Beeminder Python API Docs | dltHub

Beeminder is a goal-tracking service with an API to read and manipulate users, goals, and datapoints. The REST API base URL is https://www.beeminder.com/api/v1/ and All requests require authentication via a personal auth_token or OAuth access_token (Bearer header supported)..

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 Beeminder data in under 10 minutes.

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What data can I load from Beeminder?

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

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How do I authenticate with the Beeminder API?

Include auth_token as a query parameter auth_token=YOUR_TOKEN or access_token=YOUR_TOKEN; OAuth tokens may also be sent in Authorization: Bearer header. For personal tokens use query param name auth_token.

1. Get your credentials

1. Log into your Beeminder account at https://www.beeminder.com/. 2. Visit https://www.beeminder.com/api/v1/auth_token.json (or go to Account Settings > Apps & API) to generate/regenerate your personal auth token. 3. For OAuth clients: Register your app at https://www.beeminder.com/apps/new to obtain client_id and client_secret and follow OAuth flow; users will be redirected with access_token and username.

2. Add them to .dlt/secrets.toml

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[sources.beeminder_source]
api_key = "your_personal_auth_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 Beeminder 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 beeminder_pipeline.py

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

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Pipeline beeminder_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset beeminder_data
The duckdb destination used duckdb:/beeminder.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 beeminder_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 goals and datapoints from the Beeminder 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 beeminder_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.beeminder.com/api/v1/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "goals", "endpoint": {"path": "users/me/goals.json"}},
            {"name": "datapoints", "endpoint": {"path": "users/me/goals/{goal_slug}/datapoints.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="beeminder_pipeline",
        destination="duckdb",
        dataset_name="beeminder_data",
    )
    load_info = pipeline.run(beeminder_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("beeminder_pipeline").dataset()
sessions_df = data.datapoints.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM beeminder_data.datapoints LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("beeminder_pipeline").dataset()
data.datapoints.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 Beeminder 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 get 401 Unauthorized, verify you are using the correct parameter name: personal tokens must be passed as auth_token (or placed in Authorization: Bearer for OAuth). Calling the API with access_token vs auth_token swapped is a common mistake. Ensure you call the exact base URL https://www.beeminder.com/api/v1/ and include HTTPS and the www subdomain.

Rate limits and server errors

Check HTTP status codes: 400 (Bad Request), 401 (Unauthorized), 404 (Not Found), 406 (Not Acceptable), 500 (Internal Server Error), 503 (Service Unavailable). When 500/503 occur, retry later. Error bodies typically include a JSON object with an errors key explaining the problem.

Pagination and large result sets

Endpoints returning lists (e.g., datapoints) support pagination using page (1-indexed) and per parameters. If page is omitted the API returns all datapoints unless limited by count parameter. For datapoints you can also use count to limit results.

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