fixing console formatter

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

docs/concepts/cli.mdx

@@ -200,37 +200,6 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
     ```
     - Reads your local project configuration.
     - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
-
-### 11. Organization Management
-
-Manage your CrewAI Enterprise organizations.
-
-```shell Terminal
-crewai org [COMMAND] [OPTIONS]
-```
-
-#### Commands:
-
-- `list`: List all organizations you belong to
-```shell Terminal
-crewai org list
-```
-
-- `current`: Display your currently active organization
-```shell Terminal
-crewai org current
-```
-
-- `switch`: Switch to a specific organization
-```shell Terminal
-crewai org switch <organization_id>
-```
-
-<Note>
-You must be authenticated to CrewAI Enterprise to use these organization management commands.
-</Note>
-
-- **Create a deployment** (continued):
     - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
 
 - **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.

docs/concepts/planning.mdx

@@ -29,10 +29,6 @@ my_crew = Crew(
 
 From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
 
-<Warning>
-When planning is enabled, crewAI will use `gpt-4o-mini` as the default LLM for planning, which requires a valid OpenAI API key. Since your agents might be using different LLMs, this could cause confusion if you don't have an OpenAI API key configured or if you're experiencing unexpected behavior related to LLM API calls.
-</Warning>
-
 #### Planning LLM
 
 Now you can define the LLM that will be used to plan the tasks. 

docs/concepts/reasoning.mdx

@@ -6,11 +6,11 @@ icon: brain
 
 ## Overview
 
-Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
+Agent reasoning is a feature that allows agents to reflect on a task and create a plan before and during execution. This helps agents approach tasks more methodically and adapt their strategy as they progress through complex tasks.
 
 ## Usage
 
-To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
+To enable reasoning for an agent, set `reasoning=True` when creating the agent:
 
 ```python
 from crewai import Agent
@@ -19,13 +19,43 @@ agent = Agent(
     role="Data Analyst",
     goal="Analyze complex datasets and provide insights",
     backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
+    reasoning=True,  # Enable basic reasoning
     max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
 )
 ```
 
+### Interval-based Reasoning
+
+To enable periodic reasoning during task execution, set `reasoning_interval` to specify how often the agent should re-evaluate its plan:
+
+```python
+agent = Agent(
+    role="Research Analyst",
+    goal="Find comprehensive information about a topic",
+    backstory="You are a skilled research analyst who methodically approaches information gathering.",
+    reasoning=True,
+    reasoning_interval=3,  # Re-evaluate plan every 3 steps
+)
+```
+
+### Adaptive Reasoning
+
+For more dynamic reasoning that adapts to the execution context, enable `adaptive_reasoning`:
+
+```python
+agent = Agent(
+    role="Strategic Advisor",
+    goal="Provide strategic advice based on market research",
+    backstory="You are an experienced strategic advisor who adapts your approach based on the information you discover.",
+    reasoning=True,
+    adaptive_reasoning=True,  # Agent decides when to reason based on context
+)
+```
+
 ## How It Works
 
+### Initial Reasoning
+
 When reasoning is enabled, before executing a task, the agent will:
 
 1. Reflect on the task and create a detailed plan
@@ -33,7 +63,17 @@ When reasoning is enabled, before executing a task, the agent will:
 3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
 4. Inject the reasoning plan into the task description before execution
 
-This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
+### Mid-execution Reasoning
+
+During task execution, the agent can re-evaluate and adjust its plan based on:
+
+1. **Interval-based reasoning**: The agent reasons after a fixed number of steps (specified by `reasoning_interval`)
+2. **Adaptive reasoning**: The agent uses its LLM to intelligently decide when reasoning is needed based on:
+   - Current execution context (task description, expected output, steps taken)
+   - The agent's own judgment about whether strategic reassessment would be beneficial
+   - Automatic fallback when recent errors or failures are detected in the execution
+
+This mid-execution reasoning helps agents adapt to new information, overcome obstacles, and optimize their approach as they work through complex tasks.
 
 ## Configuration Options
 
@@ -45,35 +85,44 @@ This process helps the agent break down complex tasks into manageable steps and
     Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
 </ParamField>
 
-## Example
+<ParamField body="reasoning_interval" type="int" default="None"> 
+    Interval of steps after which the agent should reason again during execution. If None, reasoning only happens before execution.
+</ParamField>
 
-Here's a complete example:
+<ParamField body="adaptive_reasoning" type="bool" default="False"> 
+    Whether the agent should adaptively decide when to reason during execution based on context.
+</ParamField>
 
-```python
-from crewai import Agent, Task, Crew
+## Technical Implementation
 
-# Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
-    reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
-)
+### Interval-based Reasoning
 
-# Create a task
-analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
-)
+The interval-based reasoning feature works by:
 
-# Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
-result = crew.kickoff()
+1. Tracking the number of steps since the last reasoning event
+2. Triggering reasoning when `steps_since_reasoning >= reasoning_interval`
+3. Resetting the counter after each reasoning event
+4. Generating an updated plan based on current progress
 
-print(result)
-```
+This creates a predictable pattern of reflection during task execution, which is useful for complex tasks where periodic reassessment is beneficial.
+
+### Adaptive Reasoning
+
+The adaptive reasoning feature uses LLM function calling to determine when reasoning should occur:
+
+1. **LLM-based decision**: The agent's LLM evaluates the current execution context (task description, expected output, steps taken so far) to decide if reasoning is needed
+2. **Error detection fallback**: When recent messages contain error indicators like "error", "exception", "failed", etc., reasoning is automatically triggered
+
+This creates an intelligent reasoning pattern where the agent uses its own judgment to determine when strategic reassessment would be most beneficial, while maintaining automatic error recovery.
+
+### Mid-execution Reasoning Process
+
+When mid-execution reasoning is triggered, the agent:
+
+1. Summarizes current progress (steps taken, tools used, recent actions)
+2. Evaluates the effectiveness of the current approach
+3. Adjusts the plan based on new information and challenges encountered
+4. Continues execution with the updated plan
 
 ## Error Handling
 
@@ -93,7 +142,7 @@ agent = Agent(
     role="Data Analyst",
     goal="Analyze data and provide insights",
     reasoning=True,
-    max_reasoning_attempts=3
+    reasoning_interval=5  # Re-evaluate plan every 5 steps
 )
 
 # Create a task
@@ -144,4 +193,33 @@ I'll analyze the sales data to identify the top 3 trends.
 READY: I am ready to execute the task.
 ```
 
-This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.
+During execution, the agent might generate an updated plan:
+
+```
+Based on progress so far (3 steps completed):
+
+Updated Reasoning Plan:
+After examining the data structure and initial exploratory analysis, I need to adjust my approach:
+
+1. Current findings:
+   - The data shows seasonal patterns that need deeper investigation
+   - Customer segments show varying purchasing behaviors
+   - There are outliers in the luxury product category
+
+2. Adjusted approach:
+   - Focus more on seasonal analysis with year-over-year comparisons
+   - Segment analysis by both demographics and purchasing frequency
+   - Investigate the luxury product category anomalies
+
+3. Next steps:
+   - Apply time series analysis to better quantify seasonal patterns
+   - Create customer cohorts for more precise segmentation
+   - Perform statistical tests on the luxury category data
+
+4. Expected outcome:
+   Still on track to deliver the top 3 sales trends, but with more precise quantification and actionable insights.
+
+READY: I am ready to continue executing the task.
+```
+
+This mid-execution reasoning helps the agent adapt its approach based on what it has learned during the initial steps of the task.

docs/concepts/tools.mdx

@@ -32,7 +32,6 @@ The Enterprise Tools Repository includes:
 - **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
 - **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
 - **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
-- **Asynchronous Support**: Handles both synchronous and asynchronous tools, enabling non-blocking operations.
 
 ## Using CrewAI Tools
 
@@ -178,62 +177,6 @@ class MyCustomTool(BaseTool):
         return "Tool's result"
 ```
 
