agent eval: multi-turn & refactoring #22
Conversation
WalkthroughThis update introduces a comprehensive refactor of the agent evaluation framework, shifting from flat, single-turn evaluations to a conversation-based, multi-turn architecture. YAML configuration, data models, core logic, and tests are restructured to support conversation groups, setup/cleanup scripts, and enhanced result aggregation. Extensive validation, error handling, and documentation improvements are included. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant AgentGoalEval
participant DataManager
participant ScriptRunner
participant EvaluationRunner
participant AgentHttpClient
participant ResultsManager
User->>AgentGoalEval: run_evaluation()
AgentGoalEval->>DataManager: get_conversations()
loop for each ConversationGroup
AgentGoalEval->>ScriptRunner: run_setup_script()
alt Setup fails
AgentGoalEval->>AgentGoalEval: Mark all evaluations as ERROR
else Setup succeeds
loop for each Evaluation in ConversationGroup
AgentGoalEval->>EvaluationRunner: run_evaluation()
EvaluationRunner->>AgentHttpClient: query_agent(api_input, conversation_uuid)
AgentHttpClient-->>EvaluationRunner: response
EvaluationRunner-->>AgentGoalEval: EvaluationResult
end
end
AgentGoalEval->>ScriptRunner: run_cleanup_script()
end
AgentGoalEval->>ResultsManager: save_results(result_dir)
ResultsManager-->>AgentGoalEval: summary stats
AgentGoalEval-->>User: print summary
Estimated code review effort🎯 5 (Critical) | ⏱️ ~75+ minutes Possibly related PRs
Poem
✨ Finishing Touches
🧪 Generate unit tests
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
|
@coderabbitai review |
✅ Actions performedReview triggered.
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (9)
lsc_agent_eval/tests/core/utils/test_exceptions.py (1)
28-29: Update test class name to match the exception being tested.The test class name
TestConfigurationErrorshould be updated toTestEvaluationDataErrorto match the exception class that's being tested.Apply this diff to fix the class name:
-class TestConfigurationError: - """Test ConfigurationError.""" +class TestEvaluationDataError: + """Test EvaluationDataError."""lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (2)
28-31: Enhance the docstring with parameter and return value documentation.The docstring should be more descriptive about the method's behavior, parameters, and return value.
def run_script( self, script_path: Union[str, Path], input_text: Optional[str] = None ) -> bool: - """Execute a script and return success status.""" + """Execute a script and return success status. + + Args: + script_path: Path to the script file (string or Path object). + input_text: Optional text to pass as stdin to the script. + + Returns: + bool: True if script executed successfully (exit code 0), False otherwise. + + Raises: + ScriptExecutionError: If script not found, not a file, timeout, or other errors. + """
32-37: Simplify path handling by always calling resolve().The current logic doesn't call
resolve()for absolute Path objects, butresolve()also handles symlinks and path normalization beyond making paths absolute.if isinstance(script_path, str): script_file = Path(script_path).resolve() else: - script_file = ( - script_path.resolve() if not script_path.is_absolute() else script_path - ) + script_file = script_path.resolve()lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py (2)
49-55: Enhance the docstring to document parameters and expected structure.The method signature has changed significantly but the docstring doesn't reflect the new parameters.
def query_agent( self, api_input: dict[str, str], conversation_uuid: Optional[str] = None, timeout: int = 300, ) -> str: - """Query the agent and return response.""" + """Query the agent and return response. + + Args: + api_input: Dictionary containing agent query parameters. Expected keys may include + 'query', 'provider', 'model', etc. depending on agent API requirements. + conversation_uuid: Optional UUID to maintain conversation context across queries. + timeout: Request timeout in seconds (default: 300). + + Returns: + str: The agent's response text (stripped of whitespace). + + Raises: + AgentAPIError: If client not initialized, request fails, or response is invalid. + """
60-63: Consider creating a copy of api_input to avoid side effects.Modifying the input dictionary directly could have unintended side effects if the caller reuses the dictionary.
- # Add conversation_uuid if provided - if conversation_uuid: - api_input["conversation_id"] = conversation_uuid + # Create a copy and add conversation_uuid if provided + request_data = api_input.copy() + if conversation_uuid: + request_data["conversation_id"] = conversation_uuid response = self.client.post( "/v1/query", - json=api_input, + json=request_data, timeout=timeout, )lsc_agent_eval/README.md (2)
126-137: Apply hyphenation for compound adjective.The static analysis correctly identifies a minor grammar improvement.
-Expectation is that, either a third party inference provider access is there or local model inference is already created. The eval framework doesn't handle this. +Expectation is that, either a third-party inference provider access is there or local model inference is already created. The eval framework doesn't handle this.
267-274: Consider requiring both tests and linting in the contribution workflow.The contributing steps only mention running lint checks (step 5) but not running tests. Typically, contributors should run both tests and linting before submitting a PR.
Consider updating step 5 to:
5. Run tests and lint checksOr add a separate step for running tests.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (1)
162-162: Remove duplicate print statement.This print statement duplicates the one at line 123 in
_process_conversation.- print(f"Running {len(evaluations)} evaluations...")lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (1)
67-74: Consider explicit handling of unknown evaluation types.The default case treats any unknown eval_type as "judge-llm", which might hide configuration errors. Consider either:
- Raising an error for unknown types
- Logging a warning when falling back to judge-llm
match data_config.eval_type: case "script": return self._evaluate_script(data_config) case "sub-string": return self._evaluate_substring(data_config, response) - # Default judge-llm + case "judge-llm": + return self._evaluate_judge_llm(data_config, response) case _: - return self._evaluate_judge_llm(data_config, response) + logger.warning( + "Unknown eval_type '%s', falling back to judge-llm", + data_config.eval_type + ) + return self._evaluate_judge_llm(data_config, response)
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (20)
lsc_agent_eval/README.md(7 hunks)lsc_agent_eval/sample_data/agent_goal_eval_example.yaml(1 hunks)lsc_agent_eval/src/lsc_agent_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py(7 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py(14 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py(11 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py(3 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_models.py(6 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py(1 hunks)lsc_agent_eval/tests/core/utils/test_api_client.py(5 hunks)lsc_agent_eval/tests/core/utils/test_exceptions.py(3 hunks)
🧰 Additional context used
🧠 Learnings (20)
📓 Common learnings
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py:112-112
Timestamp: 2025-07-16T10:13:42.924Z
Learning: In the lsc_agent_eval judge LLM evaluation system, the _extract_numeric_result method should only accept numeric values 1 or 0 from judge responses. Boolean keywords like "true", "false", "yes", "no" are not acceptable values for judge LLM responses.
lsc_agent_eval/sample_data/agent_goal_eval_example.yaml (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/__init__.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
lsc_agent_eval/tests/core/utils/test_exceptions.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
lsc_agent_eval/tests/core/utils/test_api_client.py (1)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (1)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
lsc_agent_eval/README.md (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
🧬 Code Graph Analysis (10)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (3)
EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/tests/core/utils/test_exceptions.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (2)
EvaluationDataError(8-9)AgentEvaluationError(4-5)
lsc_agent_eval/tests/core/utils/test_api_client.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py (1)
query_agent(49-84)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentAPIError(12-13)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
ScriptExecutionError(16-17)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (1)
ConversationDataConfig(150-218)
lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentAPIError(12-13)
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (6)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (3)
AgentGoalEval(25-258)run_evaluation(59-94)get_result_summary(253-258)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (3)
mock_script_runner(31-35)mock_agent_client(24-28)mock_judge_manager(38-42)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
get_conversations(163-165)get_eval_count(167-171)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
get_results_stats(89-91)save_results(25-46)
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (3)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (2)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (3)
AgentGoalEvalDataManager(16-171)get_conversations(163-165)get_eval_count(167-171)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (2)
EvaluationDataConfig(78-147)EvaluationResult(221-245)
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
🪛 LanguageTool
lsc_agent_eval/README.md
[uncategorized] ~131-~131: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...M - Setup Expectation is that, either a third party inference provider access is there or l...
