Bonusly Python API Docs | dltHub

Bonusly is a platform for employee recognition and rewards with a RESTful JSON API to manage users, bonuses, analytics, rewards and redemptions. The REST API base URL is https://bonus.ly/api/v1/ and All requests require an API access token (Bearer token recommended) passed in the Authorization header or as access_token query parameter..

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 Bonusly data in under 10 minutes.

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What data can I load from Bonusly?

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

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How do I authenticate with the Bonusly API?

Generate an API access token in the Bonusly account (Create New API Access Token). Send it as Authorization: Bearer (recommended) or as ?access_token= query param. Admin tokens allow admin‑only endpoints; user tokens will be 403 for those.

1. Get your credentials

1. Sign in to your Bonusly account as the desired user (admin for admin tokens).
2. Visit https://bonus.ly/api (or the API keys section in account settings).
3. Click "Create New API Access Token".
4. Copy and securely store the token (the token is shown only once). Regenerate if lost.

2. Add them to .dlt/secrets.toml

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[sources.bonusly_source]
access_token = "your_bonusly_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 Bonusly 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 bonusly_pipeline.py

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

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Pipeline bonusly_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bonusly_data
The duckdb destination used duckdb:/bonusly.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 bonusly_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 bonuses from the Bonusly 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 bonusly_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://bonus.ly/api/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "result"}},
            {"name": "bonuses", "endpoint": {"path": "bonuses", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM bonusly_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bonusly_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 Bonusly 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 the token is missing/invalid you will receive 401 or 403 errors. Use Authorization: Bearer <token> (recommended). Normal user tokens lack admin privileges and will return 403 for admin‑only endpoints.

Rate limiting and HTTP errors

The API may return 429 for rate limits; implement retries with backoff. Standard HTTP codes used: 400 for bad requests, 401/403 for auth/permission errors, 404 for not found, 422 for validation errors, 500 for server errors.

Pagination

List endpoints return paginated results; check query params (page, per_page or similar) in the docs and iterate until no more results. Responses are wrapped under the top‑level result key.

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