AI Agent Evaluation Framework

A comprehensive framework for evaluating AI agent performance across multiple dimensions, including semantic accuracy, bias, toxicity, and reasoning quality.

Overview

This framework enables systematic and scalable evaluation of AI agents, focusing on response quality, factual correctness, ethical concerns, and reasoning capabilities. It provides robust metrics, visualization tools, and automated reporting for research, product development, and risk monitoring.

Features

• Multi-dimensional evaluation metrics
• Semantic similarity and embedding-based analysis
• Bias and toxicity detection (threshold-configurable)
• Factual accuracy and hallucination checks
• Reasoning quality evaluation
• Interactive performance visualization dashboard
• Automated reporting and trend analysis
• Parallel and adaptive evaluation for large datasets
• Custom scoring via LLM-based judges (e.g., GPT-4)

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/happybear-21/ai-agent-evaluation-framework
cd ai-agent-evaluation-framework
pip install -r requirements.txt

Evaluation Metrics

The framework evaluates AI responses across multiple dimensions:

• Semantic Similarity
• Factual Accuracy
• Hallucination Detection
• Bias and Toxicity Assessment
• Reasoning Quality
• Relevance to Input
• Instruction Following
• Creativity Assessment
• Consistency Evaluation

Configuration

The evaluator accepts custom configuration parameters:

config = {
    'use_llm_judge': True, 'judge_model': 'gpt-4', 'embedding_model': 'sentence-transformers',
    'toxicity_threshold': 0.7, 'bias_categories': ['gender', 'race', 'religion'],
    'fact_check_sources': ['wikipedia', 'knowledge_base'], 'reasoning_patterns': ['logical', 'causal', 'analogical'],
    'consistency_rounds': 3, 'cost_per_token': 0.00002, 'parallel_workers': 8,
    'confidence_level': 0.95, 'adaptive_sampling': True, 'metric_weights': {
        'semantic_similarity': 0.15, 'hallucination_score': 0.15, 'toxicity_score': 0.1,
        'bias_score': 0.1, 'factual_accuracy': 0.15, 'reasoning_quality': 0.15,
        'response_relevance': 0.1, 'instruction_following': 0.1
    }
}

evaluator = AdvancedAIEvaluator(agent_func, config=config)

Output

The evaluation framework provides:

1. Comprehensive evaluation reports
2. Performance visualizations
3. Risk assessments
4. Trend analysis
5. Correlation matrices
6. Statistical confidence intervals