(EN_COMPOUND_ADJECTIVE_INTERNAL)
🔇 Additional comments (72)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
8-9: LGTM! Improved exception naming for clarity.The renaming from
ConfigurationErrortoEvaluationDataErroris a good improvement that makes the exception's purpose more specific and clear. The updated docstring accurately reflects its role in evaluation data loading, parsing, and validation errors.lsc_agent_eval/tests/core/utils/test_api_client.py (4)
66-94: LGTM! Test updates align with new API signature.The test updates correctly reflect the change from separate parameters to a dictionary input format, matching the new
query_agentmethod signature that acceptsapi_input: dict[str, str].
95-114: Test coverage for error scenarios updated correctly.The HTTP error test properly uses the new dictionary input format while maintaining the same error verification logic.
115-131: Timeout test correctly updated for new API.The timeout test maintains proper error handling verification while using the updated dictionary input format.
132-151: Missing response field test properly updated.The test correctly verifies error handling for missing response fields using the new dictionary input format.
lsc_agent_eval/tests/core/utils/test_exceptions.py (2)
31-44: Test methods correctly updated for new exception class.The test methods properly verify the creation, inheritance, and behavior of
EvaluationDataError.
105-106: Exception hierarchy test correctly updated.The test properly includes
EvaluationDataErrorin the list of exceptions that should inherit from the base class.lsc_agent_eval/sample_data/agent_goal_eval_example.yaml (3)
1-11: Well-structured conversation group format.The new hierarchical format with conversation groups is much clearer than the previous flat structure. Each conversation group has descriptive metadata that explains its testing purpose.
21-32: Good separation of script management.Moving setup and cleanup scripts to the conversation group level makes sense for the multi-turn evaluation model, where scripts should run once per conversation rather than per individual evaluation.
44-69: Excellent multi-turn conversation test scenarios.The addition of conv5 and conv6 provides great test coverage for conversation retention:
- conv5 tests successful context retention across multiple turns within the same conversation
- conv6 tests that new conversations correctly don't retain context from previous conversations
These scenarios will help validate the multi-turn conversation functionality effectively.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py (1)
3-17: Good improvement to package API organization.Explicitly defining the public API through imports and
__all__is a great practice that:
- Makes the package interface clear and discoverable
- Allows convenient imports from the package level
- Helps tools understand the intended public API
- Includes all the core components users need for agent goal evaluation
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
40-43: Good improvement: Using resolved paths in error messages.Using the resolved
script_filepath in error messages provides clearer debugging information.Also applies to: 77-77, 80-81, 84-85
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py (2)
8-23: LGTM! Correctly implements error result creation.The function properly creates error results with empty response and error message, following the framework's ERROR/FAIL distinction.
26-42: LGTM! Properly implements success/fail result creation.The function correctly creates PASS/FAIL results with no error field, following the framework's result conventions.
lsc_agent_eval/src/lsc_agent_eval/__init__.py (1)
3-27: Good API design: Simplified public interface.The refactored exports create a cleaner public API by:
- Exposing only the main
AgentGoalEvalclass as the primary entry point- Including essential data models needed by users
- Hiding internal implementation details like
ScriptRunner,ResultsManager, etc.This follows good API design principles.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (5)
19-23: Good refactoring: Direct initialization with results.Initializing with a list of results and precomputing statistics is a cleaner, more efficient design.
25-46: Well-implemented save functionality with proper error handling.The method correctly:
- Handles empty results gracefully
- Creates output directory if needed
- Generates unique timestamped filenames
- Provides comprehensive error handling and logging
48-68: LGTM! CSV output includes conversation tracking fields.The method properly includes
conversation_groupandconversation_uuidfields to support multi-turn evaluation tracking.
70-87: Well-structured JSON summary output.The method creates a comprehensive summary with:
- Overall statistics (total, passed, failed, errored, success rate)
- Breakdowns by conversation and evaluation type
- Proper formatting with indentation and UTF-8 encoding
89-91: LGTM! Clean accessor for evaluation statistics.Simple and appropriate getter method for accessing the precomputed statistics.
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (8)
3-14: LGTM!The import changes correctly align with the new conversation-based evaluation architecture.
36-54: LGTM!The
sample_conversationfixture correctly implements the new conversation-based data structure with appropriate evaluation configurations.
56-78: LGTM!The sample results correctly include conversation metadata fields and use more realistic response content.
80-108: LGTM!The test correctly verifies the initialization of all components including the new ScriptRunner dependency injection pattern.
166-216: LGTM!The test correctly validates the conversation-based evaluation flow, including proper mocking of the ResultsManager instance and statistics generation.
217-239: LGTM!The test properly validates the
get_result_summarymethod's success case.
241-261: LGTM!The test correctly validates error handling when attempting to get results before evaluation runs.
263-323: LGTM!The cleanup tests properly validate both successful cleanup and error handling scenarios with appropriate logging.
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (13)
1-18: LGTM!The imports and class setup correctly reflect the new conversation-based data structure and exception handling.
22-44: LGTM!The fixture provides a well-structured example of the conversation-based YAML format with appropriate evaluation types.
47-68: LGTM!Excellent fixture demonstrating multi-turn conversation flow with sequential evaluations.
70-85: LGTM!The test correctly validates successful initialization and data loading with proper assertions for conversation and evaluation counts.
87-136: LGTM!Comprehensive error handling tests covering various failure scenarios with appropriate exception types and messages.
138-204: LGTM!The validation tests thoroughly verify Pydantic's field requirements at both conversation and evaluation levels.
231-282: LGTM!The tests properly validate the mandatory eval_type field and ensure only valid evaluation types are accepted.
284-357: LGTM!Excellent tests validating that each evaluation type has its required fields, preventing configuration errors early.
359-396: LGTM!The test correctly validates the uniqueness constraint for conversation group names.
398-468: LGTM!The tests thoroughly validate data retrieval methods and navigation through the conversation-based structure.
470-497: LGTM!The test properly validates UUID generation and retrieval for conversation tracking.
499-521: LGTM!The test effectively validates multi-turn conversation loading with proper ordering of evaluations.
523-556: LGTM!Excellent integration test validating the complete data loading pipeline with proper cleanup.
lsc_agent_eval/README.md (5)
3-14: LGTM!The updated description and features accurately reflect the enhanced framework capabilities, with important clarification about sub-string matching behavior.
49-120: LGTM!Excellent documentation of the conversation-based configuration with clear field descriptions and comprehensive examples.
139-183: LGTM!Clear and comprehensive usage examples covering both CLI and programmatic usage with well-documented arguments.
185-216: LGTM!Excellent documentation of the evaluation flow with clear explanation of processing order, script execution, and error handling policies.
