more testing

examples/lite_agent_example.py

@@ -80,62 +80,62 @@ async def main():
     # print(f"Raw response: {result.raw}")
 
     # Example 2: Query with structured output
-    print("\n=== Example 2: Structured Output ===")
-    structured_query = """
-    Research the impact of climate change on coral reefs.
+    # print("\n=== Example 2: Structured Output ===")
+    # structured_query = """
+    # Research the impact of climate change on coral reefs.
 
-    YOU MUST format your response as a valid JSON object with the following structure:
-    {
-      "main_findings": "A summary of the main findings",
-      "key_points": ["Point 1", "Point 2", "Point 3"],
-      "sources": ["Source 1", "Source 2"]
-    }
+    # YOU MUST format your response as a valid JSON object with the following structure:
+    # {
+    #   "main_findings": "A summary of the main findings",
+    #   "key_points": ["Point 1", "Point 2", "Point 3"],
+    #   "sources": ["Source 1", "Source 2"]
+    # }
 
-    Include at least 3 key points and 2 sources. Wrap your JSON in ```json and ``` tags.
-    """
+    # Include at least 3 key points and 2 sources. Wrap your JSON in ```json and ``` tags.
+    # """
 
-    result = await agent.kickoff_async(structured_query)
+    # result = await agent.kickoff_async(structured_query)
 
-    if result.pydantic:
-        # Cast to the specific type for better IDE support
-        research_result = cast(ResearchResult, result.pydantic)
-        print(f"Main findings: {research_result.main_findings}")
-        print("\nKey points:")
-        for i, point in enumerate(research_result.key_points, 1):
-            print(f"{i}. {point}")
-        print("\nSources:")
-        for i, source in enumerate(research_result.sources, 1):
-            print(f"{i}. {source}")
-    else:
-        print(f"Raw response: {result.raw}")
-        print(
-            "\nNote: Structured output was not generated. The LLM may need more explicit instructions to format the response as JSON."
-        )
-    print("Usage metrics:")
-    print(result.usage_metrics)
+    # if result.pydantic:
+    #     # Cast to the specific type for better IDE support
+    #     research_result = cast(ResearchResult, result.pydantic)
+    #     print(f"Main findings: {research_result.main_findings}")
+    #     print("\nKey points:")
+    #     for i, point in enumerate(research_result.key_points, 1):
+    #         print(f"{i}. {point}")
+    #     print("\nSources:")
+    #     for i, source in enumerate(research_result.sources, 1):
+    #         print(f"{i}. {source}")
+    # else:
+    #     print(f"Raw response: {result.raw}")
+    #     print(
+    #         "\nNote: Structured output was not generated. The LLM may need more explicit instructions to format the response as JSON."
+    #     )
+    # print("Usage metrics:")
+    # print(result.usage_metrics)
 
-    # # Example 3: Multi-turn conversation
-    # print("\n=== Example 3: Multi-turn Conversation ===")
-    # messages = [
-    #     {"role": "user", "content": "I'm planning a trip to Japan."},
-    #     {
-    #         "role": "assistant",
-    #         "content": "That sounds exciting! Japan is a beautiful country with rich culture, delicious food, and stunning landscapes. What would you like to know about Japan to help with your trip planning?",
-    #     },
-    #     {
-    #         "role": "user",
-    #         "content": "What are the best times to visit Tokyo and Kyoto?",
-    #     },
-    # ]
+    # Example 3: Multi-turn conversation
+    print("\n=== Example 3: Multi-turn Conversation ===")
+    messages = [
+        {"role": "user", "content": "I'm planning a trip to Japan."},
+        {
+            "role": "assistant",
+            "content": "That sounds exciting! Japan is a beautiful country with rich culture, delicious food, and stunning landscapes. What would you like to know about Japan to help with your trip planning?",
+        },
+        {
+            "role": "user",
+            "content": "What are the best times to visit Tokyo and Kyoto?",
+        },
+    ]
 
-    # result = await agent.kickoff_async(messages)
-    # print(f"Response: {result.raw}")
+    result = await agent.kickoff_async(messages)
+    print(f"Response: {result.raw}")
 
-    # # Print usage metrics if available
-    # if result.usage_metrics:
-    #     print("\nUsage metrics:")
-    #     for key, value in result.usage_metrics.items():
-    #         print(f"{key}: {value}")
+    # Print usage metrics if available
+    if result.usage_metrics:
+        print("\nUsage metrics:")
+        for key, value in result.usage_metrics.items():
+            print(f"{key}: {value}")
 
 
 if __name__ == "__main__":

src/crewai/llm.py

@@ -748,6 +748,7 @@ class LLM:
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
+                    print("HERE IS WHERE THE ERROR IS")
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 

src/crewai/tools/tool_usage.py

@@ -105,7 +105,6 @@ class ToolUsage:
     def use(
         self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
     ) -> str:
-        print("USING A TOOL", calling, tool_string)
         if isinstance(calling, ToolUsageErrorException):
             error = calling.message
             if self.agent.verbose:
@@ -145,8 +144,6 @@ class ToolUsage:
         tool: CrewStructuredTool,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:
-        print("USING A TOOL: ", tool)
-        print("Type of tool: ", type(tool))
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(