Betterworks Python API Docs | dltHub

Betterworks is a performance and OKR management platform providing REST APIs to retrieve and manage users, goals, feedback, recognitions, and admin resources. The REST API base URL is https://app.betterworks.com/api/v1 and All requests require an API token sent in the Authorization header as 'APIToken '..

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 Betterworks data in under 10 minutes.

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What data can I load from Betterworks?

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

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How do I authenticate with the Betterworks API?

Each call must include the API token in the HTTP Authorization header formatted as: Authorization: APIToken <your_token>. All requests must use HTTPS. Tokens are tied to a Betterworks user and inherit that user's permissions.

1. Get your credentials

1. Log into Betterworks as an Admin. 2. Go to Admin → Platform Configuration → Betterworks API. 3. Choose (or create) a user to associate with the key. 4. Click 'Generate Key'. 5. Copy and securely store the displayed API token.

2. Add them to .dlt/secrets.toml

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[sources.betterworks_source]
api_token = "your_betterworks_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 Betterworks 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 betterworks_pipeline.py

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

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Pipeline betterworks_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset betterworks_data
The duckdb destination used duckdb:/betterworks.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 betterworks_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 goals from the Betterworks 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 betterworks_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.betterworks.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users"}},
            {"name": "goals", "endpoint": {"path": "goals"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="betterworks_pipeline",
        destination="duckdb",
        dataset_name="betterworks_data",
    )
    load_info = pipeline.run(betterworks_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("betterworks_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM betterworks_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("betterworks_pipeline").dataset()
data.users.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 Betterworks 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 responses, confirm you are sending the Authorization header exactly as: Authorization: APIToken <your_token>. Ensure the token is active (Admin → Platform Configuration → Betterworks API) and that the token's user has permissions for the requested resource.

Rate limiting and throttling

The public documentation does not publish explicit rate limit values. If you receive 429 responses, implement exponential backoff and retry, and contact Betterworks support with an X-Request-Id header you generated for the failing request.

Pagination

The documentation references pagination for list endpoints. Where present, follow the API's pagination parameters (page, limit, or offset) as documented per-endpoint. If you encounter partial results, check response headers or body for pagination metadata and request subsequent pages.

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