Fix lint: Apply automatic fixes from ruff linter

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

docs/changelog.mdx

@@ -4,36 +4,6 @@ description: View the latest updates and changes to CrewAI
 icon: timeline
 ---
 
-<Update label="2025-04-07" description="v0.114.0">
-  ## Release Highlights
-  <Frame>
-    <img src="/images/v01140.png" />
-  </Frame>
-
-  **New Features & Enhancements**
-  - Agents as an atomic unit. (`Agent(...).kickoff()`)
-  - Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
-  - Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
-  - Enhanced YAML extraction.
-  - Multimodal agent validation.
-  - Added Secure fingerprints for agents and crews.
-
-  **Core Improvements & Fixes**
-  - Improved serialization, agent copying, and Python compatibility.
-  - Added wildcard support to `emit()`
-  - Added support for additional router calls and context window adjustments.
-  - Fixed typing issues, validation, and import statements.
-  - Improved method performance.
-  - Enhanced agent task handling, event emissions, and memory management.
-  - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
-
-  **Documentation & Guides**
-  - Improved documentation structure, theme, and organization.
-  - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
-  - Updated tool configuration examples, prompts, and observability docs.
-  - Guide on using singular agents within Flows.
-</Update>
-
 <Update label="2025-03-17" description="v0.108.0">
   **Features**
   - Converted tabs to spaces in `crew.py` template

docs/concepts/langchain-tools.mdx

@@ -1,10 +1,10 @@
 ---
-title: LangChain Tool
-description: The `LangChainTool` is a wrapper for LangChain tools and query engines.
+title: Using LangChain Tools
+description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
 icon: link
 ---
 
-## `LangChainTool`
+## Using LangChain Tools
 
 <Info>
     CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.

docs/concepts/llamaindex-tools.mdx

@@ -0,0 +1,71 @@
+---
+title: Using LlamaIndex Tools
+description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
+icon: toolbox
+---
+
+## Using LlamaIndex Tools
+
+<Info>
+    CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. 
+</Info>
+
+Here are the available built-in tools offered by LlamaIndex.
+
+```python Code
+from crewai import Agent
+from crewai_tools import LlamaIndexTool
+
+# Example 1: Initialize from FunctionTool
+from llama_index.core.tools import FunctionTool
+
+your_python_function = lambda ...: ...
+og_tool = FunctionTool.from_defaults(
+    your_python_function, 
+    name="<name>", 
+    description='<description>'
+)
+tool = LlamaIndexTool.from_tool(og_tool)
+
+# Example 2: Initialize from LlamaHub Tools
+from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
+wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
+wolfram_tools = wolfram_spec.to_tool_list()
+tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
+
+# Example 3: Initialize Tool from a LlamaIndex Query Engine
+query_engine = index.as_query_engine()
+query_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="Uber 2019 10K Query Tool",
+    description="Use this tool to lookup the 2019 Uber 10K Annual Report"
+)
+
+# Create and assign the tools to an agent
+agent = Agent(
+    role='Research Analyst',
+    goal='Provide up-to-date market analysis',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[tool, *tools, query_tool]
+)
+
+# rest of the code ...
+```
+
+## Steps to Get Started
+
+To effectively use the LlamaIndexTool, follow these steps:
+
+<Steps>
+    <Step title="Package Installation">
+    Make sure that `crewai[tools]` package is installed in your Python environment:
+    <CodeGroup>
+        ```shell Terminal
+        pip install 'crewai[tools]'
+        ```
+    </CodeGroup>
+    </Step>
+    <Step title="Install and Use LlamaIndex">
+    Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
+    </Step>
+</Steps>

docs/custom_llm.md

@@ -1,13 +1,9 @@
----
-title: Custom LLM Implementation
-description: Learn how to create custom LLM implementations in CrewAI.
-icon: code
----
-
-## Custom LLM Implementations
+# Custom LLM Implementations
 
 CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
 
+## Using Custom LLM Implementations
+
 To create a custom LLM implementation, you need to:
 
