fixing types and adding ability for nor using system prompts

src/crewai/agent.py

@@ -73,6 +73,10 @@ class Agent(BaseAgent):
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
+    use_system_prompt: Optional[bool] = Field(
+        default=True,
+        description="Use system prompt for the agent.",
+    )
     llm: Any = Field(
         description="Language model that will run the agent.", default="gpt-4o"
     )
@@ -206,6 +210,7 @@ class Agent(BaseAgent):
             agent=self,
             tools=tools,
             i18n=self.i18n,
+            use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
             prompt_template=self.prompt_template,
             response_template=self.response_template,

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -20,6 +20,7 @@ class CrewAgentExecutorMixin:
     task: Optional["Task"]
     iterations: int
     have_forced_answer: bool
+    max_iter: int
     _i18n: I18N
 
     def _should_force_answer(self) -> bool:

src/crewai/agents/crew_agent_executor.py

@@ -57,22 +57,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.respect_context_window = respect_context_window
         self.request_within_rpm_limit = request_within_rpm_limit
         self.ask_for_human_input = False
-        self.messages = []
+        self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.have_forced_answer = False
         self.name_to_tool_map = {tool.name: tool for tool in self.tools}
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
-            system_prompt = self._format_prompt(self.prompt["system"], inputs)
-            user_prompt = self._format_prompt(self.prompt["user"], inputs)
+            system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
+            user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
 
             self.messages.append(self._format_msg(system_prompt, role="system"))
             self.messages.append(self._format_msg(user_prompt))
         else:
-            user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
+            user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
             self.messages.append(self._format_msg(user_prompt))
-        self.ask_for_human_input = inputs.get("ask_for_human_input", False)
+        self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
 
         formatted_answer = self._invoke_loop()
 
@@ -135,7 +135,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         return formatted_answer
 
-    def _use_tool(self, agent_action: AgentAction) -> None:
+    def _use_tool(self, agent_action: AgentAction) -> Any:
         tool_usage = ToolUsage(
             tools_handler=self.tools_handler,
             tools=self.tools,
@@ -143,7 +143,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             tools_description=self.tools_description,
             tools_names=self.tools_names,
             function_calling_llm=self.function_calling_llm,
-            task=self.task,
+            task=self.task,  # type: ignore[arg-type]
             agent=self.agent,
             action=agent_action,
         )
@@ -188,7 +188,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
             summarized_contents.append(summary)
 
-        merged_summary = " ".join(summarized_contents)
+        merged_summary = " ".join(str(content) for content in summarized_contents)
 
         self.messages = [
             self._format_msg(
@@ -226,6 +226,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         ):
             training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
             if training_data.get(agent_id):
+                # type: ignore[union-attr]
                 training_data[agent_id][self.crew._train_iteration][
                     "improved_output"
                 ] = result.output
@@ -238,9 +239,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 "agent": agent_id,
                 "agent_role": self.agent.role,
             }
-            CrewTrainingHandler(TRAINING_DATA_FILE).append(
-                self.crew._train_iteration, agent_id, training_data
-            )
+            # type: ignore[union-attr]
+            if isinstance(self.crew._train_iteration, int):
+                CrewTrainingHandler(TRAINING_DATA_FILE).append(
+                    self.crew._train_iteration, agent_id, training_data
+                )
+            else:
+                self._logger.log(
+                    "error",
+                    "Invalid train iteration type. Expected int.",
+                    color="red",
+                )
 
     def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
         prompt = prompt.replace("{input}", inputs["input"])

src/crewai/crew.py

@@ -201,7 +201,8 @@ class Crew(BaseModel):
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
         self.function_calling_llm = (
             self.function_calling_llm.model_name
-            if hasattr(self.function_calling_llm, "model_name")
+            if self.function_calling_llm is not None
+            and hasattr(self.function_calling_llm, "model_name")
             else self.function_calling_llm
         )
         self._telemetry = Telemetry()

src/crewai/utilities/evaluators/task_evaluator.py

@@ -83,7 +83,7 @@ class TaskEvaluator:
             instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
 
         converter = Converter(
-            llm=self.function_calling_llm,
+            llm=self.llm,
             text=evaluation_query,
             model=TaskEvaluation,
             instructions=instructions,

src/crewai/utilities/prompts.py

@@ -11,9 +11,10 @@ class Prompts(BaseModel):
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
+    use_system_prompt: Optional[bool] = False
     agent: Any
 
-    def task_execution(self) -> str:
+    def task_execution(self) -> dict[str, str]:
         """Generate a standard prompt for task execution."""
         slices = ["role_playing"]
         if len(self.tools) > 0:
@@ -23,7 +24,11 @@ class Prompts(BaseModel):
         system = self._build_prompt(slices)
         slices.append("task")
 
-        if not self.system_template and not self.prompt_template:
+        if (
+            not self.system_template
+            and not self.prompt_template
+            and self.use_system_prompt
+        ):
             return {
                 "system": system,
                 "user": self._build_prompt(["task"]),

tests/agent_test.py

@@ -856,6 +856,31 @@ def test_interpolate_inputs():
     assert agent.backstory == "I am the master of nothing"
 
 
+def test_not_using_system_prompt():
+    agent = Agent(
+        role="{topic} specialist",
+        goal="Figure {goal} out",
+        backstory="I am the master of {role}",
+        use_system_prompt=False,
+    )
+
+    agent.create_agent_executor()
+    assert not agent.agent_executor.prompt.get("user")
+    assert not agent.agent_executor.prompt.get("system")
+
+
+def test_using_system_prompt():
+    agent = Agent(
+        role="{topic} specialist",
+        goal="Figure {goal} out",
+        backstory="I am the master of {role}",
+    )
+
+    agent.create_agent_executor()
+    assert agent.agent_executor.prompt.get("user")
+    assert agent.agent_executor.prompt.get("system")
+
+
 def test_system_and_prompt_template():
     agent = Agent(
         role="{topic} specialist",