Adala is an autonomous data labeling agent framework designed for AI engineers, researchers, and data scientists. It enables the creation of specialized agents that can independently acquire and refine skills through iterative learning, leveraging LLMs as their runtime environment, to streamline diverse data processing and labeling tasks.

How It Works

Adala agents learn by interacting with a defined environment, influenced by observations and reflections. This process is grounded in user-provided "ground truth" data, ensuring reliable and controllable outputs. Agents can be customized with specific constraints and instructions, and the framework supports flexible runtime environments, allowing skills to be deployed across different LLMs, including OpenAI models and those accessible via OpenRouter.ai.

Quick Start & Requirements

• Install via pip: pip install git+https://github.com/HumanSignal/Adala.git
• Requires an OpenAI API key (or OpenRouter API key for alternative models).
• Example usage available in Python notebooks.
• Official quickstart example: [link to quickstart example]

Highlighted Details

• Autonomous learning agents that iteratively develop skills.
• Controllable output with configurable constraints.
• Supports multiple LLM runtimes (OpenAI, OpenRouter.ai for Claude, Gemini, etc.).
• Designed for customization and extensibility.
• Includes specialized skills like Classification, Summarization, and Question Answering.

Maintenance & Community

• Active development with frequent updates.
• Community support available via Discord: [link to Discord]
• Roadmap available, with features like multi-task learning and a REST API planned.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

• The primary dependency is on OpenAI or compatible LLMs, requiring API key setup and incurring associated costs.
• The README does not specify a license, which may impact commercial use or integration into closed-source projects.