chore: remove useless comments

docs/docs.json

@@ -9,7 +9,12 @@
   },
   "favicon": "/images/favicon.svg",
   "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude"
+    ]
   },
   "navigation": {
     "languages": [
@@ -50,22 +55,32 @@
             "groups": [
               {
                 "group": "Get Started",
-                "pages": ["en/introduction", "en/installation", "en/quickstart"]
+                "pages": [
+                  "en/introduction",
+                  "en/installation",
+                  "en/quickstart"
+                ]
               },
               {
                 "group": "Guides",
                 "pages": [
                   {
                     "group": "Strategy",
-                    "pages": ["en/guides/concepts/evaluating-use-cases"]
+                    "pages": [
+                      "en/guides/concepts/evaluating-use-cases"
+                    ]
                   },
                   {
                     "group": "Agents",
-                    "pages": ["en/guides/agents/crafting-effective-agents"]
+                    "pages": [
+                      "en/guides/agents/crafting-effective-agents"
+                    ]
                   },
                   {
                     "group": "Crews",
-                    "pages": ["en/guides/crews/first-crew"]
+                    "pages": [
+                      "en/guides/crews/first-crew"
+                    ]
                   },
                   {
                     "group": "Flows",
@@ -79,6 +94,7 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
+
                     ]
                   }
                 ]
@@ -225,7 +241,6 @@
                   "en/observability/langtrace",
                   "en/observability/maxim",
                   "en/observability/mlflow",
-                  "en/observability/neatlogs",
                   "en/observability/openlit",
                   "en/observability/opik",
                   "en/observability/patronus-evaluation",
@@ -259,7 +274,9 @@
               },
               {
                 "group": "Telemetry",
-                "pages": ["en/telemetry"]
+                "pages": [
+                  "en/telemetry"
+                ]
               }
             ]
           },
@@ -268,7 +285,9 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/enterprise/introduction"]
+                "pages": [
+                  "en/enterprise/introduction"
+                ]
               },
               {
                 "group": "Features",
@@ -323,7 +342,9 @@
               },
               {
                 "group": "Resources",
-                "pages": ["en/enterprise/resources/frequently-asked-questions"]
+                "pages": [
+                  "en/enterprise/resources/frequently-asked-questions"
+                ]
               }
             ]
           },
@@ -332,7 +353,9 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/api-reference/introduction"]
+                "pages": [
+                  "en/api-reference/introduction"
+                ]
               },
               {
                 "group": "Endpoints",
@@ -342,13 +365,16 @@
           },
           {
             "tab": "Examples",
-            "groups": [
+                        "groups": [
               {
                 "group": "Examples",
-                "pages": ["en/examples/example"]
+                "pages": [
+                  "en/examples/example"
+                ]
               }
             ]
           }
+
         ]
       },
       {
@@ -399,15 +425,21 @@
                 "pages": [
                   {
                     "group": "Estratégia",
-                    "pages": ["pt-BR/guides/concepts/evaluating-use-cases"]
+                    "pages": [
+                      "pt-BR/guides/concepts/evaluating-use-cases"
+                    ]
                   },
                   {
                     "group": "Agentes",
-                    "pages": ["pt-BR/guides/agents/crafting-effective-agents"]
+                    "pages": [
+                      "pt-BR/guides/agents/crafting-effective-agents"
+                    ]
                   },
                   {
                     "group": "Crews",
-                    "pages": ["pt-BR/guides/crews/first-crew"]
+                    "pages": [
+                      "pt-BR/guides/crews/first-crew"
+                    ]
                   },
                   {
                     "group": "Flows",
@@ -600,7 +632,9 @@
               },
               {
                 "group": "Telemetria",
-                "pages": ["pt-BR/telemetry"]
+                "pages": [
+                  "pt-BR/telemetry"
+                ]
               }
             ]
           },
@@ -609,7 +643,9 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/enterprise/introduction"]
+                "pages": [
+                  "pt-BR/enterprise/introduction"
+                ]
               },
               {
                 "group": "Funcionalidades",
@@ -674,7 +710,9 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/api-reference/introduction"]
+                "pages": [
+                  "pt-BR/api-reference/introduction"
+                ]
               },
               {
                 "group": "Endpoints",
@@ -684,13 +722,16 @@
           },
           {
             "tab": "Exemplos",
-            "groups": [
+                        "groups": [
               {
                 "group": "Exemplos",
-                "pages": ["pt-BR/examples/example"]
+                "pages": [
+                  "pt-BR/examples/example"
+                ]
               }
             ]
           }
+
         ]
       }
     ]