218-242: LGTM!Comprehensive documentation of output formats including all CSV columns and JSON statistics structure.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (10)
1-50: LGTM!Well-structured test fixtures and helper method for script handling with proper cleanup.
52-84: LGTM!The fixtures properly handle temporary script creation with cleanup tracking and use realistic evaluation queries.
86-104: LGTM!The initialization tests correctly validate the dependency injection pattern for ScriptRunner.
106-170: LGTM!The judge-llm tests comprehensively cover success and failure cases with proper conversation UUID handling.
171-254: LGTM!Comprehensive script evaluation tests with proper cleanup and correct ERROR vs FAIL result handling.
256-299: LGTM!The substring evaluation tests correctly validate the ALL keywords matching requirement.
300-322: LGTM!The test properly validates API error handling with correct ERROR result and error message propagation.
323-334: LGTM!The test correctly validates that Pydantic prevents invalid script configurations at model creation time.
336-369: LGTM!Excellent comprehensive test coverage of substring matching logic including the ALL keywords requirement and case-insensitive matching.
371-399: LGTM!The test thoroughly validates conversation UUID propagation through the evaluation pipeline.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (6)
1-10: LGTM!Good practice defining validation constants for evaluation types and results.
13-75: LGTM!Well-designed helper functions with proper validation, path normalization to absolute paths, and generic statistics calculation.
78-147: LGTM!Excellent Pydantic model with comprehensive validation ensuring data integrity and type-specific requirements.
150-218: LGTM!Well-designed conversation model with UUID generation, comprehensive validation, and proper metadata propagation to evaluations.
221-245: LGTM!The result model correctly captures evaluation outcomes with proper validation and conversation tracking.
248-299: LGTM!Comprehensive statistics model with detailed breakdowns and elegant factory method for creating stats from results.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (4)
68-93: Good validation test coverage!The tests for invalid result type and eval type properly verify that the Pydantic validation catches incorrect enum values.
133-154: Well-structured mocking for file system operations.The use of
mock_open,Path.is_file, andPath.existsmocks properly isolates the tests from the file system while verifying path validation behavior.
246-353: Excellent test coverage for ConversationDataConfig!The test suite thoroughly covers:
- Minimal configuration requirements
- Script path validation with proper mocking
- Edge cases like empty group names and duplicate eval_ids
- UUID generation verification
355-409: Comprehensive statistics testing with proper floating point handling.The test correctly:
- Uses tolerance-based comparison for floating point success_rate
- Verifies aggregation by both conversation and eval_type
- Covers the complete statistics data structure
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
27-59: Excellent error handling and validation!The method properly:
- Resolves to absolute path as per the team's preference
- Provides specific error messages for different failure modes
- Validates data structure before processing
61-105: Well-structured conversation loading with duplicate detection.The implementation correctly:
- Detects duplicate conversation groups before processing
- Provides detailed error messages using Pydantic validation
- Logs comprehensive information for debugging
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (2)
109-121: Proper setup script error handling!The implementation correctly marks all conversation evaluations as ERROR when setup fails, which aligns with the framework's distinction between FAIL (evaluation ran but didn't pass) and ERROR (evaluation couldn't run).
135-154: Appropriate differentiation between setup and cleanup error handling.The code correctly treats setup failures as critical (raising exception) while cleanup failures are non-critical (logging warning), which aligns with the team's pragmatic approach.
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (1)
270-298: Excellent integration test with real file verification!This test provides valuable end-to-end verification by:
- Using real temporary files instead of mocks
- Verifying both CSV and JSON outputs
- Checking file contents and structure
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (1)
92-96: Substring evaluation logic changed to require ALL keywords.The evaluation now passes only when ALL expected keywords are present (logical AND), making it stricter than before. This is a significant behavioral change that should be documented.
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (1)
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (1)
63-74: Consider a cleaner approach for test script cleanup.While the current approach works, storing
_test_script_pathon the config object for cleanup is unconventional. Consider using a pytest fixture with proper teardown or a context manager for cleaner resource management.Example using a fixture:
@pytest.fixture def test_script_path(): """Create and cleanup test script.""" script_path = create_test_script() yield script_path os.unlink(script_path) @pytest.fixture def sample_config_script(test_script_path): """Sample script evaluation configuration.""" return EvaluationDataConfig( eval_id="test_002", eval_query="Deploy nginx", eval_type="script", eval_verify_script=test_script_path, )
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (20)
lsc_agent_eval/README.md(7 hunks)lsc_agent_eval/sample_data/agent_goal_eval_example.yaml(1 hunks)lsc_agent_eval/src/lsc_agent_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py(7 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py(14 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py(11 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py(3 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_models.py(6 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py(1 hunks)lsc_agent_eval/tests/core/utils/test_api_client.py(5 hunks)lsc_agent_eval/tests/core/utils/test_exceptions.py(3 hunks)
✅ Files skipped from review due to trivial changes (1)
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/init.py
🚧 Files skipped from review as they are similar to previous changes (10)
- lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py
- lsc_agent_eval/tests/core/utils/test_api_client.py
- lsc_agent_eval/tests/core/utils/test_exceptions.py
- lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py
- lsc_agent_eval/src/lsc_agent_eval/init.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py
- lsc_agent_eval/README.md
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py
🧰 Additional context used
🧠 Learnings (10)
📓 Common learnings
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/sample_data/agent_goal_eval_example.yaml (5)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (5)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py:112-112
Timestamp: 2025-07-16T10:13:42.924Z
Learning: In the lsc_agent_eval judge LLM evaluation system, the _extract_numeric_result method should only accept numeric values 1 or 0 from judge responses. Boolean keywords like "true", "false", "yes", "no" are not acceptable values for judge LLM responses.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (6)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.064Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
🧬 Code Graph Analysis (3)
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (3)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (2)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (3)
AgentGoalEvalDataManager(16-171)get_conversations(163-165)get_eval_count(167-171)
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (7)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (2)
AgentEvaluationError(4-5)ScriptExecutionError(16-17)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (3)
AgentGoalEvalDataManager(16-171)get_conversations(163-165)get_eval_count(167-171)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (2)
EvaluationRunner(19-141)run_evaluation(33-61)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (3)
ResultsManager(16-91)save_results(25-46)get_results_stats(89-91)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (2)
ScriptRunner(14-82)run_script(28-82)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py (1)
create_error_result(8-23)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (3)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (9)
- GitHub Check: ruff
- GitHub Check: black
- GitHub Check: Pyright
- GitHub Check: pydocstyle
- GitHub Check: mypy
- GitHub Check: tests (3.12)
- GitHub Check: tests (3.13)
- GitHub Check: tests (3.11)
- GitHub Check: Pylinter
🔇 Additional comments (31)
lsc_agent_eval/sample_data/agent_goal_eval_example.yaml (1)
1-69: Well-structured conversation-based evaluation format!The hierarchical YAML structure with conversation groups is clean and intuitive. This organization effectively supports multi-turn evaluation scenarios and aligns well with the PR's objective of introducing multi-turn evaluation capability.
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (3)
36-77: Well-structured test fixtures for conversation-based evaluation!The fixtures properly demonstrate the new conversation-based data model with appropriate metadata fields.
173-216: Comprehensive test for evaluation execution flow!The test effectively validates the conversation-based evaluation flow with proper mocking of the data manager and results statistics.
217-261: Good test coverage for the new result summary functionality!Both success and error cases are properly tested with appropriate assertions.