 1. Inherit from the `BaseLLM` abstract base class

docs/docs.json

@@ -11,21 +11,22 @@
   "navigation": {
     "tabs": [
       {
-        "tab": "Documentation",
+        "tab": "Get Started",
         "groups": [
           {
             "group": "Get Started",
             "pages": [
               "introduction",
               "installation",
-              "quickstart"
+              "quickstart",
+              "changelog"
             ]
           },
           {
             "group": "Guides",
             "pages": [
               {
-                "group": "Strategy",
+                "group": "Concepts",
                 "pages": [
                   "guides/concepts/evaluating-use-cases"
                 ]
@@ -78,6 +79,41 @@
               "concepts/event-listener"
             ]
           },
+          {
+            "group": "How to Guides",
+            "pages": [
+              "how-to/create-custom-tools",
+              "how-to/sequential-process",
+              "how-to/hierarchical-process",
+              "how-to/custom-manager-agent",
+              "how-to/llm-connections",
+              "how-to/customizing-agents",
+              "how-to/multimodal-agents",
+              "how-to/coding-agents",
+              "how-to/force-tool-output-as-result",
+              "how-to/human-input-on-execution",
+              "how-to/kickoff-async",
+              "how-to/kickoff-for-each",
+              "how-to/replay-tasks-from-latest-crew-kickoff",
+              "how-to/conditional-tasks",
+              "how-to/langchain-tools",
+              "how-to/llamaindex-tools"
+            ]
+          },
+          {
+            "group": "Agent Monitoring & Observability",
+            "pages": [
+              "how-to/agentops-observability",
+              "how-to/arize-phoenix-observability",
+              "how-to/langfuse-observability",
+              "how-to/langtrace-observability",
+              "how-to/mlflow-observability",
+              "how-to/openlit-observability",
+              "how-to/opik-observability",
+              "how-to/portkey-observability",
+              "how-to/weave-integration"
+            ]
+          },
           {
             "group": "Tools",
             "pages": [
@@ -105,7 +141,6 @@
               "tools/hyperbrowserloadtool",
               "tools/linkupsearchtool",
               "tools/llamaindextool",
-              "tools/langchaintool",
               "tools/serperdevtool",
               "tools/s3readertool",
               "tools/s3writertool",
@@ -135,40 +170,6 @@
               "tools/youtubevideosearchtool"
             ]
           },
-          {
-            "group": "Agent Monitoring & Observability",
-            "pages": [
-              "how-to/agentops-observability",
-              "how-to/arize-phoenix-observability",
-              "how-to/langfuse-observability",
-              "how-to/langtrace-observability",
-              "how-to/mlflow-observability",
-              "how-to/openlit-observability",
-              "how-to/opik-observability",
-              "how-to/portkey-observability",
-              "how-to/weave-integration"
-            ]
-          },
-          {
-            "group": "Learn",
-            "pages": [
-              "how-to/conditional-tasks",
-              "how-to/coding-agents",
-              "how-to/create-custom-tools",
-              "how-to/custom-llm",
-              "how-to/custom-manager-agent",
-              "how-to/customizing-agents",
-              "how-to/force-tool-output-as-result",
-              "how-to/hierarchical-process",
-              "how-to/human-input-on-execution",
-              "how-to/kickoff-async",
-              "how-to/kickoff-for-each",
-              "how-to/llm-connections",
-              "how-to/multimodal-agents",
-              "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/sequential-process"
-            ]
-          },
           {
             "group": "Telemetry",
             "pages": [
@@ -187,35 +188,19 @@
             ]
           }
         ]
-      },
-      {
-        "tab": "Releases",
-        "groups": [
-          {
-            "group": "Releases",
-            "pages": [
-              "changelog"
-            ]
-          }
-        ]
       }
     ],
     "global": {
       "anchors": [
         {
-          "anchor": "Website",
-          "href": "https://crewai.com",
-          "icon": "globe"
-        },
-        {
-          "anchor": "Forum",
+          "anchor": "Community",
           "href": "https://community.crewai.com",
           "icon": "discourse"
         },
         {
-          "anchor": "Get Help",
-          "href": "mailto:support@crewai.com",
-          "icon": "headset"
+          "anchor": "Tutorials",
+          "href": "https://www.youtube.com/@crewAIInc",
+          "icon": "youtube"
         }
       ]
     }
@@ -229,12 +214,6 @@
     "strict": false
   },
   "navbar": {
-    "links": [
-      {
-        "label": "Start Free Trial",
-        "href": "https://app.crewai.com"
-      }
-    ],
     "primary": {
       "type": "github",
       "href": "https://github.com/crewAIInc/crewAI"

docs/how-to/custom-manager-agent.mdx

@@ -1,5 +1,5 @@
 ---
-title: Custom Manager Agent
+title: Create Your Own Manager Agent
 description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
 icon: user-shield
 ---

docs/how-to/replay-tasks-from-latest-crew-kickoff.mdx

@@ -20,8 +20,10 @@ Here's an example of how to replay from a task:
 To use the replay feature, follow these steps:
 