docs/en/observability/neatlogs.mdx

@@ -1,140 +0,0 @@
----
-title: Neatlogs Integration
-description: Understand, debug, and share your CrewAI agent runs
-icon: magnifying-glass-chart
----
-
-# Introduction
-
-Neatlogs helps you **see what your agent did**, **why**, and **share it**.
-
-It captures every step: thoughts, tool calls, responses, evaluations. No raw logs. Just clear, structured traces. Great for debugging and collaboration.
-
----
-
-## Why use Neatlogs?
-
-CrewAI agents use multiple tools and reasoning steps. When something goes wrong, you need context — not just errors.
-
-Neatlogs lets you:
-
-- Follow the full decision path
-- Add feedback directly on steps
-- Chat with the trace using AI assistant
-- Share runs publicly for feedback
-- Turn insights into tasks
-
-All in one place.
-
-Manage your traces effortlessly
-
-![Traces](/images/neatlogs-1.png)
-![Trace Response](/images/neatlogs-2.png)
-
-The best UX to view a CrewAI trace. Post comments anywhere you want. Use AI to debug.
-
-![Trace Details](/images/neatlogs-3.png)
-![Ai Chat Bot With A Trace](/images/neatlogs-4.png)
-![Comments Drawer](/images/neatlogs-5.png)
-
----
-
-## Core Features
-
-- **Trace Viewer**: Track thoughts, tools, and decisions in sequence
-- **Inline Comments**: Tag teammates on any trace step
-- **Feedback & Evaluation**: Mark outputs as correct or incorrect
-- **Error Highlighting**: Automatic flagging of API/tool failures
-- **Task Conversion**: Convert comments into assigned tasks
-- **Ask the Trace (AI)**: Chat with your trace using Neatlogs AI bot
-- **Public Sharing**: Publish trace links to your community
-
----
-
-## Quick Setup with CrewAI
-
-<Steps>
-  <Step title="Sign Up & Get API Key">
-    Visit [neatlogs.com](https://neatlogs.com/?utm_source=crewAI-docs), create a project, copy the API key.
-  </Step>
-  <Step title="Install SDK">
-    ```bash
-    pip install neatlogs
-    ```
-    (Latest version 0.8.0, Python 3.8+; MIT license) :contentReference[oaicite:1]{index=1}
-  </Step>
-  <Step title="Initialize Neatlogs">
-    Before starting Crew agents, add:
-
-    ```python
-    import neatlogs
-    neatlogs.init("YOUR_PROJECT_API_KEY")
-    ```
-
-    Agents run as usual. Neatlogs captures everything automatically.
-
-  </Step>
-</Steps>
-
----
-
-## Under the Hood
-
-According to GitHub, Neatlogs:
-
-- Captures thoughts, tool calls, responses, errors, and token stats :contentReference[oaicite:2]{index=2}
-- Supports AI-powered task generation and robust evaluation workflows :contentReference[oaicite:3]{index=3}
-
-All with just two lines of code.
-
----
-
-## Watch It Work
-
-### 🔍 Full Demo (4 min)
-
-<iframe
-  width="100%"
-  height="315"
-  src="https://www.youtube.com/embed/8KDme9T2I7Q?si=b8oHteaBwFNs_Duk"
-  title="YouTube video player"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
-
-### ⚙️ CrewAI Integration (30 s)
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.loom.com/embed/9c78b552af43452bb3e4783cb8d91230?sid=e9d7d370-a91a-49b0-809e-2f375d9e801d"
-  title="Loom video player"
-  frameBorder="0"
-  allowFullScreen
-></iframe>
-
----
-
-## Links & Support
-
-- 📘 [Neatlogs Docs](https://docs.neatlogs.com/)
-- 🔐 [Dashboard & API Key](https://app.neatlogs.com/)
-- 🐦 [Follow on Twitter](https://twitter.com/neatlogs)
-- 📧 Contact: hello@neatlogs.com
-- 🛠 [GitHub SDK](https://github.com/NeatLogs/neatlogs) :contentReference[oaicite:4]{index=4}
-
----
-
-## TL;DR
-
-With just:
-
-```bash
-pip install neatlogs
-
-import neatlogs
-neatlogs.init("YOUR_API_KEY")
-
-You can now capture, understand, share, and act on your CrewAI agent runs in seconds.
-No setup overhead. Full trace transparency. Full team collaboration.
-```

