This is a starter for a Llama Agents. See the documentation for more info.
The backend contains a single workflow that runs LlamaCloud Extraction, given your schema. The frontend exposes an extraction review UI, where you can review and correct extractions.
The starter contains a placeholder MySchema that is used for extraction. See schema.py.
You should customize this schema.py for your use case to modify the extracted data. You can also rename the schema from MySchema to
something more appropriate for your use case. Do a find and replace on "MySchema" to also fix the frontend references.
The frontend has a copy of the schema as a json schema, that it uses to introspect and generate an editing UI. Run uv run export-types to regenerate the frontend json schema.
This is meant to just be a starting place. You can add more workflows, and trigger them from the UI. For example, you could add functionality sync to a downstream data sink to export the corrected data after review. Or you could add a workflow that monitors a data source, and automatically triggers the extraction against the file.