-## Asynchronous Tool Support
-
-CrewAI supports asynchronous tools, allowing you to implement tools that perform non-blocking operations like network requests, file I/O, or other async operations without blocking the main execution thread.
-
-### Creating Async Tools
-
-You can create async tools in two ways:
-
-#### 1. Using the `tool` Decorator with Async Functions
-
-```python Code
-from crewai.tools import tool
-
-@tool("fetch_data_async")
-async def fetch_data_async(query: str) -> str:
-    """Asynchronously fetch data based on the query."""
-    # Simulate async operation
-    await asyncio.sleep(1)
-    return f"Data retrieved for {query}"
-```
-
-#### 2. Implementing Async Methods in Custom Tool Classes
-
-```python Code
-from crewai.tools import BaseTool
-
-class AsyncCustomTool(BaseTool):
-    name: str = "async_custom_tool"
-    description: str = "An asynchronous custom tool"
-    
-    async def _run(self, query: str = "") -> str:
-        """Asynchronously run the tool"""
-        # Your async implementation here
-        await asyncio.sleep(1)
-        return f"Processed {query} asynchronously"
-```
-
-### Using Async Tools
-
-Async tools work seamlessly in both standard Crew workflows and Flow-based workflows:
-
-```python Code
-# In standard Crew
-agent = Agent(role="researcher", tools=[async_custom_tool])
-
-# In Flow
-class MyFlow(Flow):
-    @start()
-    async def begin(self):
-        crew = Crew(agents=[agent])
-        result = await crew.kickoff_async()
-        return result
-```
-
-The CrewAI framework automatically handles the execution of both synchronous and asynchronous tools, so you don't need to worry about how to call them differently.
-
 ### Utilizing the `tool` Decorator
 
 ```python Code

docs/docs.json

@@ -9,12 +9,7 @@
   },
   "favicon": "images/favicon.svg",
   "contextual": {
-    "options": [
-      "copy",
-      "view",
-      "chatgpt",
-      "claude"
-    ]
+    "options": ["copy", "view", "chatgpt", "claude"]
   },
   "navigation": {
     "tabs": [
@@ -206,7 +201,6 @@
               "observability/arize-phoenix",
               "observability/langfuse",
               "observability/langtrace",
-              "observability/maxim",
               "observability/mlflow",
               "observability/openlit",
               "observability/opik",
@@ -262,8 +256,7 @@
               "enterprise/features/tool-repository",
               "enterprise/features/webhook-streaming",
               "enterprise/features/traces",
-              "enterprise/features/hallucination-guardrail",
-              "enterprise/features/integrations"
+              "enterprise/features/hallucination-guardrail"
             ]
           },
           {

docs/enterprise/features/integrations.mdx

@@ -1,185 +0,0 @@
----
-title: Integrations
-description: "Connected applications for your agents to take actions."
-icon: "plug"
----
-
-## Overview
-
-Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services.
-
-<Frame>
-  ![Integrations](/images/enterprise/crew_connectors.png)
-</Frame>
-
-## Supported Integrations
-
-### **Communication & Collaboration**
-- **Gmail** - Manage emails and drafts
-- **Slack** - Workspace notifications and alerts
-- **Microsoft** - Office 365 and Teams integration
-
-### **Project Management**
-- **Jira** - Issue tracking and project management
-- **ClickUp** - Task and productivity management
-- **Asana** - Team task and project coordination
-- **Notion** - Page and database management
-- **Linear** - Software project and bug tracking
-- **GitHub** - Repository and issue management
-
-### **Customer Relationship Management**
-- **Salesforce** - CRM account and opportunity management
-- **HubSpot** - Sales pipeline and contact management
-- **Zendesk** - Customer support ticket management
-
-### **Business & Finance**
-- **Stripe** - Payment processing and customer management
-- **Shopify** - E-commerce store and product management
-
-### **Productivity & Storage**
-- **Google Sheets** - Spreadsheet data synchronization
-- **Google Calendar** - Event and schedule management
-- **Box** - File storage and document management
-
-and more to come!
-
-## Prerequisites
-
-Before using Authentication Integrations, ensure you have:
-
-- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial.
-
-
-## Setting Up Integrations
-
-### 1. Connect Your Account
-
-1. Navigate to [CrewAI Enterprise](https://app.crewai.com)
-2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors
-3. Click **Connect** on your desired service from the Authentication Integrations section
-4. Complete the OAuth authentication flow
-5. Grant necessary permissions for your use case
-6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
-
-<Frame>
-  ![Integrations](/images/enterprise/enterprise_action_auth_token.png)
-</Frame>
-
-### 2. Install Integration Tools
-
-All you need is the latest version of `crewai-tools` package.
-
-```bash
-uv add crewai-tools
-```
-
-## Usage Examples
-
-### Basic Usage
-<Tip>
-  All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go.
-</Tip>
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import CrewaiEnterpriseTools
-
-# Get enterprise tools (Gmail tool will be included)
-enterprise_tools = CrewaiEnterpriseTools(
-    enterprise_token="your_enterprise_token"
-)
-# print the tools
-print(enterprise_tools)
-
-# Create an agent with Gmail capabilities
-email_agent = Agent(
-    role="Email Manager",
-    goal="Manage and organize email communications",
-    backstory="An AI assistant specialized in email management and communication.",
-    tools=[enterprise_tools]
-)
-
-# Task to send an email
-email_task = Task(
-    description="Draft and send a follow-up email to john@example.com about the project update",
-    agent=email_agent,
-    expected_output="Confirmation that email was sent successfully"
-)
-
-# Run the task
-crew = Crew(
-    agents=[email_agent],
-    tasks=[email_task]
-)
-
-# Run the crew
-crew.kickoff()
-```
-
-### Filtering Tools
-
-```python
-from crewai_tools import CrewaiEnterpriseTools
-
-enterprise_tools = CrewaiEnterpriseTools(
-    actions_list=["gmail_find_email"] # only gmail_find_email tool will be available
-)
-gmail_tool = enterprise_tools[0]
-
-gmail_agent = Agent(
-    role="Gmail Manager",
-    goal="Manage gmail communications and notifications",
-    backstory="An AI assistant that helps coordinate gmail communications.",
-    tools=[gmail_tool]
-)
-
-notification_task = Task(
-    description="Find the email from john@example.com",
-    agent=gmail_agent,
-    expected_output="Email found from john@example.com"
-)
-
-# Run the task
-crew = Crew(
-    agents=[slack_agent],
-    tasks=[notification_task]
-)
-```
-
-## Best Practices
-
-### Security
-- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks
-- **Regular Audits**: Periodically review connected integrations and their permissions
-- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow
-
-
-### Filtering Tools
-On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to.
-
-<Frame>
-  ![Integrations](/images/enterprise/filtering_enterprise_action_tools.png)
-</Frame>
-
-
-### Scoped Deployments for multi user organizations
-You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account.
-
-<Tip>
-  This is useful for multi user organizations where you want to scope the integration to a specific user.
-</Tip>
-
-
-Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew.
-
-<Frame>
-  ![Integrations](/images/enterprise/user_bearer_token.png)
-</Frame>
-
-
-
-### Getting Help
-
-<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with integration setup or troubleshooting.
-</Card>

docs/guides/flows/mastering-flow-state.mdx

@@ -277,23 +277,22 @@ This pattern allows you to combine direct data passing with state updates for ma
 
 One of CrewAI's most powerful features is the ability to persist flow state across executions. This enables workflows that can be paused, resumed, and even recovered after failures.
 
-### The @persist() Decorator
+### The @persist Decorator
 
-The `@persist()` decorator automates state persistence, saving your flow's state at key points in execution.
+The `@persist` decorator automates state persistence, saving your flow's state at key points in execution.
 
 #### Class-Level Persistence
 
-When applied at the class level, `@persist()` saves state after every method execution:
+When applied at the class level, `@persist` saves state after every method execution:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from crewai.flow.persistence import persist
+from crewai.flow.flow import Flow, listen, persist, start
 from pydantic import BaseModel
 
 class CounterState(BaseModel):
     value: int = 0
 
-@persist()  # Apply to the entire flow class
+@persist  # Apply to the entire flow class
 class PersistentCounterFlow(Flow[CounterState]):
     @start()
     def increment(self):
@@ -320,11 +319,10 @@ print(f"Second run result: {result2}")  # Will be higher due to persisted state
 
 #### Method-Level Persistence
 
-For more granular control, you can apply `@persist()` to specific methods:
+For more granular control, you can apply `@persist` to specific methods:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from crewai.flow.persistence import persist
+from crewai.flow.flow import Flow, listen, persist, start
 
 class SelectivePersistFlow(Flow):
     @start()
@@ -332,7 +330,7 @@ class SelectivePersistFlow(Flow):
         self.state["count"] = 1
         return "First step"
 
-    @persist()  # Only persist after this method
+    @persist  # Only persist after this method
     @listen(first_step)
     def important_step(self, prev_result):
         self.state["count"] += 1

docs/installation.mdx

@@ -22,7 +22,7 @@ Watch this video tutorial for a step-by-step demonstration of the installation p
 <Note>
   **Python Version Requirements**
 
-  CrewAI requires `Python >=3.10 and <3.14`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <=3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```

docs/observability/maxim.mdx

@@ -1,152 +0,0 @@
----
-title: Maxim Integration
-description: Start Agent monitoring, evaluation, and observability
-icon: bars-staggered
----
-
-# Maxim Integration
-
-Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your CrewAI applications. With Maxim's one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.
-
-
-## Features: One Line Integration
-
-- **End-to-End Agent Tracing**: Monitor the complete lifecycle of your agents
-- **Performance Analytics**: Track latency, tokens consumed, and costs
-- **Hyperparameter Monitoring**: View the configuration details of your agent runs
-- **Tool Call Tracking**: Observe when and how agents use their tools
-- **Advanced Visualisation**: Understand agent trajectories through intuitive dashboards
-
-## Getting Started
-
-### Prerequisites
-
-- Python version >=3.10
-- A Maxim account ([sign up here](https://getmaxim.ai/))
-- A CrewAI project
-
-### Installation
-
-Install the Maxim SDK via pip:
-
-```python
-pip install maxim-py>=3.6.2
-```
-
-Or add it to your `requirements.txt`:
-
-```
-maxim-py>=3.6.2
-```
-
-
-### Basic Setup
-
-### 1. Set up environment variables
-
-```python
-### Environment Variables Setup
-
-# Create a `.env` file in your project root:
-
-# Maxim API Configuration
-MAXIM_API_KEY=your_api_key_here
-MAXIM_LOG_REPO_ID=your_repo_id_here
-```
-
-### 2. Import the required packages
-
-```python
-from crewai import Agent, Task, Crew, Process
-from maxim import Maxim
-from maxim.logger.crewai import instrument_crewai
-```
-
-### 3. Initialise Maxim with your API key
-
-```python
-# Initialize Maxim logger
-logger = Maxim().logger()
-
-# Instrument CrewAI with just one line
-instrument_crewai(logger)
-```
-
-### 4. Create and run your CrewAI application as usual
-
-```python
-
-# Create your agent
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Uncover cutting-edge developments in AI',
-    backstory="You are an expert researcher at a tech think tank...",
-    verbose=True,
-    llm=llm
-)
-
-# Define the task
-research_task = Task(
-    description="Research the latest AI advancements...",
-    expected_output="",
-    agent=researcher
-)
-
-# Configure and run the crew
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    verbose=True
-)
-
-try:
-    result = crew.kickoff()
-finally:
-    maxim.cleanup()  # Ensure cleanup happens even if errors occur
-```
-
-That's it! All your CrewAI agent interactions will now be logged and available in your Maxim dashboard.
-
-Check this Google Colab Notebook for a quick reference - [Notebook](https://colab.research.google.com/drive/1ZKIZWsmgQQ46n8TH9zLsT1negKkJA6K8?usp=sharing)
-
-## Viewing Your Traces
-
-After running your CrewAI application:
-
-![Example trace in Maxim showing agent interactions](https://raw.githubusercontent.com/maximhq/maxim-docs/master/images/Screenshot2025-05-14at12.10.58PM.png)
-
-1. Log in to your [Maxim Dashboard](https://getmaxim.ai/dashboard)
-2. Navigate to your repository
-3. View detailed agent traces, including:
-    - Agent conversations
-    - Tool usage patterns
-    - Performance metrics
-    - Cost analytics
-
-## Troubleshooting
-
-### Common Issues
-
-- **No traces appearing**: Ensure your API key and repository ID are correc
-- Ensure you've **called `instrument_crewai()`** ***before*** running your crew. This initializes logging hooks correctly.
-- Set `debug=True` in your `instrument_crewai()` call to surface any internal errors:
-    
-    ```python
-    instrument_crewai(logger, debug=True)
-    ```
-    
-- Configure your agents with `verbose=True` to capture detailed logs:
-    
-    ```python
-    
-    agent = CrewAgent(..., verbose=True)
-    ```
-    
-- Double-check that `instrument_crewai()` is called **before** creating or executing agents. This might be obvious, but it's a common oversight.
-
-### Support
-
-If you encounter any issues:
-
-- Check the [Maxim Documentation](https://getmaxim.ai/docs)
-- Maxim Github [Link](https://github.com/maximhq)

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.72.0",
+    "litellm==1.68.0",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",

score.json

@@ -0,0 +1,3 @@
+{
+  "score": 4
+}

src/crewai/agent.py

@@ -1,8 +1,8 @@
 import shutil
 import subprocess
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
 
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import Field, InstanceOf, PrivateAttr, field_validator, model_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -135,6 +135,21 @@ class Agent(BaseAgent):
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
+    reasoning_interval: Optional[int] = Field(
+        default=None,
+        description="Interval of steps after which the agent should reason again during execution. If None, reasoning only happens before execution.",
+    )
+
+    @field_validator('reasoning_interval')
+    @classmethod
+    def validate_reasoning_interval(cls, v):
+        if v is not None and v < 1:
+            raise ValueError("reasoning_interval must be >= 1")
+        return v
+    adaptive_reasoning: bool = Field(
+        default=False,
+        description="Whether the agent should adaptively decide when to reason during execution based on context.",
+    )
     embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
@@ -155,13 +170,6 @@ class Agent(BaseAgent):
         default=None,
         description="The Agent's role to be used from your repository.",
     )
-    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
-        default=None,
-        description="Function or string description of a guardrail to validate agent output"
-    )
-    guardrail_max_retries: int = Field(
-        default=3, description="Maximum number of retries when guardrail fails"
-    )
 
     @model_validator(mode="before")
     def validate_from_repository(cls, v):
@@ -173,6 +181,9 @@ class Agent(BaseAgent):
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
 
+        if getattr(self, "adaptive_reasoning", False) and not getattr(self, "reasoning", False):
+            self.reasoning = True
+
         self.llm = create_llm(self.llm)
         if self.function_calling_llm and not isinstance(
             self.function_calling_llm, BaseLLM
@@ -384,6 +395,41 @@ class Agent(BaseAgent):
         else:
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
+        if self.reasoning:
+            try:
+                from crewai.utilities.reasoning_handler import (
+                    AgentReasoning,
+                    AgentReasoningOutput,
+                )
+
+                reasoning_handler = AgentReasoning(
+                    task=task,
+                    agent=self,
+                    extra_context=context or "",
+                )
+
+                reasoning_output: AgentReasoningOutput = reasoning_handler.handle_agent_reasoning()
+
+                plan_text = reasoning_output.plan.plan
+
+                internal_plan_msg = (
+                    "### INTERNAL PLAN (do NOT reveal or repeat)\n" + plan_text
+                )
+
+                task_prompt = (
+                    task_prompt
+                    + "\n\n"
+                    + internal_plan_msg
+                )
+
+            except Exception as e:
+                if hasattr(self, "_logger"):
+                    self._logger.log(
+                        "error", f"Error during reasoning process: {str(e)}"
+                    )
+                else:
+                    print(f"Error during reasoning process: {str(e)}")
+
         try:
             crewai_event_bus.emit(
                 self,
@@ -787,8 +833,6 @@ class Agent(BaseAgent):
             response_format=response_format,
             i18n=self.i18n,
             original_agent=self,
-            guardrail=self.guardrail,
-            guardrail_max_retries=self.guardrail_max_retries,
         )
 
         return lite_agent.kickoff(messages)

src/crewai/agents/crew_agent_executor.py

@@ -1,4 +1,5 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+from collections import deque
+from typing import Any, Callable, Dict, List, Optional, Union, cast
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -83,6 +84,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, Union[CrewStructuredTool, BaseTool]] = {
             tool.name: tool for tool in self.tools
         }
+        self.tools_used: deque[str] = deque(maxlen=100)  # Limit history size
+        self.steps_since_reasoning = 0
         existing_stop = self.llm.stop or []
         self.llm.stop = list(
             set(
@@ -188,6 +191,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer, tool_result
                     )
 
+                if self._should_trigger_reasoning():
+                    self._handle_mid_execution_reasoning()
+                else:
+                    self.steps_since_reasoning += 1
+
                 self._invoke_step_callback(formatted_answer)
                 self._append_message(formatted_answer.text, role="assistant")
 
@@ -212,6 +220,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                         i18n=self._i18n,
+                        task_description=getattr(self.task, "description", None),
+                        expected_output=getattr(self.task, "expected_output", None),
                     )
                     continue
                 else:
@@ -232,6 +242,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self, formatted_answer: AgentAction, tool_result: ToolResult
     ) -> Union[AgentAction, AgentFinish]:
         """Handle the AgentAction, execute tools, and process the results."""
+        if hasattr(formatted_answer, 'tool') and formatted_answer.tool:
+            if formatted_answer.tool not in self.tools_used:
+                self.tools_used.append(formatted_answer.tool)
+
         # Special case for add_image_tool
         add_image_tool = self._i18n.tools("add_image")
         if (
@@ -285,39 +299,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
         )
 
-    def _summarize_messages(self) -> None:
-        messages_groups = []
-        for message in self.messages:
-            content = message["content"]
-            cut_size = self.llm.get_context_window_size()
-            for i in range(0, len(content), cut_size):
-                messages_groups.append({"content": content[i : i + cut_size]})
-
-        summarized_contents = []
-        for group in messages_groups:
-            summary = self.llm.call(
-                [
-                    format_message_for_llm(
-                        self._i18n.slice("summarizer_system_message"), role="system"
-                    ),
-                    format_message_for_llm(
-                        self._i18n.slice("summarize_instruction").format(
-                            group=group["content"]
-                        ),
-                    ),
-                ],
-                callbacks=self.callbacks,
-            )
-            summarized_contents.append({"content": str(summary)})
-
-        merged_summary = " ".join(content["content"] for content in summarized_contents)
-
-        self.messages = [
-            format_message_for_llm(
-                self._i18n.slice("summary").format(merged_summary=merged_summary)
-            )
-        ]
-
     def _handle_crew_training_output(
         self, result: AgentFinish, human_feedback: Optional[str] = None
     ) -> None:
@@ -450,3 +431,146 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             ),
             color="red",
         )
+
+    def _should_trigger_reasoning(self) -> bool:
+        """
+        Determine if mid-execution reasoning should be triggered.
+
+        Returns:
+            bool: True if reasoning should be triggered, False otherwise.
+        """
+        if self.iterations == 0:
+            return False
+
+        if not hasattr(self.agent, "reasoning") or not self.agent.reasoning:
+            return False
+
+        if hasattr(self.agent, "reasoning_interval") and self.agent.reasoning_interval is not None:
+            return self.steps_since_reasoning >= self.agent.reasoning_interval
+
+        if hasattr(self.agent, "adaptive_reasoning") and self.agent.adaptive_reasoning:
+            return self._should_adaptive_reason()
+
+        return False
+
+    def _should_adaptive_reason(self) -> bool:
+        """
+        Determine if adaptive reasoning should be triggered using LLM decision.
+        Fallback to error detection if LLM decision fails.
+
+        Returns:
+            bool: True if adaptive reasoning should be triggered, False otherwise.
+        """
+        if self._has_recent_errors():
+            try:
+                from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
+                from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+
+                crewai_event_bus.emit(
+                    self.agent,
+                    AgentAdaptiveReasoningDecisionEvent(
+                        agent_role=self.agent.role,
+                        task_id=str(self.task.id),
+                        should_reason=True,
+                        reasoning="Recent error indicators detected in previous messages.",
+                    ),
+                )
+            except Exception:
+                pass
+            return True
+
+        try:
+            from crewai.utilities.reasoning_handler import AgentReasoning
+            from crewai.agent import Agent
+
+            current_progress = self._summarize_current_progress()
+
+            reasoning_handler = AgentReasoning(task=self.task, agent=cast(Agent, self.agent))
+
+            return reasoning_handler.should_adaptive_reason_llm(
+                current_steps=self.iterations,
+                tools_used=list(self.tools_used),
+                current_progress=current_progress,
+            )
+        except Exception as e:
+            self._printer.print(
+                content=f"Error during adaptive reasoning decision: {str(e)}. Using fallback error detection.",
+                color="yellow",
+            )
+            return False
+
+    def _has_recent_errors(self) -> bool:
+        """Check for error indicators in recent messages."""
+        error_indicators = ["error", "exception", "failed", "unable to", "couldn't"]
+        recent_messages = self.messages[-3:] if len(self.messages) >= 3 else self.messages
+
+        for message in recent_messages:
+            content = message.get("content", "").lower()
+            if any(indicator in content for indicator in error_indicators):
+                return True
+        return False
+
+    def _handle_mid_execution_reasoning(self) -> None:
+        """
+        Handle mid-execution reasoning by calling the reasoning handler.
+        """
+        if not hasattr(self.agent, "reasoning") or not self.agent.reasoning:
+            return
+
+        try:
+            from crewai.utilities.reasoning_handler import AgentReasoning
+
+            current_progress = self._summarize_current_progress()
+
+            from crewai.agent import Agent
+
+            reasoning_handler = AgentReasoning(task=self.task, agent=cast(Agent, self.agent))
+
+            reasoning_output = reasoning_handler.handle_mid_execution_reasoning(
+                current_steps=self.iterations,
+                tools_used=list(self.tools_used),
+                current_progress=current_progress,
+                iteration_messages=self.messages
+            )
+
+            updated_plan_msg = (
+                self._i18n.retrieve("reasoning", "mid_execution_reasoning_update").format(
+                    plan=reasoning_output.plan.plan
+                ) +
+                "\n\nRemember: strictly follow the updated plan above and ensure the final answer fully meets the EXPECTED OUTPUT criteria."
+            )
+
+            self._append_message(updated_plan_msg, role="assistant")
+
+            self.steps_since_reasoning = 0
+
+        except Exception as e:
+            self._printer.print(
+                content=f"Error during mid-execution reasoning: {str(e)}",
+                color="red",
+            )
+
+    def _summarize_current_progress(self) -> str:
+        """
+        Create a summary of the current execution progress.
+
+        Returns:
+            str: A summary of the current progress.
+        """
+        recent_messages = self.messages[-5:] if len(self.messages) >= 5 else self.messages
+
+        summary = f"After {self.iterations} steps, "
+
+        if self.tools_used:
+            unique_tools = set(self.tools_used)
+            summary += f"I've used {len(self.tools_used)} tools ({', '.join(unique_tools)}). "
+        else:
+            summary += "I haven't used any tools yet. "
+
+        if recent_messages:
+            last_message = recent_messages[-1].get("content", "")