reproduce_issue_3165.py

@@ -1,107 +0,0 @@
-#!/usr/bin/env python3
-"""
-Reproduction script for issue #3165: LLM Failed with Custom OpenAI-Compatible Endpoint
-
-This script reproduces the bug where CrewAI shows generic "LLM Failed" errors
-instead of propagating specific error details from custom endpoints.
-"""
-
-import sys
-from crewai import Agent, Task, Crew
-from crewai.llm import LLM
-
-def test_custom_endpoint_error_handling():
-    """Test error handling with a custom OpenAI-compatible endpoint."""
-    
-    print("Testing custom endpoint error handling...")
-    
-    custom_llm = LLM(
-        model="gpt-3.5-turbo",
-        base_url="https://non-existent-endpoint.example.com/v1",
-        api_key="fake-api-key-for-testing"
-    )
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="Test custom endpoint error handling",
-        backstory="A test agent for reproducing issue #3165",
-        llm=custom_llm,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Say hello world",
-        expected_output="A simple greeting",
-        agent=agent
-    )
-    
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True
-    )
-    
-    try:
-        print("\nAttempting to run crew with custom endpoint...")
-        result = crew.kickoff()
-        print(f"Unexpected success: {result}")
-    except Exception as e:
-        print(f"\nCaught exception: {type(e).__name__}")
-        print(f"Exception message: {str(e)}")
-        
-        if "LLM Failed" in str(e) and "connection" not in str(e).lower():
-            print("\n❌ BUG CONFIRMED: Generic 'LLM Failed' error without specific details")
-            print("Expected: Specific connection/authentication error details")
-            return False
-        else:
-            print("\n✅ Good: Specific error details preserved")
-            return True
-
-def test_direct_llm_call():
-    """Test direct LLM call with custom endpoint."""
-    
-    print("\n" + "="*60)
-    print("Testing direct LLM call with custom endpoint...")
-    
-    custom_llm = LLM(
-        model="gpt-3.5-turbo", 
-        base_url="https://non-existent-endpoint.example.com/v1",
-        api_key="fake-api-key-for-testing"
-    )
-    
-    try:
-        print("Attempting direct LLM call...")
-        response = custom_llm.call("Hello world")
-        print(f"Unexpected success: {response}")
-    except Exception as e:
-        print(f"\nCaught exception: {type(e).__name__}")
-        print(f"Exception message: {str(e)}")
-        
-        error_msg = str(e).lower()
-        if any(keyword in error_msg for keyword in ["connection", "resolve", "network", "timeout", "unreachable"]):
-            print("\n✅ Good: Specific connection error details preserved")
-            return True
-        else:
-            print("\n❌ BUG CONFIRMED: Generic error without connection details")
-            print("Expected: Specific connection error details")
-            return False
-
-if __name__ == "__main__":
-    print("Reproducing issue #3165: LLM Failed with Custom OpenAI-Compatible Endpoint")
-    print("="*80)
-    
-    crew_test_passed = test_custom_endpoint_error_handling()
-    direct_test_passed = test_direct_llm_call()
-    
-    print("\n" + "="*80)
-    print("SUMMARY:")
-    print(f"Crew-level test: {'PASSED' if crew_test_passed else 'FAILED (bug confirmed)'}")
-    print(f"Direct LLM test: {'PASSED' if direct_test_passed else 'FAILED (bug confirmed)'}")
-    
-    if not crew_test_passed or not direct_test_passed:
-        print("\n❌ Issue #3165 reproduced successfully")
-        print("CrewAI is showing generic errors instead of specific endpoint error details")
-        sys.exit(1)
-    else:
-        print("\n✅ Issue #3165 appears to be fixed")
-        sys.exit(0)

src/crewai/llm.py

@@ -984,27 +984,10 @@ class LLM(BaseLLM):
                 # whether to summarize the content or abort based on the respect_context_window flag
                 raise
             except Exception as e:
-                error_info = {
-                    "error_type": type(e).__name__,
-                    "original_error": str(e),
-                    "endpoint_info": {
-                        "base_url": self.base_url,
-                        "model": self.model,
-                        "api_base": self.api_base,
-                    } if self.base_url or self.api_base else None
-                }
-                
                 assert hasattr(crewai_event_bus, "emit")
                 crewai_event_bus.emit(
                     self,
-                    event=LLMCallFailedEvent(
-                        error=str(e),
-                        error_type=error_info["error_type"],
-                        original_error=error_info["original_error"],
-                        endpoint_info=error_info["endpoint_info"],
-                        from_task=from_task, 
-                        from_agent=from_agent
-                    ),
+                    event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
                 )
                 logging.error(f"LiteLLM call failed: {str(e)}")
                 raise

src/crewai/utilities/events/event_listener.py

@@ -361,7 +361,6 @@ class EventListener(BaseEventListener):
                 self.formatter.current_tool_branch,
                 event.error,
                 self.formatter.current_crew_tree,
-                event,
             )
 
