Merge branch 'main' into lg-downgrade-litellm

The version 1.167.2 is not compatible with Windows

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.67.2",
+    "litellm==1.67.1",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",

src/crewai/agent.py

@@ -118,10 +118,6 @@ class Agent(BaseAgent):
         default=None,
         description="Knowledge context for the agent.",
     )
-    trained_data_file: Optional[str] = Field(
-        default=TRAINED_AGENTS_DATA_FILE,
-        description="Path to the trained data file to use for task prompts.",
-    )
     crew_knowledge_context: Optional[str] = Field(
         default=None,
         description="Knowledge context for the crew.",
@@ -501,24 +497,13 @@ class Agent(BaseAgent):
         return task_prompt
 
     def _use_trained_data(self, task_prompt: str) -> str:
-        """
-        Use trained data from a specified file for the agent task prompt.
-        
-        Uses the 'trained_data_file' attribute as the source of training instructions.
-        
-        Args:
-            task_prompt: The original task prompt to enhance.
-            
-        Returns:
-            Enhanced task prompt with training instructions if available.
-        """
-        if self.trained_data_file and (data := CrewTrainingHandler(self.trained_data_file).load()):
+        """Use trained data for the agent task prompt to improve output."""
+        if data := CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load():
             if trained_data_output := data.get(self.role):
-                if "suggestions" in trained_data_output:
-                    task_prompt += (
-                        "\n\nYou MUST follow these instructions: \n - "
-                        + "\n - ".join(trained_data_output["suggestions"])
-                    )
+                task_prompt += (
+                    "\n\nYou MUST follow these instructions: \n - "
+                    + "\n - ".join(trained_data_output["suggestions"])
+                )
         return task_prompt
 
     def _render_text_description(self, tools: List[Any]) -> str:

src/crewai/cli/cli.py

@@ -201,17 +201,9 @@ def install(context):
 
 
 @crewai.command()
-@click.option(
-    "-f",
-    "--trained-data-file",
-    type=str,
-    default="trained_agents_data.pkl",
-    help="Path to a trained data file to use for agent task prompts",
-)
-def run(trained_data_file: str):
+def run():
     """Run the Crew."""
-    click.echo(f"Running the Crew with agent training data file: {trained_data_file}")
-    run_crew(trained_data_file=trained_data_file)
+    run_crew()
 
 
 @crewai.command()

src/crewai/cli/run_crew.py

@@ -1,4 +1,3 @@
-import os
 import subprocess
 from enum import Enum
 from typing import List, Optional
@@ -15,16 +14,13 @@ class CrewType(Enum):
     FLOW = "flow"
 
 
-def run_crew(trained_data_file: Optional[str] = None) -> None:
+def run_crew() -> None:
     """
     Run the crew or flow by running a command in the UV environment.
 
     Starting from version 0.103.0, this command can be used to run both
     standard crews and flows. For flows, it detects the type from pyproject.toml
     and automatically runs the appropriate command.
-
-    Args:
-        trained_data_file: Optional path to a trained data file to use
     """
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"
@@ -48,35 +44,17 @@ def run_crew(trained_data_file: Optional[str] = None) -> None:
     click.echo(f"Running the {'Flow' if is_flow else 'Crew'}")
 
     # Execute the appropriate command
-    execute_command(crew_type, trained_data_file)
+    execute_command(crew_type)
 
 
-def execute_command(crew_type: CrewType, trained_data_file: Optional[str] = None) -> None:
+def execute_command(crew_type: CrewType) -> None:
     """
     Execute the appropriate command based on crew type.
 
     Args:
         crew_type: The type of crew to run
-        trained_data_file: Optional path to a trained data file to use
     """
     command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
-    
-    if trained_data_file and crew_type == CrewType.STANDARD:
-        if not trained_data_file.endswith('.pkl'):
-            click.secho(
-                f"Error: Trained data file '{trained_data_file}' must have a .pkl extension.",
-                fg="red",
-            )
-            return
-            
-        if not os.path.exists(trained_data_file):
-            click.secho(
-                f"Error: Trained data file '{trained_data_file}' does not exist.",
-                fg="red",
-            )
-            return
-            
-        command.extend(["--trained-data-file", trained_data_file])
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)

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

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-import argparse
 import sys
 import warnings
 