+            if len(last_message) > 100:
+                last_message = last_message[:100] + "..."
+            summary += f"Most recent action: {last_message}"
+
+        return summary

src/crewai/cli/organization/main.py

@@ -1,7 +1,6 @@
 from rich.console import Console
 from rich.table import Table
 
-from requests import HTTPError
 from crewai.cli.command import BaseCommand, PlusAPIMixin
 from crewai.cli.config import Settings
 
@@ -17,7 +16,7 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin):
             response = self.plus_api_client.get_organizations()
             response.raise_for_status()
             orgs = response.json()
-
+            
             if not orgs:
                 console.print("You don't belong to any organizations yet.", style="yellow")
                 return
@@ -27,14 +26,8 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin):
             table.add_column("ID", style="green")
             for org in orgs:
                 table.add_row(org["name"], org["uuid"])
-
+            
             console.print(table)
-        except HTTPError as e:
-            if e.response.status_code == 401:
-                console.print("You are not logged in to any organization. Use 'crewai login' to login.", style="bold red")
-                return
-            console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red")
-            raise SystemExit(1)
         except Exception as e:
             console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red")
             raise SystemExit(1)
@@ -44,24 +37,18 @@ class OrganizationCommand(BaseCommand, PlusAPIMixin):
             response = self.plus_api_client.get_organizations()
             response.raise_for_status()
             orgs = response.json()
-
+            
             org = next((o for o in orgs if o["uuid"] == org_id), None)
             if not org:
                 console.print(f"Organization with id '{org_id}' not found.", style="bold red")
                 return
-
+            
             settings = Settings()
             settings.org_name = org["name"]
             settings.org_uuid = org["uuid"]
             settings.dump()
-
+            
             console.print(f"Successfully switched to {org['name']} ({org['uuid']})", style="bold green")
-        except HTTPError as e:
-            if e.response.status_code == 401:
-                console.print("You are not logged in to any organization. Use 'crewai login' to login.", style="bold red")
-                return
-            console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red")
-            raise SystemExit(1)
         except Exception as e:
             console.print(f"Failed to switch organization: {str(e)}", style="bold red")
             raise SystemExit(1)

src/crewai/cli/tools/main.py

@@ -91,7 +91,6 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             console.print(
                 f"[green]Found these tools to publish: {', '.join([e['name'] for e in available_exports])}[/green]"
             )
-        self._print_current_organization()
 
         with tempfile.TemporaryDirectory() as temp_build_dir:
             subprocess.run(
@@ -137,7 +136,6 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         )
 
     def install(self, handle: str):
-        self._print_current_organization()
         get_response = self.plus_api_client.get_tool(handle)
 
         if get_response.status_code == 404:
@@ -184,7 +182,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings.dump()
 
         console.print(
-            f"Successfully authenticated to the tool repository as {settings.org_name} ({settings.org_uuid}).", style="bold green"
+            "Successfully authenticated to the tool repository.", style="bold green"
         )
 
     def _add_package(self, tool_details: dict[str, Any]):
@@ -242,10 +240,3 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         )
 
         return env
-
-    def _print_current_organization(self):
-        settings = Settings()
-        if settings.org_uuid:
-            console.print(f"Current organization: {settings.org_name} ({settings.org_uuid})", style="bold blue")
-        else:
-            console.print("No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.", style="yellow")

src/crewai/crew.py

@@ -655,6 +655,8 @@ class Crew(FlowTrackable, BaseModel):
             if self.planning:
                 self._handle_crew_planning()
 
+            metrics: List[UsageMetrics] = []
+
             if self.process == Process.sequential:
                 result = self._run_sequential_process()
             elif self.process == Process.hierarchical:
@@ -667,8 +669,11 @@ class Crew(FlowTrackable, BaseModel):
             for after_callback in self.after_kickoff_callbacks:
                 result = after_callback(result)
 
-            self.usage_metrics = self.calculate_usage_metrics()
+            metrics += [agent._token_process.get_summary() for agent in self.agents]
 
+            self.usage_metrics = UsageMetrics()
+            for metric in metrics:
+                self.usage_metrics.add_usage_metrics(metric)
             return result
         except Exception as e:
             crewai_event_bus.emit(

src/crewai/lite_agent.py

@@ -1,14 +1,9 @@
 import asyncio
-import inspect
 import uuid
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast, get_args, get_origin
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Optional, Type, Union, cast
 
-try:
-    from typing import Self
-except ImportError:
-    from typing_extensions import Self
-
-from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator, field_validator
+from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -23,7 +18,6 @@ from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities import I18N
-from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.agent_utils import (
     enforce_rpm_limit,
     format_message_for_llm,
@@ -41,7 +35,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
     show_agent_logs,
 )
-from crewai.utilities.converter import generate_model_description
+from crewai.utilities.converter import convert_to_model, generate_model_description
 from crewai.utilities.events.agent_events import (
     LiteAgentExecutionCompletedEvent,
     LiteAgentExecutionErrorEvent,
@@ -152,15 +146,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         default=[], description="Callbacks to be used for the agent"
     )
 
-    # Guardrail Properties
-    guardrail: Optional[Union[Callable[[LiteAgentOutput], Tuple[bool, Any]], str]] = Field(
-        default=None,
-        description="Function or string description of a guardrail to validate agent output"
-    )
-    guardrail_max_retries: int = Field(
-        default=3, description="Maximum number of retries when guardrail fails"
-    )
-
     # State and Results
     tools_results: List[Dict[str, Any]] = Field(
         default=[], description="Results of the tools used by the agent."
@@ -178,9 +163,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     _messages: List[Dict[str, str]] = PrivateAttr(default_factory=list)
     _iterations: int = PrivateAttr(default=0)
     _printer: Printer = PrivateAttr(default_factory=Printer)
-    _guardrail: Optional[Callable] = PrivateAttr(default=None)
-    _guardrail_retry_count: int = PrivateAttr(default=0)
-
 
     @model_validator(mode="after")
     def setup_llm(self):
@@ -202,60 +184,6 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return self
 
-    @model_validator(mode="after")
-    def ensure_guardrail_is_callable(self) -> Self:
-        if callable(self.guardrail):
-            self._guardrail = self.guardrail
-        elif isinstance(self.guardrail, str):
-            from crewai.tasks.llm_guardrail import LLMGuardrail
-            assert isinstance(self.llm, LLM)
-
-            self._guardrail = LLMGuardrail(
-                description=self.guardrail, llm=self.llm
-            )
-
-        return self
-
-    @field_validator("guardrail", mode="before")
-    @classmethod
-    def validate_guardrail_function(cls, v: Optional[Union[Callable, str]]) -> Optional[Union[Callable, str]]:
-        """Validate that the guardrail function has the correct signature.
-
-        If v is a callable, validate that it has the correct signature.
-        If v is a string, return it as is.
-
-        Args:
-            v: The guardrail function to validate or a string describing the guardrail task
-
-        Returns:
-            The validated guardrail function or a string describing the guardrail task
-        """
-        if v is None or isinstance(v, str):
-            return v
-
-        # Check function signature
-        sig = inspect.signature(v)
-        if len(sig.parameters) != 1:
-            raise ValueError(
-                f"Guardrail function must accept exactly 1 parameter (LiteAgentOutput), "
-                f"but it accepts {len(sig.parameters)}"
-            )
-
-        # Check return annotation if present
-        if sig.return_annotation is not sig.empty:
-            if sig.return_annotation == Tuple[bool, Any]:
-                return v
-
-            origin = get_origin(sig.return_annotation)
-            args = get_args(sig.return_annotation)
-
-            if origin is not tuple or len(args) != 2 or args[0] is not bool:
-                raise ValueError(
-                    "If return type is annotated, it must be Tuple[bool, Any]"
-                )
-
-        return v
-
     @property
     def key(self) -> str:
         """Get the unique key for this agent instance."""
@@ -295,7 +223,54 @@ class LiteAgent(FlowTrackable, BaseModel):
             # Format messages for the LLM
             self._messages = self._format_messages(messages)
 
-            return self._execute_core(agent_info=agent_info)
+            # Emit event for agent execution start
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionStartedEvent(
+                    agent_info=agent_info,
+                    tools=self._parsed_tools,
+                    messages=messages,
+                ),
+            )
+
+            # Execute the agent using invoke loop
+            agent_finish = self._invoke_loop()
+            formatted_result: Optional[BaseModel] = None
+            if self.response_format:
+                try:
+                    # Cast to BaseModel to ensure type safety
+                    result = self.response_format.model_validate_json(
+                        agent_finish.output
+                    )
+                    if isinstance(result, BaseModel):
+                        formatted_result = result
+                except Exception as e:
+                    self._printer.print(
+                        content=f"Failed to parse output into response format: {str(e)}",
+                        color="yellow",
+                    )
+
+            # Calculate token usage metrics
+            usage_metrics = self._token_process.get_summary()
+
+            # Create output
+            output = LiteAgentOutput(
+                raw=agent_finish.output,
+                pydantic=formatted_result,
+                agent_role=self.role,
+                usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            )
+
+            # Emit completion event
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionCompletedEvent(
+                    agent_info=agent_info,
+                    output=agent_finish.output,
+                ),
+            )
+
+            return output
 
         except Exception as e:
             self._printer.print(
@@ -313,94 +288,6 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
             raise e
 
-    def _execute_core(self, agent_info: Dict[str, Any]) -> LiteAgentOutput:
-        # Emit event for agent execution start
-        crewai_event_bus.emit(
-            self,
-            event=LiteAgentExecutionStartedEvent(
-                agent_info=agent_info,
-                tools=self._parsed_tools,
-                messages=self._messages,
-            ),
-        )
-
-        # Execute the agent using invoke loop
-        agent_finish = self._invoke_loop()
-        formatted_result: Optional[BaseModel] = None
-        if self.response_format:
-            try:
-                # Cast to BaseModel to ensure type safety
-                result = self.response_format.model_validate_json(
-                    agent_finish.output
-                )
-                if isinstance(result, BaseModel):
-                    formatted_result = result
-            except Exception as e:
-                self._printer.print(
-                    content=f"Failed to parse output into response format: {str(e)}",
-                    color="yellow",
-                )
-
-        # Calculate token usage metrics
-        usage_metrics = self._token_process.get_summary()
-
-        # Create output
-        output = LiteAgentOutput(
-            raw=agent_finish.output,
-            pydantic=formatted_result,
-            agent_role=self.role,
-            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-        )
-
-        # Process guardrail if set
-        if self._guardrail is not None:
-            guardrail_result = process_guardrail(
-                output=output,
-                guardrail=self._guardrail,
-                retry_count=self._guardrail_retry_count
-            )
-
-            if not guardrail_result.success:
-                if self._guardrail_retry_count >= self.guardrail_max_retries:
-                        raise Exception(
-                            f"Agent's guardrail failed validation after {self.guardrail_max_retries} retries. "
-                            f"Last error: {guardrail_result.error}"
-                        )
-                self._guardrail_retry_count += 1
-                if self.verbose:
-                    self._printer.print(
-                        f"Guardrail failed. Retrying ({self._guardrail_retry_count}/{self.guardrail_max_retries})..."
-                        f"\n{guardrail_result.error}"
-                    )
-
-                self._messages.append({
-                    "role": "user",
-                    "content": guardrail_result.error or "Guardrail validation failed"
-                })
-
-                return self._execute_core(agent_info=agent_info)
-
-            # Apply guardrail result if available
-            if guardrail_result.result is not None:
-                if isinstance(guardrail_result.result, str):
-                    output.raw = guardrail_result.result
-                elif isinstance(guardrail_result.result, BaseModel):
-                    output.pydantic = guardrail_result.result
-
-            usage_metrics = self._token_process.get_summary()
-            output.usage_metrics = usage_metrics.model_dump() if usage_metrics else None
-
-        # Emit completion event
-        crewai_event_bus.emit(
-            self,
-            event=LiteAgentExecutionCompletedEvent(
-                agent_info=agent_info,
-                output=agent_finish.output,
-            ),
-        )
-
-        return output
-
     async def kickoff_async(
         self, messages: Union[str, List[Dict[str, str]]]
     ) -> LiteAgentOutput:

src/crewai/task.py

@@ -35,12 +35,12 @@ from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.security import Fingerprint, SecurityConfig
+from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.constants import NOT_SPECIFIED
-from crewai.utilities.guardrail import process_guardrail, GuardrailResult
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.events import (
     TaskCompletedEvent,
@@ -431,11 +431,7 @@ class Task(BaseModel):
             )
 
             if self._guardrail:
-                guardrail_result = process_guardrail(
-                    output=task_output,
-                    guardrail=self._guardrail,
-                    retry_count=self.retry_count
-                )
+                guardrail_result = self._process_guardrail(task_output)
                 if not guardrail_result.success:
                     if self.retry_count >= self.max_retries:
                         raise Exception(
@@ -554,7 +550,8 @@ Follow these guidelines:
 - Use * for italic text
 - Use - or * for bullet points
 - Use `code` for inline code
-- Use ```language for code blocks"""
+- Use ```language for code blocks
+- Don't start your answer with a code block"""
             tasks_slices.append(markdown_instruction)
         return "\n".join(tasks_slices)
 

src/crewai/tasks/guardrail_result.py

@@ -1,7 +1,15 @@
-from typing import Any, Callable, Optional, Tuple, Union
+"""
+Module for handling task guardrail validation results.
+
+This module provides the GuardrailResult class which standardizes
+the way task guardrails return their validation results.
+"""
+
+from typing import Any, Optional, Tuple, Union
 
 from pydantic import BaseModel, field_validator
 
+
 class GuardrailResult(BaseModel):
     """Result from a task guardrail execution.
 
@@ -46,48 +54,3 @@ class GuardrailResult(BaseModel):
             result=data if success else None,
             error=data if not success else None
         )
-
-
-def process_guardrail(output: Any, guardrail: Callable, retry_count: int) -> GuardrailResult:
-    """Process the guardrail for the agent output.
-
-    Args:
-        output: The output to validate with the guardrail
-
-    Returns:
-        GuardrailResult: The result of the guardrail validation
-    """
-    from crewai.task import TaskOutput
-    from crewai.lite_agent import LiteAgentOutput
-
-    assert isinstance(output, TaskOutput) or isinstance(output, LiteAgentOutput), "Output must be a TaskOutput or LiteAgentOutput"
-
-    assert guardrail is not None
-
-    from crewai.utilities.events import (
-        LLMGuardrailCompletedEvent,
-        LLMGuardrailStartedEvent,
-    )
-    from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-
-    crewai_event_bus.emit(
-        None,
-        LLMGuardrailStartedEvent(
-            guardrail=guardrail, retry_count=retry_count
-        ),
-    )
-
-    result = guardrail(output)
-    guardrail_result = GuardrailResult.from_tuple(result)
-
-    crewai_event_bus.emit(
-        None,
-        LLMGuardrailCompletedEvent(
-            success=guardrail_result.success,
-            result=guardrail_result.result,
-            error=guardrail_result.error,
-            retry_count=retry_count,
-        ),
-    )
-
-    return guardrail_result

src/crewai/telemetry/telemetry.py

@@ -8,7 +8,7 @@ import platform
 import warnings
 from contextlib import contextmanager
 from importlib.metadata import version
-from typing import TYPE_CHECKING, Any, Callable, Optional
+from typing import TYPE_CHECKING, Any, Optional
 import threading
 
 from opentelemetry import trace
@@ -73,16 +73,11 @@ class Telemetry:
             with cls._lock:
                 if cls._instance is None:
                     cls._instance = super(Telemetry, cls).__new__(cls)
-                    cls._instance._initialized = False
         return cls._instance
 
     def __init__(self) -> None:
-        if hasattr(self, '_initialized') and self._initialized:
-            return
-        
         self.ready: bool = False
         self.trace_set: bool = False
-        self._initialized: bool = True
 
         if self._is_telemetry_disabled():
             return
@@ -118,10 +113,6 @@ class Telemetry:
             or os.getenv("CREWAI_DISABLE_TELEMETRY", "false").lower() == "true"
         )
 
-    def _should_execute_telemetry(self) -> bool:
-        """Check if telemetry operations should be executed."""
-        return self.ready and not self._is_telemetry_disabled()
-
     def set_tracer(self):
         if self.ready and not self.trace_set:
             try:
@@ -132,9 +123,8 @@ class Telemetry:
                 self.ready = False
                 self.trace_set = False
 
-    def _safe_telemetry_operation(self, operation: Callable[[], None]) -> None:
-        """Execute telemetry operation safely, checking both readiness and environment variables."""
-        if not self._should_execute_telemetry():
+    def _safe_telemetry_operation(self, operation):
+        if not self.ready:
             return
         try:
             operation()
@@ -433,8 +423,7 @@ class Telemetry:
 
             return span
 
-        self._safe_telemetry_operation(operation)
-        return None
+        return self._safe_telemetry_operation(operation)
 
     def task_ended(self, span: Span, task: Task, crew: Crew):
         """Records the completion of a task execution in a crew.
@@ -784,8 +773,7 @@ class Telemetry:
             return span
 
         if crew.share_crew:
-            self._safe_telemetry_operation(operation)
-            return operation()
+            return self._safe_telemetry_operation(operation)
         return None
 
     def end_crew(self, crew, final_string_output):

src/crewai/tools/base_tool.py

@@ -64,7 +64,7 @@ class BaseTool(BaseModel, ABC):
                 },
             },
         )
-
+        
     @field_validator("max_usage_count", mode="before")
     @classmethod
     def validate_max_usage_count(cls, v: int | None) -> int | None:
@@ -88,11 +88,11 @@ class BaseTool(BaseModel, ABC):
         # If _run is async, we safely run it
         if asyncio.iscoroutine(result):
             result = asyncio.run(result)
-
+            
         self.current_usage_count += 1
-
+        
         return result
-
+        
     def reset_usage_count(self) -> None:
         """Reset the current usage count to zero."""
         self.current_usage_count = 0
@@ -279,7 +279,7 @@ def to_langchain(
 def tool(*args, result_as_answer: bool = False, max_usage_count: int | None = None) -> Callable:
     """
     Decorator to create a tool from a function.
-
+    
     Args:
         *args: Positional arguments, either the function to decorate or the tool name.
         result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.

src/crewai/tools/structured_tool.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import asyncio
-
 import inspect
 import textwrap
 from typing import Any, Callable, Optional, Union, get_type_hints
@@ -241,17 +239,7 @@ class CrewStructuredTool:
     ) -> Any:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)
-
-        if inspect.iscoroutinefunction(self.func):
-            result = asyncio.run(self.func(**parsed_args, **kwargs))
-            return result
-
-        result = self.func(**parsed_args, **kwargs)
-
-        if asyncio.iscoroutine(result):
-            return asyncio.run(result)
-
-        return result
+        return self.func(**parsed_args, **kwargs)
 
     @property
     def args(self) -> dict:

src/crewai/translations/en.json

@@ -25,7 +25,7 @@
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
-    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
+    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the complete final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
     "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
     "knowledge_search_query": "The original query is: {task_prompt}.",
@@ -55,7 +55,12 @@
   "reasoning": {
     "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
     "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
-    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
-    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
+    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nRemember: your ultimate objective is to produce the most COMPLETE Final Answer that fully meets the **Expected output** criteria.\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
+    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nMake sure your refined strategy directly guides you toward producing the most COMPLETE Final Answer that fully satisfies the **Expected output**.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\"",
+    "adaptive_reasoning_decision": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are currently executing a task and need to decide whether to pause and reassess your plan based on the current context.",
+    "mid_execution_reasoning": "You are currently executing a task and need to reassess your plan based on progress so far.\n\nTASK DESCRIPTION:\n{description}\n\nEXPECTED OUTPUT:\n{expected_output}\n\nCURRENT PROGRESS:\nSteps completed: {current_steps}\nTools used: {tools_used}\nProgress summary: {current_progress}\n\nRECENT CONVERSATION:\n{recent_messages}\n\nYour reassessment MUST focus on steering the remaining work toward a FINAL ANSWER that is as complete as possible and perfectly matches the **Expected output**.\n\nBased on the current progress and context, please reassess your plan for completing this task.\nConsider what has been accomplished, what challenges you've encountered, and what steps remain.\nAdjust your strategy if needed or confirm your current approach is still optimal.\n\nProvide a detailed updated plan for completing the task.\nEnd with \"READY: I am ready to continue executing the task.\" if you're confident in your plan.",
+    "mid_execution_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are reassessing your plan during task execution based on the progress made so far.",
+    "mid_execution_reasoning_update": "I've reassessed my approach based on progress so far. Updated plan:\n\n{plan}",
+    "adaptive_reasoning_context": "\n\nTASK DESCRIPTION:\n{description}\n\nEXPECTED OUTPUT:\n{expected_output}\n\nCURRENT EXECUTION CONTEXT:\n- Steps completed: {current_steps}\n- Tools used: {tools_used}\n- Progress summary: {current_progress}\n\nConsider whether the current approach is optimal or if a strategic pause to reassess would be beneficial. You should reason when:\n- You might be approaching the task inefficiently\n- The context suggests a different strategy might be better\n- You're uncertain about the next steps\n- The progress suggests you need to reconsider your approach\n\nDecide whether reasoning/re-planning is needed at this point."
   }
 }

src/crewai/utilities/agent_utils.py

@@ -20,10 +20,7 @@ from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
-from rich.console import Console
-from crewai.cli.config import Settings
 
-console = Console()
 
 def parse_tools(tools: List[BaseTool]) -> List[CrewStructuredTool]:
     """Parse tools to be used for the task."""
@@ -296,6 +293,8 @@ def handle_context_length(
     llm: Any,
     callbacks: List[Any],
     i18n: Any,
+    task_description: Optional[str] = None,
+    expected_output: Optional[str] = None,
 ) -> None:
     """Handle context length exceeded by either summarizing or raising an error.
 
@@ -306,13 +305,22 @@ def handle_context_length(
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
         i18n: I18N instance for messages
+        task_description: Optional original task description
