fix: Resolve remaining lint issues (W291, W293, B904)

- Remove trailing whitespace from examples/prompt_caching_example.py
- Fix exception handling to use 'from e' for proper error chaining
- All lint checks now pass locally

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

Author: devin-ai-integration[bot]

examples/prompt_caching_example.py

@@ -11,60 +11,60 @@
 
 def create_crew_with_caching():
     """Create a crew with prompt caching enabled."""
-    
+
     llm = LLM(
         model="anthropic/claude-3-5-sonnet-20240620",
         enable_prompt_caching=True,
         temperature=0.1
     )
-    
+
     analyst = Agent(
         role="Data Analyst",
         goal="Analyze data and provide insights",
-        backstory="""You are an experienced data analyst with expertise in 
-        statistical analysis, data visualization, and business intelligence. 
-        You have worked with various industries including finance, healthcare, 
-        and technology. Your approach is methodical and you always provide 
+        backstory="""You are an experienced data analyst with expertise in
+        statistical analysis, data visualization, and business intelligence.
+        You have worked with various industries including finance, healthcare,
+        and technology. Your approach is methodical and you always provide
         actionable insights based on data patterns.""",
         llm=llm
     )
-    
+
     analysis_task = Task(
         description="""Analyze the following dataset: {dataset}
-        
+
         Please provide:
         1. Summary statistics
         2. Key patterns and trends
         3. Actionable recommendations
         4. Potential risks or concerns
-        
+
         Be thorough in your analysis and provide specific examples.""",
         expected_output="A comprehensive analysis report with statistics, trends, and recommendations",
         agent=analyst
     )
-    
+
     return Crew(agents=[analyst], tasks=[analysis_task])
 
 
 def example_kickoff_for_each():
     """Example using kickoff_for_each with prompt caching."""
     print("Running kickoff_for_each example with prompt caching...")
-    
+
     crew = create_crew_with_caching()
-    
+
     datasets = [
         {"dataset": "Q1 2024 sales data showing 15% growth in mobile segment"},
         {"dataset": "Q2 2024 customer satisfaction scores with 4.2/5 average rating"},
         {"dataset": "Q3 2024 website traffic data with 25% increase in organic search"},
         {"dataset": "Q4 2024 employee engagement survey with 78% satisfaction rate"}
     ]
-    
+
     results = crew.kickoff_for_each(datasets)
-    
+
     for i, result in enumerate(results, 1):
         print(f"\n--- Analysis {i} ---")
         print(result.raw)
-        
+
     if crew.usage_metrics:
         print(f"\nTotal usage metrics:")
         print(f"Total tokens: {crew.usage_metrics.total_tokens}")
@@ -75,21 +75,21 @@ def example_kickoff_for_each():
 async def example_kickoff_for_each_async():
     """Example using kickoff_for_each_async with prompt caching."""
     print("Running kickoff_for_each_async example with prompt caching...")
-    
+
     crew = create_crew_with_caching()
-    
+
     datasets = [
         {"dataset": "Marketing campaign A: 12% CTR, 3.5% conversion rate"},
         {"dataset": "Marketing campaign B: 8% CTR, 4.1% conversion rate"},
         {"dataset": "Marketing campaign C: 15% CTR, 2.8% conversion rate"}
     ]
-    
+
     results = await crew.kickoff_for_each_async(datasets)
-    
+
     for i, result in enumerate(results, 1):
         print(f"\n--- Async Analysis {i} ---")
         print(result.raw)
-        
+
     if crew.usage_metrics:
         print(f"\nTotal async usage metrics:")
         print(f"Total tokens: {crew.usage_metrics.total_tokens}")
@@ -98,35 +98,35 @@ async def example_kickoff_for_each_async():
 def example_bedrock_caching():
     """Example using AWS Bedrock with prompt caching."""
     print("Running Bedrock example with prompt caching...")
-    
+
     llm = LLM(
         model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
         enable_prompt_caching=True
     )
-    
+
     agent = Agent(
         role="Legal Analyst",
         goal="Review legal documents and identify key clauses",
         backstory="Expert legal analyst with 10+ years experience in contract review",
         llm=llm
     )
-    
+
     task = Task(
         description="Review this contract section: {contract_section}",
         expected_output="Summary of key legal points and potential issues",
         agent=agent
     )
-    
+
     crew = Crew(agents=[agent], tasks=[task])
-    
+
     contract_sections = [
         {"contract_section": "Section 1: Payment terms and conditions"},
         {"contract_section": "Section 2: Intellectual property rights"},
         {"contract_section": "Section 3: Termination clauses"}
     ]
-    
+
     results = crew.kickoff_for_each(contract_sections)
-    
+
     for i, result in enumerate(results, 1):
         print(f"\n--- Legal Review {i} ---")
         print(result.raw)
@@ -135,53 +135,53 @@ def example_bedrock_caching():
 def example_openai_caching():
     """Example using OpenAI with prompt caching."""
     print("Running OpenAI example with prompt caching...")
-    
+
     llm = LLM(
         model="gpt-4o",
         enable_prompt_caching=True
     )
-    
+
     agent = Agent(
         role="Content Writer",
         goal="Create engaging content for different audiences",
         backstory="Professional content writer with expertise in various writing styles and formats",
         llm=llm
     )
-    
+
     task = Task(
         description="Write a {content_type} about: {topic}",
         expected_output="Well-structured and engaging content piece",
         agent=agent
     )
-    
+
     crew = Crew(agents=[agent], tasks=[task])
-    
+
     content_requests = [
         {"content_type": "blog post", "topic": "benefits of renewable energy"},
         {"content_type": "social media post", "topic": "importance of cybersecurity"},
         {"content_type": "newsletter", "topic": "latest AI developments"}
     ]
-    
+
     results = crew.kickoff_for_each(content_requests)
-    
+
     for i, result in enumerate(results, 1):
         print(f"\n--- Content Piece {i} ---")
         print(result.raw)
 
