret.
5. Assign the necessary scopes for the data you need.
6. Use client_id and client_secret with the OAuth2 token endpoint to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.culture_amp_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 Culture amp 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 culture_amp_pipeline.py

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

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Pipeline culture_amp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset culture_amp_data
The duckdb destination used duckdb:/culture_amp.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 culture_amp_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 employee_demographics from the Culture amp 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 culture_amp_source(client_id, client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cultureamp.com/v1",
            "auth": {
                "type": "bearer",
                "access_token": client_id, client_secret,
            },
        },
        "resources": [
            {"name": "employees", "endpoint": {"path": "employees"}},
            {"name": "employee_demographics", "endpoint": {"path": "employees/{id}/demographics"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM culture_amp_data.employees LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("culture_amp_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 Culture amp 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

401 Unauthorized or 403 Forbidden: Common reasons are an expired access token (JWT) or missing required scopes. Refresh the token using client credentials and ensure the correct scopes are requested.

Rate limits

429 Too Many Requests: Indicates that usage limits have been exceeded. Consult the Usage Limits documentation and implement exponential backoff or reduce request frequency.

Pagination

Many list endpoints are paginated using a cursor parameter. Pass the returned cursor to retrieve the next page; stop when the cursor is absent.

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