Fix import sorting in litellm_patch.py

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/agents/agent_builder/base_agent.py

@@ -134,19 +134,6 @@ class BaseAgent(ABC, BaseModel):
     @model_validator(mode="before")
     @classmethod
     def process_model_config(cls, values):
-        """
-        Process model configuration values.
-        
-        Args:
-            values: Configuration values or callable agent
-                When using CrewBase decorator, this can be a callable that returns an agent
-        
-        Returns:
-            Processed configuration or callable agent
-        """
-        # Handle case where values is a function (can happen with CrewBase decorator)
-        if callable(values) and not isinstance(values, dict):
-            return values
         return process_config(values, cls)
 
     @field_validator("tools")

src/crewai/patches/__init__.py

@@ -0,0 +1,11 @@
+"""
+Patches module for CrewAI.
+
+This module contains patches for external dependencies to fix known issues.
+
+Version: 1.0.0
+"""
+
+from crewai.patches.litellm_patch import apply_patches, patch_litellm_ollama_pt
+
+__all__ = ["apply_patches", "patch_litellm_ollama_pt"]

src/crewai/patches/litellm_patch.py

@@ -0,0 +1,186 @@
+"""
+Patch for litellm to fix IndexError in ollama_pt function.
+
+This patch addresses issue #2744 in the crewAI repository, where an IndexError occurs
+in litellm's Ollama prompt template function when CrewAI Agent with Tools uses Ollama/Qwen models.
+
+Version: 1.0.0
+"""
+
+import json
+import logging
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+# Patch version
+PATCH_VERSION = "1.0.0"
+
+
+class PatchApplicationError(Exception):
+    """Exception raised when a patch fails to apply."""
+    pass
+
+
+def apply_patches() -> bool:
+    """
+    Apply all patches to fix known issues with dependencies.
+    
+    Returns:
+        bool: True if all patches were applied successfully, False otherwise.
+    """
+    success = patch_litellm_ollama_pt()
+    logger.info(f"LiteLLM ollama_pt patch applied: {success}")
+    return success
+
+
+def patch_litellm_ollama_pt() -> bool:
+    """
+    Patch the ollama_pt function in litellm to fix IndexError.
+    
+    The issue occurs when accessing messages[msg_i].get("tool_calls") without checking
+    if msg_i is within bounds of the messages list. This happens after tool execution
+    during the next LLM call.
+    
+    Returns:
+        bool: True if the patch was applied successfully, False otherwise.
+    
+    Raises:
+        PatchApplicationError: If there's an error during patch application.
+    """
+    try:
+        # Import the module containing the function to patch
+        import litellm.litellm_core_utils.prompt_templates.factory as factory
+        
+        # Define a patched version of the function
+        def patched_ollama_pt(model: str, messages: List[Dict]) -> Dict[str, Any]:
+            """
+            Patched version of ollama_pt that adds bounds checking.
+            
+            This fixes the IndexError that occurs when the assistant message is the last
+            message in the list and msg_i goes out of bounds.
+            
+            Args:
+                model: The model name.
+                messages: The list of messages to process.
+                
+            Returns:
+                Dict containing the prompt and images.
+            """
+            user_message_types = {"user", "tool", "function"}
+            msg_i = 0
+            images: List[str] = []
+            prompt = ""
+            
+            # Handle empty messages list
+            if not messages:
+                return {"prompt": prompt, "images": images}
+                
+            while msg_i < len(messages):
+                init_msg_i = msg_i
+                user_content_str = ""
+                ## MERGE CONSECUTIVE USER CONTENT ##
+                while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
+                    msg_content = messages[msg_i].get("content")
+                    if msg_content:
+                        if isinstance(msg_content, list):
+                            for m in msg_content:
+                                if m.get("type", "") == "image_url":
+                                    if isinstance(m["image_url"], str):
+                                        images.append(m["image_url"])
+                                    elif isinstance(m["image_url"], dict):
+                                        images.append(m["image_url"]["url"])
+                                elif m.get("type", "") == "text":
+                                    user_content_str += m["text"]
+                        else:
+                            # Tool message content will always be a string
+                            user_content_str += msg_content
+
+                    msg_i += 1
+
+                if user_content_str:
+                    prompt += f"### User:\n{user_content_str}\n\n"
+
+                system_content_str, msg_i = factory._handle_ollama_system_message(
+                    messages, prompt, msg_i
+                )
+                if system_content_str:
+                    prompt += f"### System:\n{system_content_str}\n\n"
+
+                assistant_content_str = ""
+                ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+                while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+                    assistant_content_str += factory.convert_content_list_to_str(messages[msg_i])
+                    msg_i += 1
+
+                    # Add bounds check before accessing messages[msg_i]
+                    # This is the key fix for the IndexError
+                    if msg_i < len(messages):
+                        tool_calls = messages[msg_i].get("tool_calls")
+                        ollama_tool_calls = []
+                        if tool_calls:
+                            for call in tool_calls:
+                                call_id = call["id"]
+                                function_name = call["function"]["name"]
+                                arguments = json.loads(call["function"]["arguments"])
+
+                                ollama_tool_calls.append(
+                                    {
+                                        "id": call_id,
+                                        "type": "function",
+                                        "function": {
+                                            "name": function_name,
+                                            "arguments": arguments,
+                                        },
+                                    }
+                                )
+
+                        if ollama_tool_calls:
+                            assistant_content_str += (
+                                f"Tool Calls: {json.dumps(ollama_tool_calls, indent=2)}"
+                            )
+
+                            msg_i += 1
+
+                if assistant_content_str:
+                    prompt += f"### Assistant:\n{assistant_content_str}\n\n"
+
+                if msg_i == init_msg_i:  # prevent infinite loops
+                    raise factory.litellm.BadRequestError(
+                        message=factory.BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
+                        model=model,
+                        llm_provider="ollama",
+                    )
+
+            response_dict = {
+                "prompt": prompt,
+                "images": images,
+            }
+
+            return response_dict
+        
+        # Replace the original function with our patched version
+        factory.ollama_pt = patched_ollama_pt
+        
+        logger.info(f"Successfully applied litellm ollama_pt patch version {PATCH_VERSION}")
+        return True
+    except Exception as e:
+        error_msg = f"Failed to apply litellm ollama_pt patch: {e}"
+        logger.error(error_msg)
+        return False
+
+
+# For backwards compatibility
+def patch_litellm() -> bool:
+    """
+    Legacy function for backwards compatibility.
+    
+    Returns:
+        bool: True if the patch was applied successfully, False otherwise.
+    """
+    try:
+        return patch_litellm_ollama_pt()
+    except Exception as e:
+        logger.error(f"Failed to apply legacy litellm patch: {e}")
+        return False

