Adala
Autonomous DAta (Labeling) Agent framework
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for Adala, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
Adala is an open-source framework designed to automate data labeling and other data processing tasks using autonomous AI agents. These agents leverage large language models (LLMs) to perform tasks such as data classification, summarization, and generation. Adala's agents learn and improve over time by interacting with ground truth datasets and incorporating human feedback, ensuring high reliability and efficiency in data processing workflows.
Key features and capabilities
- Autonomous learning from human feedback, Integration with large language models (LLMs), Dynamic memory for storing and retrieving knowledge, Modular and extensible architecture, Open-source framework
Use cases
- Automated data labeling for machine learning, Data classification and organization, Summarization of large datasets, Data generation for training models, Enhancing data quality and consistency