+        expected_output: Optional expected output
     """
     if respect_context_window:
         printer.print(
             content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
             color="yellow",
         )
-        summarize_messages(messages, llm, callbacks, i18n)
+        summarize_messages(
+            messages,
+            llm,
+            callbacks,
+            i18n,
+            task_description=task_description,
+            expected_output=expected_output,
+        )
     else:
         printer.print(
             content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
@@ -328,6 +336,8 @@ def summarize_messages(
     llm: Any,
     callbacks: List[Any],
     i18n: Any,
+    task_description: Optional[str] = None,
+    expected_output: Optional[str] = None,
 ) -> None:
     """Summarize messages to fit within context window.
 
@@ -336,6 +346,8 @@ def summarize_messages(
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
         i18n: I18N instance for messages
+        task_description: Optional original task description
+        expected_output: Optional expected output
     """
     messages_string = " ".join([message["content"] for message in messages])
     messages_groups = []
@@ -368,12 +380,19 @@ def summarize_messages(
 
     merged_summary = " ".join(content["content"] for content in summarized_contents)
 
+    # Build the summary message and optionally inject the task reminder.
+    summary_message = i18n.slice("summary").format(merged_summary=merged_summary)
+
+    if task_description or expected_output:
+        summary_message += "\n\n"  # blank line before the reminder
+        if task_description:
+            summary_message += f"Original task: {task_description}\n"
+        if expected_output:
+            summary_message += f"Expected output: {expected_output}"
+
+    # Replace the conversation with the new summary message.
     messages.clear()
-    messages.append(
-        format_message_for_llm(
-            i18n.slice("summary").format(merged_summary=merged_summary)
-        )
-    )
+    messages.append(format_message_for_llm(summary_message))
 
 
 def show_agent_logs(
@@ -438,13 +457,6 @@ def show_agent_logs(
             )
 
 
-def _print_current_organization():
-    settings = Settings()
-    if settings.org_uuid:
-        console.print(f"Fetching agent from organization: {settings.org_name} ({settings.org_uuid})", style="bold blue")
-    else:
-        console.print("No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.", style="yellow")
-
 def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
     attributes: Dict[str, Any] = {}
     if from_repository:
@@ -454,18 +466,15 @@ def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
         from crewai.cli.plus_api import PlusAPI
 
         client = PlusAPI(api_key=get_auth_token())
-        _print_current_organization()
         response = client.get_agent(from_repository)
         if response.status_code == 404:
             raise AgentRepositoryError(
-                f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization."
-                f"\nIf you are using the wrong organization, switch to the correct one using `crewai org switch <org_id>` command.",
+                f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization"
             )
 
         if response.status_code != 200:
             raise AgentRepositoryError(
                 f"Agent {from_repository} could not be loaded: {response.text}"
-                f"\nIf you are using the wrong organization, switch to the correct one using `crewai org switch <org_id>` command.",
             )
 
         agent = response.json()

src/crewai/utilities/events/event_listener.py

@@ -61,6 +61,8 @@ from .reasoning_events import (
     AgentReasoningStartedEvent,
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
+    AgentMidExecutionReasoningStartedEvent,
+    AgentMidExecutionReasoningCompletedEvent,
 )
 
 
@@ -108,6 +110,7 @@ class EventListener(BaseEventListener):
                 event.crew_name or "Crew",
                 source.id,
                 "completed",
+                final_result=final_string_output,
             )
 
         @crewai_event_bus.on(CrewKickoffFailedEvent)
@@ -437,8 +440,6 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
-        # ----------- REASONING EVENTS -----------
-
         @crewai_event_bus.on(AgentReasoningStartedEvent)
         def on_agent_reasoning_started(source, event: AgentReasoningStartedEvent):
             self.formatter.handle_reasoning_started(
@@ -462,5 +463,37 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
+        @crewai_event_bus.on(AgentMidExecutionReasoningStartedEvent)
+        def on_mid_execution_reasoning_started(source, event: AgentMidExecutionReasoningStartedEvent):
+            self.formatter.handle_reasoning_started(
+                self.formatter.current_agent_branch,
+                event.attempt if hasattr(event, "attempt") else 1,
+                self.formatter.current_crew_tree,
+                current_step=event.current_step,
+                reasoning_trigger=event.reasoning_trigger,
+            )
+
+        @crewai_event_bus.on(AgentMidExecutionReasoningCompletedEvent)
+        def on_mid_execution_reasoning_completed(source, event: AgentMidExecutionReasoningCompletedEvent):
+            self.formatter.handle_reasoning_completed(
+                event.updated_plan,
+                True,
+                self.formatter.current_crew_tree,
+                duration_seconds=event.duration_seconds,
+                current_step=event.current_step,
+                reasoning_trigger=event.reasoning_trigger,
+            )
+
+        from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
+
+        @crewai_event_bus.on(AgentAdaptiveReasoningDecisionEvent)
+        def on_adaptive_reasoning_decision(source, event: AgentAdaptiveReasoningDecisionEvent):
+            self.formatter.handle_adaptive_reasoning_decision(
+                self.formatter.current_agent_branch,
+                event.should_reason,
+                event.reasoning,
+                self.formatter.current_crew_tree,
+            )
+
 
 event_listener = EventListener()

src/crewai/utilities/events/reasoning_events.py

@@ -19,6 +19,7 @@ class AgentReasoningCompletedEvent(BaseEvent):
     plan: str
     ready: bool
     attempt: int = 1
+    duration_seconds: float = 0.0  # Time taken for reasoning in seconds
 
 
 class AgentReasoningFailedEvent(BaseEvent):
@@ -28,4 +29,37 @@ class AgentReasoningFailedEvent(BaseEvent):
     agent_role: str
     task_id: str
     error: str
-    attempt: int = 1
+    attempt: int = 1
+
+
+class AgentMidExecutionReasoningStartedEvent(BaseEvent):
+    """Event emitted when an agent starts mid-execution reasoning."""
+
+    type: str = "agent_mid_execution_reasoning_started"
+    agent_role: str
+    task_id: str
+    current_step: int
+    reasoning_trigger: str  # "interval" or "adaptive"
+
+
+class AgentMidExecutionReasoningCompletedEvent(BaseEvent):
+    """Event emitted when an agent completes mid-execution reasoning."""
+
+    type: str = "agent_mid_execution_reasoning_completed"
+    agent_role: str
+    task_id: str
+    current_step: int
+    updated_plan: str
+    reasoning_trigger: str
+    duration_seconds: float = 0.0  # Time taken for reasoning in seconds
+
+
+class AgentAdaptiveReasoningDecisionEvent(BaseEvent):
+    """Event emitted after the agent decides whether to trigger adaptive reasoning."""
+
+    type: str = "agent_adaptive_reasoning_decision"
+    agent_role: str
+    task_id: str
+    should_reason: bool  # Whether the agent decided to reason
+    reasoning: str  # Brief explanation / rationale from the LLM
+    reasoning_trigger: str = "adaptive"  # Always adaptive for this event

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

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+import threading
 
 from rich.console import Console
 from rich.panel import Panel
@@ -18,6 +19,14 @@ class ConsoleFormatter:
     tool_usage_counts: Dict[str, int] = {}
     current_reasoning_branch: Optional[Tree] = None  # Track reasoning status
     current_llm_tool_tree: Optional[Tree] = None
+    current_adaptive_decision_branch: Optional[Tree] = None  # Track last adaptive decision branch
+    # Spinner support ---------------------------------------------------
+    _spinner_frames = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
+    _spinner_index: int = 0
+    _spinner_branches: Dict[Tree, tuple[str, str, str]] = {}  # branch -> (icon, name, style)
+    _spinner_thread: Optional[threading.Thread] = None
+    _stop_spinner_event: Optional[threading.Event] = None
+    _spinner_running: bool = False
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
@@ -49,6 +58,8 @@ class ConsoleFormatter:
 
         for label, value in fields.items():
             content.append(f"{label}: ", style="white")
+            if label == "Result":
+                content.append("\n")
             content.append(
                 f"{value}\n", style=fields.get(f"{label}_style", status_style)
             )
@@ -138,6 +149,7 @@ class ConsoleFormatter:
         crew_name: str,
         source_id: str,
         status: str = "completed",
+        final_result: Optional[str] = None,
     ) -> None:
         """Handle crew tree updates with consistent formatting."""
         if not self.verbose or tree is None:
@@ -163,15 +175,26 @@ class ConsoleFormatter:
             style,
         )
 
+        # Prepare additional fields for the completion panel
+        additional_fields: Dict[str, Any] = {"ID": source_id}
+
+        # Include the final result if provided and the status is completed
+        if status == "completed" and final_result is not None:
+            additional_fields["Result"] = final_result
+
         content = self.create_status_content(
             content_title,
             crew_name or "Crew",
             style,
-            ID=source_id,
+            **additional_fields,
         )
 
         self.print_panel(content, title, style)
 
+        # Clear all spinners when crew completes or fails
+        if status in {"completed", "failed"}:
+            self._clear_all_spinners()
+
     def create_crew_tree(self, crew_name: str, source_id: str) -> Optional[Tree]:
         """Create and initialize a new crew tree with initial status."""
         if not self.verbose:
@@ -219,6 +242,15 @@ class ConsoleFormatter:
         # Set the current_task_branch attribute directly
         self.current_task_branch = task_branch
 
+        # When a new task starts, clear pointers to previous agent, reasoning,
+        # and tool branches so that any upcoming Reasoning / Tool logs attach
+        # to the correct task.
+        if self.current_tool_branch:
+            self._unregister_spinner_branch(self.current_tool_branch)
+        self.current_agent_branch = None
+        # Keep current_reasoning_branch; reasoning may still be in progress
+        self.current_tool_branch = None
+
         return task_branch
 
     def update_task_status(
@@ -266,6 +298,17 @@ class ConsoleFormatter:
         )
         self.print_panel(content, panel_title, style)
 
+        # Clear task-scoped pointers after the task is finished so subsequent
+        # events don't mistakenly attach to the old task branch.
+        if status in {"completed", "failed"}:
+            self.current_task_branch = None
+            self.current_agent_branch = None
+            self.current_tool_branch = None
+            # Ensure spinner is stopped if reasoning branch exists
+            if self.current_reasoning_branch is not None:
+                self._unregister_spinner_branch(self.current_reasoning_branch)
+                self.current_reasoning_branch = None
+
     def create_agent_branch(
         self, task_branch: Optional[Tree], agent_role: str, crew_tree: Optional[Tree]
     ) -> Optional[Tree]:
@@ -502,19 +545,20 @@ class ConsoleFormatter:
         # Update tool usage count
         self.tool_usage_counts[tool_name] = self.tool_usage_counts.get(tool_name, 0) + 1
 
-        # Find or create tool node
-        tool_branch = self.current_tool_branch
-        if tool_branch is None:
-            tool_branch = branch_to_use.add("")
-            self.current_tool_branch = tool_branch
+        # Always create a new branch for each tool invocation so that previous
+        # tool usages remain visible in the tree.
+        tool_branch = branch_to_use.add("")
+        self.current_tool_branch = tool_branch
 
         # Update label with current count
+        spinner_char = self._next_spinner()
         self.update_tree_label(
             tool_branch,
-            "🔧",
+            f"🔧 {spinner_char}",
             f"Using {tool_name} ({self.tool_usage_counts[tool_name]})",
             "yellow",
         )
+        self._register_spinner_branch(tool_branch, "🔧", f"Using {tool_name} ({self.tool_usage_counts[tool_name]})", "yellow")
 
         # Print updated tree immediately
         self.print(tree_to_use)
@@ -544,9 +588,7 @@ class ConsoleFormatter:
             f"Used {tool_name} ({self.tool_usage_counts[tool_name]})",
             "green",
         )
-
-        # Clear the current tool branch as we're done with it
-        self.current_tool_branch = None
+        self._unregister_spinner_branch(tool_branch)
 
         # Only print if we have a valid tree and the tool node is still in it
         if isinstance(tree_to_use, Tree) and tool_branch in tree_to_use.children:
@@ -574,6 +616,7 @@ class ConsoleFormatter:
                 f"{tool_name} ({self.tool_usage_counts[tool_name]})",
                 "red",
             )
+            self._unregister_spinner_branch(tool_branch)
             if tree_to_use:
                 self.print(tree_to_use)
                 self.print()
@@ -613,7 +656,9 @@ class ConsoleFormatter:
         # Only add thinking status if we don't have a current tool branch
         if self.current_tool_branch is None:
             tool_branch = branch_to_use.add("")
-            self.update_tree_label(tool_branch, "🧠", "Thinking...", "blue")
+            spinner_char = self._next_spinner()
+            self.update_tree_label(tool_branch, f"🧠 {spinner_char}", "Thinking...", "blue")
+            self._register_spinner_branch(tool_branch, "🧠", "Thinking...", "blue")
             self.current_tool_branch = tool_branch
             self.print(tree_to_use)
             self.print()
@@ -647,6 +692,8 @@ class ConsoleFormatter:
             for parent in parents:
                 if isinstance(parent, Tree) and tool_branch in parent.children:
                     parent.children.remove(tool_branch)
+                    # Stop spinner for the thinking branch before removing
+                    self._unregister_spinner_branch(tool_branch)
                     removed = True
                     break
 
@@ -671,6 +718,7 @@ class ConsoleFormatter:
         # Update tool branch if it exists
         if tool_branch:
             tool_branch.label = Text("❌ LLM Failed", style="red bold")
+            self._unregister_spinner_branch(tool_branch)
             if tree_to_use:
                 self.print(tree_to_use)
                 self.print()
@@ -1106,17 +1154,23 @@ class ConsoleFormatter:
         agent_branch: Optional[Tree],
         attempt: int,
         crew_tree: Optional[Tree],
+        current_step: Optional[int] = None,
+        reasoning_trigger: Optional[str] = None,
     ) -> Optional[Tree]:
         """Handle agent reasoning started (or refinement) event."""
         if not self.verbose:
             return None
 
-        # Prefer LiteAgent > Agent > Task branch as the parent for reasoning
-        branch_to_use = (
-            self.current_lite_agent_branch
-            or agent_branch
-            or self.current_task_branch
-        )
+        # Prefer to nest under the latest adaptive decision branch when this is a
+        # mid-execution reasoning cycle so the tree indents nicely.
+        if current_step is not None and self.current_adaptive_decision_branch is not None:
+            branch_to_use = self.current_adaptive_decision_branch
+        else:
+            branch_to_use = (
+                self.current_lite_agent_branch
+                or agent_branch
+                or self.current_task_branch
+            )
 
         # We always want to render the full crew tree when possible so the
         # Live view updates coherently. Fallbacks: crew tree → branch itself.
@@ -1132,11 +1186,21 @@ class ConsoleFormatter:
             reasoning_branch = branch_to_use.add("")
             self.current_reasoning_branch = reasoning_branch
 
-        # Build label text depending on attempt
-        status_text = (
-            f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
-        )
-        self.update_tree_label(reasoning_branch, "🧠", status_text, "blue")
+        # Build label text depending on attempt and whether it's mid-execution
+        if current_step is not None:
+            status_text = "Mid-Execution Reasoning"
+        else:
+            status_text = (
+                f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
+            )
+
+        # ⠋ is the first frame of a braille spinner – visually hints progress even
+        # without true animation.
+        spinner_char = self._next_spinner()
+        self.update_tree_label(reasoning_branch, f"🧠 {spinner_char}", status_text, "yellow")
+
+        # Register branch for continuous spinner
+        self._register_spinner_branch(reasoning_branch, "🧠", status_text, "yellow")
 
         self.print(tree_to_use)
         self.print()
@@ -1148,6 +1212,9 @@ class ConsoleFormatter:
         plan: str,
         ready: bool,
         crew_tree: Optional[Tree],
+        duration_seconds: float = 0.0,
+        current_step: Optional[int] = None,
+        reasoning_trigger: Optional[str] = None,
     ) -> None:
         """Handle agent reasoning completed event."""
         if not self.verbose:
@@ -1161,10 +1228,31 @@ class ConsoleFormatter:
             or crew_tree
         )
 
-        style = "green" if ready else "yellow"
-        status_text = "Reasoning Completed" if ready else "Reasoning Completed (Not Ready)"
+        # Completed reasoning should always display in green.
+        style = "green"
+        # Build duration part separately for cleaner formatting
+        duration_part = f"{duration_seconds:.2f}s" if duration_seconds > 0 else ""
 
-        if reasoning_branch is not None:
+        if current_step is not None:
+            # Build label manually to style duration differently and omit trigger info.
+            if reasoning_branch is not None:
+                label = Text()
+                label.append("✅ ", style=f"{style} bold")
+                label.append("Mid-Execution Reasoning Completed", style=style)
+                if duration_part:
+                    label.append(f" ({duration_part})", style="cyan")
+                reasoning_branch.label = label
+
+            status_text = None  # Already set label manually
+        else:
+            status_text = (
+                f"Reasoning Completed ({duration_part})" if duration_part else "Reasoning Completed"
+            ) if ready else (
+                f"Reasoning Completed (Not Ready • {duration_part})" if duration_part else "Reasoning Completed (Not Ready)"
+            )
+
+        # If we didn't build a custom label (non-mid-execution case), use helper
+        if status_text and reasoning_branch is not None:
             self.update_tree_label(reasoning_branch, "✅", status_text, style)
 
         if tree_to_use is not None:
@@ -1172,9 +1260,17 @@ class ConsoleFormatter:
 
         # Show plan in a panel (trim very long plans)
         if plan:
+            # Derive duration text for panel title
+            duration_text = f" ({duration_part})" if duration_part else ""
+
+            if current_step is not None:
+                title = f"🧠 Mid-Execution Reasoning Plan{duration_text}"
+            else:
+                title = f"🧠 Reasoning Plan{duration_text}"
+
             plan_panel = Panel(
                 Text(plan, style="white"),
-                title="🧠 Reasoning Plan",
+                title=title,
                 border_style=style,
                 padding=(1, 2),
             )
@@ -1182,9 +1278,17 @@ class ConsoleFormatter:
 
         self.print()
 
+        # Unregister spinner before clearing
+        if reasoning_branch is not None:
+            self._unregister_spinner_branch(reasoning_branch)
+
         # Clear stored branch after completion
         self.current_reasoning_branch = None
 
+        # After reasoning finished, we also clear the adaptive decision branch to
+        # avoid nesting unrelated future nodes.
+        self.current_adaptive_decision_branch = None
+
     def handle_reasoning_failed(
         self,
         error: str,
@@ -1204,6 +1308,7 @@ class ConsoleFormatter:
 
         if reasoning_branch is not None:
             self.update_tree_label(reasoning_branch, "❌", "Reasoning Failed", "red")
+            self._unregister_spinner_branch(reasoning_branch)
 
         if tree_to_use is not None:
             self.print(tree_to_use)
@@ -1219,3 +1324,115 @@ class ConsoleFormatter:
 
         # Clear stored branch after failure
         self.current_reasoning_branch = None
+
+    # ----------- ADAPTIVE REASONING DECISION EVENTS -----------
+
+    def handle_adaptive_reasoning_decision(
+        self,
+        agent_branch: Optional[Tree],
+        should_reason: bool,
+        reasoning: str,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        """Render the decision on whether to trigger adaptive reasoning."""
+        if not self.verbose:
+            return
+
+        # Prefer LiteAgent > Agent > Task as parent
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+
+        if branch_to_use is None or tree_to_use is None:
+            return
+
+        decision_branch = branch_to_use.add("")
+
+        decision_text = "YES" if should_reason else "NO"
+        style = "green" if should_reason else "yellow"
+
+        self.update_tree_label(
+            decision_branch,
+            "🤔",
+            f"Adaptive Reasoning Decision: {decision_text}",
+            style,
+        )
+
+        # Print tree first (live update)
+        self.print(tree_to_use)
+
+        # Also show explanation if available
+        if reasoning:
+            truncated_reasoning = reasoning[:500] + "..." if len(reasoning) > 500 else reasoning
+            panel = Panel(
+                Text(truncated_reasoning, style="white"),
+                title="🤔 Adaptive Reasoning Rationale",
+                border_style=style,
+                padding=(1, 2),
+            )
+            self.print(panel)
+
+        self.print()
+
+        # Store the decision branch so that subsequent mid-execution reasoning nodes
+        # can be rendered as children of this decision (for better indentation).
+        self.current_adaptive_decision_branch = decision_branch
+
+    # ------------------------------------------------------------------
+    # Spinner helpers
+    # ------------------------------------------------------------------
+
+    def _next_spinner(self) -> str:
+        """Return next spinner frame."""
+        frame = self._spinner_frames[self._spinner_index]
+        self._spinner_index = (self._spinner_index + 1) % len(self._spinner_frames)
+        return frame
+
+    def _register_spinner_branch(self, branch: Tree, icon: str, name: str, style: str):
+        """Start animating spinner for given branch."""
+        self._spinner_branches[branch] = (icon, name, style)
+        if not self._spinner_running:
+            self._start_spinner_thread()
+
+    def _unregister_spinner_branch(self, branch: Optional[Tree]):
+        if branch is None:
+            return
+        self._spinner_branches.pop(branch, None)
+        if not self._spinner_branches:
+            self._stop_spinner_thread()
+
+    def _start_spinner_thread(self):
+        if self._spinner_running:
+            return
+        self._stop_spinner_event = threading.Event()
+        self._spinner_thread = threading.Thread(target=self._spinner_loop, daemon=True)
+        self._spinner_thread.start()
+        self._spinner_running = True
+
+    def _stop_spinner_thread(self):
+        if self._stop_spinner_event:
+            self._stop_spinner_event.set()
+        self._spinner_running = False
+
+    def _clear_all_spinners(self):
+        """Clear all active spinners. Used as a safety mechanism."""
+        self._spinner_branches.clear()
+        self._stop_spinner_thread()
+
+    def _spinner_loop(self):
+        import time
+        while self._stop_spinner_event and not self._stop_spinner_event.is_set():
+            if self._live and self._spinner_branches:
+                for branch, (icon, name, style) in list(self._spinner_branches.items()):
+                    spinner_char = self._next_spinner()
+                    self.update_tree_label(branch, f"{icon} {spinner_char}", name, style)
+                # Refresh live view
+                try:
+                    self._live.update(self._live.renderable, refresh=True)
+                except Exception:
+                    pass
+            time.sleep(0.15)

src/crewai/utilities/reasoning_handler.py

@@ -38,7 +38,7 @@ class AgentReasoning:
     Handles the agent reasoning process, enabling an agent to reflect and create a plan
     before executing a task.
     """
-    def __init__(self, task: Task, agent: Agent):
+    def __init__(self, task: Task, agent: Agent, extra_context: str | None = None):
         if not task or not agent:
             raise ValueError("Both task and agent must be provided.")
         self.task = task
@@ -46,6 +46,7 @@ class AgentReasoning:
         self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
         self.i18n = I18N()
+        self.extra_context = extra_context or ""
 
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
         """
@@ -55,6 +56,9 @@ class AgentReasoning:
         Returns:
             AgentReasoningOutput: The output of the agent reasoning process.
         """
+        import time
+        start_time = time.time()
+
         # Emit a reasoning started event (attempt 1)
         try:
             crewai_event_bus.emit(
@@ -72,6 +76,8 @@ class AgentReasoning:
         try:
             output = self.__handle_agent_reasoning()
 
+            duration_seconds = time.time() - start_time
+
             # Emit reasoning completed event
             try:
                 crewai_event_bus.emit(
@@ -82,6 +88,7 @@ class AgentReasoning:
                         plan=output.plan.plan,
                         ready=output.plan.ready,
                         attempt=1,
