Bubble Python API Docs | dltHub

Bubble is a no‑code web application platform that provides a RESTful Data API and Workflow API for programmatic access to app data and workflows. The REST API base URL is https://{your_app}.bubbleapps.io/version-test/api/1.1 and Requests require a Bearer token (admin API token) or a user token from login workflows; some endpoints can be configured for no authentication..

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 Bubble data in under 10 minutes.

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What data can I load from Bubble?

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

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How do I authenticate with the Bubble API?

Include the admin API token in the HTTP header Authorization: Bearer <token>. User tokens obtained from login workflows are used with the same header format.

1. Get your credentials

1. Open your Bubble app editor.
2. Navigate to Settings → API.
3. Enable the Data API and/or Workflow API and ensure "Expose app as an API" is on.
4. Click "Generate a new API token" to create a 32‑character token.
5. Copy the token and store it securely; use it as a Bearer token in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.bubble_source]
api_token = "your_32_char_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 Bubble 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 bubble_pipeline.py

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

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Pipeline bubble_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bubble_data
The duckdb destination used duckdb:/bubble.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 bubble_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 data_types and single_record from the Bubble 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 bubble_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_app}.bubbleapps.io/version-test/api/1.1",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "data_types", "endpoint": {"path": "api/1.1/obj/{data_type}", "data_selector": "response"}},
            {"name": "single_record", "endpoint": {"path": "api/1.1/obj/{data_type}/{id}", "data_selector": "response"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bubble_pipeline",
        destination="duckdb",
        dataset_name="bubble_data",
    )
    load_info = pipeline.run(bubble_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("bubble_pipeline").dataset()
sessions_df = data.data_types.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bubble_data.data_types LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bubble_pipeline").dataset()
data.data_types.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 Bubble 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, verify that the Authorization: Bearer <token> header is present, the token is valid, the Data API is enabled, and the requested data type is exposed.

Pagination and limits

Bubble paginates large result sets. Use the limit and cursor (or page) query parameters. Request additional pages when the response contains a cursor for the next page.

Privacy rules and missing fields

Empty or missing fields often indicate that privacy rules are restricting access. An admin token bypasses privacy rules; user tokens are subject to them.

Rate limits and errors

Bubble may return HTTP 429 when rate limits are exceeded. Retry with exponential backoff on 5xx server errors. Workflow errors return a JSON body with status: "error" and an explanatory message.

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