Adala

What is Adala?

Adala (Agent Difference Agent Labeling Assistant) is an open-source framework developed by Heartex (the team behind the popular software Label Studio). Launched in 2023, Adala was conceived to automate repetitive and complex annotation, labeling, and curation tasks for text data using multiple autonomous AI agents.

How it works

The framework implements specialized, independent LLM agents that perform annotations on raw data. What makes Adala unique is its feedback and learning system: each agent not only generates labels but also evaluates the decisions of other agents (internal auditing). Based on human guidelines and validation datasets, the agents autonomously refine their instructions (prompts) over time to maximize labeling accuracy.

Key features

• Multi-Agent Labeling: Distributes annotation tasks among different agents with specific roles and instructions.
• Adaptive Learning: Agents review their own errors based on validation samples (ground truth) and rewrite their instructions automatically.
• Decision Auditing: Offers complete traceability of the reasoning and decisions made by each agent in the labeling process.
• Open-Source: Provides total freedom to integrate into local pipelines or proprietary clouds without vendor lock-in.

Who is it for

• Machine Learning Engineers and Data Scientists who handle large volumes of raw, unstructured text data (NLP).
• Data Operations Teams (DataOps) looking to reduce the cost and time of manual labeling.
• AI Researchers interested in multi-agent architectures and autonomous prompt refinement processes.

Pricing

Pros and cons

Pros:

• Labeling automation with a high degree of reliability and self-refinement.
• Highly adaptable architecture for different text processing tasks.
• Open-source and free licensing.

Cons:

• Requires own infrastructure to run the LLMs (cost of API tokens or local hardware).
• Steep technical learning curve to configure and initialize agents.

Alternatives to Adala

If you are looking for alternatives for automated data curation and annotation, the main recommended options include Label Studio, Scale AI, Prodigy, and Snorkel Flow.