 
 if __name__ == "__main__":
     print("=== CrewAI Prompt Caching Examples ===\n")
-    
+
     example_kickoff_for_each()
-    
+
     print("\n" + "="*50 + "\n")
-    
+
     asyncio.run(example_kickoff_for_each_async())
-    
+
     print("\n" + "="*50 + "\n")
-    
+
     example_bedrock_caching()
-    
+
     print("\n" + "="*50 + "\n")
-    
+
     example_openai_caching()

src/crewai/llm.py

@@ -740,7 +740,7 @@ def _handle_streaming_response(
             # Catch context window errors from litellm and convert them to our own exception type.
             # This exception is handled by CrewAgentExecutor._invoke_loop() which can then
             # decide whether to summarize the content or abort based on the respect_context_window flag.
-            raise LLMContextLengthExceededException(str(e))
+            raise LLMContextLengthExceededException(str(e)) from e
         except Exception as e:
             logging.error(f"Error in streaming response: {e!s}")
             if full_response.strip():
@@ -762,7 +762,7 @@ def _handle_streaming_response(
                         error=str(e), from_task=from_task, from_agent=from_agent
                     ),
                 )
-            raise Exception(f"Failed to get streaming response: {e!s}")
+            raise Exception(f"Failed to get streaming response: {e!s}") from e
 
     def _handle_streaming_tool_calls(
         self,
@@ -891,7 +891,7 @@ def _handle_non_streaming_response(
         except ContextWindowExceededError as e:
             # Convert litellm's context window error to our own exception type
             # for consistent handling in the rest of the codebase
-            raise LLMContextLengthExceededException(str(e))
+            raise LLMContextLengthExceededException(str(e)) from e
         # --- 2) Extract response message and content
         response_message = cast(Choices, cast(ModelResponse, response).choices)[
             0