SYSTEM ACTIVE
Home›Agents›Adala

Adala

HumanSignal

76·Strong

Overall Trust Score

Autonomous data labeling agent framework for creating self-improving AI systems. Combines LLMs with ground truth learning to automate and improve data annotation tasks, enabling continuous learning loops.

data-labeling
open-source
Version: 0.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

77
data labeling accuracy
82
Methodology
Labeling accuracy testing
Evidence
Adala Documentation
Autonomous agents learn from ground truth to improve labeling
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09
self improvement
80
Methodology
Learning capability testing
Evidence
Learning Loop
Agents improve through feedback and learning cycles
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
skill acquisition
78
Methodology
Skill capability assessment
Evidence
Skills System
Modular skills for classification, NER, summarization
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
batch processing
76
Methodology
Batch processing testing
Evidence
Architecture
Designed for batch data processing workflows
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
ground truth learning
84
Methodology
Learning effectiveness testing
Evidence
Learning Mechanism
Uses ground truth data to refine agent performance
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
latency
Value: Variable (batch processing)
Methodology
Performance monitoring
Evidence
Performance
Optimized for batch workflows, not real-time
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

73
data handling
75
Methodology
Data security review
Evidence
Data Pipeline
Handles sensitive labeling data, requires secure setup
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
self hosting
85
Methodology
Deployment security assessment
Evidence
Deployment
Python framework, full self-hosting control
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source
88
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0 license, 1k+ stars, transparent code
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
llm security
68
Methodology
LLM security assessment
Evidence
LLM Integration
Security depends on configured LLM provider
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
access control
65
Methodology
Access control assessment
Evidence
Framework Design
Basic framework, access control user-implemented
Date: 2024-09-15
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

76
data privacy
78
Methodology
Privacy architecture review
Evidence
Data Processing
Labeling data processed locally or sent to LLM provider
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
gdpr compliance
75
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted
GDPR compliance possible with proper configuration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment
88
Methodology
Deployment options assessment
Evidence
Installation
Python package, full local deployment supported
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
training data privacy
72
Methodology
Training data privacy assessment
Evidence
Learning System
Ground truth data used for training, privacy considerations
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
llm data sharing
70
Methodology
Data flow analysis
Evidence
LLM Integration
Labeling data sent to configured LLM provider
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

79
documentation quality
78
Methodology
Documentation completeness review
Evidence
Documentation
Good README and examples, documentation growing
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09
learning transparency
82
Methodology
Transparency assessment
Evidence
Learning Metrics
Agent learning progress trackable through metrics
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source
88
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0, developed by HumanSignal (Label Studio team)
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
skill visibility
76
Methodology
Explainability assessment
Evidence
Skills Framework
Skill definitions and improvements visible
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
community support
72
Methodology
Community engagement analysis
Evidence
Community
Growing community, backed by Label Studio team
Date: 2024-10-10
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

75
ease of integration
80
Methodology
Integration complexity assessment
Evidence
Python Package
Simple pip install, Python API
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
label studio integration
85
Methodology
Integration assessment
Evidence
Label Studio
Native integration with Label Studio for labeling workflows
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
72
Methodology
Scalability testing
Evidence
Architecture
Batch processing design, scalability requires infrastructure
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
88
Methodology
Pricing model analysis
Evidence
Pricing
Free Apache 2.0, costs only for LLM API usage
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
70
Methodology
Monitoring features assessment
Evidence
Metrics
Learning metrics available, limited production monitoring
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
70
Methodology
Production readiness assessment
Evidence
Maturity
Active development, production use requires careful setup
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Specialized for autonomous data labeling with self-improvement
  • •Ground truth learning enables continuous agent refinement
  • •Open source (Apache 2.0) from trusted HumanSignal team
  • •Native integration with Label Studio annotation platform
  • •Modular skills system for classification, NER, summarization
  • •Designed specifically for data annotation workflows

⚠️ Limitations

  • •Narrow focus on data labeling, not general-purpose agents
  • •Requires ground truth data for effective learning
  • •Smaller community and ecosystem than general frameworks
  • •Limited production features and documentation
  • •Best suited for batch processing, not real-time inference
  • •Requires expertise in data labeling workflows

📊 Metadata

license: Apache 2.0
supported models:
0: OpenAI
1: Anthropic
2: Custom LLMs
programming languages:
0: Python
deployment type: Self-hosted Python library
tool support:
0: Classification
1: NER
2: Summarization
3: Custom skills
pricing model: Free open source
github stars: 1289+
first release: 2024
parent project: HumanSignal (Label Studio)
use case focus: Autonomous data labeling and annotation
pricing: Free (Apache-2.0 license)
updated: November 6, 2025

Use Case Ratings

customer support

76

Good for training support classification agents

code generation

68

Limited applicability to code generation

research assistant

80

Good for learning to summarize research documents

data analysis

88

Excellent for autonomous data labeling and classification

content creation

74

Can train content classification agents

education

78

Can build self-improving educational content classifiers

healthcare

83

Good for medical text classification and NER tasks

financial analysis

81

Useful for document classification in compliance workflows

legal compliance

85

Excellent for legal document classification and entity extraction

creative writing

65

Limited applicability to creative tasks