# User Memory Evaluation Framework - Complete Implementation

## Overview
A comprehensive evaluation framework for testing AI agent memory systems across three progressive complexity levels. The framework uses realistic US business conversations to evaluate whether agents can store, retrieve, disambiguate, and synthesize information effectively.

## Implementation Status

### ✅ Framework Core (100% Complete)
- **Evaluation Engine**: LLM-as-judge using Kimi K2 or OpenAI
- **Test Case Models**: Full Pydantic models for validation
- **Framework Interface**: Complete API for test case management
- **Interactive CLI**: User-friendly interface for testing
- **Batch Processing**: Automated evaluation of multiple test cases
- **Reporting System**: Comprehensive evaluation reports

### ✅ Test Case Examples (Representative Set)
Created detailed examples demonstrating all three evaluation layers:

#### Layer 1: Basic Recall (10 complete examples)
- Banking, insurance, medical, travel, telecommunications
- Each with 50+ conversation rounds
- Single conversation per test case
- Clear, unambiguous information retrieval

#### Layer 2: Contextual Reasoning (3 complete examples)
- Multiple vehicles requiring disambiguation
- Multiple properties (home vs rental)
- Multiple credit cards with different features
- Each with 2-3 conversation histories
- Tests ALL information retrieval and disambiguation

#### Layer 3: Cross-Session Synthesis (3 complete examples)  
- International travel with passport expiration issue
- Medical procedure with insurance coordination
- Home purchase with timeline dependencies
- Each with 3-4 conversation histories across time
- Tests proactive issue identification

## Key Features Demonstrated

### 1. Realistic Conversations
Every test case includes:
- **50+ conversation rounds** mimicking real phone calls
- **Natural corrections**: "Oh wait, I meant..." 
- **Tangential discussions**: Exploring options not chosen
- **Business processes**: Hold times, transfers, verifications
- **Confusing information**: Similar numbers, multiple references

### 2. Progressive Complexity
- **Layer 1**: Single fact retrieval (account numbers, dates)
- **Layer 2**: Multiple entity disambiguation (which car? which property?)
- **Layer 3**: Temporal synthesis (passport expires before travel!)

### 3. Semantic Evaluation
- No string matching - uses LLM understanding
- Evaluates intent and completeness
- Recognizes partial success
- Provides detailed reasoning

## Usage Examples

### Running Interactive Evaluation
```bash
python main.py --mode interactive

# Menu Options:
# 1. List test cases by category
# 2. View detailed test case
# 3. Submit agent response for evaluation
# 4. Generate evaluation report
```

### Programmatic Testing
```python
from framework import UserMemoryEvaluationFramework

framework = UserMemoryEvaluationFramework()

# Get test case
test_case = framework.get_test_case("layer2_01_multiple_vehicles")

# Show conversation histories to agent
for history in test_case.conversation_histories:
    print(f"Conversation: {history.conversation_id}")
    for message in history.messages:
        print(f"{message.role}: {message.content}")

# Get agent's response
agent_response = your_agent.process(test_case.user_question)

# Evaluate
result = framework.submit_and_evaluate(
    test_case.test_id,
    agent_response
)

print(f"Reward: {result.reward:.3f}")  # Continuous score (0.0-1.0)
print(f"Passed: {result.reward >= 0.6}")  # Pass threshold at 0.6
print(f"Reasoning: {result.reasoning}")
```

### Batch Evaluation
```python
# Prepare responses
agent_responses = {
    "layer1_01_bank_account": "Your account number is 4429853327",
    "layer2_01_multiple_vehicles": "You have a Honda and Tesla. Which car?",
    "layer3_01_travel_coordination": "WARNING: Passport expires too soon!"
}

# Run batch evaluation
results = framework.evaluate_batch(agent_responses)

# Generate report
report = framework.generate_report(results, "evaluation_report.txt")
```

## Test Case Design Principles

### Information Architecture
Each conversation strategically places information:
1. **Key facts** stated clearly but surrounded by noise
2. **Corrections** that override earlier statements  
3. **Options discussed** but not selected
4. **Future considerations** that may become relevant
5. **Technical details** that create confusion

### Realism Factors
- Agent introduces themselves with names
- Security verification processes
- System limitations ("I can't do that but...")
- Price negotiations and discounts discovered
- Schedule checking and availability
- Hold music and transfers mentioned

### Evaluation Criteria Structure
```yaml
evaluation_criteria:
  description: What the agent must accomplish
  required_information:
    - Specific facts that must be retrieved
    - All relevant details for disambiguation
  success_indicators:
    - Signs of proper understanding
    - Evidence of complete retrieval
  failure_indicators:
    - Common mistakes to avoid
    - Signs of incomplete processing
```

## Creating Additional Test Cases

### Template Structure
```yaml
test_id: [layer]_[number]_[descriptor]
category: layer1|layer2|layer3
title: Brief descriptive title
description: What this test evaluates
conversation_histories:
  - conversation_id: unique_id
    timestamp: ISO format
    metadata:
      business: Company name
      department: Department
      call_duration: Duration
    messages:
      # 50+ rounds of conversation
      - role: user|assistant
        content: Message content
user_question: The ambiguous or specific question
evaluation_criteria:
  # Detailed evaluation rules
expected_behavior: Ideal agent response
```

### Conversation Patterns to Include
1. **Opening**: Greeting, agent introduction, purpose
2. **Verification**: Security questions, account lookup
3. **Information Gathering**: Progressive detail collection
4. **Options Exploration**: Multiple choices discussed
5. **Decision Making**: Selecting from options
6. **Corrections**: Changing earlier statements
7. **Tangents**: Related but unselected options
8. **Confirmation**: Repeating key details
9. **Future Planning**: Next steps, follow-ups
10. **Closing**: Summary, reference numbers

## Performance Considerations

### Optimization Tips
- Cache conversation histories for repeated testing
- Use batch evaluation for multiple test cases
- Implement retry logic for API failures
- Consider parallel evaluation for large sets

### Scalability
- Framework handles 100+ test cases efficiently
- Conversations stored as YAML for easy editing
- Modular design allows custom evaluators
- Results export to JSON/CSV for analysis

## Extension Possibilities

### Additional Test Domains
- Healthcare coordination across providers
- Financial planning across accounts
- Educational records and requirements
- Government services and documentation
- E-commerce orders and returns

### Enhanced Evaluation
- Multi-language support
- Industry-specific terminology validation
- Regulatory compliance checking
- Sentiment preservation validation
- Time-sensitive information handling

## Conclusion

This framework provides a robust, scalable solution for evaluating AI agent memory systems using realistic scenarios. The three-layer approach ensures comprehensive testing from basic recall through complex synthesis, while the LLM-as-judge evaluation ensures semantic understanding rather than rigid pattern matching.

The framework is production-ready and can be extended with additional test cases following the established patterns. All conversations are carefully crafted to reflect authentic US business interactions with the complexity, confusion, and corrections that occur in real phone calls.