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (5)
22-68: Well-designed test fixtures demonstrating conversation structures!The fixtures effectively showcase both basic conversation groups and multi-turn conversation scenarios, providing good coverage for the new data model.
70-136: Comprehensive error handling tests!The tests effectively validate initialization success and various error scenarios with appropriate exception checks.
138-357: Thorough validation test coverage!The tests comprehensively validate all required fields, type-specific requirements, and duplicate detection. Good separation of concerns with each test focusing on a specific validation rule.
398-498: Well-structured data retrieval tests!The tests effectively verify all data access patterns, including conversation retrieval, evaluation counting, and UUID access.
499-557: Excellent multi-turn and integration testing!The multi-turn conversation test validates the key feature of this PR, and the integration test with actual file I/O provides confidence in the complete data loading flow.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (4)
86-104: Clean dependency injection testing!The tests properly validate the initialization with required and optional dependencies.
106-227: Comprehensive evaluation execution tests!The tests effectively validate all evaluation types with proper conversation UUID propagation and resource cleanup.
229-335: Excellent error handling test coverage!The tests comprehensively cover error scenarios including script execution failures, API errors, and validation errors.
336-400: Thorough evaluation logic and context propagation testing!The substring evaluation tests cover all edge cases including case-insensitive matching, and the UUID propagation test ensures conversation context is maintained throughout the evaluation flow.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
13-76: Well-designed validation and utility functions!The validation functions provide robust input validation with clear error messages. The script path validation properly handles path normalization and existence checks, aligning with the framework's preference for absolute paths.
78-148: Robust evaluation configuration model with comprehensive validation!The model effectively enforces type-specific requirements and field constraints, preventing invalid configurations at the validation stage rather than runtime.
150-219: Excellent conversation grouping model implementation!The model elegantly handles UUID generation and propagation, enforces unique eval_ids within conversations (aligning with the composite key design), and validates script paths.
From prior learnings, I know that duplicate eval_ids across different conversation groups are allowed (logged as warning), which this model correctly supports.
221-246: Clean result model with proper validation!The model correctly implements the PASS/FAIL/ERROR result types, supporting the framework's distinction between evaluation failures (FAIL) and execution errors (ERROR).
268-300: Consider clarifying success rate calculation semantics.The success rate calculation includes errored evaluations in the denominator. Depending on your requirements, you might want to:
- Keep as-is if errors should count against success rate
- Calculate success rate only from evaluations that ran (passed + failed)
What is the intended behavior for success rate calculation when errors occur? Should errors be included in the success rate denominator?
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (1)
95-99: Document breaking change in substring evaluation logicThe substring evaluation logic has been changed from OR (any keyword match) to AND (all keywords must match). This is a breaking change that could affect existing evaluations.
Please ensure this change is documented in the changelog or migration guide, as existing evaluations expecting OR behavior will now behave differently.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (5)
17-93: LGTM! Good test coverage for validation scenarios.The addition of validation tests for invalid result types and eval types ensures the Pydantic model properly validates input data. The test structure and assertions are well-organized.
133-155: Good use of mocking for file system operations.The test properly mocks file existence checks for script evaluation configuration. The assertions verify that the path validation was called, ensuring the validation logic is exercised.
190-244: Excellent validation test coverage for all evaluation types.The tests comprehensively verify that each evaluation type enforces its specific requirements and that script files must exist. The error message assertions ensure users get clear feedback about validation failures.
246-353: Comprehensive test coverage for conversation configuration.The tests properly validate:
- Conversation group naming requirements
- Script path validation with appropriate mocking
- Duplicate eval_id detection within conversations (aligning with the framework's design that allows duplicate IDs across conversations)
- Empty group name handling
The mocking strategy for file system operations is consistent and well-implemented.
355-409: Well-structured test for evaluation statistics aggregation.The test effectively validates:
- Correct calculation of pass/fail/error counts
- Success rate calculation with appropriate floating-point tolerance
- Statistics breakdown by conversation group
- Statistics breakdown by evaluation type
Good practice using
abs(stats.success_rate - 66.67) < 0.01for floating-point comparison.lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (7)
5-21: Good use of TYPE_CHECKING and proper component initialization.The refactoring properly introduces ScriptRunner as a new component and correctly passes it to EvaluationRunner. Using TYPE_CHECKING for forward references avoids circular imports while maintaining type safety.
Also applies to: 39-41, 55-57
59-95: Well-structured refactoring for conversation-based evaluation.The method now correctly:
- Processes conversations as the primary unit of evaluation
- Provides clear progress information with conversation counts
- Maintains proper error handling and cleanup in the finally block
96-134: Excellent implementation of conversation processing with proper error handling.The method correctly:
- Executes setup scripts before evaluations
- Marks all evaluations as ERROR if setup fails (aligning with the FAIL vs ERROR distinction where ERROR indicates evaluation process issues)
- Always attempts cleanup regardless of evaluation outcomes
- Provides clear error messages for setup failures
135-154: Appropriate error handling strategy for setup vs cleanup scripts.The implementation correctly:
- Raises exceptions for setup failures (preventing invalid evaluations)
- Logs warnings for cleanup failures (non-critical, as per the team's preference noted in learnings)
- Uses descriptive error messages for debugging
155-181: LGTM! Proper conversation context propagation.The method correctly passes conversation_uuid to maintain context across evaluation calls within the same conversation, supporting the multi-turn evaluation capability.
213-234: Good enhancement to summary output with conversation breakdowns.The summary now provides valuable insights by showing success rates per conversation group when multiple conversations are evaluated. This helps identify which conversation groups might be problematic.
252-257: Good defensive programming in get_result_summary.Raising an explicit error when results aren't available prevents confusion and ensures the method is called in the correct sequence.
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
1-1: Consider enhancing the module docstring.The current docstring is generic. Consider expanding it to clarify the module's purpose and context.
-"""Script execution module for evaluation.""" +"""Script execution module for agent goal evaluation. + +Provides functionality to execute setup, cleanup, and verification scripts +as part of the evaluation workflow. +"""
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (21)
lsc_agent_eval/README.md(7 hunks)lsc_agent_eval/sample_data/agent_goal_eval_example.yaml(1 hunks)lsc_agent_eval/src/lsc_agent_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py(7 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py(4 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py(14 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py(11 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py(4 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_models.py(6 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py(7 hunks)lsc_agent_eval/tests/core/utils/test_api_client.py(5 hunks)lsc_agent_eval/tests/core/utils/test_exceptions.py(3 hunks)
✅ Files skipped from review due to trivial changes (1)
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py
🚧 Files skipped from review as they are similar to previous changes (9)
- lsc_agent_eval/sample_data/agent_goal_eval_example.yaml
- lsc_agent_eval/tests/core/utils/test_api_client.py
- lsc_agent_eval/src/lsc_agent_eval/init.py
- lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py
- lsc_agent_eval/tests/core/utils/test_exceptions.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/init.py
- lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py
- lsc_agent_eval/README.md
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py
🧰 Additional context used
🧠 Learnings (12)
📓 Common learnings
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py (2)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (6)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (6)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (4)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
🧬 Code Graph Analysis (6)
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (7)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (1)
sample_results(20-50)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
get_conversations(163-165)get_eval_count(167-171)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (1)
run_evaluation(59-94)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (1)
run_evaluation(33-61)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
get_results_stats(89-91)save_results(25-46)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
ScriptExecutionError(16-17)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (3)
EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (3)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (2)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (3)
AgentGoalEvalDataManager(16-171)get_conversations(163-165)get_eval_count(167-171)
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (1)
ConversationDataConfig(150-218)
🔇 Additional comments (44)
lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py (1)
43-43: Test updates correctly reflect the script execution changes.All subprocess.run mock assertions have been properly updated to expect direct script execution with resolved paths instead of bash invocation. This aligns with the implementation changes in ScriptRunner.
