improve

src/crewai/cli/crew_chat.py

@@ -27,9 +27,17 @@ def run_chat():
     crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
     system_message = build_system_message(crew_chat_inputs)
 
+    # Call the LLM to generate the introductory message
+    introductory_message = chat_llm.call(
+        messages=[{"role": "system", "content": system_message}]
+    )
+    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
+
     messages = [
         {"role": "system", "content": system_message},
+        {"role": "assistant", "content": introductory_message},
     ]
+
     available_functions = {
         crew_chat_inputs.crew_name: create_tool_function(crew, messages),
     }
@@ -77,6 +85,8 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
         "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
         "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
         "If you are ever unsure about a user's request or need clarification, ask the user for more information."
+        "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
+        "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
         f"\nCrew Name: {crew_chat_inputs.crew_name}"
         f"\nCrew Description: {crew_chat_inputs.crew_description}"
     )

src/crewai/cli/templates/crew/main.py

@@ -1,10 +1,8 @@
 #!/usr/bin/env python
 import sys
-import json
 import warnings
 
 from {{folder_name}}.crew import {{crew_name}}
-from crewai.utilities.llm_utils import create_llm
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
@@ -15,13 +13,10 @@
 
 def run():
     """
-    Run the crew, allowing CLI overrides for required inputs.
-    Usage example:
-        uv run run_crew -- --topic="New Topic" --some_other_field="Value"
+    Run the crew.
     """
     inputs = {
         'topic': 'AI LLMs'
-        # Add any other default fields here
     }
 
     try:

src/crewai/crew.py

@@ -209,10 +209,6 @@ class Crew(BaseModel):
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
-    chat_inputs: Optional[ChatInputs] = Field(
-        default=None,
-        description="Holds descriptions of the crew as well as named inputs for chat usage.",
-    )
     _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
     )

src/crewai/llm.py

@@ -146,7 +146,6 @@ def __init__(
         self.callbacks = callbacks
         self.context_window_size = 0
 
-        # For safety, we disable passing init params to next calls
         litellm.drop_params = True
 
         self.set_callbacks(callbacks)
@@ -247,40 +246,36 @@ def call(
                 function_name = tool_call.function.name
 
                 if function_name in available_functions:
-                    # Parse arguments
                     try:
                         function_args = json.loads(tool_call.function.arguments)
                     except json.JSONDecodeError as e:
                         logging.warning(f"Failed to parse function arguments: {e}")
-                        return text_response  # Fallback to text response
+                        return text_response
 
                     fn = available_functions[function_name]
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
 
-                        # Return the result directly
                         return result
 
                     except Exception as e:
                         logging.error(
                             f"Error executing function '{function_name}': {e}"
                         )
-                        return text_response  # Fallback to text response
+                        return text_response
 
                 else:
                     logging.warning(
                         f"Tool call requested unknown function '{function_name}'"
                     )
-                    return text_response  # Fallback to text response
+                    return text_response
 
             except Exception as e:
-                # Check if context length was exceeded, otherwise log
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
                     logging.error(f"LiteLLM call failed: {str(e)}")
-                # Re-raise the exception
                 raise
 
     def supports_function_calling(self) -> bool:

src/crewai/task.py

@@ -393,7 +393,7 @@ def _execute_core(
                 self.retry_count += 1
                 context = self.i18n.errors("validation_error").format(
                     guardrail_result_error=guardrail_result.error,
-                    task_output=task_output.raw
+                    task_output=task_output.raw,
                 )
                 printer = Printer()
                 printer.print(
@@ -431,9 +431,7 @@ def _execute_core(
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 
@@ -453,11 +451,12 @@ def prompt(self) -> str:
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-
-    def interpolate_inputs_and_add_conversation_history(self, inputs: Dict[str, Union[str, int, float]]) -> None:
+    def interpolate_inputs_and_add_conversation_history(
+        self, inputs: Dict[str, Union[str, int, float]]
+    ) -> None:
         """Interpolate inputs into the task description, expected output, and output file path.
            Add conversation history if present.
-        
+
         Args:
             inputs: Dictionary mapping template variables to their values.
                    Supported value types are strings, integers, and floats.
@@ -497,16 +496,15 @@ def interpolate_inputs_and_add_conversation_history(self, inputs: Dict[str, Unio
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
-                raise ValueError(f"Error interpolating output_file path: {str(e)}") from e
-
+                raise ValueError(
+                    f"Error interpolating output_file path: {str(e)}"
+                ) from e
+
         if "crew_chat_messages" in inputs and inputs["crew_chat_messages"]:
-            # Fetch the conversation history instruction using self.i18n.slice
             conversation_instruction = self.i18n.slice(
                 "conversation_history_instruction"
             )
-            print("crew_chat_messages:", inputs["crew_chat_messages"])
 
-            # Ensure that inputs["crew_chat_messages"] is a string
             crew_chat_messages_json = str(inputs["crew_chat_messages"])
 
             try:
@@ -515,15 +513,15 @@ def interpolate_inputs_and_add_conversation_history(self, inputs: Dict[str, Unio
                 print("An error occurred while parsing crew chat messages:", e)
                 raise
 
-            # Process the messages to build conversation history
             conversation_history = "\n".join(
                 f"{msg['role'].capitalize()}: {msg['content']}"
                 for msg in crew_chat_messages
                 if isinstance(msg, dict) and "role" in msg and "content" in msg
             )
 
-            # Add the instruction and conversation history to the description
-            self.description += f"\n\n{conversation_instruction}\n\n{conversation_history}"
+            self.description += (
+                f"\n\n{conversation_instruction}\n\n{conversation_history}"
+            )
 
     def interpolate_only(
         self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]

src/crewai/utilities/llm_utils.py

@@ -34,7 +34,6 @@ def create_llm(
     if isinstance(llm_value, str):
         try:
             created_llm = LLM(model=llm_value)
-            print(f"LLM created with model='{llm_value}'")
             return created_llm
         except Exception as e:
             print(f"Failed to instantiate LLM with model='{llm_value}': {e}")
@@ -197,7 +196,6 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     # Try creating the LLM
     try:
         new_llm = LLM(**llm_params)
-        print(f"LLM created with model='{model_name}'")
         return new_llm
     except Exception as e:
         print(