Implement comprehensive LLMOps (evaluating, testing, and monitoring) with Opik integration

[Copilot]

This PR implements a complete LLMOps solution for Browser.AI that provides evaluation, testing, and monitoring capabilities using Opik integration. The implementation works alongside the existing LMNR observability infrastructure to provide comprehensive insights into agent performance and task execution quality.

Overview

The new LLMOps framework addresses three core areas:

• Evaluation: Automatic assessment of task completion quality and LLM performance
• Testing: Comprehensive test suite framework for browser automation workflows
• Monitoring: Real-time metrics tracking and performance analysis

Key Features

🔍 Automatic Evaluation

• Task completion scoring with customizable success criteria
• Step efficiency evaluation based on execution time and success rates
• Content extraction quality assessment
• Custom evaluation function support

🧪 Comprehensive Testing Framework

• Multi-scenario test suite management with JSON configuration
• Automated success/failure detection based on configurable criteria
• Batch testing capabilities with performance comparison
• Detailed reporting with summary statistics and performance metrics

📊 Real-time Monitoring

• Action execution tracking with success rates and timing
• LLM call monitoring (tokens, cost, latency)
• Error rate analysis and performance bottleneck identification
• Dual observability support (existing LMNR + new Opik)

Implementation Details

Core Components

browser_ai/llmops/opik_integration.py

• OpikConfig: Configuration management for Opik integration
• OpikTracer: Execution tracing and span logging
• OpikEvaluator: Task and step evaluation with scoring
• OpikMonitor: Real-time performance monitoring
• OpikLLMOps: Main integration class with decorator support

browser_ai/llmops/test_framework.py

• BrowserAITestSuite: Test suite management and execution
• TestScenario: Test case definition with success criteria
• TestResult: Detailed outcome tracking and analysis

Agent Integration

The Agent class now supports Opik configuration through new parameters:

agent = Agent(
    task="Go to Google and search for 'OpenAI'",
    llm=llm,
    opik_config=OpikConfig(project_name="my-project", enabled=True),
    enable_opik_llmops=True
)

Automatic tracing is applied to the run() and step() methods when Opik is enabled, providing zero-configuration observability.

Usage Example

from browser_ai.llmops import BrowserAITestSuite, TestScenario, OpikConfig

# Create test suite with Opik monitoring
test_suite = BrowserAITestSuite(
    opik_config=OpikConfig(project_name="browser-ai-tests"),
    results_dir="./test_results"
)

# Add test scenarios
test_suite.add_scenario(TestScenario(
    name="google_search",
    task_description="Go to Google and search for 'Browser.AI'",
    success_criteria=["browser", "ai", "search"],
    max_steps=10
))

# Run tests and generate reports
results = await test_suite.run_all_scenarios(agent_factory)
test_suite.print_report(results)

Backward Compatibility

The implementation maintains full backward compatibility:

• Existing LMNR observability continues to work unchanged
• Opik integration is opt-in via configuration parameters
• All existing Agent and Controller functionality preserved
• No changes to public APIs or method signatures

Documentation and Examples

• docs/llmops-opik-integration.md: Comprehensive usage guide with examples
• examples/llmops_demo.py: Complete demo showcasing all features
• examples/test_scenarios.json: Sample test scenarios for various use cases
• test_opik_integration.py: Integration tests ensuring functionality

Testing

All new components include comprehensive tests:

• ✅ OpikConfig creation and configuration
• ✅ OpikLLMOps tracing and evaluation functionality
• ✅ BrowserAITestSuite scenario management
• ✅ Evaluation scoring with mock data
• ✅ Disabled configuration behavior

The integration provides powerful LLMOps capabilities while maintaining the simplicity and reliability of the existing Browser.AI framework.

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