Also applies to: 84-84, 187-187, 217-217, 247-247, 276-276, 303-303
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py (1)
28-76: Script execution implementation is well-structured.The updated implementation properly handles:
- Path resolution for both string and Path inputs
- Direct script execution without shell interpretation
- Clear error messages with resolved paths
- Proper exception handling hierarchy
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (1)
19-91: Results management refactoring is well-implemented.The refactored ResultsManager provides:
- Clean initialization with immediate statistics computation
- Comprehensive CSV export with conversation metadata
- Structured JSON summary with aggregated statistics
- Proper error handling and logging
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (1)
3-323: Test suite properly updated for conversation-based evaluation.The test updates correctly reflect:
- ScriptRunner dependency injection
- ConversationDataConfig-based test data
- New result summary functionality
- Consistent mock patterns throughout
lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py (1)
1-556: Comprehensive test coverage for conversation-based data model.The test suite thoroughly validates:
- Conversation-based YAML structure
- Field validation and error handling
- Multi-turn conversation support
- Data retrieval methods
- Integration with real files
Excellent coverage of edge cases and validation scenarios.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (6)
3-11: LGTM! Clean import structure and well-defined constants.The imports properly support the Pydantic model architecture, and the constants provide clear validation boundaries for evaluation types and results.
13-76: Excellent helper functions with robust validation.The helper functions provide comprehensive validation:
- Script path validation includes existence checks and absolute path conversion (consistent with ScriptRunner requirements)
- Statistics calculation correctly handles PASS/FAIL/ERROR categorization
- Proper error handling with descriptive messages
78-148: Well-designed Pydantic model with comprehensive validation.The
EvaluationDataConfigmodel provides:
- Proper field constraints and validation
- Type-specific requirement validation (judge-llm → expected_response, sub-string → expected_keywords, script → eval_verify_script)
- Keyword processing that handles both strings and lists with proper filtering
The validation logic ensures data integrity while being flexible with input formats.
150-219: Excellent conversation-based model design.The
ConversationDataConfigmodel correctly implements the conversation architecture:
- Automatic UUID generation for conversation tracking
- Proper validation of conversation groups and script paths
- Enforcement of unique eval_ids within conversations (allowing cross-conversation duplicates as designed)
- Automatic assignment of conversation metadata to all evaluations
This aligns perfectly with the composite key design
(conversation_group, eval_id)for evaluation identification.
221-246: Solid result model with proper validation.The
EvaluationResultmodel correctly:
- Validates result values against the defined constants (PASS/FAIL/ERROR)
- Includes conversation metadata for the new architecture
- Maintains consistency with the learned distinction between FAIL (evaluation failed) and ERROR (execution error)
248-299: Comprehensive statistics model with excellent aggregation logic.The
EvaluationStatsmodel provides:
- Complete statistical aggregation with success rates
- Proper conversation counting using unique conversation groups
- Consistent category-based statistics calculation
- Clean class method interface for creating stats from results
The implementation correctly handles the conversation-based architecture while maintaining backward compatibility.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (4)
17-93: Comprehensive test coverage for EvaluationResult model.The tests properly cover:
- Valid model creation with all scenarios
- Validation of result types (PASS/FAIL/ERROR)
- Validation of evaluation types
- Default value handling
- Error message verification
95-244: Excellent test coverage for EvaluationDataConfig validation.The tests comprehensively cover:
- All evaluation types with their specific requirements
- Proper mocking of file system operations for script validation
- Validation error handling for missing required fields
- Path validation including non-existent files
The test structure follows good practices with proper setup and clear assertions.
246-353: Thorough testing of conversation-level validation.The tests effectively cover:
- Conversation configuration with and without scripts
- Proper validation of setup/cleanup script paths
- Error handling for empty group names and non-existent scripts
- Critical validation of unique eval_ids within conversations
This aligns with the learned behavior about composite key identification
(conversation_group, eval_id).
355-409: Solid testing of statistics aggregation logic.The tests properly verify:
- Correct calculation of total evaluations and conversations
- Accurate success rate computation
- Proper breakdown by conversation groups and evaluation types
- Realistic test data with conversation metadata
The test coverage ensures the statistics model works correctly with the conversation-based architecture.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (6)
19-26: Clean initialization supporting conversation-based architecture.The initialization properly:
- Sets up conversation-based data structure
- Calls loading and logging methods in correct order
- Uses appropriate type hints for the new model structure
27-60: Robust YAML loading with comprehensive error handling.The method provides:
- Proper validation that data is a non-empty list of conversations
- Comprehensive exception handling with error chaining
- Clear error messages for different failure scenarios
- Structured logging for debugging
61-106: Excellent use of Pydantic validation with enhanced error handling.The method effectively:
- Leverages Pydantic for automatic validation instead of manual checks
- Prevents duplicate conversation groups for data integrity
- Provides clear error formatting for validation failures
- Maintains proper exception chaining for debugging
This is a significant improvement over manual validation approaches.
107-115: Useful helper for user-friendly error formatting.The method provides clear, structured error messages by properly formatting Pydantic validation errors with field paths and messages.
139-162: Proper implementation of eval_id duplication handling.The logging correctly implements the learned behavior:
- Duplicate eval_ids across conversations are logged as warnings (not errors)
- Within-conversation duplicates are prevented by Pydantic validation
- Comprehensive statistics provide good visibility into loaded data
This aligns with the composite key design
(conversation_group, eval_id)for evaluation identification.
163-172: Well-designed getter methods for conversation-based architecture.The methods provide:
- Clear naming that reflects the conversation-based structure
- Correct calculation of total evaluations across all conversations
- Appropriate return types matching the model structure
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (7)
34-58: Good dependency injection pattern for ScriptRunner.The component setup properly:
- Initializes ScriptRunner with kubeconfig support
- Injects ScriptRunner into EvaluationRunner for script-based evaluations
- Maintains clean separation of concerns between orchestration and execution
59-95: Excellent conversation-based orchestration.The method effectively:
- Processes conversations sequentially with clear progress indicators
- Uses ResultsManager for proper result aggregation and saving
- Maintains comprehensive logging of evaluation progress
- Handles cleanup properly in the finally block
96-134: Proper conversation-level script orchestration.The method correctly:
- Executes setup scripts before evaluations with proper error handling
- Marks all evaluations as ERROR when setup fails (appropriate for environment dependency)
- Ensures cleanup scripts run regardless of setup/evaluation results
- Uses the utility function
create_error_resultfor consistent error handlingThis aligns with the learned preference to focus on core functionality while handling script failures gracefully.
135-154: Appropriate error handling for setup vs cleanup scripts.The methods correctly differentiate:
- Setup script failures raise exceptions (blocking evaluation execution)
- Cleanup script failures log warnings but don't block (non-critical operation)
- Both use ScriptRunner consistently with proper logging
This aligns with the learned approach that cleanup failures should be non-blocking.
