fix: resolve lint and type checking issues

- Add per-file ignores for S101 (assert statements) in test files
- Add per-file ignore for PERF203 (try-except in loop) in lite_agent.py
- Replace assert with proper error handling in lite_agent.py
- Replace eval() with ast.literal_eval() for security compliance

Co-Authored-By: João <joao@crewai.com>

tests/agents/test_lite_agent.py

@@ -1,19 +1,18 @@
 from collections import defaultdict
 from typing import cast
-from unittest.mock import Mock
+from unittest.mock import Mock, patch
 
 import pytest
 from pydantic import BaseModel, Field
 
 from crewai import LLM, Agent
-from crewai.flow import Flow, start
-from crewai.lite_agent import LiteAgent, LiteAgentOutput
-from crewai.tools import BaseTool
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.agent_events import LiteAgentExecutionStartedEvent
 from crewai.events.types.tool_usage_events import ToolUsageStartedEvent
+from crewai.flow import Flow, start
+from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llms.base_llm import BaseLLM
-from unittest.mock import patch
+from crewai.tools import BaseTool
 
 
 # A simple test tool
@@ -37,10 +36,9 @@ class WebSearchTool(BaseTool):
         # This is a mock implementation
         if "tokyo" in query.lower():
             return "Tokyo's population in 2023 was approximately 21 million people in the city proper, and 37 million in the greater metropolitan area."
-        elif "climate change" in query.lower() and "coral" in query.lower():
+        if "climate change" in query.lower() and "coral" in query.lower():
             return "Climate change severely impacts coral reefs through: 1) Ocean warming causing coral bleaching, 2) Ocean acidification reducing calcification, 3) Sea level rise affecting light availability, 4) Increased storm frequency damaging reef structures. Sources: NOAA Coral Reef Conservation Program, Global Coral Reef Alliance."
-        else:
-            return f"Found information about {query}: This is a simulated search result for demonstration purposes."
+        return f"Found information about {query}: This is a simulated search result for demonstration purposes."
 
 
 # Define Mock Calculator Tool
@@ -52,11 +50,12 @@ class CalculatorTool(BaseTool):
 
     def _run(self, expression: str) -> str:
         """Calculate the result of a mathematical expression."""
+        import ast
         try:
-            result = eval(expression, {"__builtins__": {}})
+            result = ast.literal_eval(expression)
             return f"The result of {expression} is {result}"
         except Exception as e:
-            return f"Error calculating {expression}: {str(e)}"
+            return f"Error calculating {expression}: {e!s}"
 
 
 # Define a custom response format using Pydantic
@@ -522,14 +521,14 @@ def test_lite_agent_with_custom_llm_and_guardrails():
 
 def test_lite_agent_structured_output_with_malformed_json():
     """Test that LiteAgent can handle malformed JSON wrapped in markdown blocks."""
-    
+
     class FounderNames(BaseModel):
         names: list[str] = Field(description="List of founder names")
-    
+
     class MockLLMWithMalformedJSON(BaseLLM):
         def __init__(self):
             super().__init__(model="mock-model")
-        
+
         def call(self, messages, **kwargs):
             return '''Thought: I need to extract the founder names
 Final Answer: ```json
@@ -537,18 +536,18 @@ Final Answer: ```json
   "names": ["John Smith", "Jane Doe"]
 }
 ```'''
-        
+
         def supports_function_calling(self):
             return False
-        
+
         def supports_stop_words(self):
             return False
-        
+
         def get_context_window_size(self):
             return 4096
-    
+
     mock_llm = MockLLMWithMalformedJSON()
-    
+
     agent = Agent(
         role="Data Extraction Specialist",
         goal="Extract founder names from text",
@@ -556,12 +555,12 @@ Final Answer: ```json
         llm=mock_llm,
         verbose=True,
     )
-    
+
     result = agent.kickoff(
         messages="Extract founder names from: 'The company was founded by John Smith and Jane Doe.'",
         response_format=FounderNames
     )
-    
+
     assert result.pydantic is not None, "Should successfully parse malformed JSON"
     assert isinstance(result.pydantic, FounderNames), "Should return correct Pydantic model"
     assert result.pydantic.names == ["John Smith", "Jane Doe"], "Should extract correct founder names"