src/crewai/project/annotations.py

@@ -65,27 +65,6 @@ def cache_handler(func):
     return memoize(func)
 
 
-def _resolve_agent(task_instance):
-    """
-    Resolve an agent from a task instance.
-    
-    If the agent is a callable (e.g., a method from CrewBase), call it to get the agent instance.
-    
-    Args:
-        task_instance: The task instance containing the agent
-    
-    Returns:
-        The resolved agent instance or None if no agent is present
-    """
-    if not hasattr(task_instance, 'agent') or not task_instance.agent:
-        return None
-        
-    if callable(task_instance.agent) and not isinstance(task_instance.agent, type):
-        return task_instance.agent()
-        
-    return task_instance.agent
-
-
 def crew(func) -> Callable[..., Crew]:
 
     @wraps(func)
@@ -100,14 +79,7 @@ def crew(func) -> Callable[..., Crew]:
 
         # Instantiate tasks in order
         for task_name, task_method in tasks:
-            # Get the task instance
             task_instance = task_method(self)
-            
-            # Resolve the agent
-            agent = _resolve_agent(task_instance)
-            if agent:
-                task_instance.agent = agent
-                
             instantiated_tasks.append(task_instance)
             agent_instance = getattr(task_instance, "agent", None)
             if agent_instance and agent_instance.role not in agent_roles:

src/crewai/task.py

@@ -61,25 +61,6 @@ class Task(BaseModel):
         output_pydantic: Pydantic model for task output.
         tools: List of tools/resources limited for task execution.
     """
-    
-    def __init__(self, **data):
-        # Handle case where agent is a callable (can happen with CrewBase decorator)
-        if 'agent' in data and callable(data['agent']) and not isinstance(data['agent'], type):
-            try:
-                # Call the agent method to get the agent instance
-                agent = data['agent']()
-                
-                # Verify that the agent is a valid instance
-                from crewai.agents.agent_builder.base_agent import BaseAgent
-                if agent is not None and not isinstance(agent, BaseAgent):
-                    raise ValueError(f"Expected BaseAgent instance, got {type(agent)}")
-                    
-                data['agent'] = agent
-            except Exception as e:
-                raise ValueError(f"Failed to initialize agent from callable: {e}")
-        
-        # Call the parent class __init__ method
-        super().__init__(**data)
 