         @crewai_event_bus.on(LLMStreamChunkEvent)

src/crewai/utilities/events/llm_events.py

@@ -67,9 +67,6 @@ class LLMCallFailedEvent(LLMEventBase):
 
     error: str
     type: str = "llm_call_failed"
-    error_type: Optional[str] = None
-    original_error: Optional[str] = None
-    endpoint_info: Optional[Dict[str, Any]] = None
 
 
 class FunctionCall(BaseModel):

src/crewai/utilities/events/utils/console_formatter.py

@@ -721,7 +721,7 @@ class ConsoleFormatter:
                 self.print()
 
     def handle_llm_call_failed(
-        self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree], event: Optional[Any] = None
+        self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
     ) -> None:
         """Handle LLM call failed event."""
         if not self.verbose:
@@ -764,19 +764,9 @@ class ConsoleFormatter:
                 self.print(tree_to_use)
                 self.print()
 
-        # Show detailed error panel
+        # Show error panel
         error_content = Text()
         error_content.append("❌ LLM Call Failed\n", style="red bold")
-        
-        if event and hasattr(event, 'error_type') and event.error_type:
-            error_content.append(f"Error Type: {event.error_type}\n", style="yellow")
-        
-        if event and hasattr(event, 'endpoint_info') and event.endpoint_info:
-            endpoint = event.endpoint_info.get('base_url') or event.endpoint_info.get('api_base')
-            if endpoint:
-                error_content.append(f"Endpoint: {endpoint}\n", style="cyan")
-                error_content.append(f"Model: {event.endpoint_info.get('model', 'unknown')}\n", style="cyan")
-        
         error_content.append("Error: ", style="white")
         error_content.append(str(error), style="red")
 

