feat: implement evaluation framework for praisonaiagents #976
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- Add comprehensive evaluation framework with minimal client-side code - Implement AccuracyEval with simple similarity and LLM-based scoring - Implement ReliabilityEval for tool usage validation - Implement PerformanceEval for runtime, memory, and token benchmarking - Add EvalSuite for automated test suites with CI/CD integration - Include EvalCriteria for multi-dimensional evaluation scoring - Support statistical reliability with multiple iterations and confidence intervals - Add result export capabilities (JSON, HTML, Markdown) - Integrate with existing Agent, Task, and PraisonAIAgents classes - Ensure backward compatibility with lazy loading - Include comprehensive test suite and usage examples 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <MervinPraison@users.noreply.github.com>
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Summary of Changes
Hello @MervinPraison, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request delivers a robust and extensible evaluation framework for PraisonAI agents, addressing the need for systematic quality assessment. It provides developers with tools to measure and improve agent performance, reliability, and accuracy through configurable tests, statistical analysis, and automation features, ultimately enhancing the overall quality assurance pipeline for agent development.
Highlights
- New Evaluation Framework: Introduced a comprehensive evaluation framework for PraisonAI agents, including core classes like
AccuracyEval,ReliabilityEval,PerformanceEval,EvalSuite,TestCase, andEvalCriteria. - Multi-faceted Evaluation Capabilities: The framework supports diverse evaluation types: accuracy (via simple similarity or LLM-based multi-criteria scoring), reliability (tool usage validation, including order and additional tool tolerance), and performance (benchmarking runtime, memory, token usage, and time to first token).
- Automation and Reporting: Features include statistical reliability with confidence intervals, automated test suites with scheduling and alerts, and flexible result export options (JSON, HTML, Markdown) for continuous integration and quality assurance.
- Backward Compatibility: The new evaluation components are integrated using lazy loading to ensure full backward compatibility with existing PraisonAI agent implementations.
- Example and Test Coverage: A new example file (
example_eval_usage.py) demonstrates the framework's capabilities, and a dedicated test script (test_eval_framework.py) validates its core components.
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Code Review
This pull request introduces a comprehensive evaluation framework for PraisonAI agents, including modules for accuracy, reliability, and performance testing. The implementation is well-structured with clear separation of concerns. I've identified a few areas for improvement, including a high-severity performance issue in report generation, a medium severity bug in result saving, and opportunities to make evaluation thresholds more configurable for better flexibility. Overall, this is a great addition to the library.
| # Run the evaluation | ||
| result = self.run() |
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The generate_report method currently calls self.run() internally. This is highly inefficient, as it will re-run the entire evaluation suite every time a report is generated, which can be very time-consuming and expensive.
The report generation should be decoupled from the test execution. A better approach is to have run() return the results, and then pass those results to generate_report().
I suggest changing the signature of generate_report to accept an EvalSuiteResult object.
def generate_report(
self,
result: EvalSuiteResult,
format: str = "json",
include_graphs: bool = False,
compare_with: Optional[str] = None
) -> str:
"""
Generate a comprehensive evaluation report.
Args:
result: The result object from an EvalSuite run.
format: Report format ("json", "html", "markdown")
include_graphs: Whether to include performance graphs
compare_with: Compare with previous results (e.g., "last_week")
Returns:
Report content or file path
"""
try:
# No longer runs the evaluation, uses the passed-in result object| if hasattr(self, 'verbose') and self.verbose: | ||
| print(f"Results saved to {self.save_results}") |
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The condition hasattr(self, 'verbose') and self.verbose will always evaluate to false because verbose is a parameter of the run method and is not set as an attribute on the class instance. This means the confirmation message for saving results is never printed, which can be confusing for users.
A better approach would be to use the logging module to inform the user that the file has been saved. This is more idiomatic for a library and allows the user to control visibility via their logging configuration.
logger.info(f"Results saved to {self.save_results}")|
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| return { | ||
| 'type': 'accuracy', | ||
| 'passed': result.success and result.score >= 7.0, # Default threshold |
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The passing threshold for accuracy tests is hardcoded to 7.0. This reduces the flexibility of the evaluation suite, as different tests might require different passing criteria.
Consider making this threshold configurable by adding a property to the TestCase dataclass, for example min_accuracy_score: Optional[float] = 7.0. This would allow users to define custom thresholds for each test case.
| 'passed': result.success and result.score >= 7.0, # Default threshold | |
| 'passed': result.success and result.score >= (test_case.min_score if hasattr(test_case, 'min_score') else 7.0), # Default threshold |
| ) | ||
| result = evaluator.run() | ||
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| passed = result.success and result.success_rate >= 80.0 # Default threshold |
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Similar to the accuracy test, the success rate threshold for reliability tests is hardcoded to 80.0. This should be configurable to accommodate different reliability requirements.
I recommend adding a min_success_rate field to the TestCase dataclass to allow users to set this value on a per-test basis.
| passed = result.success and result.success_rate >= 80.0 # Default threshold | |
| passed = result.success and result.success_rate >= (test_case.min_success_rate if hasattr(test_case, 'min_success_rate') else 80.0) # Default threshold |
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Bug: Verbose Mode Not Functional
The _save_results method attempts to use self.verbose to control a print statement, but the verbose parameter is only passed to the run() method and never stored as an instance attribute. This causes the hasattr(self, 'verbose') check to always be False, preventing the "Results saved to..." message from being printed even when verbose mode is intended. The verbose parameter should be stored as an instance attribute or passed directly to _save_results.
src/praisonai-agents/praisonaiagents/eval/accuracy_eval.py#L279-L281
PraisonAI/src/praisonai-agents/praisonaiagents/eval/accuracy_eval.py
Lines 279 to 281 in 3fa2a13
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@claude review this pull request and do a detailed analysis and fix this if the existing code doesn't have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. |
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Claude encountered an error —— View job I'll analyze this and get back to you. |
Implements comprehensive evaluation framework as requested in issue #967
Closes #967
Generated with Claude Code