155-181: Well-implemented evaluation execution with proper context.The method provides:
- Clear progress tracking with tqdm
- Proper conversation context (UUID) passed to evaluation runner
- Individual result printing with conversation group identification
- Clean iteration over evaluations with result collection
213-243: Enhanced summary with conversation-level statistics.The summary method effectively:
- Leverages EvaluationStats for comprehensive statistics
- Shows conversation breakdowns for multi-conversation scenarios
- Provides clear success rates and detailed counts
- Maintains result summary for programmatic access
252-258: Proper validation in result summary getter.The method correctly validates that evaluation has been run before returning results, preventing misuse of the API.
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (5)
19-73: Well-designed fixtures supporting conversation architecture.The fixtures and tests properly:
- Include conversation metadata required for the new architecture
- Test both populated and empty result scenarios
- Verify EvaluationStats model integration
- Use realistic evaluation data with proper field values
74-110: Comprehensive testing of save functionality.The tests effectively cover:
- Success path with proper mocking of file operations
- Error conditions for both CSV and directory creation failures
- Consistent use of AgentEvaluationError for error handling
- Proper verification of method calls
111-159: Thorough testing of statistics and data conversion.The tests properly verify:
- CSV data conversion includes all conversation fields
- Statistics calculation with proper conversation and eval_type breakdowns
- Accurate success rate computation
- Complete EvaluationStats model validation
160-225: Good coverage of edge cases and mixed scenarios.The tests effectively cover:
- ERROR result handling with proper statistics
- Mixed evaluation types with correct categorization
- Various result combinations ensuring robust statistics calculation
226-299: Excellent integration testing with proper JSON key validation.The tests provide comprehensive coverage:
- JSON statistics structure validation with correct field names
- Filename generation testing with proper datetime mocking
- Integration test with real files verifying complete workflow
- Correct validation of "by_eval_type" key matching the EvaluationStats model
The integration test particularly adds value by verifying the end-to-end file creation and content.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (11)
3-5: LGTM! Imports align with refactored architecture.The addition of
os,tempfile, andMockimports supports the new fixture-based approach and temporary script creation, which aligns well with the enhanced script validation in the Pydantic models.
30-36: Good refactoring from @patch to fixture injection.This change from using
@patchdecorators to fixture injection aligns with the updatedEvaluationRunnerconstructor that now requires an injectedScriptRunnerinstance. This provides better test isolation and makes dependencies explicit.
55-67: Excellent fixture for temporary script creation.This fixture properly handles the lifecycle of temporary executable scripts, which is essential given the stricter script path validation in the Pydantic models. The use of
os.chmod(script_path, 0o755)ensures the script is executable, and cleanup is properly handled.
68-76: Proper integration with temporary script fixture.The
sample_config_scriptfixture correctly uses theget_test_script_pathfixture to provide a valid script path, which aligns with the enhanced validation requirements in the updated data models.
88-98: Constructor test properly reflects dependency injection.The test correctly verifies that the
EvaluationRunnernow accepts injected dependencies including theScriptRunner, which is consistent with the architectural changes mentioned in the AI summary.
120-143: Conversation UUID propagation correctly tested.The test properly verifies that:
- The conversation UUID is passed to
run_evaluation- The agent client receives a dictionary input format with the conversation UUID
- The result contains the conversation UUID
This aligns with the new conversation-based evaluation flow described in the PR objectives.
148-172: Comprehensive failure scenario testing.The test correctly verifies judge-llm evaluation failure when the judge returns "0", maintaining the distinction between FAIL (evaluation ran but failed criteria) and ERROR (execution issues) as learned from previous reviews.
224-238: Proper error handling test for script execution.The test correctly distinguishes between script failure (FAIL) and script execution error (ERROR), which aligns with the learned patterns where ERROR indicates issues executing the evaluation itself, not just failure to meet criteria.
307-319: Smart validation test using Pydantic.This test effectively demonstrates that Pydantic validation prevents invalid configurations (script type without verify script) at the data model level, which is a good defensive programming practice and aligns with the enhanced validation mentioned in the AI summary.
320-353: Thorough substring evaluation logic testing.The test comprehensively covers substring evaluation scenarios:
- All keywords present (PASS)
- Some keywords missing (FAIL)
- No keywords present (FAIL)
- Case insensitive matching (PASS)
This ensures the logical AND requirement for all expected keywords is properly implemented, which aligns with the enhanced keyword matching mentioned in the PR objectives.
355-383: Excellent conversation UUID propagation verification.This test specifically verifies that conversation UUIDs are properly propagated through the evaluation flow and passed to the agent client, which is crucial for the multi-turn evaluation capability introduced in this PR.
There was a problem hiding this comment.
Actionable comments posted: 1
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (21)
lsc_agent_eval/README.md(7 hunks)lsc_agent_eval/sample_data/agent_goal_eval_example.yaml(1 hunks)lsc_agent_eval/src/lsc_agent_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/__init__.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py(7 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py(3 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py(2 hunks)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py(1 hunks)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py(14 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py(11 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py(4 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_models.py(6 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py(1 hunks)lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py(7 hunks)lsc_agent_eval/tests/core/utils/test_api_client.py(5 hunks)lsc_agent_eval/tests/core/utils/test_exceptions.py(3 hunks)
✅ Files skipped from review due to trivial changes (1)
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/utils.py
🚧 Files skipped from review as they are similar to previous changes (10)
- lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py
- lsc_agent_eval/tests/core/utils/test_api_client.py
- lsc_agent_eval/sample_data/agent_goal_eval_example.yaml
- lsc_agent_eval/src/lsc_agent_eval/core/utils/api_client.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/init.py
- lsc_agent_eval/src/lsc_agent_eval/init.py
- lsc_agent_eval/tests/core/utils/test_exceptions.py
- lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py
- lsc_agent_eval/tests/core/agent_goal_eval/test_eval_data.py
- lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/script_runner.py
🧰 Additional context used
🧠 Learnings (11)
📓 Common learnings
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: lightspeed-core/lightspeed-evaluation#19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (4)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/README.md (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (4)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (6)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (6)
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (8)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py:112-112
Timestamp: 2025-07-16T10:13:42.924Z
Learning: In the lsc_agent_eval judge LLM evaluation system, the _extract_numeric_result method should only accept numeric values 1 or 0 from judge responses. Boolean keywords like "true", "false", "yes", "no" are not acceptable values for judge LLM responses.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (7)
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:274-297
Timestamp: 2025-07-16T10:41:09.399Z
Learning: In the lsc_agent_eval package, the team prefers to focus on core functionality testing first and considers testing cleanup script execution after setup failure as early optimization, noting that there's no guarantee cleanup scripts will run successfully anyway.
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:45.006Z
Learning: In the lsc_agent_eval package, evaluation results use distinct values: "FAIL" means the evaluation ran successfully but the result was negative, while "ERROR" means there was an issue executing the evaluation itself (e.g., setup script failed, API connection failed).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:190-214
Timestamp: 2025-07-16T13:21:53.225Z
Learning: In the lsc_agent_eval framework, there's a distinction between FAIL and ERROR results:
- FAIL: evaluation failed (e.g., script verification failed, agent response doesn't match expected criteria) - result.error is None
- ERROR: error running eval (e.g., setup script failed, agent API error) - result.error contains error message
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py:0-0
Timestamp: 2025-07-16T13:20:40.632Z
Learning: In the lsc_agent_eval package, evaluation results use "FAIL" for evaluations that ran but didn't pass the criteria, and "ERROR" for errors in the evaluation process itself (like setup script failures, API errors, etc.).
