BigBlueButton Python API Docs | dltHub

BigBlueButton API allows creating, joining, and ending meetings via HTTP requests. Essential parameters include meetingID, attendeePW, and moderatorPW. Use POST requests with URL-encoded data for API calls. The REST API base URL is https://{your-bbb-host}/bigbluebutton/api and Server-to-server shared secret: API calls require a checksum computed with the server's shared secret..

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 BigBlueButton data in under 10 minutes.

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What data can I load from BigBlueButton?

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

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How do I authenticate with the BigBlueButton API?

BigBlueButton uses a shared secret per server. Each API call is an HTTPS GET (or POST for some methods) to https://{host}/bigbluebutton/api/? and must include a checksum parameter (checksum=sha1(<shared_secret>)). Responses are returned as XML, not JSON.

1. Get your credentials

1. On the BigBlueButton server, run: bbb-conf --secret (or check /etc/bigbluebutton/bigbluebutton.properties).
2. Note the URL (server hostname) and the Secret printed.
3. Use that Secret on your backend only; do not expose it to browsers.

2. Add them to .dlt/secrets.toml

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[sources.bigbluebutton_source]
shared_secret = "your_shared_secret_here"
host = "https://your-bbb-host"

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 BigBlueButton 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 bigbluebutton_pipeline.py

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

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Pipeline bigbluebutton_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bigbluebutton_data
The duckdb destination used duckdb:/bigbluebutton.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 bigbluebutton_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 get_meetings and get_recordings from the BigBlueButton 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 bigbluebutton_source(shared_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-bbb-host}/bigbluebutton/api",
            "auth": {
                "type": "api_key",
                "shared_secret": shared_secret,
            },
        },
        "resources": [
            {"name": "get_meetings", "endpoint": {"path": "getMeetings", "data_selector": "meetings.meeting"}},
            {"name": "get_recordings", "endpoint": {"path": "getRecordings", "data_selector": "recordings.recording"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bigbluebutton_pipeline",
        destination="duckdb",
        dataset_name="bigbluebutton_data",
    )
    load_info = pipeline.run(bigbluebutton_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("bigbluebutton_pipeline").dataset()
sessions_df = data.get_meetings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bigbluebutton_data.get_meetings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bigbluebutton_pipeline").dataset()
data.get_meetings.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 BigBlueButton 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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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