 <Steps>
-   <Step title="Open your terminal or command prompt."></Step>
-   <Step title="Navigate to the directory where your CrewAI project is located."></Step>
+   <Step title="Open your terminal or command prompt.">
+   </Step>
+   <Step title="Navigate to the directory where your CrewAI project is located.">
+   </Step>
    <Step title="Run the following commands:">
       To view the latest kickoff task_ids use:
 

docs/quickstart.mdx

@@ -336,22 +336,9 @@ email_summarizer_task:
       - research_task
 ```
 
-## Deploying Your Crew
+## Deploying Your Project
 
-The easiest way to deploy your crew to production is through [CrewAI Enterprise](http://app.crewai.com).
-
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
+The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
 
 <CardGroup cols={2}>
   <Card

src/crewai/crew.py

@@ -720,8 +720,13 @@ class Crew(BaseModel):
     def _handle_crew_planning(self):
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")
+        
+        agent_llm = self.agents[0].llm if self.agents and hasattr(self.agents[0], 'llm') else None
+        
         result = CrewPlanner(
-            tasks=self.tasks, planning_agent_llm=self.planning_llm
+            tasks=self.tasks, 
+            planning_agent_llm=self.planning_llm,
+            agent_llm=agent_llm
         )._handle_crew_planning()
 
         for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):

src/crewai/llm.py

@@ -358,12 +358,7 @@ class LLM(BaseLLM):
         }
 
         # Remove None values from params
-        params = {k: v for k, v in params.items() if v is not None}
-        
-        if "o4-mini" in self.model:
-            params.pop("stop", None)
-            
-        return params
+        return {k: v for k, v in params.items() if v is not None}
 
     def _handle_streaming_response(
         self,

src/crewai/utilities/planning_handler.py

@@ -28,11 +28,23 @@ class PlannerTaskPydanticOutput(BaseModel):
 
 class CrewPlanner:
     """Plans and coordinates the execution of crew tasks."""
-    def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
+    def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None, agent_llm: Optional[Any] = None):
         self.tasks = tasks
 
         if planning_agent_llm is None:
-            self.planning_agent_llm = "gpt-4o-mini"
+            if agent_llm is not None and hasattr(agent_llm, "base_url") and agent_llm.base_url is not None:
+                from crewai.llm import LLM
+                self.planning_agent_llm = LLM(
+                    model="gpt-4o-mini",
+                    base_url=agent_llm.base_url,
+                    api_key=getattr(agent_llm, "api_key", None),
+                    organization=getattr(agent_llm, "organization", None),
+                    api_version=getattr(agent_llm, "api_version", None),
+                    extra_headers=getattr(agent_llm, "extra_headers", None)
+                )
+            else:
+                from crewai.llm import LLM
+                self.planning_agent_llm = LLM(model="gpt-4o-mini")
         else:
             self.planning_agent_llm = planning_agent_llm
 

tests/llm_test.py

@@ -533,39 +533,3 @@ def test_handle_streaming_tool_calls_no_tools(mock_emit):
         expected_completed_llm_call=1,
         expected_final_chunk_result=response,
     )
-
-
-def test_llm_o4_mini_stop_parameter():
-    """Test that o4-mini model works correctly without stop parameter."""
-    llm = LLM(model="o4-mini", stop=["STOP", "END"])
-    
-    # Check that stop parameter is set
-    assert llm.stop == ["STOP", "END"]
-    
-    params = llm._prepare_completion_params(messages=[{"role": "user", "content": "Hello"}])
-    
-    assert "stop" not in params
-    
-    with patch("litellm.completion") as mock_completion:
-        # Create a mock response
-        mock_message = MagicMock()
-        mock_message.content = "Test response"
-        mock_choice = MagicMock()
-        mock_choice.message = mock_message
-        mock_response = MagicMock()
-        mock_response.choices = [mock_choice]
-        mock_response.usage = {
-            "prompt_tokens": 5,
-            "completion_tokens": 5,
-            "total_tokens": 10,
-        }
-        
-        # Set up the mock to return our response
-        mock_completion.return_value = mock_response
-        
-        response = llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
-        
-        assert response == "Test response"
-        
-        call_args = mock_completion.call_args[1]
-        assert "stop" not in call_args

tests/utilities/test_planning_auth/test_planning_auth_inheritance.py

@@ -0,0 +1,28 @@
+import unittest
+from unittest.mock import MagicMock
+
+from crewai import Task
+from crewai.utilities.planning_handler import CrewPlanner
+
+
+def test_planning_llm_inherits_auth_params():
+    """Test that planning LLM inherits authentication parameters from agent LLM."""
+    mock_llm = MagicMock()
+    mock_llm.base_url = "https://api.custom-provider.com/v1"
+    mock_llm.api_version = "2023-05-15"
+    
+    task = Task(
+        description="Test Task",
+        expected_output="Test Output"
+    )
+    
+    planner = CrewPlanner(
+        tasks=[task],
+        planning_agent_llm=None,  # This should trigger the inheritance logic
+        agent_llm=mock_llm
+    )
+    
+    assert hasattr(planner, 'planning_agent_llm')
+    assert hasattr(planner.planning_agent_llm, 'base_url')
+    assert planner.planning_agent_llm.base_url == "https://api.custom-provider.com/v1"
+    assert planner.planning_agent_llm.api_version == "2023-05-15"