@@ -18,23 +17,13 @@ def run():
     """
     Run the crew.
     """
-    parser = argparse.ArgumentParser(description="Run the crew")
-    parser.add_argument(
-        "--trained-data-file", 
-        "-f", 
-        type=str, 
-        default="trained_agents_data.pkl",
-        help="Path to a trained data file to use for agent task prompts"
-    )
-    args, _ = parser.parse_known_args()
-    
     inputs = {
         'topic': 'AI LLMs',
         'current_year': str(datetime.now().year)
     }
     
     try:
-        {{crew_name}}().crew(trained_data_file=args.trained_data_file).kickoff(inputs=inputs)
+        {{crew_name}}().crew().kickoff(inputs=inputs)
     except Exception as e:
         raise Exception(f"An error occurred while running the crew: {e}")
 

src/crewai/crew.py

@@ -122,10 +122,6 @@ class Crew(BaseModel):
     tasks: List[Task] = Field(default_factory=list)
     agents: List[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
-    trained_data_file: Optional[str] = Field(
-        default=None,
-        description="Path to the trained data file to use for agent task prompts.",
-    )
     verbose: bool = Field(default=False)
     memory: bool = Field(
         default=False,
@@ -1200,12 +1196,7 @@ class Crew(BaseModel):
             "manager_llm",
         }
 
-        cloned_agents = []
-        for agent in self.agents:
-            cloned_agent = agent.copy()
-            if self.trained_data_file:
-                cloned_agent.trained_data_file = self.trained_data_file
-            cloned_agents.append(cloned_agent)
+        cloned_agents = [agent.copy() for agent in self.agents]
         manager_agent = self.manager_agent.copy() if self.manager_agent else None
         manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
 

tests/agent_test.py

@@ -1196,102 +1196,6 @@ def test_agent_use_trained_data(crew_training_handler):
     )
 
 
-@patch("crewai.agent.CrewTrainingHandler")
-def test_agent_use_custom_trained_data_file(crew_training_handler):
-    task_prompt = "What is 1 + 1?"
-    custom_file = "custom_trained_data.pkl"
-    agent = Agent(
-        role="researcher",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-        trained_data_file=custom_file
-    )
-    crew_training_handler().load.return_value = {
-        agent.role: {
-            "suggestions": [
-                "The result of the math operation must be right.",
-                "Result must be better than 1.",
-            ]
-        }
-    }
-
-    result = agent._use_trained_data(task_prompt=task_prompt)
-
-    assert (
-        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(custom_file), mock.call().load()]
-    )
-
-
-@patch("crewai.agent.CrewTrainingHandler")
-def test_agent_with_none_trained_data_file(crew_training_handler):
-    task_prompt = "What is 1 + 1?"
-    agent = Agent(
-        role="researcher",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-        trained_data_file=None
-    )
-    
-    result = agent._use_trained_data(task_prompt=task_prompt)
-    
-    assert result == task_prompt
-    crew_training_handler.assert_not_called()
-
-
-@patch("crewai.agent.CrewTrainingHandler")
-def test_agent_with_missing_role_in_trained_data(crew_training_handler):
-    task_prompt = "What is 1 + 1?"
-    agent = Agent(
-        role="researcher",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-        trained_data_file="trained_agents_data.pkl"
-    )
-    crew_training_handler().load.return_value = {
-        "other_role": {
-            "suggestions": ["This should not be used."]
-        }
-    }
-    
-    result = agent._use_trained_data(task_prompt=task_prompt)
-    
-    assert result == task_prompt
-    crew_training_handler.assert_has_calls(
-        [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
-    )
-
-
-@patch("crewai.agent.CrewTrainingHandler")
-def test_agent_with_missing_suggestions_in_trained_data(crew_training_handler):
-    task_prompt = "What is 1 + 1?"
-    agent = Agent(
-        role="researcher",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-        trained_data_file="trained_agents_data.pkl"
-    )
-    crew_training_handler().load.return_value = {
-        "researcher": {
-            "other_key": ["This should not be used."]
-        }
-    }
-    
-    result = agent._use_trained_data(task_prompt=task_prompt)
-    
-    assert result == task_prompt
-    crew_training_handler.assert_has_calls(
-        [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
-    )
-
-
 def test_agent_max_retry_limit():
     agent = Agent(
         role="test role",

tests/llm_test.py

@@ -1,5 +1,4 @@
 import os
-import sys
 from time import sleep
 from unittest.mock import MagicMock, patch
 
@@ -286,9 +285,6 @@ def test_gemini_models(model):
     ],
 )
 def test_gemma3(model):
-    if sys.version_info.major == 3 and sys.version_info.minor == 11:
-        pytest.skip("Skipping test_gemma3 on Python 3.11 due to segmentation fault")
-    
     llm = LLM(model=model)
     result = llm.call("What is the capital of France?")
     assert isinstance(result, str)