+                        duration_seconds=duration_seconds,
                     ),
                 )
             except Exception:
@@ -317,7 +324,7 @@ class AgentReasoning:
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
-            description=self.task.description,
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
             expected_output=self.task.expected_output,
             tools=available_tools
         )
@@ -368,7 +375,7 @@ class AgentReasoning:
         plan = response
         ready = False
 
-        if "READY: I am ready to execute the task." in response:
+        if "READY: I am ready to execute the task." in response or "READY: I am ready to continue executing the task." in response:
             ready = True
 
         return plan, ready
@@ -385,3 +392,303 @@ class AgentReasoning:
             "The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
         )
         return self.handle_agent_reasoning()
+
+    def _emit_reasoning_event(self, event_class, **kwargs):
+        """Centralized method for emitting reasoning events."""
+        try:
+            reasoning_trigger = "interval"
+            if hasattr(self.agent, 'adaptive_reasoning') and self.agent.adaptive_reasoning:
+                reasoning_trigger = "adaptive"
+
+            crewai_event_bus.emit(
+                self.agent,
+                event_class(
+                    agent_role=self.agent.role,
+                    task_id=str(self.task.id),
+                    reasoning_trigger=reasoning_trigger,
+                    **kwargs
+                ),
+            )
+        except Exception:
+            # Ignore event bus errors to avoid breaking execution
+            pass
+
+    def handle_mid_execution_reasoning(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> AgentReasoningOutput:
+        """
+        Handle reasoning during task execution with context about current progress.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            AgentReasoningOutput: Updated reasoning plan based on current context
+        """
+        import time
+        start_time = time.time()
+
+        from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningStartedEvent
+
+        self._emit_reasoning_event(
+            AgentMidExecutionReasoningStartedEvent,
+            current_step=current_steps
+        )
+
+        try:
+            output = self.__handle_mid_execution_reasoning(
+                current_steps, tools_used, current_progress, iteration_messages
+            )
+
+            duration_seconds = time.time() - start_time
+
+            # Emit completed event
+            from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningCompletedEvent
+
+            self._emit_reasoning_event(
+                AgentMidExecutionReasoningCompletedEvent,
+                current_step=current_steps,
+                updated_plan=output.plan.plan,
+                duration_seconds=duration_seconds
+            )
+
+            return output
+        except Exception as e:
+            # Emit failed event
+            from crewai.utilities.events.reasoning_events import AgentReasoningFailedEvent
+
+            self._emit_reasoning_event(
+                AgentReasoningFailedEvent,
+                error=str(e),
+                attempt=1
+            )
+
+            raise
+
+    def __handle_mid_execution_reasoning(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> AgentReasoningOutput:
+        """
+        Private method that handles the mid-execution reasoning process.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            AgentReasoningOutput: The output of the mid-execution reasoning process.
+        """
+        mid_execution_prompt = self.__create_mid_execution_prompt(
+            current_steps, tools_used, current_progress, iteration_messages
+        )
+
+        if self.llm.supports_function_calling():
+            plan, ready = self.__call_with_function(mid_execution_prompt, "mid_execution_plan")
+        else:
+            # Use the same prompt for system context
+            system_prompt = self.i18n.retrieve("reasoning", "mid_execution_plan").format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory()
+            )
+
+            response = self.llm.call(
+                [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": mid_execution_prompt}
+                ]
+            )
+
+            plan, ready = self.__parse_reasoning_response(str(response))
+
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
+        return AgentReasoningOutput(plan=reasoning_plan)
+
+    def __create_mid_execution_prompt(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str,
+        iteration_messages: list
+    ) -> str:
+        """
+        Creates a prompt for the agent to reason during task execution.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+            iteration_messages: Recent conversation messages
+
+        Returns:
+            str: The mid-execution reasoning prompt.
+        """
+        tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
+
+        recent_messages = ""
+        if iteration_messages:
+            recent_msgs = iteration_messages[-6:] if len(iteration_messages) > 6 else iteration_messages
+            for msg in recent_msgs:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if content:
+                    recent_messages += f"{role.upper()}: {content[:200]}...\n\n"
+
+        return self.i18n.retrieve("reasoning", "mid_execution_reasoning").format(
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
+            expected_output=self.task.expected_output,
+            current_steps=current_steps,
+            tools_used=tools_used_str,
+            current_progress=current_progress,
+            recent_messages=recent_messages
+        )
+
+    def should_adaptive_reason_llm(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str
+    ) -> bool:
+        """
+        Use LLM function calling to determine if adaptive reasoning should be triggered.
+
+        Args:
+            current_steps: Number of steps executed so far
+            tools_used: List of tools that have been used
+            current_progress: Summary of progress made so far
+
+        Returns:
+            bool: True if reasoning should be triggered, False otherwise.
+        """
+        try:
+            decision_prompt = self.__create_adaptive_reasoning_decision_prompt(
+                current_steps, tools_used, current_progress
+            )
+
+            if self.llm.supports_function_calling():
+                should_reason, reasoning_expl = self.__call_adaptive_reasoning_function(decision_prompt)
+            else:
+                should_reason, reasoning_expl = self.__call_adaptive_reasoning_text(decision_prompt)
+
+            # Emit an event so the UI/console can display the decision
+            try:
+                from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
+
+                self._emit_reasoning_event(
+                    AgentAdaptiveReasoningDecisionEvent,
+                    should_reason=should_reason,
+                    reasoning=reasoning_expl,
+                )
+            except Exception:
+                # Ignore event bus errors to avoid breaking execution
+                pass
+
+            return should_reason
+        except Exception as e:
+            self.logger.warning(f"Error during adaptive reasoning decision: {str(e)}. Defaulting to no reasoning.")
+            return False
+
+    def __call_adaptive_reasoning_function(self, prompt: str) -> tuple[bool, str]:
+        """Call LLM with function calling for adaptive reasoning decision."""
+        function_schema = {
+            "type": "function",
+            "function": {
+                "name": "decide_reasoning_need",
+                "description": "Decide whether reasoning is needed based on current task execution context",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "should_reason": {
+                            "type": "boolean",
+                            "description": "Whether reasoning/re-planning is needed at this point in task execution."
+                        },
+                        "reasoning": {
+                            "type": "string",
+                            "description": "Brief explanation of why reasoning is or isn't needed."
+                        }
+                    },
+                    "required": ["should_reason", "reasoning"]
+                }
+            }
+        }
+
+        def _decide_reasoning_need(should_reason: bool, reasoning: str):
+            """Return the reasoning decision result in JSON string form."""
+            return json.dumps({"should_reason": should_reason, "reasoning": reasoning})
+
+        system_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        response = self.llm.call(
+            [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt}
+            ],
+            tools=[function_schema],
+            available_functions={"decide_reasoning_need": _decide_reasoning_need},
+        )
+
+        try:
+            result = json.loads(response)
+            return result.get("should_reason", False), result.get("reasoning", "No explanation provided")
+        except (json.JSONDecodeError, KeyError):
+            return False, "No explanation provided"
+
+    def __call_adaptive_reasoning_text(self, prompt: str) -> tuple[bool, str]:
+        """Fallback text-based adaptive reasoning decision."""
+        system_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        response = self.llm.call([
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": prompt + "\n\nRespond with 'YES' if reasoning is needed, 'NO' if not."}
+        ])
+
+        return "YES" in str(response).upper(), "No explanation provided"
+
+    def __create_adaptive_reasoning_decision_prompt(
+        self,
+        current_steps: int,
+        tools_used: list,
+        current_progress: str
+    ) -> str:
+        """Create prompt for adaptive reasoning decision."""
+        tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
+
+        # Use the prompt from i18n and format it with the current context
+        base_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory()
+        )
+
+        context_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_context").format(
+            description=self.task.description + (f"\n\nContext:\n{self.extra_context}" if self.extra_context else ""),
+            expected_output=self.task.expected_output,
+            current_steps=current_steps,
+            tools_used=tools_used_str,
+            current_progress=current_progress
+        )
+
+        prompt = base_prompt + context_prompt
+
+        return prompt

tests/adaptive_reasoning_test.py

@@ -0,0 +1,89 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+
+
+def _create_executor(agent):  # noqa: D401,E501
+    """Utility to build a minimal CrewAgentExecutor with the given agent.
+
+    A real LLM call is not required for these unit-tests, so we stub it with
+    MagicMock to avoid any network interaction.
+    """
+    return CrewAgentExecutor(
+        llm=MagicMock(),
+        task=MagicMock(),
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock(),
+    )
+
+
+def test_agent_adaptive_reasoning_default():
+    """Agent.adaptive_reasoning should be False by default."""
+    agent = Agent(role="Test", goal="Goal", backstory="Backstory")
+    assert agent.adaptive_reasoning is False
+
+
+@pytest.mark.parametrize("adaptive_decision,expected", [(True, True), (False, False)])
+def test_should_trigger_reasoning_with_adaptive_reasoning(adaptive_decision, expected):
+    """Verify _should_trigger_reasoning defers to _should_adaptive_reason when
+    adaptive_reasoning is enabled and reasoning_interval is None."""
+    # Use a lightweight mock instead of a full Agent instance to isolate the logic
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = None
+    agent.adaptive_reasoning = True
+
+    executor = _create_executor(agent)
+
+    # Ensure the helper returns the desired decision
+    with patch.object(executor, "_should_adaptive_reason", return_value=adaptive_decision) as mock_adaptive:
+        assert executor._should_trigger_reasoning() is expected
+        mock_adaptive.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_adaptive_reasoning_full_execution():
+    """End-to-end test that triggers adaptive reasoning in a real execution flow.
+
+    The task description intentionally contains the word "error" to activate the
+    simple error-based heuristic inside `_should_adaptive_reason`, guaranteeing
+    that the agent reasons mid-execution without relying on patched internals.
+    """
+    agent = Agent(
+        role="Math Analyst",
+        goal="Solve arithmetic problems flawlessly",
+        backstory="You excel at basic calculations and always double-check your steps.",
+        llm="gpt-4o-mini",
+        reasoning=True,
+        adaptive_reasoning=True,
+        verbose=False,
+    )
+
+    task = Task(
+        description="There was an unexpected error earlier. Now, please calculate 3 + 5 and return only the number.",
+        expected_output="The result of the calculation (a single number).",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+
+    result = crew.kickoff()
+
+    # Validate the answer is correct and numeric
+    assert result.raw.strip() == "8"
+
+    # Confirm that an adaptive reasoning message (Updated plan) was injected
+    assert any(
+        "updated plan" in msg.get("content", "").lower()
+        for msg in agent.agent_executor.messages
+    )

tests/agent_test.py

@@ -2126,60 +2126,3 @@ def test_agent_from_repository_agent_not_found(mock_get_agent, mock_get_auth_tok
         match="Agent test_agent does not exist, make sure the name is correct or the agent is available on your organization",
     ):
         Agent(from_repository="test_agent")
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-@patch("crewai.utilities.agent_utils.Settings")
-@patch("crewai.utilities.agent_utils.console")
-def test_agent_from_repository_displays_org_info(mock_console, mock_settings, mock_get_agent, mock_get_auth_token):
-    mock_settings_instance = MagicMock()
-    mock_settings_instance.org_uuid = "test-org-uuid"
-    mock_settings_instance.org_name = "Test Organization"
-    mock_settings.return_value = mock_settings_instance
-
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 200
-    mock_get_response.json.return_value = {
-        "role": "test role",
-        "goal": "test goal",
-        "backstory": "test backstory",
-        "tools": []
-    }
-    mock_get_agent.return_value = mock_get_response
-
-    agent = Agent(from_repository="test_agent")
-
-    mock_console.print.assert_any_call(
-        "Fetching agent from organization: Test Organization (test-org-uuid)",
-        style="bold blue"
-    )
-
-    assert agent.role == "test role"
-    assert agent.goal == "test goal"
-    assert agent.backstory == "test backstory"
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-@patch("crewai.utilities.agent_utils.Settings")
-@patch("crewai.utilities.agent_utils.console")
-def test_agent_from_repository_without_org_set(mock_console, mock_settings, mock_get_agent, mock_get_auth_token):
-    mock_settings_instance = MagicMock()
-    mock_settings_instance.org_uuid = None
-    mock_settings_instance.org_name = None
-    mock_settings.return_value = mock_settings_instance
-
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 401
-    mock_get_response.text = "Unauthorized access"
-    mock_get_agent.return_value = mock_get_response
-
-    with pytest.raises(
-        AgentRepositoryError,
-        match="Agent test_agent could not be loaded: Unauthorized access"
-    ):
-        Agent(from_repository="test_agent")
-
-    mock_console.print.assert_any_call(
-        "No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.",
-        style="yellow"
-    )

tests/cassettes/test_agent_output_when_guardrail_returns_base_model.yaml

@@ -1,137 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '694'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//nFfNchtHDr7rKVBz0a6KVJGUZMm6SVrJcSw6Ktmb7NY6pQJ7wBlEPd1T
-        6B5S3JTP+yw55AVy9T7YFnr4Jy7pRLmwioP+wfcB+Br4eQ8g4zw7h8yUGE1V2+5l0dh/xOvXo5MT
-        ufDvJk//lNvJm+9HvR9+errPOrrDj34iExe7Do2vakuRvWvNRggj6an90+PXJ69OX50dJUPlc7K6
-        rahj99h3K3bcHfQGx93eabd/Nt9dejYUsnP41x4AwM/pV/10OT1l59DrLL5UFAIWlJ0vFwFk4q1+
-        yTAEDhFdzDoro/Eukkuufyx9U5TxHN6C81Mw6KDgCQFCof4DujAlAfjkbtihhYv0/xw+lgRjb62f
-        siuAAyCEKI2JjVAOfkIyYZqCH0MsCUwjQi5C9DX0exC8MSRQW5yRBGCXFk292BxGGPSA9IkFapKx
-        lwqdodABNCXThCpyUf+59ia0Friq0cT5PiiwIsCg139noh+RwKA3ODr/5D65/iEcHNyyd2RhSCHw
-        wQH85a2LJDBkrPivnxwAdOHg4M4H1ngeHJzDjZcpSr60XfnGRZmp6UIKcpEdLo0Xa27qilO4RGu9
-        g3z/OwHUg0IHqsZGri3B369vLuCqxKpm7wLcEhYNQeRoFbMlzJXj5TUQvaLpw5WvES6qL78IG0xs
-        DHqDAdy8vbmAHxKZV00NEzbRy+xQsQ8U+7uZZXQwHGFdf/lF0d+hcIAPyC5235BUyO7FLNyIxmgn
-        BRtOTdk5Eo38oNc/64BrKhLfBLhlxd5fon90fupg7AVKDhBqorwDufBoZNkVbQ4UHm03GC9KUy1+
-        SiEkuEcK91p0JXyDaNHlCneIzpQUNOJXHGcvhvpeTbPdUDFECnGe3hotITTlCqP6m7J+a+A7aaO6
-        jGCkMYwWtJp1w4bn+wGi0MhSV/nULYEweNfyOhioqhwlJo5T4GnCDv5GcCnNzNEfpmLI+ZjJ5iTb
-        2LgkW3BT7aTjHc0SogofSVIkNy5dq4Q7oYpJNktAg/w8EejJUK3+oYUJB/YuLapV7pS5EVuObc6f
-        KPQr4RAZnYd73ZN7BX9hu+8xBHlxAtx5iU2Bdifmk60Fj9Z2I1e0LGlNBNDEbUtBlWtHSszrxY9X
-        XNl1jnT7tSs0wzvwoUZ2LWtvI9qWhldKw3uaVSjwrRzO8X/DFu2L8V8K/pt3o3/3LFRjiyzJmfDI
-        1nagJCgxwNS7jWpvQ6hVkwPChONa5rdX7gdwOA97JKwgNMZQCB1gZ2yTSF2UgrI5V0hSgUwsnCoL
-        9/ogRLilKbrcT8NjegIuUQxZ7/BPpIPyvpOOe1I+KE+MPNOqeZp2Ehfqb1LJSxWPIbn9AHethKQE
-        mhd1byH0/Q7oOy48aqIeVhJO2M5Uby51l4Nh49iU+2HBEgUY0dgLQeUniSJdOked6DlLb2PziDD0
-        ufB//6PE3BNaGGIunP8ZfbgSj5F3P476ADwrlzbXNaTaUeg6tAp+zY/9sOW9FG6qumyzaFFh88sV
-        qfI71h4mLLqSdPPyTUoEvFYChr7EinL4gBZLZeCWJyS19y+vlOtiVsfflca5Li6v0Rqx9TyJKyUk
-        +buhjorz662DrljqxRtvc3Jw6X2cKxJsPTfx0O8pEd+z+/Kr4SbAt19+c+xlazp8lY+vSMf2YuEk
-        4CGibEg+FqlY1pVkqRU1T/y6WvxOqsxbogV+LaZuap3a5zMx8LGkQMsWtcQJablpM00u2hnkZDVc
-        lAM9RUEvOTuU2bOGddGOpupIjpfe5hC4cDxmgy4Cu7FtyBmCKcfyWSfsx/NGeaPQ21xmSQoY9teq
-        OzVDKI6SurDLecJ5gxbQGG8xp3C4PgYIjZuAOoq4xto1AzrnY9LZNID8OLd8Xo4c1he1+FHY2JqN
-        2XEoHyTRqONFiL7OkvXzHsCPabRpnk0rWS2+quND9I+UrusPBu152WqiWllPjxbWqBFfGc5Ojjtb
-        DnzIKSLbsDYdZQZNSflq62qUwiZnv2bYW4P9/+5sO7uFzq74I8evDEbbGcofaqGczXPIq2VCOnHu
-        WrakOTmcBR3BDD1EJtFQ5DTGxrZzYBZmIVL1MGZXkNTC7TA4rh9eDXBwhGd9Gmd7n/f+BwAA//8D
-        AMMI9CsaDwAA
-    headers:
-      CF-RAY:
-      - 94d9be627c40f260-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 15:02:05 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=qYkxv9nLxeWAtPBvECxNw8fLnoBHLorJdRI8.xVEVEA-1749567725-1.0.1.1-75sp4gwHGJocK1MFkSgRcB4xJUiCwz31VRD4LAmQGEmfYB0BMQZ5sgWS8e_UMbjCaEhaPNO88q5XdbLOCWA85_rO0vYTb4hp6tmIiaerhsM;
-        path=/; expires=Tue, 10-Jun-25 15:32:05 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=HRKCwkyTqSXpCj9_i_T5lDtlr_INA290o0b3k.26oi8-1749567725794-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '42674'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '42684'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999859'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_d92e6f33fa5e0fbe43349afee8f55921
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_cache_hitting.yaml

@@ -1020,4 +1020,63 @@ interactions:
       - req_83a900d075a98ab391c27c5d1cd4fbcb
     http_version: HTTP/1.1
     status_code: 200
+- request:
+    body: !!binary |
+      CtcMCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSrgwKEgoQY3Jld2FpLnRl
+      bGVtZXRyeRKUCAoQot+4QIkYBzRK/SkLTbr2XBIIKFjBQjUmSxQqDENyZXcgQ3JlYXRlZDABOdDB
+      ddshVEMYQbBDfdshVEMYShsKDmNyZXdhaV92ZXJzaW9uEgkKBzAuMTIxLjBKGgoOcHl0aG9uX3Zl
+      cnNpb24SCAoGMy4xMS43Si4KCGNyZXdfa2V5EiIKIDY5NDY1NGEzMThmNzE5ODgzYzA2ZjhlNmQ5
+      YTc1NDlmSjEKB2NyZXdfaWQSJgokYTgxMTFiOTktZWJkMy00ZWYzLWFmNmQtMTk1ZDhiYjNhN2Jl
+      ShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3
+      X251bWJlcl9vZl90YXNrcxICGAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUo6ChBjcmV3
+      X2ZpbmdlcnByaW50EiYKJGQ2YTE3OTk4LTQ0ODgtNDQ0Mi1iY2I3LWZiYzdlMDU1NjE4MUo7Chtj
+      cmV3X2ZpbmdlcnByaW50X2NyZWF0ZWRfYXQSHAoaMjAyNS0wNS0yN1QwMToxMzowNC43NDEyNzRK
+      zAIKC2NyZXdfYWdlbnRzErwCCrkCW3sia2V5IjogIjU1ODY5YmNiMTYzMjNlNzEyOWQyNTIzNjJj
+      ODU1ZGE2IiwgImlkIjogIjdiOTMxZWIzLTRiM2YtNGI3OC1hOWEzLTY4ODZiNTE1M2QxZiIsICJy
+      b2xlIjogIlNheSBIaSIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyNSwgIm1heF9y
+      cG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJ0ZXN0LW1vZGVs
+      IiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6
+      IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119XUr7AQoKY3Jl
+      d190YXNrcxLsAQrpAVt7ImtleSI6ICJkZTI5NDBmMDZhZDhhNDE2YzI4Y2MwZjI2MTBmMTgwYiIs
+      ICJpZCI6ICI5MWU0NjUyYy1kMzk0LTQyMGQtYTIwOS0wNzlhYThkN2E5MDAiLCAiYXN5bmNfZXhl
+      Y3V0aW9uPyI6IGZhbHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9yb2xlIjogIlNh
+      eSBIaSIsICJhZ2VudF9rZXkiOiAiNTU4NjliY2IxNjMyM2U3MTI5ZDI1MjM2MmM4NTVkYTYiLCAi
+      dG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKABAoQG7YeGAh0HoexHn1XjCcuCRIIHGHDZGTQ
+      CskqDFRhc2sgQ3JlYXRlZDABObDFkdshVEMYQThWktshVEMYSi4KCGNyZXdfa2V5EiIKIDY5NDY1
+      NGEzMThmNzE5ODgzYzA2ZjhlNmQ5YTc1NDlmSjEKB2NyZXdfaWQSJgokYTgxMTFiOTktZWJkMy00
+      ZWYzLWFmNmQtMTk1ZDhiYjNhN2JlSi4KCHRhc2tfa2V5EiIKIGRlMjk0MGYwNmFkOGE0MTZjMjhj
+      YzBmMjYxMGYxODBiSjEKB3Rhc2tfaWQSJgokOTFlNDY1MmMtZDM5NC00MjBkLWEyMDktMDc5YWE4
+      ZDdhOTAwSjoKEGNyZXdfZmluZ2VycHJpbnQSJgokZDZhMTc5OTgtNDQ4OC00NDQyLWJjYjctZmJj
+      N2UwNTU2MTgxSjoKEHRhc2tfZmluZ2VycHJpbnQSJgokZDk1ZDk1ZmYtYTBhNC00NmFjLWI2NWUt
+      MWE4Njg5ODYwOWQySjsKG3Rhc2tfZmluZ2VycHJpbnRfY3JlYXRlZF9hdBIcChoyMDI1LTA1LTI3
+      VDAxOjEzOjA0Ljc0MTIyNko7ChFhZ2VudF9maW5nZXJwcmludBImCiRkNTc5ZDA2Ny03YzU1LTQw
+      NmQtYTg5Zi1mZTM1MzU0ZGFlYTJ6AhgBhQEAAQAA
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '1626'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.31.1
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Tue, 27 May 2025 08:13:05 GMT
+    status:
+      code: 200
+      message: OK
 version: 1

tests/cassettes/test_guardrail_is_called_using_callable.yaml

@@ -1,130 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 1 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '693'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//TFPRbttGEHzPVwz04taQBFt1nFZvsgsnRlvYqI0aafOyPK7IjY97xO1R
-        CpMv6nf0x4qlZLcvBHh3Mzs7s/vtDTCTerbGLLRUQtfHxVUzPF6udsaXHx/Cn7//8fG8e9rf/XxF
-        X+vqaTZ3RKo+cygvqGVIXR+5SNLDdchMhZ31/N3FT28vL99enE0XXao5Oqzpy+IiLTpRWazOVheL
-        s3eL8x+P6DZJYJut8dcbAPg2fV2n1vxltsbENZ10bEYNz9avj4BZTtFPZmQmVkjLbP7fZUhaWCfp
-        j20amrascQtNewRSNLJjEBrXD1LbcwY+6Y0oRWym/zUeW0ZJPfpII2eIorSMfcqxnoMMaYu7UFLF
-        Gauz1Q9ziOFXScoRv7GZLPEkNccRmRvKNdcTSNmBzjRVZyuwFALnY52Jl2JEkY7nBya0ZGipBikk
-        xsFKljQYAmXmjNBSplA4y1euUY3gLyVTyrUo5RH2LDHaHDsxSToHaY2Q1E1jDePSO/+kv/CITWiF
-        d9yxFlv78QKnp9dxqOz0dH2UYj1rmfR39DllKaMLbuVVDRXcXOOKcuCYlObYt5wZLaPiQB1P2BCH
-        6sS8z4X3ichUizawkDLnJT5MzxQlk9qWc+YaJeGeshgeSLQs3nPuSBTf3T+8/97TWZ2tzpcHzbda
-        OCv5pFJ07R+8RI1NbliLKDnZTkJJeXwJ1uG4Tj1h0/3zd5ZAk1PHqxVubm82ePL0cT30cxiHIbtm
-        7z1yQ2H0gAkvddFyTsuji5s95fp/Nnqi+6Tohlikj4wrijEp6pO7DJoez9FK00Zp2vJSxgqVwbxM
-        mfy08jKdTUwVxTgiKYx3nCkihUAeuS094PtIo/M8lDHylO5BiRieNe0V25SnIqIhcy1VZNRZqio6
-        iiqJUsY5+sxBjNH72mlzGKc+pyhbCWgSxYWHKNos8cGd8ZVjz8PnpMm085HxZveGVjpPoiPlYccZ
-        pc2+qyjeNGreshobjKmLbBbHOTp6PrjRgaYx9s13oK9yOkS5FY71chrrW93GgTUcOr7iMWk92ShW
-        JNhxwU4M0vUUXhka8uU7uHEk8CuyXqbMj7t66H5Kpk+5WEdqrfRo6V8AAAD//4xVy24CIRTdz1cQ
-        1m1Tx+nCr+gXGHKFO85NGSDAaF347w2ggtUmXR84931O0hcdLJP5mlC14yNzPfmJQlrB0qkkWWQW
-        VMyhH62fIUVNApUXgWk8oGZH1JqRiTYzzqRe1+8hDRFYEhOY83kWVKEimbcx55mFoLTlM2+IvuoL
-        fuPs0gQxsOMEkVFkM4IJiWmHD9vWaiGLVsHprRVfj+MSIBmAWbRuADDGxpxQlv3tBTnfhF7bvfN2
-        F3595SMZCpPwCMGaJOohWsczeu4Y22ZDWe48gjtvZxdFtF+Yw636vvDx6mMV7TebCxptBF2BoV+9
-        PCEUCiOQDo0ncQlyQlW/VgODRZFtgK4p+zGdZ9yldDL7/9BXQEp0EZVw6aTlfcn1mcfk8389u7U5
-        J8wD+gNJFJHQp1EoHGHRxX15OIWIsxjJ7NE7T8WCRyfWA3wMgJu15N25+wEAAP//AwDdzCHTkAgA
-        AA==
-    headers:
-      CF-RAY:
-      - 94d9a27f5dc000f9-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:42:51 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=7hq1JYlSmmLvjUR7npK1vcLJYOvCPn947S.EYBtvTcQ-1749566571-1.0.1.1-11XCSwdUqYCYC3zE9DZk20c_BHXTPqEi6YMhVtX9dekgrj0J3a4EHGdHvcnhBNkIxYzhM4zzQsetx2sxisMk62ywkO8Tzo3rlYdo__Kov7w;
-        path=/; expires=Tue, 10-Jun-25 15:12:51 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=bhxj6kzt6diFCyNbiiw60v4lKiUKaoHjQ3Yc4KWW4OI-1749566571331-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '30419'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '30424'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999859'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_b5983a9572e28ded39da7b12e678e2b7
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_guardrail_is_called_using_string.yaml

@@ -1,643 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '694'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAA6RXXW4cNxJ+1ykK8+JdoyVII/lH8yZprUi25BVsB0GwDoQadk13ZdgkU2TPaBIY
-        2Lc9wx5hz7F7kj1JUOye38iBgrwImiZZrPr41VdVv+wBDLgcjGBgakymCXb/vGrfyvX4+zCZ/fTd
-        3dWPD69v+N3V7bfjDw/N20GhJ/z4RzJpeerA+CZYSuxdt2yEMJFaPXp1cvri5YvTl6/yQuNLsnqs
-        Cmn/xO837Hh/eDg82T98tX/0uj9dezYUByP4xx4AwC/5r/rpSnoYjOCwWH5pKEasaDBabQIYiLf6
-        ZYAxckzo0qBYLxrvErns+qfat1WdRnANzs/BoIOKZwQIlfoP6OKcBOCzu2SHFs7y7xFckRCgEKSa
-        IPkAR4cwppggemNIIFhckERgl3fMvdgSMIKfwN9N8mMSGB4OjwsYY6QSfN7GAoFk4qVBZ6gAbgKa
-        VAC6EvyMBK0F9V143CrQEZLP5itsaPTZfXZHB/D8+Q17RxZuKUZ+/hz+cu0SCdwyNvzXzw4A9uHO
-        R1YLI7j0Mkcp++8XvnVJFiM4k4pcYof9whVXteWqTnHUGc6OsGspOxFrPzcYCWqOQA+GglpHC6Xw
-        eGzZVQXMOLJ3XTQKT4NTdhXgmC2nRQGWsNQPq6vV8IxN8rJY4jg8HA7h8vryDL7LiF60IduLXDme
-        sEGX7GIDI1epEXXK2Hb8LEJsjaEYKR4oXEOF693CMjq4HWMI//2PAnaHwhE+Iru0/w1Jg+yeDNyl
-        6Ns9gto75+cOJl6yO2PLMZGogzEQlTkKY9mxQQsTdhzrjFrvFnAEhCktemZlQ2Ofarj7+E0+rPBc
-        CjlTg8Me/UTYFDDdunmDYZmeN1y1BEfZBitT1qd9Kw4bcqlD61jReiP6mnCFaNGVitYtOlOTRgMX
-        nBZPRuq9fl48glRv+1mEyqPdj8ZnnIL4OcXYvXcSclWqocYZQYOlMq8B70gzTKFofEwwIRTKu3na
-        Z+ObVnygAmqyQa3ueA+RTCsEkXK+QeJkM07GtpmdavvbN5dncFFjE3IS3hBWLWWITjKhaMYO/kZw
-        Lu3C0ZMwuuVywmRLkl2YzslW3DaP4LS64f///Pd21gWVPWVPzgRvu6TrEhAanP4GrSxdfVLuylYB
-        7GKSVomAVpd2osnXl75hp6TSDaVvKCY2+doOcXSgFYJSDrgj1AtF60I4Jkbn4YP6XnrF68zCe4xR
-        nkymOy+prdA+ClMMnJQRs14PNLSP2JYMd+L75yuW1z9TKJOgl5IdygISWnIph7LFx164YEoUMp49
-        aMa7GUnMWQTY+J40CnGhfNWzNZaAYKkiV+odBoVIAI34GKFpbeJgSSWxanuteqlgvadFgwJv5aBH
-        6Yot2iejdC74Mz+GUWf3WQSsejXO5Ztn+f+ccVO2tku3KYWUA0bVJC1+vaaQKAZdBSxyPe3rQg6Z
