Overview
Adala is an autonomous data (labeling) agent framework designed to build adaptable data pipelines, autonomous skills, and runtime configurations. It aims to streamline dataset creation and annotation workflows by composing agents and skills that can learn and operate with minimal manual intervention.
Key features
- Autonomous agents and skills for data labeling and dataset management.
- Colab notebooks and example projects demonstrating common workflows.
- Multiple runtime and storage integrations to support end-to-end pipelines.
- Installable via pip and runnable from source; Apache-2.0 licensed.
Use cases
- Automated dataset labeling and management for ML training.
- Rapid prototyping of data processing agents and labeling strategies.
- Building reproducible pipelines for data collection and annotation.
Technical details
Adala provides a modular architecture for composing agents, skills, and runtimes. The project includes example notebooks and usage patterns showing how to create agents, connect to model providers (e.g., OpenAI), and run labeling tasks programmatically.