     __hash__ = object.__hash__  # type: ignore
     logger: ClassVar[logging.Logger] = logging.getLogger(__name__)

tests/patches/test_litellm_patch.py

@@ -0,0 +1,71 @@
+"""
+Test for the litellm patch that fixes the IndexError in ollama_pt function.
+"""
+
+import sys
+import unittest
+from unittest.mock import MagicMock, patch
+
+import litellm
+import pytest
+from litellm.litellm_core_utils.prompt_templates.factory import ollama_pt
+
+from crewai.patches.litellm_patch import patch_litellm_ollama_pt
+
+
+class TestLitellmPatch(unittest.TestCase):
+    def test_ollama_pt_patch_fixes_index_error(self):
+        """Test that the patch fixes the IndexError in ollama_pt."""
+        # Create a message list where the assistant message is the last one
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there"},
+        ]
+        
+        # Store the original function to restore it after the test
+        original_ollama_pt = litellm.litellm_core_utils.prompt_templates.factory.ollama_pt
+        
+        try:
+            # Apply the patch
+            success = patch_litellm_ollama_pt()
+            self.assertTrue(success, "Patch application failed")
+            
+            # Use the function from the module directly to ensure we're using the patched version
+            result = litellm.litellm_core_utils.prompt_templates.factory.ollama_pt("qwen3:4b", messages)
+            
+            # Verify the result is as expected
+            self.assertIn("prompt", result)
+            self.assertIn("images", result)
+            self.assertIn("### User:\nHello", result["prompt"])
+            self.assertIn("### Assistant:\nHi there", result["prompt"])
+        finally:
+            # Restore the original function to avoid affecting other tests
+            litellm.litellm_core_utils.prompt_templates.factory.ollama_pt = original_ollama_pt
+    
+    def test_ollama_pt_patch_with_empty_messages(self):
+        """Test that the patch handles empty message lists."""
+        messages = []
+        
+        # Store the original function to restore it after the test
+        original_ollama_pt = litellm.litellm_core_utils.prompt_templates.factory.ollama_pt
+        
+        try:
+            # Apply the patch
+            success = patch_litellm_ollama_pt()
+            self.assertTrue(success, "Patch application failed")
+            
+            # Use the function from the module directly to ensure we're using the patched version
+            result = litellm.litellm_core_utils.prompt_templates.factory.ollama_pt("qwen3:4b", messages)
+            
+            # Verify the result is as expected
+            self.assertIn("prompt", result)
+            self.assertIn("images", result)
+            self.assertEqual("", result["prompt"])
+            self.assertEqual([], result["images"])
+        finally:
+            # Restore the original function to avoid affecting other tests
+            litellm.litellm_core_utils.prompt_templates.factory.ollama_pt = original_ollama_pt
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_crewbase_agent_method.py

@@ -1,51 +0,0 @@
-import unittest
-
-from crewai import Agent, Task
-
-
-class TestTaskInitFix(unittest.TestCase):
-    """Test the fix for issue #2219 where agent methods are not handled correctly in tasks."""
-
-    def test_task_init_handles_callable_agent(self):
-        """Test that the Task.__init__ method correctly handles callable agents."""
-        
-        # Create an agent instance
-        agent_instance = Agent(
-            role="Test Agent",
-            goal="Test Goal",
-            backstory="Test Backstory"
-        )
-        
-        # Create a callable that returns the agent instance
-        def callable_agent():
-            return agent_instance
-        
-        # Create a task with the callable agent
-        task = Task(
-            description="Test Task",
-            expected_output="Test Output",
-            agent=callable_agent
-        )
-        
-        # Verify that the agent in the task is an instance, not a callable
-        self.assertIsInstance(task.agent, Agent)
-        self.assertEqual(task.agent.role, "Test Agent")
-        self.assertIs(task.agent, agent_instance)
-        
-    def test_task_init_handles_invalid_callable_agent(self):
-        """Test that the Task.__init__ method correctly handles invalid callable agents."""
-        
-        # Create a callable that returns an invalid agent (not an Agent instance)
-        def invalid_callable_agent():
-            return "Not an agent"
-        
-        # Create a task with the invalid callable agent
-        with self.assertRaises(ValueError) as context:
-            task = Task(
-                description="Test Task",
-                expected_output="Test Output",
-                agent=invalid_callable_agent
-            )
-        
-        # Verify that the error message is correct
-        self.assertIn("Expected BaseAgent instance", str(context.exception))