-        Yw/lil67bwDzmq2mM7uE7DLImrwK4ZamQRCK5EyXdK8UmQ9aUxPc0Bxd6edxmkvfOYoh6x3+ASKp
-        9jwC0aeaVIKslpmlouwUQeMlP7+2HJQrkeorqsTirgxvSHzofco/FGskYbTryrhbz1ZR5czbLmmv
-        swKhcAPn5H6mBnumXKfENZZ/vpApEP1rfyC0cIulsIosCgjp4y1p0T13ASVH1Rb1Xos8Say5q9uJ
-        TN2VvMyv2LGmR3XJmj45vkKaHPVp7nvaKcKtL4X/9y8NesO7PyK4F+Ix8WONzxk0mFXPT6Dpzy8l
-        tmtrlrrbe7HGwwG7iW21scn1vdIig2kTwWI3W+ghkLDSPBufeJ/G2gJqlbaWq1UCHB1q+Le+xoZK
-        +IgWa43/hmckwfteH7766JvBv6kWIS2/bsaerWZtDNZHbZED9o79vipmkTDkkqwD1yRYebd82Duh
-        hkmWWvDV0lFsCYQCFROmNub2el3SyKroT7o4u9z4VFOkVWuetaykxmt101Flq4RmPhbrbDa9DnY9
-        ee8zK+NjIJMUEFWmrj3pdbuA6C2XPFn0nYM2+P0TxJ3SsFVyDzanFaFJG1EnJtdau7GAzvmUZTHP
-        ST/0K19Wk5H1VRA/jjtHB5323Ath9E6noJh8GOTVL3sAP+QJrN0aqgZBfBPSffJTytcdDYedvcF6
-        8Fuvvjw96leTT2jXC6+PjotHDN6XlJBt3BjiBgZNTeX66HriUwXwGwt7G2H/1p3HbHehs6ueYn69
-        YJQfVN4HoZLNdsjrbUI6GH9t2wrm7PAgkszY0H1iEn2KkibY2m5cHcRFTNTcT9hVJEG4m1kn4f74
-        BF+cIJ0em8Hel71fAQAA//8DAICIe4nBDwAA
-    headers:
-      CF-RAY:
-      - 94d9947e9abcf1fe-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:33:27 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=UscrLNUFPQp17ivpT2nYqbIb8GsK9e0GpWC7sIKWwnU-1749566007-1.0.1.1-fCm5jdN02Agxc9.Ep4aAnKukyBp9S3iOLK9NY51NLG1zib3MnfjyCm5HhsWtUvr2lIjQpD_EWosVk4JuLbGxrKYZBa4WTendGsY9lCU9naU;
-        path=/; expires=Tue, 10-Jun-25 15:03:27 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=CGbpwQsAGlm5OcWs4NP0JuyqJh.mIqHyUZjIdKm8_.I-1749566007688-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '40370'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '40375'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999859'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_94fb13dc93d3bc9714811ff4ede4c08f
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Guardrail Agent. You
-      are a expert at validating the output of a task. By providing effective feedback
-      if the output is not valid.\nYour personal goal is: Validate the output of the
-      task\n\nTo give my best complete final answer to the task respond using the
-      exact following format:\n\nThought: I now can give a great answer\nFinal Answer:
-      Your final answer must be the great and the most complete as possible, it must
-      be outcome described.\n\nI MUST use these formats, my job depends on it!\nIMPORTANT:
-      Your final answer MUST contain all the information requested in the following
-      format: {\n  \"valid\": bool,\n  \"feedback\": str | None\n}\n\nIMPORTANT: Ensure
-      the final output does not include any code block markers like ```json or ```python."},
-      {"role": "user", "content": "\n        Ensure the following task result complies
-      with the given guardrail.\n\n        Task result:\n        Here are the top
-      10 best soccer players in the world as of October 2023, based on their performance,
-      impact, and overall contributions to the game:\n\n1. **Lionel Messi** (Inter
-      Miami)\n   - Position: Forward\n   - Country: Argentina\n   - Highlights: Messi
-      continues to showcase his exceptional dribbling, vision, and playmaking ability,
-      leading Argentina to victory in the 2022 FIFA World Cup and significantly contributing
-      to his club''s successes.\n\n2. **Kylian Mbapp\u00e9** (Paris Saint-Germain)\n   -
-      Position: Forward\n   - Country: France\n   - Highlights: Known for his blistering
-      speed and clinical finishing, Mbapp\u00e9 is a key player for both PSG and the
-      French national team, known for his performances in Ligue 1 and international
-      tournaments.\n\n3. **Erling Haaland** (Manchester City)\n   - Position: Forward\n   -
-      Country: Norway\n   - Highlights: Haaland''s goal-scoring prowess and strength
-      have made him one of the most feared strikers in Europe, helping Manchester
-      City secure several titles including the UEFA Champions League.\n\n4. **Kevin
-      De Bruyne** (Manchester City)\n   - Position: Midfielder\n   - Country: Belgium\n   -
-      Highlights: De Bruyne\u2019s exceptional passing, control, and vision make him
-      one of the best playmakers in the world, instrumental in Manchester City\u2019s
-      dominance in domestic and European competitions.\n\n5. **Cristiano Ronaldo**
-      (Al Nassr)\n   - Position: Forward\n   - Country: Portugal\n   - Highlights:
-      Despite moving to the Saudi Pro League, Ronaldo''s extraordinary talent and
-      goal-scoring ability keep him in the conversation among the best, having had
-      a legendary career across multiple leagues.\n\n6. **Neymar Jr.** (Al Hilal)\n   -
-      Position: Forward\n   - Country: Brazil\n   - Highlights: Neymar''s agility,
-      creativity, and skill have kept him as a top performer in soccer, now showcasing
-      his talents in the Saudi Pro League while maintaining a strong national team
-      presence.\n\n7. **Robert Lewandowski** (Barcelona)\n   - Position: Forward\n   -
-      Country: Poland\n   - Highlights: The prolific striker continues to score consistently
-      in La Liga, known for his finishing, positioning, and aerial ability, contributing
-      to Barcelona\u2019s successes.\n\n8. **Karim Benzema** (Al Ittihad)\n   - Position:
-      Forward\n   - Country: France\n   - Highlights: The former Real Madrid star
-      remains a top talent, displaying leadership and technical skills, now continuing
-      his career in the Saudi Pro League.\n\n9. **Luka Modri\u0107** (Real Madrid)\n   -
-      Position: Midfielder\n   - Country: Croatia\n   - Highlights: A master of midfield
-      control and passing, Modri\u0107 remains an influential figure at Real Madrid,
-      showcasing his experience and football intelligence.\n\n10. **Mohamed Salah**
-      (Liverpool)\n    - Position: Forward\n    - Country: Egypt\n    - Highlights:
-      Salah''s explosive pace and goal-scoring ability keep him as a central figure
-      for Liverpool in the Premier League and European competitions, maintaining his
-      status as one of the elite forwards.\n\nThese players have demonstrated exceptional
-      skill, consistency, and impact in their respective teams and leagues, solidifying
-      their positions among the best in the world.\n\n        Guardrail:\n        Only
-      include Brazilian players, both women and men\n        \n        Your task:\n        -
-      Confirm if the Task result complies with the guardrail.\n        - If not, provide
-      clear feedback explaining what is wrong (e.g., by how much it violates the rule,
-      or what specific part fails).\n        - Focus only on identifying issues \u2014
-      do not propose corrections.\n        - If the Task result complies with the
-      guardrail, saying that is valid\n        "}], "model": "gpt-4o-mini", "stop":
-      ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4676'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=UscrLNUFPQp17ivpT2nYqbIb8GsK9e0GpWC7sIKWwnU-1749566007-1.0.1.1-fCm5jdN02Agxc9.Ep4aAnKukyBp9S3iOLK9NY51NLG1zib3MnfjyCm5HhsWtUvr2lIjQpD_EWosVk4JuLbGxrKYZBa4WTendGsY9lCU9naU;
-        _cfuvid=CGbpwQsAGlm5OcWs4NP0JuyqJh.mIqHyUZjIdKm8_.I-1749566007688-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAA4xUwWobMRC9+ysGnVqwjZ3YjuNbQim0hULBtIc6mLE0uzuxVtpqtHbckH8v2o29
-        TpNCLwuapzfzZvRmH3sAio1agNIFRl1WdnCb119+LIvZh+/+h9zM55Op3Wzv5dt4rKei+onhN/ek
-        45E11L6sLEX2roV1IIyUso6vJtfT2Ww0mjdA6Q3ZRMurOJj4QcmOBxeji8lgdDUYz5/ZhWdNohbw
-        swcA8Nh8k05n6EEtYNQ/RkoSwZzU4nQJQAVvU0ShCEtEF1W/A7V3kVwjfVn4Oi/iAj6B83vQ6CDn
-        HQFCnvQDOtlTAFi5j+zQwk1zXsDjygGs1A4tm5VaQIZWqN8GMyKzQb1N8ZVaFgQRZQuBpLYRjCcB
-        5yM0AzvAnmMBsSDIawwmIFtAAY7ATtvakEBl8UBBIAu+hB0G9rWA9rWLgUkAnQHv7AFKcmn8AnHv
-        4Tbgb7aM7kR/95UOJQb4HIYNpz2+h33BloAeUjV2+Rlz70vq+Cl7IHsYwvKFWqlIc8YarT1AoF81
-        SRSI/thAK+6Vnj5sfCyeayQ9Jbl+EqML4HZCWW0ztpbMcKVW7un8CQNltWCykautPQPQOR+xmUMy
-        z90z8nSyi/V5FfxG/qKqjB1LsQ6E4l2yhkRfqQZ96gHcNbasXzhNVcGXVVxHv6Wm3PV01uZT3TZ0
-        6PwIRh/RdvHxaHLRfyPh2lBEtnLmbKVRF2Q6brcGWBv2Z0DvrO3Xct7K3bbOLv+f9B2gNVWRzLoK
-        ZFi/bLm7Fij9Lf517TTmRrASCjvWtI5MIT2FoQxr2+6wkoNEKtcZu5xCFbhd5KxaX05wOkG6vtSq
-        99T7AwAA//8DABZK9w/WBAAA
-    headers:
-      CF-RAY:
-      - 94d9957c4b46f1fe-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:33:46 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '18280'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '18287'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149998889'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_853da6b9ca8f386ff1427c837346290d
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
-      great answer  \nFinal Answer: Here are the top 10 best soccer players in the
-      world as of October 2023, based on their performance, impact, and overall contributions
-      to the game:\n\n1. **Lionel Messi** (Inter Miami)\n   - Position: Forward\n   -
-      Country: Argentina\n   - Highlights: Messi continues to showcase his exceptional
-      dribbling, vision, and playmaking ability, leading Argentina to victory in the
-      2022 FIFA World Cup and significantly contributing to his club''s successes.\n\n2.
-      **Kylian Mbapp\u00e9** (Paris Saint-Germain)\n   - Position: Forward\n   - Country:
-      France\n   - Highlights: Known for his blistering speed and clinical finishing,
-      Mbapp\u00e9 is a key player for both PSG and the French national team, known
-      for his performances in Ligue 1 and international tournaments.\n\n3. **Erling
-      Haaland** (Manchester City)\n   - Position: Forward\n   - Country: Norway\n   -
-      Highlights: Haaland''s goal-scoring prowess and strength have made him one of
-      the most feared strikers in Europe, helping Manchester City secure several titles
-      including the UEFA Champions League.\n\n4. **Kevin De Bruyne** (Manchester City)\n   -
-      Position: Midfielder\n   - Country: Belgium\n   - Highlights: De Bruyne\u2019s
-      exceptional passing, control, and vision make him one of the best playmakers
-      in the world, instrumental in Manchester City\u2019s dominance in domestic and
-      European competitions.\n\n5. **Cristiano Ronaldo** (Al Nassr)\n   - Position:
-      Forward\n   - Country: Portugal\n   - Highlights: Despite moving to the Saudi
-      Pro League, Ronaldo''s extraordinary talent and goal-scoring ability keep him
-      in the conversation among the best, having had a legendary career across multiple
-      leagues.\n\n6. **Neymar Jr.** (Al Hilal)\n   - Position: Forward\n   - Country:
-      Brazil\n   - Highlights: Neymar''s agility, creativity, and skill have kept
-      him as a top performer in soccer, now showcasing his talents in the Saudi Pro
-      League while maintaining a strong national team presence.\n\n7. **Robert Lewandowski**
-      (Barcelona)\n   - Position: Forward\n   - Country: Poland\n   - Highlights:
-      The prolific striker continues to score consistently in La Liga, known for his
-      finishing, positioning, and aerial ability, contributing to Barcelona\u2019s
-      successes.\n\n8. **Karim Benzema** (Al Ittihad)\n   - Position: Forward\n   -
-      Country: France\n   - Highlights: The former Real Madrid star remains a top
-      talent, displaying leadership and technical skills, now continuing his career
-      in the Saudi Pro League.\n\n9. **Luka Modri\u0107** (Real Madrid)\n   - Position:
-      Midfielder\n   - Country: Croatia\n   - Highlights: A master of midfield control
-      and passing, Modri\u0107 remains an influential figure at Real Madrid, showcasing
-      his experience and football intelligence.\n\n10. **Mohamed Salah** (Liverpool)\n    -
-      Position: Forward\n    - Country: Egypt\n    - Highlights: Salah''s explosive
-      pace and goal-scoring ability keep him as a central figure for Liverpool in
-      the Premier League and European competitions, maintaining his status as one
-      of the elite forwards.\n\nThese players have demonstrated exceptional skill,
-      consistency, and impact in their respective teams and leagues, solidifying their
-      positions among the best in the world."}, {"role": "user", "content": "The task
-      result does not comply with the guardrail as it includes players from various
-      countries and only mentions two Brazilian players (Neymar Jr. and Neymar) while
-      excluding Brazilian women players entirely. The guardrail specifically requests
-      to include only Brazilian players, both women and men, which is not fulfilled."}],
-      "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4338'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=UscrLNUFPQp17ivpT2nYqbIb8GsK9e0GpWC7sIKWwnU-1749566007-1.0.1.1-fCm5jdN02Agxc9.Ep4aAnKukyBp9S3iOLK9NY51NLG1zib3MnfjyCm5HhsWtUvr2lIjQpD_EWosVk4JuLbGxrKYZBa4WTendGsY9lCU9naU;
-        _cfuvid=CGbpwQsAGlm5OcWs4NP0JuyqJh.mIqHyUZjIdKm8_.I-1749566007688-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//lFfNbhxHDr7rKYi5BDBagiTLsj032QvHceC1ExubIOtAYFdzuhlVs3pZ
-        1TMeBwb2Nfa857xArnqTfZIFq2e6R9J417oImmYVfz5+JIu/HwDMuJrNYeYaTK7t/OGzuv8+vPuu
-        O/2H+9k//vnF8flP4Zcf6uclPj35dlbYjVD+Ri5tbx250HaeEgcZxE4JE5nWk8dnTx+dnx+fnmdB
-        Gyrydq3u0uFZOGxZ+PD0+PTs8Pjx4cmTze0msKM4m8PfDwAAfs9/zU+p6ONsDsfF9ktLMWJNs/l4
-        CGCmwduXGcbIMaGkWTEJXZBEkl1/34S+btIcvgMJK3AoUPOSAKE2/wElrkgBPsgLFvRwkX/P4SUp
-        ASpBaghS6ODkGEqKCWJwjhQ6j2vSCCz5xCqorwpYBNdHlhroo/N95CX5NQSBZ4qf2DPK9l4BZUgN
-        rEJLAigVtCQFlBipsvOpIVboSBdBWxRH+Qy3Hbq0NVljS4ARwgLeuBRKUjg9Pn04/yAf5OQIHjz4
-        K61bVHilRw8eWIAAcAhvQ2TL4RxeBF2hVqPkue/LOVx4eMke/fj5JdeN57pJcQ5vhMycWW+DYXHF
-        3lMFi0FVhNqHEr1fF7Axbplg6SlCClDhp0+eYMWpgYYjVMpl6VnqIsPS4lX+30KtA/rD6IIamFiy
-        57QuoMFl/g2Ra+EFO5S0RSXIAKkpdr4vsxrzdMJe0CJHD4mwPTKcTg2nv7Fc/+G4j/Dq+k/hoPeA
-        60dCD6+xUq72IjbpbjACtaQ1VZY1hCtab9hg+O1qKkBCGmDN4cSOqCogkWuEHfoB9zgg5bR3jD4j
-        lunIbRfUKgJaTK6hWEBswsphJqbpS+hJJsRGtFgS6QiSpyX5mGF6aDD9yK5B9RyD3AOg9yElkgZb
-        eBlS7HrdC9P3ElYyxrskjZhyzrNfSGoRjjTY8STDWpILVguw5IQeWmqtGDZEvZH0rM6umMRht1Fp
-        5HQNSk1TZY0s7XBTfrfomJE5M2Reoya8ByZv1KNUAd4qV7QXjwtI2hN4qkkqiyQ3im/ipvsUkE3m
-        SFwQ64Akya+3iaYKGtKBJgV4woo0Ntztqa1Ow4piLIBQxT5I35KGPgIOJZ0T4FzwWFkVN2r9NPQp
-        WxgcRF2DQyXSjMkjw+RCKrW+DRd+SXEvOK+5WjD5ivR2UYUWvwBLx8tgOd4UDssdZEbDBk7FMZ8c
-        8KCPjroNG4YSGpoEupTLyiImoRiHumAZSsM05IPGz//XUc5z8B4F4R1KCvtD/wstSO4G/ozEmtr+
-        2LNOjqDkQi38adsgSKEydTZttukcsrZD7iBJueyTzbIcA6aE7qoAiglLz7HJzYE0kl/s7U9DcyW9
-        2VzvAvDYAPgWS2Xy8IpiH+8zfTSSfGH4/GQFibDwyINHYz1arPkZkMfHDdsGGApQjCTJmkiHmva2
-        hgKubjShVdArUEw0tkbvuSabxcOAhkzeHPMTi/k9Xv8R4ULx+s/fwv0I/+763wHeYu/DF2ivnJt3
-        TJg5PxHwNvsHJ4z6NbJQtWWLOTDSJSYNVvrT8yIPjnbj3o15kdOei2UzbQgTL7ft72kOvGGsA7xj
-        v8T7sP15Qz7SF9guQB87UjbAq2xayTOWnsCRzanDMvM3W/3PP/8Vd9pcPj7VxFAHTIbLkqDFiqDh
-        FhBcUCGNKQhlcLYefc3T4eTYYn/GCL+gkGJKU9L/B9PH6N+ib4kV4yS4NQHylJ5eVwWYsZ15t8NX
-        0ptdfRxtFrVFM72vxsfD2BPyx+lB5XPH+Npyf2+14jkmaEbv81EWp1SxJWzz3sj8GY0GyU/XOy9j
-        Y+KWzTs8HF7E267tNGy79MB/072tBRvfsYDU9NYsO6Vc+1JvbH0TQdk124e8pxrd+mh3fVBa9BFt
-        hZHe+x0BioSUIciLy68byedxVfGh7jSU8dbV2YKFY3OphDGIrSUxhW6WpZ8PAH7NK1F/Y8uZdRra
-        Ll2mcEXZ3JMnJ4O+2bSJTdLz883CNEs2ICfByaOz7b0bGi8rSsg+7qxVM4euoWq6O+1g2FccdgQH
-        O3Hf9Wef7iF2lvpr1E8CZyObqsvOyORuxjwdU7JV9UvHRpyzw7NIumRHl4lJLRcVLbD3wwI5i+uY
-        qL1csNSknfKwRS66y4dn+OgM6elDNzv4fPBfAAAA//8DAGsE37hTDwAA
-    headers:
-      CF-RAY:
-      - 94d995f0ca0cf1fe-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:34:22 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '35941'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '35946'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149998983'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_c874fed664f962f5ee90decb8ebad875
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Guardrail Agent. You
-      are a expert at validating the output of a task. By providing effective feedback
-      if the output is not valid.\nYour personal goal is: Validate the output of the
-      task\n\nTo give my best complete final answer to the task respond using the
-      exact following format:\n\nThought: I now can give a great answer\nFinal Answer:
-      Your final answer must be the great and the most complete as possible, it must
-      be outcome described.\n\nI MUST use these formats, my job depends on it!\nIMPORTANT:
-      Your final answer MUST contain all the information requested in the following
-      format: {\n  \"valid\": bool,\n  \"feedback\": str | None\n}\n\nIMPORTANT: Ensure
-      the final output does not include any code block markers like ```json or ```python."},
-      {"role": "user", "content": "\n        Ensure the following task result complies
-      with the given guardrail.\n\n        Task result:\n        Here are the top
-      10 best soccer players in the world, focusing exclusively on Brazilian players,
-      both women and men, based on their performance and impact in the game as of
-      October 2023:\n\n1. **Neymar Jr.**  \n   - Position: Forward  \n   - Club: Al
-      Hilal  \n   - Highlights: One of the most skilled forwards globally, Neymar
-      continues to dazzle with his dribbling, playmaking, and goal-scoring ability,
-      having a significant impact on both his club and the Brazilian national team.\n\n2.
-      **Vin\u00edcius J\u00fanior**  \n   - Position: Forward  \n   - Club: Real Madrid  \n   -
-      Highlights: Vin\u00edcius has emerged as a key player for Real Madrid, noted
-      for his speed, technical skills, and crucial goals in important matches, showcasing
-      his talent on both club and international levels.\n\n3. **Richarlison**  \n   -
-      Position: Forward  \n   - Club: Tottenham Hotspur  \n   - Highlights: Known
-      for his versatility and aerial ability, Richarlison has become a vital member
-      of the national team and has the capability to change the game with his pace
-      and scoring ability.\n\n4. **Marta**  \n   - Position: Forward  \n   - Club:
-      Orlando Pride  \n   - Highlights: A true legend of women''s soccer, Marta has
-      consistently showcased her skill, leadership, and goal-scoring prowess, earning
-      numerous awards and accolades throughout her legendary career.\n\n5. **Andressa
-      Alves**  \n   - Position: Midfielder  \n   - Club: Roma  \n   - Highlights:
-      A pivotal player in women''s soccer, Andressa has displayed her exceptional
-      skills and tactical awareness both in club play and for the Brazilian national
-      team.\n\n6. **Alana Santos**  \n   - Position: Defender  \n   - Club: Benfica  \n   -
-      Highlights: Alana is recognized for her defensive prowess and ability to contribute
-      to the attack, establishing herself as a key player for both her club and the
-      national team.\n\n7. **Gabriel Jesus**  \n   - Position: Forward  \n   - Club:
-      Arsenal  \n   - Highlights: With a flair for scoring and assisting, Gabriel
-      Jesus is an essential part of the national team, known for his work rate and
-      intelligence on the field.\n\n8. **Ta\u00eds Ara\u00fajo**  \n   - Position:
-      Midfielder  \n   - Club: S\u00e3o Paulo  \n   - Highlights: A rising star in
-      Brazilian women''s soccer, Ta\u00eds has gained recognition for her strong performances
-      in midfield, showcasing both skill and creativity.\n\n9. **Thiago Silva**  \n   -
-      Position: Defender  \n   - Club: Chelsea  \n   - Highlights: An experienced
-      and reliable center-back, Silva\u2019s leadership and defensive abilities have
-      made him a cornerstone for Chelsea and the Brazilian national team.\n\n10. **Bia
-      Zaneratto**  \n    - Position: Forward  \n    - Club: Palmeiras  \n    - Highlights:
-      A talented forward, Bia has become known for her goal-scoring capabilities and
-      playmaking skills, contributing significantly to both her club and the national
-      team.\n\nThis list highlights the incredible talent and contributions of Brazilian
-      players in soccer, showcasing their skills across both men''s and women''s games,
-      thus representing Brazil''s rich soccer legacy.\n\n        Guardrail:\n        Only
-      include Brazilian players, both women and men\n        \n        Your task:\n        -
-      Confirm if the Task result complies with the guardrail.\n        - If not, provide
-      clear feedback explaining what is wrong (e.g., by how much it violates the rule,
-      or what specific part fails).\n        - Focus only on identifying issues \u2014
-      do not propose corrections.\n        - If the Task result complies with the
-      guardrail, saying that is valid\n        "}], "model": "gpt-4o-mini", "stop":
-      ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4571'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=UscrLNUFPQp17ivpT2nYqbIb8GsK9e0GpWC7sIKWwnU-1749566007-1.0.1.1-fCm5jdN02Agxc9.Ep4aAnKukyBp9S3iOLK9NY51NLG1zib3MnfjyCm5HhsWtUvr2lIjQpD_EWosVk4JuLbGxrKYZBa4WTendGsY9lCU9naU;
-        _cfuvid=CGbpwQsAGlm5OcWs4NP0JuyqJh.mIqHyUZjIdKm8_.I-1749566007688-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAA4ySTW/bMAyG7/4VhM72kLj5WHxrDwEKtNilGDYshcFItK1WlgRJTjYE+e+D7DR2
-        tw7YxYD58KX4kjwlAEwKVgDjDQbeWpXd1d3Dl+5Yfb3dPD6K7bri26cw899f5vruG0ujwuxfiIc3
-        1SduWqsoSKMHzB1hoFh1vl5slqvVbJX3oDWCVJTVNmQLk7VSyyyf5Ytsts7mny/qxkhOnhXwIwEA
-        OPXf2KcW9JMVMEvfIi15jzWx4poEwJxRMcLQe+kD6sDSEXKjA+m+9afGdHUTCrgHbY7AUUMtDwQI
-        dewfUPsjOYCd3kqNCm77/wJOOw2wYwdUUuxYAcF1lA6xikjskb/GsO6U2unz9HFHVedRXeAEoNYm
-        YBxgb/v5Qs5Xo8rU1pm9/0PKKqmlb0pH6I2OpnwwlvX0nAA89wPt3s2IWWdaG8pgXql/bpMvh3ps
-        3ONI8/UFBhNQTVTLPP2gXikooFR+shLGkTckRum4P+yENBOQTFz/3c1HtQfnUtf/U34EnJMNJErr
-        SEj+3vGY5iie+b/SrlPuG2ae3EFyKoMkFzchqMJODcfH/C8fqC0rqWty1snhAitb3ixwuUDa3HCW
-        nJPfAAAA//8DAOHOvQuPAwAA
-    headers:
-      CF-RAY:
-      - 94d996d2b877f1fe-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:34:38 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '15341'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '15343'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149998917'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_22419b91d42cf1e03278a29f3093ed3d
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_guardrail_reached_attempt_limit.yaml

@@ -1,726 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '694'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAA5yXzVIbORDH7zxF11xiqLHLNp/xDUhMssEJm49N1S4pqq1pz/SikYaWxo6Tynmf
-        ZQ/7AnvNPtiWZIMNGAK5UHhaLfVPrW799XUNIOEs6UGiCvSqrHTzIK9HH/yzi6Mv44uT3z93X5fn
-        Fxefs49bR3vya5IGDzv8k5S/9GopW1aaPFszMysh9BRm7exuPd3e2d1s70VDaTPSwS2vfHPLNks2
-        3Oy2u1vN9m6zszf3LiwrckkP/lgDAPga/4Y4TUafkx6008svJTmHOSW9q0EAiVgdviToHDuPxifp
-        wqis8WRi6O8LW+eF78FLMHYCCg3kPCZAyEP8gMZNSABOTZ8NatiPv3vwviDwtoJOG4bkPDirFAlU
-        GqckDtiALwgmVnSWAjqwI3ijvB2SQLfd3UzjSkMCzsh4HjFlMERHGdjoyQJCioyHimRkpUSjyKXg
-        zllrlwKXFSofBudYBgOaDOyYBLWGgCc8rEMuHHg7n9ATlq4FL0gI2MX4nJda+VooA83O907Nqem0
-        YGPjmK0hDQNyjqHx0ngSGDCWDIf99Y2NUwMATTixjsMiPehbmaBk8++vaAr7fhYDuR48Ex4ONZs8
-        hTE7tiaF3KJuOmWFTQ44ZM1+2pq776uCaUwlGe96MKi150oTHKDW1kD25I3AhI0hSeHQVgj7JQkr
-        BFVgWcXZP4Z9h8O6uvoGjW67211vBcJuIHw11YwGBkOsqu9/Q+MEhR28Qza+eURSIpvHg76riLIU
-        PKnC8EVNKYzYsCvY5Kvh+i/7+3dE29lbT+GY85qgA569DmkuLzcjsyU5zwpUXblItRmonkvYZniB
-        qMOJaAzQqIJcSN8h++njiU7sJOyzm4Fdyxob59kov5rsoDYZOc05xjoJTmGiD8/7+3A4x3RwTBgA
-        Z+mMOdpcT+FEqGSSS+sMPkJuxdTRmA08IziQemroQZQDzkZMOiO5CzR0iuUTGvsXj398LldGm0Lg
-        rCv3IOTIth3ZULiEAzJfqERo7OvmS++5wOzxqTtaSlYaO1OJ5/F/j8qzQg1sPGnNORlFqylD1ays
-        vKuTeJPMwYR9AW8JNQwwE84i3U6ge03TEgV+kVZAgxesUT8erK+RJYVs0VUenKurstHXk3UrPVIH
-        zGZdxeB3Q/BvQ/f2cEwTNJmduHOGRv8QDlAUaWvwJ0Aum0MK1dwl/kASRn1/W4yd4yBcPQMyTxyc
-        xJtnKS/LBTinPMbQUPB6U5yfvr2AOLAFlpTBO9RYQOOYxySVtfqnO+FoQXiVrtU8N6po0ctndXR/
-        GUWCp4HgNzbf/1FcO/jl+7+GrUBj6ST+NMcjztoPy727fn8mOu14A9fnCAObCf/310qG+9rbQ/rb
-        PU3g7vq5xXV5tG63iPkdFpHeF+yiwgjCIwhF1rcFj6pFguIJcieFCt1N8RNutDu0jZCrSPmg3KLM
-        CQJsaH2xKPjgHEjFYPBFHeMgHzfQteb1A4Im9EgX9dkYZbqIsiJx0dFWbELOwpRhWR6Fe1jYh7KF
-        OgwfWQEao67jYikMax8idXSlEAscU6AJ8pSM11MYEhkQylEyyoJmdLakIByDUosic1lVrhCVrWWN
-        KzSqHQadbWqtlwxojPUxrKiuP80t3670tLZ5JXbobrgms1o+E0JnTdDOztsqidZvawCfom6vr0nx
-        pBJbVv7M23OKy3W63dl8yeK5sLDubG3Ord561AvD7s52umLCs4w8snZL0j9RqArKFq6LdwLWGdsl
-        w9oS9u1wVs09Q2eTP2T6hUEpqjxlZ5VQxuo68mKYUHhO3TXsaptjwIkjGbOiM88kIRUZjbDWs0dO
-        4qbOU3k2YpOTVMKzl86oOtvcwu0tpKebKln7tvY/AAAA//8DAJ67Vp33DQAA
-    headers:
-      CF-RAY:
-      - 94d9b5400dcd624b-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:55:42 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=8Yv8F0ZCFAo2lf.qoqxao70yxyjVvIV90zQqVF6bVzQ-1749567342-1.0.1.1-fZgnv3RDfunvCO1koxwwFJrHnxSx_rwS_FHvQ6xxDPpKHwYr7dTqIQLZrNgSX5twGyK4F22rUmkuiS6KMVogcinChk8lmHtJBTUVTFjr2KU;
-        path=/; expires=Tue, 10-Jun-25 15:25:42 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=wzh8YnmXvLq1G0RcIVijtzboQtCZyIe2uZiochkBLqE-1749567342267-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '33288'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '33292'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999859'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_6a587ea22edef774ecdada790a320cab
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
-      great answer  \nFinal Answer: The top 10 best soccer players in the world, as
-      of October 2023, can be identified based on their recent performances, skills,
-      impact on games, and overall contributions to their teams. Here is the structured
-      list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
-      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair,
-      dribbling, creativity.\n   - Achievements: Multiple domestic league titles,
-      Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   -
-      Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   -
-      Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion
-      (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League
-      champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior
-      (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling,
-      creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga
-      champion (2023).\n\n10. **Luka Modri\u0107 (Real Madrid)**\n    - Position:
-      Midfielder\n    - Key Attributes: Passing, vision, tactical intelligence.\n    -
-      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis
-      list is compiled based on their current form, past performances, and contributions
-      to their respective teams in both domestic and international competitions. Player
-      rankings can vary based on personal opinion and specific criteria used for evaluation,
-      but these players have consistently been regarded as some of the best in the
