fix: correct default model (gpt-4o), correct token counts, and correct TaskOutput attributes (added agent) (#749)

* fix: 'from datetime import datetime for logging' to print the timestamp

* fix: correct default model (gpt-4o), correct token counts, and correct TaskOutput attributes (added agent)

* test: verify Task callback data is an instance of TaskOutput

src/crewai/utilities/logger.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 from crewai.utilities.printer import Printer
 
 
@@ -13,6 +14,7 @@ class Logger:
     def log(self, level, message, color="bold_green"):
         level_map = {"debug": 1, "info": 2}
         if self.verbose_level and level_map.get(level, 0) <= self.verbose_level:
-            timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-            self._printer.print(f"[{timestamp}][{level.upper()}]: {message}", color=color)
-
+            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            self._printer.print(
+                f"[{timestamp}][{level.upper()}]: {message}", color=color
+            )

tests/agent_test.py

@@ -29,7 +29,7 @@ def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     assert isinstance(agent.llm, ChatOpenAI)
-    assert agent.llm.model_name == "gpt-4"
+    assert agent.llm.model_name == "gpt-4o"
     assert agent.llm.temperature == 0.7
     assert agent.llm.verbose is False
     assert agent.allow_delegation is True
@@ -732,7 +732,7 @@ def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
 
     assert len(agent1.llm.callbacks) == 1
     assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
-    assert agent1.llm.callbacks[0].model == "gpt-4"
+    assert agent1.llm.callbacks[0].model == "gpt-4o"
     assert (
         agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
     )

tests/crew_test.py

@@ -445,10 +445,14 @@ def test_async_task_execution():
             start.return_value = thread
             with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
                 list_ideas.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", raw_output="ok"
+                    description="A 4 paragraph article about AI.",
+                    raw_output="ok",
+                    agent="writer",
                 )
                 list_important_history.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", raw_output="ok"
+                    description="A 4 paragraph article about AI.",
+                    raw_output="ok",
+                    agent="writer",
                 )
                 crew.kickoff()
                 start.assert_called()
@@ -677,9 +681,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
 
     result = crew.kickoff()
     assert result == '"Howdy!"'
+    print(crew.usage_metrics)
     assert crew.usage_metrics == {
-        "total_tokens": 1666,
-        "prompt_tokens": 1383,
+        "total_tokens": 1664,
+        "prompt_tokens": 1381,
         "completion_tokens": 283,
         "successful_requests": 3,
     }

tests/task_test.py

@@ -1,5 +1,7 @@
 """Test Agent creation and execution basic functionality."""
 
+import json
+
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -7,6 +9,7 @@ from pydantic import BaseModel
 from pydantic_core import ValidationError
 
 from crewai import Agent, Crew, Process, Task
+from crewai.tasks.task_output import TaskOutput
 
 
 def test_task_tool_reflect_agent_tools():
@@ -105,6 +108,46 @@ def test_task_callback():
         task_completed.assert_called_once_with(task.output)
 
 
+def test_task_callback_returns_task_ouput():
+    researcher = Agent(
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+        allow_delegation=False,
+    )
+
+    task_completed = MagicMock(return_value="done")
+
+    task = Task(
+        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
+        expected_output="Bullet point list of 5 interesting ideas.",
+        agent=researcher,
+        callback=task_completed,
+    )
+
+    with patch.object(Agent, "execute_task") as execute:
+        execute.return_value = "exported_ok"
+        task.execute()
+        # Ensure the callback is called with a TaskOutput object serialized to JSON
+        task_completed.assert_called_once()
+        callback_data = task_completed.call_args[0][0]
+
+        # Check if callback_data is TaskOutput object or JSON string
+        if isinstance(callback_data, TaskOutput):
+            callback_data = json.dumps(callback_data.model_dump())
+
+        assert isinstance(callback_data, str)
+        output_dict = json.loads(callback_data)
+        expected_output = {
+            "description": task.description,
+            "exported_output": "exported_ok",
+            "raw_output": "exported_ok",
+            "agent": researcher.role,
+            "summary": "Give me a list of 5 interesting ideas to explore...",
+        }
+        assert output_dict == expected_output
+
+
 def test_execute_with_agent():
     researcher = Agent(
         role="Researcher",