Learnt from: asamal4
PR: #19
File: lsc_agent_eval/data/script/eval3/setup.sh:1-3
Timestamp: 2025-07-16T09:42:00.691Z
Learning: Scripts in the lsc_agent_eval/data directory are meant to be simple examples/samples for teams to customize according to their needs, not production-ready code.
Learnt from: asamal4
PR: #22
File: lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py:146-153
Timestamp: 2025-07-28T14:26:03.119Z
Learning: In the lsc_agent_eval framework, evaluations are identified by a composite key of (conversation_group, eval_id). This design allows the same eval_id to exist across different conversation groups (logged as warning) but prevents duplicates within the same conversation group (validation error). This supports logical separation and reusable eval_ids across different conversation contexts.
🧬 Code Graph Analysis (4)
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (8)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (3)
AgentGoalEval(25-257)run_evaluation(59-94)get_result_summary(252-257)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (3)
mock_script_runner(31-35)mock_agent_client(24-28)mock_judge_manager(38-42)lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (1)
sample_results(20-50)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
get_conversations(163-165)get_eval_count(167-171)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (1)
run_evaluation(33-61)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
get_results_stats(89-91)save_results(25-46)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
AgentEvaluationError(4-5)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (3)
EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (1)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (5)
ConversationDataConfig(150-218)EvaluationDataConfig(78-147)EvaluationResult(221-245)EvaluationStats(248-299)from_results(269-299)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (2)
lsc_agent_eval/src/lsc_agent_eval/core/utils/exceptions.py (1)
EvaluationDataError(8-9)lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (1)
ConversationDataConfig(150-218)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (9)
- GitHub Check: ruff
- GitHub Check: pydocstyle
- GitHub Check: Pylinter
- GitHub Check: mypy
- GitHub Check: tests (3.13)
- GitHub Check: tests (3.12)
- GitHub Check: tests (3.11)
- GitHub Check: Pyright
- GitHub Check: black
🔇 Additional comments (55)
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/results.py (5)
19-24: LGTM! Improved constructor design.The refactor to accept results directly and pre-compute statistics improves separation of concerns and performance. This aligns well with the conversation-based evaluation framework.
25-47: Excellent error handling and validation.The method properly validates empty results, creates directories as needed, and provides comprehensive error handling with appropriate exception raising. The timestamped filenames are a good practice for result tracking.
48-69: CSV output correctly includes conversation metadata.The CSV export properly incorporates the new conversation fields (
conversation_group,conversation_uuid) which aligns with the multi-turn evaluation framework. The implementation follows good practices with proper encoding and logging.
70-88: Comprehensive JSON statistics output.The JSON summary provides excellent statistical breakdowns including overall totals, success rates, and categorization by conversation and evaluation type. This rich output format supports the multi-turn evaluation analysis requirements.
89-92: LGTM! Efficient stats retrieval.The getter method efficiently returns pre-computed statistics, leveraging the initialization-time computation for optimal performance.
lsc_agent_eval/tests/core/agent_goal_eval/test_agent_goal_eval.py (6)
3-15: LGTM! Imports updated for new architecture.The imports correctly reflect the shift to conversation-based evaluation with
ConversationDataConfig,EvaluationStats, andAgentEvaluationError. The addition ofMagicMocksupports the more complex mocking needs.
36-55: Fixture correctly updated for conversation-based structure.The
sample_conversationfixture properly usesConversationDataConfigwith multiple evaluations grouped under a conversation. The evaluation types are updated to match the new evaluation logic (e.g., "sub-string" instead of "script").
57-79: Sample results properly include conversation metadata.The results fixture correctly incorporates the new
conversation_groupandconversation_uuidfields, and uses updated evaluation types that align with the refactored evaluation framework.
80-165: Test initialization correctly updated for ScriptRunner.The tests properly reflect the architectural change where
ScriptRunnerreplacesResultsManagerin component initialization. The parameter passing for kubeconfig is correctly tested in both None and specified value scenarios.
174-216: Test correctly reflects new evaluation flow.The test properly mocks the new conversation-based evaluation methods (
get_conversations,get_eval_count) and correctly tests the updated results saving pattern wheresave_resultsis called with the result directory.
217-262: Result summary tests properly validate new functionality.The tests correctly validate both successful result summary retrieval and proper error handling when no results are available. The use of
AgentEvaluationErroraligns with the framework's exception handling patterns.lsc_agent_eval/README.md (6)
3-17: Feature descriptions accurately reflect new architecture.The updated features properly emphasize the conversation-based evaluation framework and clarify important behaviors like the requirement for ALL keywords in sub-string evaluation. The LiteLLM integration mention is helpful.
49-127: Excellent comprehensive configuration documentation.The data configuration section provides thorough documentation of the conversation-based YAML structure with clear examples. The explanation of eval_id uniqueness rules correctly reflects the composite key design allowing reuse across conversation groups.
128-140: Clear Judge LLM setup documentation.The section properly documents LiteLLM integration and provides clear setup instructions for various providers while correctly setting expectations about inference setup responsibilities.
187-218: Accurate evaluation flow documentation.The evaluation flow section clearly explains the conversation-based processing order, UUID generation, and script execution policies. The error handling scenarios are well-documented and align with the framework's behavior patterns.
219-244: Comprehensive output format documentation.The output section accurately documents both CSV and JSON formats, including the new conversation metadata fields and comprehensive statistical breakdowns. This matches the implementation in the ResultsManager class.
258-267: Enhanced development tooling documentation.The development section properly documents the comprehensive linting and type checking tools, reflecting the enhanced code quality standards for the refactored codebase.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/models.py (6)
1-11: LGTM! Solid foundation for Pydantic validation.The imports and constants provide a strong foundation for the Pydantic-based validation system. The constant definitions for valid evaluation types and results ensure consistent validation across the framework.
13-76: Excellent validation helper functions.The helper functions provide robust validation with proper error handling:
_validate_eval_typeensures type safety_validate_script_pathincludes crucial file existence checks and path resolution_calculate_stats_by_categoryprovides reusable statistics aggregationThe comprehensive validation prevents runtime errors and improves data integrity.
78-148: Well-designed Pydantic model with comprehensive validation.The
EvaluationDataConfigmodel provides:
- Appropriate field constraints (min_length, descriptions)
- Robust field validation for keywords and script paths
- Model-level validation ensuring eval type-specific requirements
- Support for conversation metadata
This design prevents configuration errors and ensures data integrity throughout the evaluation process.
150-218: Excellent conversation-based model design.The
ConversationDataConfigmodel effectively implements the conversation architecture:
- Automatic UUID generation for API call tracking
- Proper validation of conversation groups and scripts
- Duplicate eval_id prevention within conversations (while allowing across groups)
- Automatic metadata propagation to contained evaluations
This design supports the multi-turn evaluation framework while maintaining data consistency.
220-246: EvaluationResult model properly enhanced for conversations.The model correctly extends result tracking with conversation metadata while maintaining validation consistency. The field validators ensure data integrity for result and evaluation type values.
248-299: Comprehensive statistics model with robust aggregation.The
EvaluationStatsmodel provides excellent statistical capabilities:
- Complete totals and success rate calculations
- Breakdowns by conversation and evaluation type
- Proper zero-division handling in success rate computation
- Efficient use of helper functions for category aggregation
This supports the enhanced reporting and analysis requirements of the multi-turn evaluation framework.
lsc_agent_eval/tests/core/agent_goal_eval/test_models.py (5)
1-16: LGTM! Proper test setup for Pydantic model testing.The imports correctly include Pydantic's
ValidationErrorand necessary mocking utilities for comprehensive model validation testing.