-      world as of October 2023."}, {"role": "user", "content": "You are not allowed
-      to include Brazilian players"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3594'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=8Yv8F0ZCFAo2lf.qoqxao70yxyjVvIV90zQqVF6bVzQ-1749567342-1.0.1.1-fZgnv3RDfunvCO1koxwwFJrHnxSx_rwS_FHvQ6xxDPpKHwYr7dTqIQLZrNgSX5twGyK4F22rUmkuiS6KMVogcinChk8lmHtJBTUVTFjr2KU;
-        _cfuvid=wzh8YnmXvLq1G0RcIVijtzboQtCZyIe2uZiochkBLqE-1749567342267-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//nJfNctpIEMfvfoouXYJdwgXYjm1u4JjEsam4kuzuYZ1yNaNG6njUo50Z
-        QUgq5zzLHvYF9pp9sK0ZwOAEx3EuFKjVTf+mP/TXpy2AhLOkC4kq0Kuy0s1+Xucvb06e+X7ndDzp
-        DflscN6yRBfng75O0uBhRu9J+aXXrjJlpcmzkblZWUJPIWr7cP/44Onh3sFxNJQmIx3c8so3902z
-        ZOFmp9XZb7YOm+2jhXdhWJFLuvDnFgDAp/gZ8pSMPiRdaKXLKyU5hzkl3dubABJrdLiSoHPsPIpP
-        0pVRGfEkMfW3hanzwnfhDMRMQaFAzhMChDzkDyhuShbgSgYsqKEXf3fhBVkCdoACdZUFUNDsPJgx
-        +ILAmwraLRiR8+CMUmSh0jgj64Al3jE1VmeALni8Ut6MyEKn1dlLgT4oXWcsOfQtfmTNKEvn7pVc
-        SXsXdnYu2AhpGJJzDI0z8WRhyFgynAy2d3auBACacGkch4p0YWDsFG22uH5OM+h5b3lUe3JdeGZ5
-        NNIseQoTdmwkhdygbjplbEgER6zZz3YX7j1VME2oJPGuC8Nae640QR+1NgLZk1cWpixCNoUTUyH0
-        SrKsEFSBZRWj/xHpT+rq9ho0Oq1OZ3s3EHYC4fkskg9HWFVf/4bGJVp28AZZfPM52RJZHg/6piLK
-        UvCkCuG/akphzMKuYMk3ww3OBr17sm0fbadwwXlN0AbPXpNLoVweRmZKcp4VqLpykWovUJ3acMzw
-        AlGjZNAYoqiCXCjfCfvZ44kuzTScs5uD3akai/Msym8m69eSkdOcY+zW4BQC/XY66MHJAtPBBWEA
-        nJcz1mhvO4VLSyWTXVrn8BFyP5aOJizwjKBv65nQT1EOORsz6YzsfaBhlNc7NC4YnjzclxuzTSFw
-        1pX7KeTIdhDZ0HIJfZKPVCI0erp55j0XmD2+dM/XipXGES/xJn73qDwr1MDiSWvOSRRtpgxTs3Hy
-        bjvxWzIHU/YFvCbUMMTMchbpnga612EPebigKUpmpu6GoTE4gT5aRdoIPh5ysJyvFKqFS/yBZBn1
-        jzdLHL5+2KFDkicOLuMWXENb7+FFVS8wzCTe3SuLAh4GxKEpsKQM3qDGAhoXPCFbGaN/eZmMV4TZ
-        co9u5vmmEVfrcN6KP+7ESHAUCH5n+fqP4trBy6//ChsLjbVi/jJHtnoIPDRaD05MZ/vHlTiOz7D6
-        BmFoMsv/fXkQ4fH74RFDNLxvVm7b6vsJWzwCIk67FXheoLUzOMew8fqhUwWGtbAqljz31uTB5bD2
-        wFrtid2l712Y50ZnJNA3xt8ug3tWYKzjaW1NRSgr+IIrsHXwbNZVBHxbsJsLnAIdjIgEMHtfu6B7
-        vFlIFvpesEB4yI2Nqh05MEH5GEcwLQwUOCEoMSNwnAuPWaF44LJC5ZciiS0oXY/mUcxcN4ViWsFw
-        hqjBecxpg4rahVNUxSKLoNMsKZMLf6SQjl0Epw+KqmWkG9bapVCRHRtboqhwQOGPce10d9dlpKVx
-        7TBIWam1XjOgiPExxShg3y0sn28lqzZ5Zc3IfeOazFfJtSV0RoI8dd5USbR+3gJ4F6VxfUftJpU1
-        ZeWvvbmh+HeHR+15vGSlyFfWp+2FcE688ahXhvbe8dLvTsTrjDyydmvyOlGoCspWvistjnXGZs2w
-        tcb9fT6bYs/ZWfKfCb8yqFBJyq4rSxmru8yr2yyFV5b7brs955hw4shOWNG1Z7KhFhmNsdbzF4nE
-        zZyn8nrMkpOtLM/fJsbV9d4+HuwjHe+pZOvz1v8AAAD//wMAVoKTVVsNAAA=
-    headers:
-      CF-RAY:
-      - 94d9b6782db84d3b-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:56:30 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '31484'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '31490'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999166'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_aa737cf40bb76af9f458bfd35f7a77a1
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
-      great answer  \nFinal Answer: The top 10 best soccer players in the world, as
-      of October 2023, can be identified based on their recent performances, skills,
-      impact on games, and overall contributions to their teams. Here is the structured
-      list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
-      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair,
-      dribbling, creativity.\n   - Achievements: Multiple domestic league titles,
-      Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   -
-      Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   -
-      Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion
-      (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League
-      champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior
-      (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling,
-      creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga
-      champion (2023).\n\n10. **Luka Modri\u0107 (Real Madrid)**\n    - Position:
-      Midfielder\n    - Key Attributes: Passing, vision, tactical intelligence.\n    -
-      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis
-      list is compiled based on their current form, past performances, and contributions
-      to their respective teams in both domestic and international competitions. Player
-      rankings can vary based on personal opinion and specific criteria used for evaluation,
-      but these players have consistently been regarded as some of the best in the
-      world as of October 2023."}, {"role": "user", "content": "You are not allowed
-      to include Brazilian players"}, {"role": "assistant", "content": "Thought: I
-      now can give a great answer  \nFinal Answer: Here is an updated list of the
-      top 10 best soccer players in the world as of October 2023, excluding Brazilian
-      players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
-      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes:
-      Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s
-      Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed
-      Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing,
-      dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions
-      League winner.\n\n8. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position:
-      Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements:
-      UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107
-      (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision,
-      tactical intelligence.\n   - Achievements: Multiple Champions League titles,
-      Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position:
-      Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements:
-      Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis
-      list has been adjusted to exclude Brazilian players and focuses on those who
-      have made significant impacts in their clubs and on the international stage
-      as of October 2023. Each player is recognized for their exceptional skills,
-      performances, and achievements."}, {"role": "user", "content": "You are not
-      allowed to include Brazilian players"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6337'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=8Yv8F0ZCFAo2lf.qoqxao70yxyjVvIV90zQqVF6bVzQ-1749567342-1.0.1.1-fZgnv3RDfunvCO1koxwwFJrHnxSx_rwS_FHvQ6xxDPpKHwYr7dTqIQLZrNgSX5twGyK4F22rUmkuiS6KMVogcinChk8lmHtJBTUVTFjr2KU;
-        _cfuvid=wzh8YnmXvLq1G0RcIVijtzboQtCZyIe2uZiochkBLqE-1749567342267-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//rJfNbhs3EMfvforBXiIHK0OSv3WTHCsxYiFunKJo6sAYkaPdqbkkS3Il
-        K0HOfZY+R/tgBSnJkhPZtdteDC+HQ82P87H//bIFkLHMupCJEoOorGr2i7rwvWp2tt+qfzi4tCf2
-        6Pjjx5/36FX74nOWRw8z+pVEWHrtCFNZRYGNnpuFIwwUT20f7h3vHxzuHu8lQ2UkqehW2NDcM82K
-        NTc7rc5es3XYbB8tvEvDgnzWhV+2AAC+pL8xTi3pNutCK1+uVOQ9FpR17zYBZM6ouJKh9+wD6pDl
-        K6MwOpBOoX8oTV2UoQtnoM0UBGooeEKAUMT4AbWfkgO40gPWqKCXnrvwhhwBewglgaMJe5Kg2Acw
-        47QWjIV2C0bkA3gjBDmwCmfkPLBOO6bGKQnoo8c7EcyIHHRand0c6NYqFhzUDOhWqFqyLqDv8DMr
-        Rr08p3ulr3R7B16+PGejScGQvGdonOlADoaMFcPJYPvlyysNAE24MJ5jdrowMG6KTi7W39IMeiE4
-        HtWBfBdeOR6NFOsihwl7NjqHwqBqemFcDARHrDjMdhbuPVEyTagiHXwXhrUKbBVBH5UyGuSLdw6m
-        rDW5HE6MRehV5FggiBIrm07/KV3ESW3v1qDRaXU62zuRsBMJ384S+XCE1v75BzQu0LGHS2Qdmq/J
-        Vcj6+aCXlkjmEEiUmn+rKYcxa/Yl62Iz3OBs0Hsg2vbRdg7VEv6ci5qgDYGDIp8DagkTdGxqD9JU
-        5AMLELX1iXA3Ep66eOXwBlHF3Y0halGSj6k84TB7Pt2FmcY793PIexlk7QNrETZT9mstySsuMBVx
-        dIoH/Xg66MHJAtnDOWFknKc25Wt3O4cLRxWTW1rn/AlyL6WRJqzhFUHf1TNNT6IcshwzKUnuIdDY
-        4uvVmgYPT/65RjdGm0PkrK1/EnJi209s6LiCPunPVCE0eqp5FgKXKJ+futdrycpTu1d4k/4PKAIL
-        VMA6kFJckBa0mTJ20MYuvKvSb8k8TDmU8J5QwRClY5noDiLd+zieApzTFLU0U3/D0BicQB+dIGU0
-        Ph9ysOy1HOzCJT0gOUb1+JRJjdiPo3VI+oWHizQR19DWa3iR1XOMbYn3Z8wigYcRcWhKrEjCJSos
-        oXHOE3LWGPWvB8t4RSiXM3UzzzeFuBqN81J8vBITwVF6D9Q3CEMjHf/1OzTW8vj/9NUzim/4UI3d
-        peP7ylyM0YRzHHHeoHMzeItxUPRjgjUMa82i/K8dtTbxV821GeS1UZI09I0Jdw30wNhIOTqtnbGE
-        egVesgVXR89mbRNcuzXvKOn4wQn4WKo2vbNpTNpH3eJvWCmfg11mb9lcsaem6EiT9zvLcx4tw3tg
-        TxiDH0r2cw3EHkTtovqDacmKAGVJ6dUTzEIx+eBYxLii/HlE5ezAKYpy8QSs40aSUKIHSZXRPsx/
-        h24F2QXn/A7SW9dRhe4GR4rAkhsbV6EWEcobxZLHsxRUSezABwy1j4LMm4qWOi4JuH9QbDvr2tLR
-        uPYY9a2ulVozoNYmYAwxqdpPC8vXOx2rTGGdGflvXLP5HLl2hN7oqFl9MDZL1q9bAJ+SXq7vSeDM
-        OlPZcB3MDaWfa++1DucHZiudvjIftPYX1mACqpWh02p38g1HXksKyMqvie5MoChJrnxXCh1ryWbN
-        sLUG/n08m86ew7MunnL8yiBiUZC8to7kvNw2bXMUP2Qe2nZ30SngzJObsKDrwORiMiSNsVbzz4vM
-        z3yg6nrMuiBnHc+/Mcb2+qCDnV08atM42/q69TcAAAD//wMAvqcBwHENAAA=
-    headers:
-      CF-RAY:
-      - 94d9b7561f204d3b-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:56:46 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '12189'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '12193'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149998513'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_1098f5a5384f4a26aecf0c9e4e4d1fc0
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
-      great answer  \nFinal Answer: The top 10 best soccer players in the world, as
-      of October 2023, can be identified based on their recent performances, skills,
-      impact on games, and overall contributions to their teams. Here is the structured
-      list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
-      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair,
-      dribbling, creativity.\n   - Achievements: Multiple domestic league titles,
-      Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   -
-      Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   -
-      Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion
-      (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League
-      champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior
-      (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling,
-      creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga
-      champion (2023).\n\n10. **Luka Modri\u0107 (Real Madrid)**\n    - Position:
-      Midfielder\n    - Key Attributes: Passing, vision, tactical intelligence.\n    -
-      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis
-      list is compiled based on their current form, past performances, and contributions
-      to their respective teams in both domestic and international competitions. Player
-      rankings can vary based on personal opinion and specific criteria used for evaluation,
-      but these players have consistently been regarded as some of the best in the
-      world as of October 2023."}, {"role": "user", "content": "You are not allowed
-      to include Brazilian players"}, {"role": "assistant", "content": "Thought: I
-      now can give a great answer  \nFinal Answer: Here is an updated list of the
-      top 10 best soccer players in the world as of October 2023, excluding Brazilian
-      players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
-      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes:
-      Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s
-      Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed
-      Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing,
-      dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions
-      League winner.\n\n8. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position:
-      Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements:
-      UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107
-      (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision,
-      tactical intelligence.\n   - Achievements: Multiple Champions League titles,
-      Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position:
-      Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements:
-      Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis
-      list has been adjusted to exclude Brazilian players and focuses on those who
-      have made significant impacts in their clubs and on the international stage
-      as of October 2023. Each player is recognized for their exceptional skills,
-      performances, and achievements."}, {"role": "user", "content": "You are not
-      allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought:
-      I now can give a great answer  \nFinal Answer: Here is the revised list of the
-      top 10 best soccer players in the world as of October 2023, explicitly excluding
-      Brazilian players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
-      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
-      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
-      champion (2018), multiple Ligue 1 titles, and various domestic cups.\n\n3. **Erling
-      Haaland (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power,
-      speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA
-      Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne
-      (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing,
-      vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups,
-      UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   -
-      Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   -
-      Achievements: 2022 Ballon d''Or winner, multiple Champions Leagues with Real
-      Madrid.\n\n6. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   -
-      Key Attributes: Finishing, positioning, aerial ability.\n   - Achievements:
-      FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7.
-      **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes:
-      Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA
-      Cup, UEFA Champions League winner.\n\n8. **Luka Modri\u0107 (Real Madrid)**\n   -
-      Position: Midfielder\n   - Key Attributes: Passing, vision, tactical intelligence.\n   -
-      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\n9.
-      **Harry Kane (Bayern Munich)**\n   - Position: Forward\n   - Key Attributes:
-      Goal-scoring, technique, playmaking.\n   - Achievements: Golden Boot winner,
-      Premier League titles, UEFA European Championship runner-up.\n\n10. **Rodri
-      (Manchester City)**\n    - Position: Midfielder\n    - Key Attributes: Defensive
-      skills, passing, positional awareness.\n    - Achievements: Premier League titles,
-      UEFA Champions League winner (2023).\n\nThis list is curated while adhering
-      to the restriction of excluding Brazilian players. Each player included has
-      demonstrated exceptional skills and remarkable performances, solidifying their
-      status as some of the best in the world as of October 2023."}, {"role": "user",
-      "content": "You are not allowed to include Brazilian players"}], "model": "gpt-4o-mini",
-      "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '9093'
-      content-type:
-      - application/json
-      cookie:
-      - __cf_bm=8Yv8F0ZCFAo2lf.qoqxao70yxyjVvIV90zQqVF6bVzQ-1749567342-1.0.1.1-fZgnv3RDfunvCO1koxwwFJrHnxSx_rwS_FHvQ6xxDPpKHwYr7dTqIQLZrNgSX5twGyK4F22rUmkuiS6KMVogcinChk8lmHtJBTUVTFjr2KU;
-        _cfuvid=wzh8YnmXvLq1G0RcIVijtzboQtCZyIe2uZiochkBLqE-1749567342267-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.78.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.78.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//rJfNThtJEMfvPEVpLjFojGxjEuIbJhCi4ASFRKvVJkLl7vJMLT3Vk+4e
-        O06U8z7LPsfug626bbAhJgm7e0F4aqqmfl0f858vWwAZ62wAmSoxqKo27WHRlEc0edUdv+q9pl/f
-        2P2jsnz38dXw7J3GLI8edvw7qXDttatsVRsKbGVhVo4wUIzafdJ/uv/4Sb/bT4bKajLRrahDu2/b
-        FQu3e51ev9150u4eLL1Ly4p8NoDftgAAvqS/MU/R9CkbQCe/vlKR91hQNri5CSBz1sQrGXrPPqCE
-        LF8ZlZVAklJ/W9qmKMMAXoDYGSgUKHhKgFDE/AHFz8gBvJcTFjRwmH4P4JQcAXtAcDRhIQ2GfQA7
-        gVASBFtDtwNj8gG8VYoc1Abn5DywpDtm1hkN6KPHaxXsmBz0Or29HEh841gKCCUGEAtDh5/ZMMpN
-        DIzPFmUaTXrwXt5Ldxd2ds7YChkYkfcMrRcSyMGIsWI4Otne2XkvANCGc+s5FmkAJ9bN0Onl9Zc0
-        h8MQHI+bQH4AzxyPx4alyGHKnq3kUFg0ba9syg7HbDjMd5fuh6pkmlJFEvwARo0JXBuCIRpjBfSj
-        1w5mLEIuhyNbIxxW5FghqBKrOkX/JZ3IUVPfXINWr9Prbe9Gwl4kfDlPxzAaY13/9Se0ztGxhwtk
-        Ce3n5CpkeTjoRU2kcwikSuGPDeUwYWFfshSb4U5enBzek233YDuH6hr+jIuGoAuBgyGfwxQd28aD
-        thX5wApUU/tEtxfpjl08bjhFNCgaWiMUVZKPZTziMH842bmdxfP2C8Bb1WPxgUWFzYTDRjR5wwWm
-        To5OMdC745NDOFriejgjjHyLsqZa7W3ncO6oYnLX1gV7guynEtKUBZ4RDF0zF/opyhHrCZPR5O4D
-        jVO+3qlp9/D0x/25MdscImdT+59CTmz7iQ0dVzAk+UwVQuvQtF+EwCXqh5fu+Vqx8jT3FV6l/wOq
-        wAoNsAQyhgsSRZsp4/RsnMCbDr1L5mHGoYQ3hAZGqB3rRPc40r2JOyrAGc1QtJ35K4bWyREM0Sky
-        VvDhkCfXc5ZDvXRJP5Aco/n+hklDOIz7dUTyyMN5Wo1raOs9vKzqGcaRxNv7ZVnAJxFxZEusSMMF
-        GiyhdcZTcrW15l8vlcmKUF/v0808dxpxtRYXrfj9TkwEB+kd0FwhjKx2/Pcf0Fqr4/8zVw9ovtF9
-        PXZTjm87c7lCE87TiHOKzs3hJcZFMYwFFhg1wqr8rxO1tu1Xw7UZ5Lk1mgSG1oZvB+ie/ZGKddw4
-        WxPK6gRKrsE1MUS7qRNltxMxL6zAKTVSRC0Erbc2BJISKzi1wdeNu6a9F/enOvAu6I968Lvg6++w
-        9SX/tmS/kEIlehgTCSh0NGmMmYOjKXvSECzQpyRfAI3ZIHBmJRuCkovScFGG+MbytqJreVVZHyCg
-        IQmkgUXzlHWDJqmrpd76VlrtwjGqcvmMlJ4v7UxhzMhRhe4Kx4aAJhNSgack5D3EN7G/YmNyiKox
-        HW9KhwvhCSuUYOYRKJTEDgJh5cEKjG0oV4cUo8SRcYKxeGjAByzI766r0nhKHqMylsaYNQOK2JAc
-        kx7+sLR8vVHAxha1s2N/xzVbFP/SEXorUe36YOssWb9uAXxISru5JZ6z2tmqDpfBXlF6XK/TO1gE
-        zFYKf2V+vBD1AFmwAc2a3+N+L98Q8lJTQDZ+Ta5nClVJeuXb7R2s5D02mu3K1tlaY/82pU3hF/ws
-        xVqUe8OvDEpRHUhf1o40q9vYq9scxa+g+267OeuUcObJTVnRZWBysR6aJtiYxbdJ5uc+UHU5YSnI
-        1Y4XHyiT+nKvj/t9pKd7Ktv6uvUPAAAA//8DAMc1SFGuDQAA
-    headers:
-      CF-RAY:
-      - 94d9b7d24d991d2c-GRU
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 10 Jun 2025 14:57:29 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '35291'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-envoy-upstream-service-time:
-      - '35294'
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149997855'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_4676152d4227ac1825d1240ddef231d6
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_using_contextual_memory.yaml

@@ -199,4 +199,829 @@ interactions:
       - req_2ac1e3cef69e9b09b7ade0e1d010fc08
     http_version: HTTP/1.1
     status_code: 200
+- request:
+    body: '{"input": ["I now can give a great answer. Final Answer:  **Topic**: Basic
+      Addition  **Explanation**: Addition is a fundamental concept in math that means
+      combining two or more numbers to get a new total. It''s like putting together
+      pieces of a puzzle to see the whole picture. When we add, we take two or more
+      groups of things and count them all together.  **Angle**: Use relatable and
+      engaging real-life scenarios to illustrate addition, making it fun and easier
+      for a 6-year-old to understand and apply.  **Examples**:  1. **Counting Apples**:    Let''s
+      say you have 2 apples and your friend gives you 3 more apples. How many apples
+      do you have in total?    - You start with 2 apples.    - Your friend gives you
+      3 more apples.    - Now, you count all the apples together: 2 + 3 = 5.    -
+      So, you have 5 apples in total.  2. **Toy Cars**:    Imagine you have 4 toy
+      cars and you find 2 more toy cars in your room. How many toy cars do you have
+      now?    - You start with 4 toy cars.    - You find 2 more toy cars.    - You
+      count them all together: 4 + 2 = 6.    - So, you have 6 toy cars in total.  3.
+      **Drawing Pictures**:    If you draw 3 pictures today and 2 pictures tomorrow,
+      how many pictures will you have drawn in total?    - You draw 3 pictures today.    -
+      You draw 2 pictures tomorrow.    - You add them together: 3 + 2 = 5.    - So,
+      you will have drawn 5 pictures in total.  4. **Using Fingers**:    Let''s use
+      your fingers to practice addition. Show 3 fingers on one hand and 1 finger on
+      the other hand. How many fingers are you holding up?    - 3 fingers on one hand.    -
+      1 finger on the other hand.    - Put them together and count: 3 + 1 = 4.    -
+      So, you are holding up 4 fingers.  By using objects that kids are familiar with,
+      such as apples, toy cars, drawings, and even their own fingers, we can make
+      the concept of addition relatable and enjoyable. Practicing with real items
+      helps children visualize the math and understand that addition is simply combining
+      groups to find out how many there are altogether."], "model": "text-embedding-3-small",
+      "encoding_format": "base64"}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '2092'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.68.2
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.68.2
+      x-stainless-read-timeout:
+      - '600'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.7
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA1SaSw+yTrfl5++nePKf0idyEarqnXEXASkEr51OBxAREJFLFVAn57t38OmcTk8c
+        IBGE2muv9dv1n//68+efNq3ybPzn33/+eZfD+M//WI89kjH5599//ue//vz58+c/f5//35l5k+aP
+        R/kpfqf/viw/j3z+599/+P8+8v9O+veff+ZECgOidjuPT/hggOYz8/B5WXJjKhdrgcXu+QpqzjpU
+        fHRkJbTCW4nP3zI23k+iNugeb0qq9aECplduqMgw4oEQ79P11KnpRe7JhmCtVCkjaRmdQPymFnlr
+        alLNxrOD0FHcF7aCU+zx4if3AdioPj4Y2K+W8UIT2IeKGkz3BnhfmzMXhMhxhw/EruKFp/kWHL5S
+        Rd399RvP3VZSAdhPHc2G/mvQ7ZJPECZiSrWnMXjLt25zkGDgYS1J7X4e5CVBrDtrNIqXOObbdEhg
+        ROwPxh/vyKb6ClTY3d0d9UThysbDPCwwwwnEKvAoYO0YdUhr0iGY+TruhV2VnaAT4xrbtfoFQywZ
+        KozwpAdXsNlVAhNFE87oqWHTwbAfzlrpoFZo1GB5DAmTpjsNAZeNO5yk8SedYWISNNrcg+KxbEC2
+        C4sJ9dXtQk+NdKgk+XtbIH+J+gCiAqXU6h86VKTwRC/vsUynp+tE6P60Q6py75e3zCGywe65SMH2
+        ethV/FLVIthevw69JLYe88BmOVTVvAvGgtcqnhNLHbXz/h7wdW2nou/vM9jm1QcHlhIZ00s+OrBi
+        QkSfwsiBiVotQSdU37A6uDmYLevmQzxFEd7nxdJTgNISnqOhoImdvPvFfNEcpqpQYtuzjh4f3r4+
+        CM5KS+Y+VBizw6H5+z6vocWDIVgYROWyhThwhI2x8PSigNcMG+yj4pHOvfsqlfen4OnFferp0m0P
+        BXQsNaHnk6FXYsjXLcKhltFry12qqZNuPqxvAoeNnXz25nl4qjDUlxbvz8HEFul5KEGjcg71d9Wr
+        J49BTqARKjMNqH6PheNO7dD1MX+pIfQ3JpzdRYeNNXLY/AZ6NRePYwl3pmlS3XOu3nD2DqLiwFbC
+        l1ta9PzEpwoUIiAEKLo/PSkLM1P+trNIxm1z7ykpThw6yJDDYWQ4vRhvXglyVasmUOOXeIzFYwFP
+        cuzTdT1V88OJE2TfnATne+gwiRZKB+F4mQOFM2WwlGflBKO+PmJf45d0fkh2A0vd1PDxMzqxSK/d
+        BD/TwaCPQBwq3qNFCEmS7rGWaueUdVlfw3PouzivjachVAfVhZcr1KkT2Q828dnGAbODn2QWvnYv
+        GMElQFonxngv3U+pVNC4gVKZNuSSkA+QhjPawn5oGFXtrK2Wx7GK0A4ML3y+cDQeQHh14TeeXRzG
+        08ug+CsvUDFfDtk+rFssHIWLA9vZu2M3+pzSuXkVNTpPoMO7d1p7U+/FOrzdRYL3U/7piXfGGbix
+        UKdHGs39OJzVGrG8PeDw0c5sycqtDvnmZuM7oVtv7rYbFV4jU6RxWpdVS54sQ6Nwe/zepyFwqWMj
+        EJsV3d32eirQl8TBjmg3et9/dxX/yg0dvnMf4IPLC2zOhk8Ji86K6HOvW5449e8SNaKN6eFEHjHv
+        yR4EBbt7+EbSXb+g8QGh38oH7Jbinc0mew9IAmNOw+3W6dnTNW20i44BPUWqXkmc5BVw9CRC7etc
+        G0s5OzlsP1KBD+ZTYmycvwPwkvNCd35ZpkxhkYMeYU6wZWZlz+rvK4L32kb0AHWtEk/biw7N3t0E
+        pfjOKnYwzQbdx2NCVW+IK0l9mDXyNE3BVqraHo0d+QLrk+1gk1ZqJV5bs4NvxXCpWn/8mE/54Qat
+        e73BGl/HFf/Y+1u4JVmHb9rxWy1m4N7AkKoMa2QoGTvHYQLbiMNUhxedSUyYJhQr40xKeTOy6SS1
+        A8jApcGH/OH30/c9LoCiTYgDaTj3bJOlNrL2z5p6THGB8MaiA/2Hf8fnu5yl/EnzM/DuRRVj7zyk
+        0+tDCLgkF4T30JgYm17hBblhsCEKs56p4D1KHQmRLOBdJBUxFWIeovFbBzgOQtcQV32F7qnwsHeO
+        ZMaie3GDR7cssJoC2Vj20T2HQZxENIvdV7V4Xj+Bjfwc8L5NEu9Xj6AZpgtObtw+FdlxypB+D2N6
+        MV0zFfYjSxDpyhrr78lJJYDiAn08E2A36TUgnGW89uekDHripdWS2KQErplMBDrMZ9MzriBkVqBg
+        9XrM1nqVXEhx1+PoReZ+kA8vB8Yb9xZ0PB8AYeZV++//u+jpibGDUvDQsfQkgM9rmM6DrCSw8gMe
+        62v/EpRCuUGznUxq4bttvFXKnyDXaCp9uPwZTC9fDNHenyKcp+JkLFtFvoB0+9xg0yQFmzoXncDv
+        98yNq1ZS7+U8iIfy89OHfnCEKdpqjx3D1uLTeBFOJIdO4EvUKV5qKmaozRRyUwOalGHvsaj6Tkje
+        DjLdyySqFk3+KvADZJFi9day0boJIbraiY6NV8biqYY1j7Ran+m+LPexCHijRXXpSUSpkNEv0LEz
+        WFhmEwg23TO+c9EFXIxXSq/385NNxRbkqDzVV4qjU8OoJ48Z5EQ3pansXoxxYl0ClyII8PMFbMCP
+        58VE589nxpohDN6kA38Lq3Mxkhm2midJp+mEWl3kqb8zHzHlyTuBrcQB7Avnd7qkt5CDeRs0WKU1
+        BbN8mXU4vMRLMNDmzCQuOg7IO2cvmoqCBFjvXXiAU8fFjzhfDMZS9YS6z/mIdeHaprPQDAOEdsrT
+        nXzIPEm/5ArEp0tLlI1XVYM+GwU6+nJKcfOuPJ4EDYGin1dk+529lP3WI0z4FB+PsxtLrjopqHod
+        XtjIfCeeA4vWkAfJEkhH9wNoKS8tSgieSN+oRj9VhxSi0+NaBnz/2gOpdQoePbxcowfbn9IF3bdb
+        SJ+TjePeK3u2hN8Q1oe9izH/7PoJf8Pwr77GvCp5yyY2fHD4ZC1+bBu5mrYHL4OyUS80Izetl3b9
+        MkBOdFLqmP6lWuovssH7reYBr2+O8XTIZR96yXUJGHftAcMoF8HyvfUYX6CfsnZMOnTvlCcO1n4r
+        NP0XwlVvSHWXOm/5hKqKxEDK6WF3rNIZL4WLFi69YjMOLoAah5sCizzJqRnGncdIkXGA25EUO2/K
+        V/S4Ezhg7ewvtn2+6PmZO0cw7wikJslEMPB168OhdwJq3eUs5pHon6BbnBKK97plCEWDfPh4wCt2
+        IDpVdPWXCgmGM72qQI8ZheoEf3qmX3ZzNdVXpiMmagINci9I+QmXNqqI+KY+U3fevFR3HZ6F0xHv
+        dmER0w+nDagsYg87ihoC6RzfEqhz4YZsIxux71N7D4j2mUJmwJkVT8C+QY9PGAZbPG/6cWzfHYwT
+        /Yj11rIrNn6KC+AyuiMzF+tgEh9KATTZeFBtrYepa4wMICGwMNb7ySPbZ+9DJc0saq9+l46f9gLn
+        p5tRcyuUVce7OAHg3MhEOjhHb75/Y15ePNvGFrRFwJbwFYG1XnFoXc/VdNL8HNRjopJvdvBikaWZ
+        q1yMKqWed4tSWl85Hr7zAFD11Tk9w+jCK93negy4NHylS4gVHsovcA06QX6x9X5OcPXbVN0wIaaw
+        rExYqaWGD9HWiMnbKiBK81qjUSoWYNa81gcgS1KyHQg2pHisXDR6AqHuxjP6v/04r+QE4+ZteMLP
+        v7X37kIPVu0C/llPBCLve6GuFgOwTP1YKuN5/yHzsHHTyQBljXKrFqjxLQog3g/yFrYRxARMhcyW
+        8WAMIN9eNMK0+mpQWiwdaG5nir30I7HyV3/5mYTBIpkFYGz/2MLWwhRj4pyrJXJoBkQ/q9Z+6sdk
+        Q44XiOvcoMFz27DpM8QZkl/ylernlw4mKQ99eNudbvi+zASw2JkvaHvtHRrDjqV/+2UPh4BGTHc9
+        UUGfi8K3SkZ1H7+8KbsziHbO5vzLAx6Vv+EC62TBhH68GQz8PjHhYOs7MgXakU15nargg2+74Bhd
+        ZW+Y730OO2LcqMciKxV5etnCl3tyCODLsJqqpc+hbrclzVT6jJflGE2A7WiI93o3evOQ1yHkzvIb
+        H6L7xlvuX0eHN5PXcTi4HGDF6ZBBA/IJtcpq2887rswR2VISREzvjGWCS/Q3T9+tT8j4y/Q4AVWX
+        3mQq3uee321iCJf37hkoG8+o2JuVLbztLjdsfQ48G5qQtrD8vo2g0JMjWMKujxS+NJVgU+2BMbrb
+        KIN5N8AAOa+gJ30s3+DRGDgy7hutWrLjoQHqdrcjkbqHjDmp1/1dn0YUFYwmFlKBVW632PXMF5u/
+        qpsBVRfeRFIzzls0WpRo0jeIWr88/wnlAhhJ8MW2nvJsWjgFwp9/uHWbTV8f0UTAej80H/m3txAg
+        Kmjth1QlTwjI8Q4DGG+cGz1N89lYIqHVEfwab2xMD7Pib1/awbc5t9QASetNt8KDAC/fw/r8JsbY
+        ZnER+iJA/e9RAqPvnyNYeNOIrWfrVTS9bxbw6o0WmzvN8VhlHhdUGeoVW9Flz+bNlpTgvfcx2W6o
+        6k3SByjwfb3fycwUyRs4sVQhXK55AF9D0C+S1+YwcO076ZO7UUlL+ArR6m8p5q7Am992ksGLGH6w
+        phwHg73kjFP0vbPFp3N1NsZvTAu41hc9VVCtiHEYCJQa8UAOw71jc3rXMzTPU4B3d742WAKPEXI+
+        nYetfScC+mZdC3/Pe99YANBm987AqenTYDM9GBtXvyufP++ZTM9zFy96e3bBwt2veP9KN/20rjdo
+        MT3+63/YbVZ8+DjwkPQRAj05P8YG6FMPCDLwUC0vb4KoUgsN30X2rqbw9goQSqc5UK7SJiX3pSLw
+        GR02dNVXNjf25MDFXhTCe99DRV6KnYF8t5nIzz8J0w4XUAyEnGpdNxijGxcdbF7flnB72LJZsYYQ
+        8g/7QJjKtfHq37ZwrY/gUT6NeOUROUB6JtNjnOtAUoL2AnHquthhxsGjuaoTONjqjhrGEVfDY28q
+        8Bjzd4wfcugxf9IWxI7aHueKxsDS4O4El70n0MA/7jwxrtMQ6oJdEvPA9WDZS14It7OdUfd6nHp2
+        L945ZMMIsV/OWsXQTulgm7kD1rO8qSaPFtHPP2KvaaKUCTGE8Drd02B7FQ4GrZTwgqoraMm0069x
+        1z3ONjQ2h3Ow7BUd/OUJBxK5QfdQC29ESXeDMjhgbHdoTNf3FciOIJzx+XM4rfpxPkFjg8+klmzm
+        fSfW3ZTVb9PHYX/yFkHb36BPB0rjrT4ytjX3IaqbJg+mVa+Wbj+piseYjTXEcxVBlRJBO+dfeEcs
+        NRaeRK3RpH50aj+MxljurxxCrIlRgBPOMNgjAS5cogehh8dNrkhgcja8sOVIFHjfGmy6f0L443s7
+        MlpA5KKxVDRr8KkF8qOxXPMWwvKyiagbnkZvfn2iLZIBxj9+ELNV30FW37/0serZfFVcRanBkwbF
+        6WYD/g4iG9HnYmMruRX9rOWfBpZCLGE/px2Y1voFF/dQrv23ipn77gjIFmdP17zR0wQeQ/ispYl6
