fixing tasks order

src/crewai/agents/crew_agent_executor.py

@@ -70,7 +70,10 @@ def __init__(
         self.log_error_after = 3
         self.have_forced_answer = False
         self.name_to_tool_map = {tool.name: tool for tool in self.tools}
-        self.llm.stop = self.stop
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:

src/crewai/project/annotations.py

@@ -1,6 +1,7 @@
 from functools import wraps
 
 from crewai.project.utils import memoize
+from crewai import Crew
 
 
 def task(func):
@@ -72,7 +73,7 @@ def pipeline(func):
     return memoize(func)
 
 
-def crew(func):
+def crew(func) -> "Crew":
     def wrapper(self, *args, **kwargs):
         instantiated_tasks = []
         instantiated_agents = []

src/crewai/project/crew_base.py

@@ -5,11 +5,8 @@
 import yaml
 from dotenv import load_dotenv
 
-from crewai.crew import Crew
-
 load_dotenv()
 
-
 T = TypeVar("T", bound=Type[Any])
 
 
@@ -37,19 +34,6 @@ def __init__(self, *args, **kwargs):
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
-        def crew(self) -> "Crew":
-            agents = [
-                getattr(self, name)()
-                for name, func in self._get_all_functions().items()
-                if hasattr(func, "is_agent")
-            ]
-            tasks = [
-                getattr(self, name)()
-                for name, func in reversed(self._get_all_functions().items())
-                if hasattr(func, "is_task")
-            ]
-            return Crew(agents=agents, tasks=tasks)
-
         @staticmethod
         def load_yaml(config_path: Path):
             try:

tests/agent_test.py

@@ -1093,7 +1093,7 @@ def test_agent_training_handler(crew_training_handler):
 
     result = agent._training_handler(task_prompt=task_prompt)
 
-    assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good"
+    assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good"
 
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("training_data.pkl"), mock.call().load()]
@@ -1121,8 +1121,8 @@ def test_agent_use_trained_data(crew_training_handler):
     result = agent._use_trained_data(task_prompt=task_prompt)
 
     assert (
-        result == "What is 1 + 1?You MUST follow these feedbacks: \n "
-        "The result of the math operation must be right.\n - Result must be better than 1."
+        result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
+        " - The result of the math operation must be right.\n - Result must be better than 1."
     )
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
@@ -1205,7 +1205,7 @@ def test_agent_with_custom_stop_words():
     )
 
     assert isinstance(agent.llm, LLM)
-    assert agent.llm.stop == stop_words
+    assert agent.llm.stop == stop_words + ["\nObservation:"]
 
 
 def test_agent_with_callbacks():
@@ -1368,7 +1368,7 @@ def test_agent_with_all_llm_attributes():
     assert agent.llm.temperature == 0.7
     assert agent.llm.top_p == 0.9
     assert agent.llm.n == 1
-    assert agent.llm.stop == ["STOP", "END"]
+    assert agent.llm.stop == ["STOP", "END", "\nObservation:"]
     assert agent.llm.max_tokens == 100
     assert agent.llm.presence_penalty == 0.1
     assert agent.llm.frequency_penalty == 0.1

tests/utilities/evaluators/test_task_evaluator.py

@@ -50,11 +50,12 @@ def test_evaluate_training_data(converter_mock):
                 text="Assess the quality of the training data based on the llm output, human feedback , and llm "
                 "output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
                 "1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
-                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- "
-                "Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs "
-                "provide action items based on human_feedback for future tasks\n- A score from 0 to 10 evaluating "
-                "on completion, quality, and overall performance from the improved output to the initial output "
-                "based on the human feedback\n",
+                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- Provide "
+                "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
+                "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
+                "action items for future tasks. Ensure all key and specificpoints from the human feedback are "
+                "incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and "
+                "overall performance from the improved output to the initial output based on the human feedback\n",
                 model=TrainingTaskEvaluation,
                 instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
                 "following structure, with the following keys:\n{\n    suggestions: List[str],\n    quality: float,\n    final_summary: str\n}",