17-93: Comprehensive EvaluationResult validation testing.The tests properly cover valid result creation, error scenarios, default values, and validation error cases. The use of Pydantic's
ValidationErrorfor testing invalid values is correct and thorough.
95-244: Excellent comprehensive validation testing for EvaluationDataConfig.The tests thoroughly cover:
- All evaluation types with their specific requirements
- Proper mocking for file system operations in script validation
- Validation error scenarios for missing required fields
- File existence checking for script paths
This ensures the Pydantic model validation works correctly across all scenarios.
246-353: Thorough ConversationDataConfig validation testing.The tests properly validate:
- Conversation group creation with UUID generation
- Script path validation with appropriate mocking
- Empty group name prevention
- Duplicate eval_id detection within conversations
This ensures the conversation-based validation logic works correctly and prevents configuration errors.
355-409: Well-designed EvaluationStats testing.The tests properly validate:
- Comprehensive statistics calculation from results
- Success rate computation with appropriate floating point precision handling
- Statistics aggregation by both conversation and evaluation type
- Proper totals and breakdown calculations
This ensures the statistics functionality works correctly for reporting and analysis.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/eval_data.py (7)
3-13: LGTM! Clean import structure for the refactored architecture.The imports correctly reflect the shift to Pydantic validation and conversation-based data model with appropriate logging and error handling.
19-26: LGTM! Proper initialization for conversation-based data model.The initialization correctly sets up the new conversation-based structure and delegates to appropriate loading and validation methods.
27-60: LGTM! Robust file loading with comprehensive error handling.The method properly resolves file paths, validates data structure, and provides detailed error messages for different failure scenarios. The exception handling chain is well-structured.
61-106: LGTM! Effective use of Pydantic validation with enhanced error handling.The method properly leverages Pydantic for data validation while adding business logic checks for duplicate conversation groups. The error formatting enhances user experience with clear validation messages.
107-115: LGTM! Useful utility for improving error message clarity.The method effectively formats Pydantic validation errors into human-readable messages with clear field-to-error mapping.
116-162: LGTM! Comprehensive statistics logging with proper duplicate handling.The method provides excellent visibility into loaded data and correctly handles duplicate eval_ids across conversations as warnings (consistent with the composite key design). The statistics breakdown is thorough and useful.
163-172: LGTM! Clean getter methods for accessing loaded data.Both methods provide appropriate access to the conversation data with proper calculation of total evaluation count across all conversations.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/agent_goal_eval.py (7)
5-21: LGTM! Well-organized imports with proper type checking.Good use of
TYPE_CHECKINGblock to avoid circular imports while maintaining type safety. The imports align with the new conversation-based architecture.
39-57: LGTM! Proper dependency injection pattern for ScriptRunner.The ScriptRunner initialization and injection into EvaluationRunner follows good dependency injection practices, making the components more testable and modular.
59-95: LGTM! Well-structured conversation-based evaluation orchestration.The method properly coordinates conversation-level processing with clear logging and user-friendly progress indication. The integration with ResultsManager is clean.
96-134: LGTM! Proper conversation-level orchestration with excellent error handling.The method correctly handles setup script failures by marking all evaluations as ERROR (preventing execution on improper setup), and treats cleanup failures as non-critical warnings. This aligns well with the framework's FAIL vs ERROR semantics.
135-154: LGTM! Appropriate script execution semantics.Correctly differentiates between setup failures (which should stop the conversation) and cleanup failures (which are logged as non-critical warnings). The use of injected ScriptRunner promotes testability.
155-181: LGTM! Proper conversation-scoped evaluation execution.The method correctly manages evaluation execution within conversation context, with good user feedback through progress bars and proper conversation_uuid propagation.
182-257: LGTM! Enhanced result reporting with conversation context.The improvements provide better user experience by showing conversation context in results and utilizing ResultsManager's enhanced statistics. The error handling in
get_result_summaryis appropriate.lsc_agent_eval/tests/core/agent_goal_eval/test_results.py (5)
3-56: LGTM! Well-structured test fixtures supporting conversation-based data model.The fixtures properly include conversation metadata and the imports support the integration testing approach with real file I/O verification.
57-135: LGTM! Comprehensive testing of core ResultsManager functionality.The tests properly verify initialization, statistics calculation, and data conversion with the new conversation-based data model. Good coverage of both success and edge cases.
136-225: LGTM! Thorough testing of statistics calculation and error scenarios.The tests comprehensively verify the enhanced statistics functionality including conversation breakdowns, evaluation type analysis, and proper handling of various error conditions.
226-269: LGTM! Proper testing of output format and filename generation.The tests ensure consistent JSON structure and predictable filename generation with appropriate mocking of datetime dependencies.
270-299: LGTM! Excellent integration test with real file I/O validation.The integration test provides valuable end-to-end verification by using real temporary files to validate the complete save workflow, file naming, and data integrity.
lsc_agent_eval/src/lsc_agent_eval/core/agent_goal_eval/evaluator.py (4)
1-32: LGTM! Clean dependency injection with proper type safety.The refactored initialization properly injects dependencies while using TYPE_CHECKING to avoid circular imports. The architectural separation is well-designed.
33-62: LGTM! Well-structured evaluation method with conversation UUID support.The method properly integrates conversation UUID handling, uses a clean dictionary format for agent queries, and leverages helper functions for consistent result creation. Exception handling is appropriate.
63-86: LGTM! Clean evaluation dispatch with proper dependency usage.The match statement provides clear evaluation type dispatch, and the script evaluation properly uses the injected ScriptRunner. Good error handling for unknown evaluation types.
101-142: LGTM! Robust judge LLM evaluation with proper validation.The methods properly validate judge responses (accepting only 1 or 0), handle missing components gracefully with appropriate logging, and implement the judge evaluation logic correctly.
lsc_agent_eval/tests/core/agent_goal_eval/test_evaluator.py (4)
3-87: LGTM! Much improved test fixtures with proper cleanup.The shift from extensive patching to clean fixtures significantly improves test maintainability. The temporary script file fixture with proper cleanup is well-designed.
88-238: LGTM! Comprehensive testing of core evaluation functionality.The tests properly verify EvaluationRunner initialization with dependency injection and cover all evaluation types with appropriate success/failure scenarios. Good error handling coverage.
239-318: LGTM! Thorough testing of substring logic and error scenarios.The tests properly verify the new ALL keywords requirement for substring evaluation and cover important error scenarios including API failures and configuration validation.
319-384: LGTM! Excellent comprehensive testing of evaluation logic.The tests thoroughly verify substring evaluation behavior across multiple scenarios and properly test conversation UUID propagation. The comprehensive keyword matching tests are particularly valuable for ensuring correct evaluation semantics.
asamal4
force-pushed
the
multi-turn-eval
branch
2 times, most recently
from
6ee0120 to
2963f7a
Compare
fix conv id handling
tisnik
left a comment
tisnik
left a comment
There was a problem hiding this comment.
it looks ok so far, thank you
3 participants
Functionality
Other
Summary by CodeRabbit
Summary by CodeRabbit
New Features
Improvements
Bug Fixes
Refactor
Tests