Flatpak Python API Docs | dltHub

Flatpak is a system for building, distributing, and running sandboxed desktop applications; the XDG Desktop Portal exposes host system functionality to sandboxed apps via D-Bus portal interfaces. The REST API base URL is `` and access via D-Bus session/system bus; no HTTP auth..

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 Flatpak data in under 10 minutes.

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What data can I load from Flatpak?

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

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How do I authenticate with the Flatpak API?

The portals are D-Bus interfaces available on the session (and in some cases system) bus under well‑known names such as org.freedesktop.portal.Desktop and org.freedesktop.portal.*. Authorization to call certain methods is controlled by Flatpak sandbox policies and, for system helpers, polkit; there is no HTTP header‑based authentication.

1. Get your credentials

No API credentials to obtain. Access is via D-Bus from a host application or a sandboxed app with appropriate Flatpak permissions; system‑level operations may require polkit authorization configured by the system administrator.

2. Add them to .dlt/secrets.toml

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[sources.flatpak_portal_api_source]
# No HTTP auth required; configure D-Bus connection parameters in your environment or connector config.

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 Flatpak 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 flatpak_portal_api_pipeline.py

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

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Pipeline flatpak_portal_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset flatpak_portal_api_data
The duckdb destination used duckdb:/flatpak_portal_api.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 flatpak_portal_api_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 documents_list and permission_store_list from the Flatpak 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 flatpak_portal_api_source(dbus_client=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "dbus",
                "": dbus_client,
            },
        },
        "resources": [
            {"name": "documents_list", "endpoint": {"path": "org.freedesktop.portal.Documents.List"}},
            {"name": "permission_store_list", "endpoint": {"path": "org.freedesktop.impl.portal.PermissionStore.List"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flatpak_portal_api_pipeline",
        destination="duckdb",
        dataset_name="flatpak_portal_api_data",
    )
    load_info = pipeline.run(flatpak_portal_api_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("flatpak_portal_api_pipeline").dataset()
sessions_df = data.documents_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM flatpak_portal_api_data.documents_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("flatpak_portal_api_pipeline").dataset()
data.documents_list.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 Flatpak 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 / Authorization failures

Portals are accessible via D-Bus; calls from sandboxed apps may be blocked if the Flatpak sandbox manifest does not grant talk access to the portal bus name or if the operation requires host privileges. System‑level methods require polkit authorization; check journalctl and polkit policies. Ensure your process has access to the session bus (DBUS_SESSION_BUS_ADDRESS) and appropriate Flatpak permissions.

Operation cancelled or user interaction required

Many portal methods are asynchronous and emit a Response signal. A Response value of 1 indicates user‑cancelled; 2 indicates other errors. Some operations require showing UI (webflow or auth dialogs); running headless with no‑interaction may cause failures.

Not available inside sandbox / permission store quirks

Some methods (e.g., Documents.List, Documents.Info) are not available inside the sandbox and are intended to be called on the host. The permission store uses flexible tables and may return dictionary structures (a{say}); map these GVariant types properly when converting to records.

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