tests/test_custom_endpoint_error_handling.py

@@ -1,185 +0,0 @@
-"""
-Tests for custom endpoint error handling (issue #3165).
-
-These tests verify that CrewAI properly propagates specific error details
-from custom OpenAI-compatible endpoints instead of showing generic "LLM Failed" errors.
-"""
-
-import pytest
-from unittest.mock import patch, MagicMock
-from crewai.llm import LLM
-from crewai.utilities.events.llm_events import LLMCallFailedEvent
-from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
-import requests
-
-
-class TestCustomEndpointErrorHandling:
-    """Test error handling for custom OpenAI-compatible endpoints."""
-
-    def test_connection_error_preserves_details(self):
-        """Test that connection errors preserve specific error details."""
-        custom_llm = LLM(
-            model="gpt-3.5-turbo",
-            base_url="https://non-existent-endpoint.example.com/v1",
-            api_key="fake-api-key"
-        )
-        
-        with patch('litellm.completion') as mock_completion:
-            mock_completion.side_effect = requests.exceptions.ConnectionError(
-                "Failed to establish a new connection: [Errno -2] Name or service not known"
-            )
-            
-            with pytest.raises(requests.exceptions.ConnectionError) as exc_info:
-                custom_llm.call("Hello world")
-            
-            assert "Name or service not known" in str(exc_info.value)
-
-    def test_authentication_error_preserves_details(self):
-        """Test that authentication errors preserve specific error details."""
-        custom_llm = LLM(
-            model="gpt-3.5-turbo",
-            base_url="https://api.openai.com/v1",
-            api_key="invalid-api-key"
-        )
-        
-        with patch('litellm.completion') as mock_completion:
-            mock_completion.side_effect = Exception(
-                "AuthenticationError: Incorrect API key provided"
-            )
-            
-            with pytest.raises(Exception) as exc_info:
-                custom_llm.call("Hello world")
-            
-            assert "AuthenticationError" in str(exc_info.value)
-            assert "Incorrect API key" in str(exc_info.value)
-
-    def test_llm_call_failed_event_enhanced_fields(self):
-        """Test that LLMCallFailedEvent includes enhanced error information."""
-        custom_llm = LLM(
-            model="gpt-3.5-turbo",
-            base_url="https://custom-endpoint.example.com/v1",
-            api_key="test-key"
-        )
-        
-        captured_events = []
-        
-        def capture_event(sender, event):
-            captured_events.append(event)
-        
-        with patch('crewai.utilities.events.crewai_event_bus.crewai_event_bus.emit', side_effect=capture_event):
-            with patch('litellm.completion') as mock_completion:
-                mock_completion.side_effect = requests.exceptions.ConnectionError(
-                    "Connection failed"
-                )
-                
-                with pytest.raises(requests.exceptions.ConnectionError):
-                    custom_llm.call("Hello world")
-        
-        assert len(captured_events) == 2  # Started and Failed events
-        failed_event = captured_events[1]
-        assert isinstance(failed_event, LLMCallFailedEvent)
-        assert failed_event.error_type == "ConnectionError"
-        assert failed_event.original_error == "Connection failed"
-        assert failed_event.endpoint_info is not None
-        assert failed_event.endpoint_info["base_url"] == "https://custom-endpoint.example.com/v1"
-        assert failed_event.endpoint_info["model"] == "gpt-3.5-turbo"
-
-    def test_console_formatter_displays_enhanced_error_info(self):
-        """Test that console formatter displays enhanced error information."""
-        formatter = ConsoleFormatter(verbose=True)
-        
-        mock_event = MagicMock()
-        mock_event.error_type = "ConnectionError"
-        mock_event.endpoint_info = {
-            "base_url": "https://custom-endpoint.example.com/v1",
-            "model": "gpt-3.5-turbo"
-        }
-        
-        captured_output = []
-        
-        def mock_print_panel(content, title, style):
-            captured_output.append(str(content))
-        
-        formatter.print_panel = mock_print_panel
-        
-        formatter.handle_llm_call_failed(
-            tool_branch=None,
-            error="Connection failed",
-            crew_tree=None,
-            event=mock_event
-        )
-        
-        output = captured_output[0]
-        assert "Error Type: ConnectionError" in output
-        assert "Endpoint: https://custom-endpoint.example.com/v1" in output
-        assert "Model: gpt-3.5-turbo" in output
-        assert "Connection failed" in output
-
-    def test_backward_compatibility_without_enhanced_fields(self):
-        """Test that console formatter works without enhanced fields for backward compatibility."""
-        formatter = ConsoleFormatter(verbose=True)
-        
-        captured_output = []
-        
-        def mock_print_panel(content, title, style):
-            captured_output.append(str(content))
-        
-        formatter.print_panel = mock_print_panel
-        
-        formatter.handle_llm_call_failed(
-            tool_branch=None,
-            error="Generic error message",
-            crew_tree=None,
-            event=None
-        )
-        
-        output = captured_output[0]
-        assert "❌ LLM Call Failed" in output
-        assert "Generic error message" in output
-        assert "Error Type:" not in output
-        assert "Endpoint:" not in output
-
-    def test_streaming_response_error_handling(self):
-        """Test that streaming responses also preserve error details."""
-        custom_llm = LLM(
-            model="gpt-3.5-turbo",
-            base_url="https://custom-endpoint.example.com/v1",
-            api_key="test-key",
-            stream=True
-        )
-        
-        with patch('litellm.completion') as mock_completion:
-            mock_completion.side_effect = requests.exceptions.ConnectionError(
-                "Streaming connection failed"
-            )
-            
-            with pytest.raises(Exception) as exc_info:
-                custom_llm.call("Hello world")
-            
-            assert "Streaming connection failed" in str(exc_info.value)
-
-    def test_non_custom_endpoint_error_handling(self):
-        """Test that standard OpenAI endpoint errors are handled normally."""
-        standard_llm = LLM(
-            model="gpt-3.5-turbo",
-            api_key="test-key"
-        )
-        
-        captured_events = []
-        
-        def capture_event(sender, event):
-            captured_events.append(event)
-        
-        with patch('crewai.utilities.events.crewai_event_bus.crewai_event_bus.emit', side_effect=capture_event):
-            with patch('litellm.completion') as mock_completion:
-                mock_completion.side_effect = Exception("Standard API error")
-                
-                with pytest.raises(Exception):
-                    standard_llm.call("Hello world")
-        
-        assert len(captured_events) == 2  # Started and Failed events
-        failed_event = captured_events[1]
-        assert isinstance(failed_event, LLMCallFailedEvent)
-        assert failed_event.error_type == "Exception"
-        assert failed_event.original_error == "Standard API error"
-        assert failed_event.endpoint_info is None  # No custom endpoint info