+        t6E3WiGeXUTr7w0b7JJUkw5MBU6c3NCTftd78SGebURBc6YWlY7GcJ2ADt/FGOFIC+1etG7eAqMo
+        vGN75WuDG8McgnMtU9vrn2yauXMID157x8+Mf3gLKEUF2m6q4AB/tXRqwk8HDVHf0l+9jzDxCfwm
+        zUB3e1tJZ99/hL98ToRT73h/n//1tr2Ry3vU46Xd3LYoTbyE2ppgG9M3cgi4lv6RJqPUG9O5STlo
+        DGRDpOvTYlI7Ji1Y+VfAUV1OZxpEChpt+KCPOI+87zEcLlDdRf7K9zgwK/jdoeoqt9iF95ux8qcb
+        MB8vheIdSL1lkNUtMNvFDDZZEMRLAKITyBB9UY0p19Wfez6smzoP0LVowShoyhY8HtyV6iQxeyE4
+        hB36LjtK0C8/RunTB4pZOQSsPHbZp4KDNvJjoJalz/HyUoIcjhK7YXwYtHRAx6SAP/8DoUJ6qrfa
+        BTp30GO7rzJjGeYrD1G6zNSaG7Wf+ublwsUzbfzXnzySY4uOD13Eq5/2Fi56F7CmE6B7kVm9WGj3
+        Lbh+dmXAbxbbEL+RqEKn02ysOwc3FstnrgOK255eTptbNWTce4ErrybS3k7iZXrVDshzZQx6rdCA
+        EIvHEh2k2ljzyataBtlRUPZQNfxs3pVBAD7y0DooHnWmyWKsjcwC7mliBDeuvcTLq3klCOyXbvVj
+        Qs/gLuNgfTIdfBO4xBDse8RBS0gDvL+AfcyP36sDf/lit+ax6Yi2BKz+hcb5E/dz+JpttK2uEGuz
+        9jK67GjVMBbEgnqcbMXCYOQuNJ1XgB+NLrApvechPB4vBj7ortzTSrmd4O7SJNhldVURK2htKAiv
+        Bseffc9W/1FD2UIj9Vce8hY9GP7WL46G0TcmlIgBsKL5gTGah3QmX+8C1w4X3NgXsNnivQTOfFQH
+        U/EWqjnlwwa07/yNtdMgxYvkFdmPl6z++xyzrtmFci3bfCAkN7UfRH1IQC8oA3kvPk4Hy94P8MqT
+        D0FkiAxem0YRnI6LG0hKhL3VH7UQOicL78vyG7PRj3z55aQS9cv51U/fSCXIvadZwPvyFoxhaHdQ
+        S8iWrnmPTSlWaqBm+wfVY9+spOPdJWBdj0ScldlYyOF1g3HsU3xLJQNI5tImsJSHnD4W0rJ55rQc
+        xepwp/rhIcTT01UjWEq+g73b4HmSJ78zCDewxJmdB9XCxDL81QPhsIs8ejD9BgI/Vsm7CI/p+NK/
+        NvzgZIft9fp/9W/NQ6Rud1vAuOhI0JqPg6pnjiH+eORvfoLT+vjrhxFUHoKKHfVbe0ueDq0iHIBD
+        xIqL2DIarQ795/tEz6naGNOTLCZQpmzBfndxGRNPUQtXfcIBP7fs626TDFx24gfbq3599fO3hvh0
+        avEZ2hcw7TYp95e3OIP9rYaNtnfhwevu1H17N6/7JNoNrX4C7wyRGsPKt4DNuC/dn0UxnfbvvQ/n
+        4+uGcc+u6XKqJw7Zo9bjw7a5VzQarRw2b8gH7cpfV17RgCCrKV7nJ3/1Dd32Y4w13YmrVY8HyI7G
+        nsiNlbL50qgOuATdHrvXY1gt7y6aYMWkiDq5X3okrtMIao5fUsMN4nRBGzVBD66XAhgZVzAmt2GB
+        a97EwXyVqnkerjqCTdthzzL4dLkZUwE7zmFE/j5Hb1C1pYPotMHYrGs7/hqKbMtTYPfB8TRc4yVF
+        7gSuU5pS21U5Y0SOBEHvnxE206iOx9IvM3jl9Cf17GlgU9d4GQTWMw+mWQ48FiybBfZVcvnx3n7p
+        yEsE+Ss1sbtr74x91c2kqLvQxykNST9fjaYD+EKrgK28Z1r91s8/Yo2LdTbja6TCrFQJ3YlbBMi1
+        9TswnPAxEO1HX9HMnhKUU9Whz/ATpOKxem4VqBUVdUep9xZur/FwYpcg2Kz8hd+29wb+eK/Z7rZs
+        7XeKsj4PaqqD6/F+Hhdwo0mYgM3zZCys3vDwEFZ9IHhXmq68eAInraN0X+DSmPZ7YMPH1RFouOaR
+        sbKu9m++FWxW/eejyokgaHcYuzxPGPNPoAWf8sMR8fHlGZ1e4QndnYhQZ9Un1j+0CAbWfKDqOr/8
+        PEJ5UnZLYdLUeVg9Xx/kDloctdf5KAZ8e+xVeHRUlzSaYHstFEgLf/161fNqHpXQhl1a6UGv22G/
+        jO2dhz6SZWqs/HkGvOCikBuOGFtqEIt6+3D/9utg92Zsviq6AhTovfFhne+JF3pT4ai6AGMmmUDK
+        NrtQOQuXI1UDbQZ/5ynqvt/h4PhM00HVlBYGFjsE2218ixn+zhPkzuCNnZXnDHtSb9GT89qVp4k9
+        XecDcJ0P0B9fXCpzrH/9m+orb2Z+jnQ4deqZPu9Ht19y97mFewtO2B2xVf30X9nhPqPmZrG95XaR
+        FKin34Bq0fXurfmh/PFAen2a2CPfus3gpjSt1c/HgD83MYTTfavgYMncfuWbBIKN7hM4A95bx2uc
+        ojlBSd7BiRl05rhJKS8oWvP802BZeDLR1hNlrGllzeb7N+XhjDWN7prvNl4W0Wwg1Y1P8FjPn/Ps
+        4kLEYn71C29v+PHkw1eoqCskqJ86V7iAdZ5DFreYq3k5khr85kP7/XfXM3B7ERTDTsf2uE3S2Y3b
+        9ufPME64ylhOcVCCUx0W9LKVeDCt80QIl3NOg/7ZsDHFmo329V4h0nD7AH6j7R249KxZ8/AVzPEG
+        +cA+XrbY1oTGmMenBtE/v10B//WvP3/+12+HQdM+8ve6MWDM5/E//nurwH9I/zE0yfv9dxsCGZIi
+        /+ff/3cHwj/fvm2+4/8e2zr/DP/8+48sin83G/wztmPy/v+++Nd6sf/61/8BAAD//wMAMvIyqeIg
+        AAA=
+    headers:
+      CF-RAY:
+      - 94640d271c367db6-LAX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 27 May 2025 08:13:10 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=zxXbTMyK.67_c.SQXNivPXTcfsIBL5Vl1Q7WXFcTgxU-1748333590-1.0.1.1-ArIOxtxz6HMOCmEGGFc.Hxs19gY1LkxaxTZYc9hAE7zSdmh2fCrczDquGUovgGjYHvCJ94TxWQTCVlo1v7kDhnnrF0jwHy_U_LaR6AbA.94;
+        path=/; expires=Tue, 27-May-25 08:43:10 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=4YFW5U1WwjrbdrZ7OWIvzgymvtGAnmnPEu1zeEdQsGg-1748333590310-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-allow-origin:
+      - '*'
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-model:
+      - text-embedding-3-small
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '180'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      via:
+      - envoy-router-5f689c5f9d-mdwg9
+      x-envoy-upstream-service-time:
+      - '185'
+      x-ratelimit-limit-requests:
+      - '10000'
+      x-ratelimit-limit-tokens:
+      - '10000000'
+      x-ratelimit-remaining-requests:
+      - '9999'
+      x-ratelimit-remaining-tokens:
+      - '9999496'
+      x-ratelimit-reset-requests:
+      - 6ms
+      x-ratelimit-reset-tokens:
+      - 3ms
+      x-request-id:
+      - req_421cd0437639cc5312b23b1d7727f0a4
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nResearch a topic to teach a kid aged 6 about math.\n\nExpected
+      Output:\nA topic, explanation, angle, and examples.\n\nActual Output:\nI now
+      can give a great answer.\nFinal Answer: \n**Topic**: Basic Addition\n\n**Explanation**:\nAddition
+      is a fundamental concept in math that means combining two or more numbers to
+      get a new total. It''s like putting together pieces of a puzzle to see the whole
+      picture. When we add, we take two or more groups of things and count them all
+      together.\n\n**Angle**:\nUse relatable and engaging real-life scenarios to illustrate
+      addition, making it fun and easier for a 6-year-old to understand and apply.\n\n**Examples**:\n\n1.
+      **Counting Apples**:\n   Let''s say you have 2 apples and your friend gives
+      you 3 more apples. How many apples do you have in total?\n   - You start with
+      2 apples.\n   - Your friend gives you 3 more apples.\n   - Now, you count all
+      the apples together: 2 + 3 = 5.\n   - So, you have 5 apples in total.\n\n2.
+      **Toy Cars**:\n   Imagine you have 4 toy cars and you find 2 more toy cars in
+      your room. How many toy cars do you have now?\n   - You start with 4 toy cars.\n   -
+      You find 2 more toy cars.\n   - You count them all together: 4 + 2 = 6.\n   -
+      So, you have 6 toy cars in total.\n\n3. **Drawing Pictures**:\n   If you draw
+      3 pictures today and 2 pictures tomorrow, how many pictures will you have drawn
+      in total?\n   - You draw 3 pictures today.\n   - You draw 2 pictures tomorrow.\n   -
+      You add them together: 3 + 2 = 5.\n   - So, you will have drawn 5 pictures in
+      total.\n\n4. **Using Fingers**:\n   Let''s use your fingers to practice addition.
+      Show 3 fingers on one hand and 1 finger on the other hand. How many fingers
+      are you holding up?\n   - 3 fingers on one hand.\n   - 1 finger on the other
+      hand.\n   - Put them together and count: 3 + 1 = 4.\n   - So, you are holding
+      up 4 fingers.\n\nBy using objects that kids are familiar with, such as apples,
+      toy cars, drawings, and even their own fingers, we can make the concept of addition
+      relatable and enjoyable. Practicing with real items helps children visualize
+      the math and understand that addition is simply combining groups to find out
+      how many there are altogether.\n\nPlease provide:\n- Bullet points suggestions
+      to improve future similar tasks\n- A score from 0 to 10 evaluating on completion,
+      quality, and overall performance- Entities extracted from the task output, if
+      any, their type, description, and relationships"}], "model": "gpt-4o-mini",
+      "tool_choice": {"type": "function", "function": {"name": "TaskEvaluation"}},
+      "tools": [{"type": "function", "function": {"name": "TaskEvaluation", "description":
+      "Correctly extracted `TaskEvaluation` with all the required parameters with
+      correct types", "parameters": {"$defs": {"Entity": {"properties": {"name": {"description":
+      "The name of the entity.", "title": "Name", "type": "string"}, "type": {"description":
+      "The type of the entity.", "title": "Type", "type": "string"}, "description":
+      {"description": "Description of the entity.", "title": "Description", "type":
+      "string"}, "relationships": {"description": "Relationships of the entity.",
+      "items": {"type": "string"}, "title": "Relationships", "type": "array"}}, "required":
+      ["name", "type", "description", "relationships"], "title": "Entity", "type":
+      "object"}}, "properties": {"suggestions": {"description": "Suggestions to improve
+      future similar tasks.", "items": {"type": "string"}, "title": "Suggestions",
+      "type": "array"}, "quality": {"description": "A score from 0 to 10 evaluating
+      on completion, quality, and overall performance, all taking into account the
+      task description, expected output, and the result of the task.", "title": "Quality",
+      "type": "number"}, "entities": {"description": "Entities extracted from the
+      task output.", "items": {"$ref": "#/$defs/Entity"}, "title": "Entities", "type":
+      "array"}}, "required": ["entities", "quality", "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '4092'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=SlnUP7AT9jJlQiN.Fm1c7MDyo78_hBRAz8PoabvHVSU-1736018539826-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.68.2
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.68.2
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-read-timeout:
+      - '600.0'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.7
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAAwAAAP//zFZLbxs3EL77Vwx4lgzb8lM3PxrAOaRu4xSIs4YwImd3x+GSLB+KFob/
+        e0GuLcm2CqSnRgdB4ry+b+bbHT7uAAhWYgpCthhl5/T4Yv7A9qG7O4qX8k9X790df17SZ3NFV7ef
+        JmKUI+z8gWR8idqVtnOaIlszmKUnjJSz7p8cnk4mk6OzvWLorCKdwxoXx4d23LHh8cHeweF472S8
+        f/oc3VqWFMQUvu0AADyW74zTKFqKKZRc5aSjELAhMV05AQhvdT4RGAKHiCaK0doorYlkMnSTtN4w
+        RGv1TKLW68LD53Hj97pZqPXs6mJ5+fWu/th9bRbu03J54/GPj3ft6Ua9IXXvCqA6Gblq0oZ9dT59
+        UwxAGOxK7C2G778tUCfckgFAoG9SRyZm9OKxEiE1DYXsGyox/VaJL4EgcJ6TB42mSdgQhMQR55qg
+        th4Qjsc9oR9brUaAAYLtCGjpNJpSNUCHPYTIWsOcYJj6crcSo0pcG6mTIuisJ2ATyaOMvMjxmN0C
+        5Ar5iCNTgNhiBNmyVp4MSDTg2j5w7msPjnxtfQfRApkmA/3BsQVUijOOoeKlNYEVeWAjrXfWY2TT
+        wIJDQl3KKY8/2DQhc8mQGo8654wtQSSUbfZ33koKYQRkWjQyHyWjyGflqPwvt6a3yTSgCb0hH4b6
+        N94uWBFEdqE4OfR5AGANtPZHLuSJTW29pFJSWiPJRcAIbe5s4VQnAw12Q39oQb5X2G80arcS96NK
+        /J1Qc+wrMT0bVYJMLMY82ceqaKQS00pcYGAJ589tKiiz9ort1jqW5UhRkJ7d4JItLWUYCrN+UK9w
+        soEOY0sdRpYBbJ0nPmeTm2JSNycfMsksgxQJEKKNqIfmeNKDZFp2zwK8DoAbCVGDdeRxhfQ6AIWQ
+        qaEu/ST0uh800lqrSuwwAzZNJe6fRpvcz11W2WvOv5fX1FbSH3ziGCAFUpkomhehZkqsdQrRYySY
+        l5a+Vt4Wcl9e8oC0yRQhbuQrYlsleQf91vZwif7nwd/a/heBfsMyJv8f+v5XeTwBfQTHJOkX4fGB
+        TUNvJ3BhVQ836LczOTcYbVeE7DKd/4vH/ZN4tQyedrb9vt9YdZ7qFFC/34FojI0DnrwE758tT6t9
+        q23jvJ2HN6GiZsOhnXnCUNaYCNG6AVaGUIqL9GpVC+dt5+Is2u9Uyp2cnQ35xPo6sbYeHB8+W8tb
+        Zm3Y3zuejLZknCmKyGWbry4QEmVLah27vkhgUmw3DDsbvN/j2ZZ74M6m+Zn0a4PMr1pSM+dJsXzN
+        ee3mKT9Q/+a26nMBLAL5BUuaRSafZ6GoxqSHW5AIfYjUzeqid+e5XIVE7WaTQzw6RDqbSLHztPMP
+        AAAA//8DAGHAjEAYCgAA
+    headers:
+      CF-RAY:
+      - 94640d2c4fbb14f8-LAX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 27 May 2025 08:13:14 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=2gcR.TdsIISbWE0mGGmusReUBNsIatAwc18zV72zD20-1748333594-1.0.1.1-qeHSEYFoSHrtU4ZrkbG05aW.fPl53pBh5THKK8DsmUMObmaOM2VjUu.LX4CG.kTiSHKPDctGkrALLResb.5.jJ8KafoAb00ULAntajitgj0;
+        path=/; expires=Tue, 27-May-25 08:43:14 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=JuFQuJzf0SDdTXeiRyPt8YpIoGL3iM9ZMap_LG1xa5k-1748333594521-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '3966'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-envoy-upstream-service-time:
+      - '3976'
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999368'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_da07dbd4e017b1837841676fb84c2e7f
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"input": ["Apples(Object): Fruits used in an example to illustrate basic
+      addition."], "model": "text-embedding-3-small", "encoding_format": "base64"}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '150'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=zxXbTMyK.67_c.SQXNivPXTcfsIBL5Vl1Q7WXFcTgxU-1748333590-1.0.1.1-ArIOxtxz6HMOCmEGGFc.Hxs19gY1LkxaxTZYc9hAE7zSdmh2fCrczDquGUovgGjYHvCJ94TxWQTCVlo1v7kDhnnrF0jwHy_U_LaR6AbA.94;
+        _cfuvid=4YFW5U1WwjrbdrZ7OWIvzgymvtGAnmnPEu1zeEdQsGg-1748333590310-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.68.2
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.68.2
+      x-stainless-read-timeout:
+      - '600'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.7
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA1SaSxO6Orfm5++n2LWn9im5SZI9Q0BEbkFAxa6uLkBEUOSaADl1vnsX/t863T1x
+        AEEIZK31PL+V//zXX3/93aRVno1///PX359yGP/+H+uxRzImf//z1//8119//fXXf/5+/7+ReZ3m
+        j0f5LX7DfyfL7yOf//7nL+6/j/zfQf/89Xckbi/UPZZxOtWfDQHNrcjxw2hOjJN9zoAvyTuRGUlV
+        T9NahrB6Dxgn0OyqvsmqDrm5AKlaYacfKd86cCu5jreZq6Ia4JiZ8P7EFdkJkcE4P0kncCxiF1vv
+        61TNsaboCLKxwApwr/Z0eu1r5LYfCe+b7WkdHy5ovrgC1rbFbC/qronk21avyEabHbYMe6mBTVwJ
+        2LmMijZ71sWAb5vzqaWWhU1sx4jkmxO/vK2xe/UTSt8ejCXpi0P/iwAzToWBJqUR6dWTylD4it8O
+        hnJxw17/9tjEudoCu3OqUs23hGo+aEaOAk854zg6l9qUyhsTPJetRIjs7W3B0WEBD+0lx/eLy8C0
+        bZsGbp/+SB1yNyuhLx1P3r1zCasWS+x5R90Y4OtxQ5W7MGpz2KUZHBy9w4H0cdnQgyuEu2SMqDYD
+        1abK9qajvIU7bK3vf0b9XUaSsjvj25an6fKdHgL0vjzwIGnznmvUq4CGyhmw+3xpvdA6CMpu6qn4
+        4G75dGyU7A2E6G7TaLAP9jLJ04KyqLxg3ftaFTflL4K8j2LQm7n/9DOvVDFKJkiIPL1bNnDqp4O3
+        FzFoSOnTpkKQXYFxfZ9waH2+4WLqXQn7TLHwtXv0GvOqpwCdg7Kj1qvbV4KqiwQdVPwgY7qxAPO5
+        ooaAfzyovpEAm9bnh/EObAkUN2JFuHKvoNvVOlCNQj/k6IcVsLgFuid5z1c1bwLfRBcBx/T0kLiw
+        bEUlQ4tVJfR06YBNMrNtEJpsg2oH7aHxHphj9LGMEJtFYNlifQYFGJ2ThQ+3+dLPrH8YcnbaDh48
+        BXzPfCAncPpsUmoZF48tzwO9Qj0vVfLVAafNT5h4MLi/A2zYy8SoNU0B0i705rEW5WB2pNSAF3/H
+        YafXXjZ/r+cNPEb7I32U3R0wWJQLGi/HliqbyWPsUqkd0vXpSf0mJPa8t4wANpal0GczBjav+5ME
+        mRYZ+OBlWcVXRHnD19CY2JDGhAlJw3tofZ/4MUeOzfGDZMEM5cybFMTC5SvSBrw3RYCvh+oUCiZW
+        GqTSOKH5qXJDfic8PWhtOw7vrZfCxHzudcjVeoMfZVIxsazuGYyL7YsqiqYD8jx8r1D3owKflRKH
+        yxI5NVzOfYb17Kun4kz8BD3nTeEtwy3quezFxWBrWgMB0x2w6bs5B8gq/RtOsFv0U8BoAYOPFeI7
+        bvRKqPZMQJbyHbH9/hxsZteJBbkvOGB9Z6S9YJsmB7NrtHi83mUpJz+fBH5ki1Gj2AchcyxHh2Ve
+        Hch8l/xUtOvARDGFJsa0XuysHIQCfjV78fhHddWmLCnf6JpFe/o8Mt+ea36IAW3GCSsG1JmoS08f
+        xqZwpMeBCPb0MJcOTUmcUN/XHSa8X4UHF3E6UMVtGBt4EC5oY+oONZnQ9azJ+g7WOqDUzUVbW/hb
+        KUM5GADh3e0lnVEBJtjC44NILyeuOADECT3EQKTGMCyMJfPdQEYFvzT0b0cmvBtZgWja2FQF+QgY
+        6WQD3u7LFmt6zQNqbVMJWp/dmTrhC2kTz502ckg2V+xdZqOazxY1oH6YRW/hnzCcr+Nu2T2Ozg2H
+        n7Fhi/4IArgISKFKHWUhr/uSBPo2bnAw3KKKr0lSQ5S1G2wR0wdtLLUyigv0oml5GrS5qPwc3IXL
+        h6r+R64Y3191eBfNDvt8yoPp9ME63O6UMz3cNjWbk4ulgjSlLbXy+RVyCs44eN2qGtUqYIacuzdV
+        8OiJQPfHlKZdKJ9M8K27HfbARQH8+7yUCJz2IvUdckj5DbYF8PAtATuBdUz55NlYkNu/T16RlZm9
+        DH4UgKYynvhoc5E2ATXOUUHiA1Zoy6ds+94bkJ0p9ODb2mtiWtUNuueOTUOOU1MqES5B2UtmWN8e
+        XK27NZWDvlP8wPbof3qypNcE0i+JaXxuRNBcc11HVWLXVKlsQ5sk8JlgWH7P+Piy+HQedtGAnndl
+        T9d6ACipGh/8vsel3Sg9dxnoAKvFGEkvwijlzW0iwa2EHXwUims6Crt7Ae1dJtETrEi/nJNoAVxu
+        v/6cZ/SCOICJcMJx95r7iU7nTo7lUceBm71TsnVPNbQcuODrgO1qPtV3E8L6plIz18qQLr2ugwZ/
+        bvj2+GaVYJuKALfk0VDv2l+0obuNBiS9v9Cn8jAZL9lhgUgfLF7Xq9twts6RhJptKxLUl3Y/yfFB
+        ArwdO9S9lIEmXtKJg+m89/C9M9xeUE1dBev8CJrENxj0ve3BELwo1uv7u5+en4CAVU/Q583a9my4
+        VBwaMH1TdWFjOKEoSWSt5xSaXvbvkJ7OmoS42mioDuih4uuvMiGM3C29n5OPxmRFiNETlQVWk+Mb
+        LNgMOFTVxUAN/v2uxLhuNrvP2bvQi9SL1Z94yxcQk00sQ0BT4iyA3xgeve1OSsU9btcAeQqVsSlQ
+        CywPYdeBl/F6YvX+GMKhP/AmaC5kT0/T5RJO+l5z0HFcOuqgKbaXbGNnwA+jFl+9/MkWg0YOfN/i
+        M74fnJ51Dio74B+XBuub5gAW+z5NYHczErJttF3PLMnS4RovnnA+WtqSDKcCzldBoUfBV8IREw/C
+        zzGfqULbS8on7uQg2dze8K/ezZgYEAn+dKY3ZnlAGE5WDpspQ1TjiQ9Y8HxHKGm5AT+cpAunHuQb
+        eHkernj/FF9g1U8ZusHHnvoyKrSh51wTntRbh7Uspow8hLlBYCg3WPcsLSVSmSqwQXyCXXF6g8kP
+        9xDlenvxYF+YqcjxnAMPqQ0IvY5xOJWDUMJg3zvYHkcHsDU/g8McfIj0beZ0CK+Fj0gQRfR+cGw2
+        vFVHgPnjy6jeHli/zm8DhvvTxIfxKIDh9EABjKQlwUdhPPaDu1dUeAy8Dus3WFVT4cSbf3/Piz1V
+        i/56LjDJco/iRe3tOaz2DVyEreJJAu3AZDz6AML6ouLsLW5SVsYggshuanzKD59+Ibk/oH254bGl
+        7XjWOiOEoHxeLfrLH0s3UQGa+uHrbWI5Y4vIWRv4GZYLAeJz1BbwmSN04T3FE6ezx5Y+bN5Qu4w3
+        aurjPmTwcc2Bcfu8vLm+PvvpdXI6cFjOCFuvxxRSM/I9xLnWCe+pN6fTMzdlZOYNptllVOy5JkGN
+        XNzsqaMhGC7nF3lDKQ8JmXrB6MXn5aHAfv8svXo8ydpCnWJCbyBN9HhmH8YKGHpILg53fJA8P6WZ
+        u3OgaukCtvv8YC/BPlXl0NNnrN1Gaq/5Mt4dvLqkh0dY2FOkS1c4R07ugZfqpD3OPAWy5y73WiO0
+        e+I2JIHh+06ow6VPbVmvh5GILtSIFCulrxkZwPuKwGM8mcB8AFwNgOGoP/8TipdU4mB0lHh8V5Gu
+        cbC2rkB5vKdf/PaC1UQxmq+cQpPn/pYuezu+gtL4bKlmfb7pJN6Y/ItnbxvIbT+cZdP86SdvtuYr
+        YH1X6DBnwwMfo2fEWCKVEhxvo00vbWTa8y8/9s23wqeuigDRX7cFKhxZPPg8PACbjokHN5vUJDJ2
+        lV7oPnG5kws5pzbGGuCCJnZgHMUmzZ6HB2NgCQZ4Ui8dtad61JaLo5no6WbKT89Xc/X9QiCcOoy1
+        wt70w+pn4Gdkoycws++n07XP4OYktR7juDLtPunxDVd9Si6Rh+w/ekrPCxUnE7dl/cnd1jv5BQTs
+        rHp9aXzLh/7pcyDzSTRDDtZqhPxz6RA4wn3Yd1orAb0/pPjpmm06trMkI+My6zSwqrf9uz+4Pm53
+        6syBCQRjKUrYGY1BXbSk6eTfjhmqcNxRx4mP9kS5m/nHb1gvpbWpzp0n5LmSTh3i3NLp/upisOze
+        pSflRds/MvJYwO0+bal+J7q2pBjkYHu8fckuzX7/j3MZdmzvgR2JGJO+ZQ3PXEDIu5rkdLi84hgq
+        Gu9iTE8dW66iIYDzc/GwZvEoZZwSxOgT8Zi6aOv0yzlUN0gU7ZEwn6NaczsdObnUFsubFapU/Jp/
+        4GlbR/Qgpa9e6Npcgf1Udli1FTtk2wIuADvmEQfN+8NGLH10cLvBkjoTv68YWWwZXB7BDh8FDdsL
+        3u8VeLPeaz3f2f3EarcA23AYsf0FTjgnF1VFGKQlGdZ4Zq+uCpAivDARD+YnHN5Ot5EPiVVhe7qn
+        YL7zjxoGWGvp4TF9U7KuP9hsmie1pqPaLyoaBdhcxzu2mHy224z/1uDbPK+esGWOvZyTbIHJ41HT
+        y9DRqjOQKMAL7yj0vOaj4ck20Z/5XKT0Vc2Pp2yhOu8DAjCuGPNaKQNldzCIfME3Wzg/Xjq8ed7G
+        mzdLoE0WejXwMVYh3u9bbC+jmOTwvK8HT2CLbi82Thp4Fg42Nr92p03d7WOg8hlZ9LHGPzk2VgLZ
+        eYTeHG9ejBHe92C5KC7OZHebrryhg8Ot64mgui/Gfjzkl58Pg2uGXCQHJko+8Ug2HKb9xHIwwBTF
+        IVZkpGgCVa0Otsd688cviT+/LOwGSh9aUIY//Qdur8GgycQ9Qac0tzck8nMkGydzK/5zjBLkvd09
+        jiPS/jtfy2Gh4OTkUW1Jk/MbWnHJqAc+V/bTKz9/TkB869mwxou06hGMe6aloguaBAhHZcTHMzsw
+        PiOXCZz9+YsP27lhw+yLPlyC/LX6g7pirioHUhT0M3XX+j67masCV+kTuv+0ZTj5GHWw1486xuFb
+        1RblnRgQnDTRgyZPUmampQpUOEn4+OBGwKK59lFWGFeq758lGz7yAoH4VMNV/xugfVsFQfBrHn9+
+        T2N2ChW45geyLc93jduliSeLWh5iQ9jbgN8b5gSu2Baw1+SPalIfZx+CMdKwJbA5ZcHF8yBcBpNq
+        HFeGLVwqgrAcWzg1Wa+Rb+so0MmXjBrnY2dPbXtRISqnhqCr0/ULVO85WNc/kdRzk87r+9oVNH1Q
+        S91+0y5q7g4sdVZitc2jcBHbt4Bw0XzxxQ6NkOMOOwifiZB7vD251RxWpw62g1RiN91YbH6f5RKW
+        nWzj/T5DYPnF1xMVBfacXQRmcCwUxKPzibCmbm22b9sG7mcrxunTt5lYntvy56epaeiiRrqhu/7i
+        /6dvKoaOrQJXf02Tl9JqP32IHHEA9CYg1E/Xq+zLZeO31Fn1SZMOSQl3TjV7Wy5/a5MntBxc6yc9
+        bnka/u4PfterdzmsxKZCORzgBlNXo23F4rrYoPOSn6neBjqb8k2UQTBeNezJvZlydHQjeK4lnwYZ
+        Y4A+41eEBm9w6D11Dunc7tQBxSXbENZqU7gwNsE/PO7wMAhgOnefdu5y1UcAOSPkPqy1YFryPXbi
+        Wkm5ZbxFsGsjE2uUPrWvV90EGaZvY62XFSP0FGXQiVyOAOMW2KT7+AX8XPuBsG1xtr8ntQlQF+In
+        /sM3DwDWu10si9SN0yX9o8fod4hpmNQ07Ky+GOBpjhcc6Ec7nWJ5sMB2aG18aESFEXHYWXAjLx51
+        Y+leTXKkxEj3rwV2LgUBc7uzBhC8Lx6+fzuuX3budUGnb+95UoImQM7vRUZHcDKx8zorQCDl1YF9
+        ploY+/tTRWe+82RpXgTqvXsCmJqKDjpzPqFPcNM1evWMGHpxq9CsbIRqppAp8L2tOqxswm/Fhvcx
+        Q280uvjKPgug1VFbeQ0syFS+3H6+E3sB3WEPvWluvlq7KOcE7VONYG/NT1xOJQ4a1eaLD1HqpayD
+        voJWfY2tg3Ts5zzABFY46Va9JaWEMQnC5PGs8W+9Tas/Ar6g9nhve7nNZxKQ4XJuM6xln5c25haS
+        5VVfEREGVzZT3ZHgs06e1BFffjj33l0FkCs++Hmsc8aeWc1BG4QXrBBqgPHHW0FHXtgjD6yt/KUB
+        fFl6ZIPeTcVWvvqrnx6fiF97OguMQ8oiXbAbhl26fIc4Au2V7eghVx6p6BvxG9i7XKLuu7UrPoB2
+        DFZ98oeXUNArMvpEIqbqbR+GrDraiWzYikmVT+KyZa0HYIY3kR4H8E1ZsoUEclvOJiORmDY9c0VG
+        byBPWGuLW89OrlhDJmrFn/HTUX+VcL0eWwhbYSNx4QYWcZhhfPpMPVuqJYB7L79gw96WmvBK+BqK
+        gnXBP73Az8E+Qsb9vvFYUuOUFtqog5VneRt0E9Nl2AwBlPbkiv/wb61sBPmguo9VjysaN4P9Ihcn
+        /fXHD9Cfn1n9o8cv7d5mojLE8OWfNZwGpq5Nn1ZTUXjjLtjfJ3I1GNd7Biq3dukJS0k1dH4Y/PQu
+        trqLqk39AVk7neO29OeHRludA4QS/oU1LxbsqS1tD+7Vo+9Nz2b88cxud1WmPTXP8YtNaz1BLz/U
+        fvmCjcTrVSiYvEX1fTBXi6mXJdIPTCSC36lgVmcn++kVbL+eYSWSqvChUZoW1W9Q61vjSQq4+l9s
+        wUWzR64QNnBdP1SnG9Yvx2Kq0UKFzW89h1QzTgtc+ba3W/0dgeo5h9g9PVf/Bv/oc1QL7/uv3jP+
+        Ai8xOn93DQ6oPNrsLNUG/HZnRpXGyMCiJ+4GbjuBULN7nasJ1uoVXb86WudzrujKE8HKt7GFH9eq
+        y18ohvhUfqi+h53WrOMhaGSdmtIm6KfIRNxPf3sd/JzByN5aAIn5ngg8PQ2wlAfTgtUcNh54DW82
+        csUGwogqgkcbvwOj8H29oWVbF7rGdzWlGzWDTLsadD93Vrjk7dGDP78U3vwjYPWZFXA2u4cnd5fS
+        niSx8SF9sjPWNrXajysfhP6oO/jsKpt+zMhjgqW6PWBV+oxsFgTXg70WbgmaI2f1M7YF1/kTSBwx
+        pCtfBvFJfHqCnp2qRel7CUhJw1Hd1s9sfsTNVfZcWfeKLDBsQRMiFR0q6YizU8BXbW87BJyfk0dd
+        R5z74Z7nhtzcyhwf2o1SiZNixpAYS+imGtjZLH22phybexVrTX2ylyXS39BC3A1bh14IhxmcJujV
+        Wk7NL69WLOKWGu2qnnmiOjjhopDJQ/e+bgm/+uH5t77DYuJ+er0Sk6NRI/SJLepxGPfCztA34H39
+        OPi4jRuNFHQnQF/OV3/wWPpxb5gLPD+sgSqcrmhzHhwHtPI/rEfOjk1Hk6rgI0VXbxdErGJkdxl+
+        /T3qGHVdsaWSfbj6CxrVgg2mH+/45Sf3E761WWvlDhYvDLyN3DfhsvJWuOpP+uOjw+GNjN98vZXn
+        VcvjcxrA9XG54/vYGiFT1D6Xv3PKkyHUebby/RJ+cSF6xdFJGCfUzxKu/SciTzepZ64UGX/8gj40
+        Q0h+vKMNrS0+wSsKx+XYe/DHe12uC6v5c4zin7+mhp6dej6GNAZttM28r4AePUFTm0ED6A221FKx
+        hW4or0AT4z0Nsxgz9nvfaaxTet5cLDAwPzbgeKO2J7+rPZg6Pw3AK5A++HIuPXvKPuYAY2pr5C5E
+        NaBldc4RlhPLA6u/4rdK4chKa86kuwybcHEbEkPeSd90n0WnlJv9rQ/2XnbB4eOJe8FBXQP3iz3R
+        Y/ck4avkkxgsJkH0CGmuzVIwRmDN/1653RvVMrgYQrdeNOpx6ialpFsMOF4OLfap42kCA9YCFQZG
+        b9mes6qX7aiE2vHF4dM7GsOJjocI8u9rT9XkJjOy8kV4BLbpiQkY+zGFivPr32Jz7a+ROe8GKIqn
+        EXvodgv/+Juo4CN8qcpjz9J6gSh0w4Eaqry12cGfJbTySqpYwZDOxL4SSM4vTF3iyRW5ve4xvCHf
+        IIut2ClrPVMBv/5yuMSncIzivoRlv6vor3/Ef+DGg+rmc6G2c1p6Um+UAP301Ek+Xnr24vcx8A/Q
+        xqb62oSEm4YGVs3tRGQzUnr+51+l/EyorvNdOgvopshXz8qpAjqUjlVV5oittdA673DFzx5SYL/c
+        jmSWt1W6/J539U/klUVtuPYbLTglSUIxGbRKMIRa2QH++cBrfu6XK9tHqAucI31o22c/XYbvAPv9
+        o8TuM1rC2YTeG2xMw/HEJDdWXn8uf/kWa8fh+G9/a2o6xlp924fTMIArrIf9tPJ7ISStKeXQ0Zzs
+        3/z8Iy8bdNbup5VX64A7mlQBn1BZaFAun7RVQflGa/+F8O1Mw9mUTjr8+7cr4L/+9ddf/+u3w6Bu
+        Hvln3Rgw5vP4H/+9VeA/xP8Y6uTz+bMNgQxJkf/9z793IPzd9k3djv97bN75d/j7n7/43Z+9Bn+P
+        zZh8/t/j/1pv9V//+j8AAAD//wMAYp9Xq+AgAAA=
+    headers:
+      CF-RAY:
+      - 94640d46896fb6c9-LAX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 27 May 2025 08:13:14 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-allow-origin:
+      - '*'
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-model:
+      - text-embedding-3-small
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '196'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      via:
+      - envoy-router-5f689c5f9d-svjx7
+      x-envoy-upstream-service-time:
+      - '200'
+      x-ratelimit-limit-requests:
+      - '10000'
+      x-ratelimit-limit-tokens:
+      - '10000000'
+      x-ratelimit-remaining-requests:
+      - '9999'
+      x-ratelimit-remaining-tokens:
+      - '9999982'
+      x-ratelimit-reset-requests:
+      - 6ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_51978202e4a91acdb0b2cf314d536032
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"input": ["Pictures(Object): Visual art pieces used in an example to illustrate
+      basic addition."], "model": "text-embedding-3-small", "encoding_format": "base64"}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '163'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=zxXbTMyK.67_c.SQXNivPXTcfsIBL5Vl1Q7WXFcTgxU-1748333590-1.0.1.1-ArIOxtxz6HMOCmEGGFc.Hxs19gY1LkxaxTZYc9hAE7zSdmh2fCrczDquGUovgGjYHvCJ94TxWQTCVlo1v7kDhnnrF0jwHy_U_LaR6AbA.94;
+        _cfuvid=4YFW5U1WwjrbdrZ7OWIvzgymvtGAnmnPEu1zeEdQsGg-1748333590310-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.68.2
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.68.2
+      x-stainless-read-timeout:
+      - '600'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.7
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA1R6RxOyTLvm/vsVT71b5hRJ6O5vRxJJ0khSp6amQBFJooQG+tT571P6TE3YuEAK
+        6HDfV+r//NefP//0eV3cpn/+/eefthqnf/7b99o9m7J//v3nv//rz58/f/7z9/v/3Vl0eXG/V6/y
+        d/vvz+p1L9Z//v2H+z9X/u9N//7zT8ilLlHq2s55S89TOKWb6C88dWvqEyrDt+NzfnEL13o7XJAA
+        I/3oYMWmTDS7HfWRwR74+XZYjkCo27JElbiaOPPTyt3G6d3AQORG7KTLTl9fDcsBpGgywYXe1mtk
+        vi8Qvy4GPj/aiztA5JpwNGOMD+8eR2LPmzOc3+yOeHvJdEWK3yE8nLTLzC10G1bdAA1Mr/Xdf6qq
+        kU9908uQDPWTaEcC61k6cRlUH3nid6uq6vRzKH0AfDHGeqbV7pqtOIMn6ZKQ61O81qLSWanMVryI
+        D2fRigQhJRkcuitD9AJwEXkZTYaexsBh7z6zdGvNKQC/8T7GrXaX+3oykOBTgDVZHnK610IDpYF7
+        IkcJ7CPOEzNDfl5Dl1hmplNhH15iGN3alLiHd1pz1zof4a6cC3xzzNVdT28/htfu3vgUNQtYPZUa
+        yChONjH4x46u9DjPkI+Ws/958Zdh3bTWQZdbT+Ydcj75snFrhvgn9yHHrlQHjt4bCCPKHLCxhhPd
+        GKuNYfm5ARIngww2x1YDKYDlhh8e0+rLUW44WOH27aN6ZMAC76wH3YxT8eXk3N3puN0z4NemRu4H
+        ce9Si198dBtY1+fNRoqmJxpLiUcsxvrqpgM9ISmA+ettzxKZ9vW6+DsNydtIiEMSdRBvHWtAbmhk
+        UkzOQeczd/7AIXq3JCay6gp0b8Uojt4bNvcqpcs0XAxotNN7Xm5PxaVkBjcIRG0lOD5K9bbTSwNR
+        uH7I5ZqO+TtoQYOAY/vELxmRru2qjSjAb+xD+5XQ8Y5dEzpV3+KgwZhuhC4OssZN+rufhuz49JCn
+        A4n47YdxqZpWMyL2zvZLCvV6Uce7D5M1DLGSDpRuAUhNZFoKxpbz8sEmrH4jueIaYdP0klx0n0IF
+        Gi5oScw4ek7nyy6F94aNfeb9rvJReeYfGD0S4jOusgccS4cM7rFikuvSie5W7y47ENjqjXjaRY14
+        9xYGckwbHaf5iafbw3t2iDwygPfTTsm5eyrJQD0lur8GQI+E1OhLwFo94zNbFdWc7fMlanz+SQrL
+        fdWrFl89aDbsEx9xy9EpLxQDgc/OxRdrOehiF2sKcCZYELWvN3d5HeYKpDhtiLtwbb3ph2mGB6hC
+        v44mMKwyO3bweQ1ckje5m3Mw5D8wBNsHO0Wg6t/6MOHb38k4KkCc8+8X7RF3yjRcZHJDP3n/1NBZ
+        kX3su4+nTpGSyFC9rNWcC8MxFx6PKwOdiSmIgzUrFwtPgagcL73/xtJh4GeX88Di+y45NIEGBP0w
+        jbBrbw7+zWfHd0cFElu28UFgFVdoR1NBEpONxOSqlk5Npzq/fkf20W2m2/vMX+T7updJnhzDYaIU
+        F9K3XmZ6Ou/qCVelAJqR25M8FUk+no+vEvaM4ZOI9adhxXgpUCgaDbnq0yXiTKuD8Fa+Q5w0TzIs
+        /r67wXUkJraX7qxvPrdr0HnfxCTsP+98ZYaIQeK110i2H841X9yYBdaMwmMtNhp3kT+O+ZtvfDyv
+        hs5fJ+4DeOcu+cp6aKNPvVQGlKrgQPJrhoahy3ccoA/uQgo6JfpysC6K9GwvKzGTbNZHeV1ktHSP
+        88wb8q0Wbna7wMdHYLETZ7y76OSqQa27r9g8HWt9/RxvBfzWBwmGk+TS7Xwxocg6HPGDFtWbo4YF
+        CsrL0S/PAp8vzL0M0N1SNHLo7xNdM7fr4a7qXZJ2jE0XutxTuQ8OA9mbzTXi5HUnQ/gpD9/+LdUk
+        fosdbFeGwzqje+6KkouARldJSOioI1ij62kBaOelOHsyAdji174AXOXPxBrXQKfzZUnRKSUDtmxg
+        DeKYcBXipciemWvQu9uzZQupZG2VHDTzWdMkXQL0wxN7ao7DItaggsPa29gTUadvFpM2iFyohbUu
+        7vRx46QMZsVbIQ8k1kMP76KHdsIxIEWIs5ze1CIFNYEVVvPilPOtz40Iu+fjr56GxbmKBvDgIfHp
+        +Ahy8ZNdGmSPF56cWu2Yz6bzrODVELyZ/87PwpXiCEIudkkuOtbAdcXGoPI1VvhUf8qaDDunhC9p
+        8/A1Kdh8CVDZo6HLGew8Q2dYqjLyYW4tDi62ig7kgfMZmEMbf/t3W//GB7RbsMOh8i4j7pRnHyDr
+        0gO7wz0e1vWg+jB0jiq57aVOn2YXesCIZTQPkp/XvNVzBowhg7EbpZ/hYya1CV16OJLjM81/9ZVB
+        TXEif2kwBkvbPTd4OcA3NvDrABaHfXCw2T+Sma/TiVKlU2IorbZJslvX5BR4/YICanUkGhm3Xo+V
+        uMDmWXokf0FCqZ2fFPTtp/iLj7XwIiqHxhd6kTxKP/UaKCGHvI3YPtCWfb49Ni2FmFafuT2/r5Qz
+        HjSD92zLyXn8XMAUM1sFTzobEvt0inPiJ7cZpseLSDJv3EfCbaAbOlpHgJOdzURf/DLh/ZRhnO5V
+        Cii+vG7wVnXsjGLY0i1Ldj165FAhCdGfVDCTwYCP541iz2y9nNuy0IdxAK7YrUkD/u6/S6pB7GWj
+        EFGls2Io2tPqS8xNdzlFf44wzR/OvANsXy/WTo7h9/n4OjXHemL21IPTGL3J3jeqnPs4VYzOev/C
+        JrmwYHTYM4f2H30gxyR/upzDFSHs/MPVf/Z16FLRVxX093n1M4226X2sYAXvni9rb1+ny2Z6KHvm
+        GVE5OOZbxFx3QGqVEmePXBvETj19oAPFOzaL6BGt9xPy4GEbFqzfjbGeiVk2yLyNyfxp4thdYn8n
+        iD++59y6Jlq/fAHcHlyLVcAk9VRo5waAUztjU7G0YRmZ8wUd94+KHNloBWvkX3wUbpeCJOe3BNZY
+        1Gf45UvYaWJOfwe7AYIMPVniDndu2FKjr6DDUAer6TboTS2kKXpLioFzXg3qTWJd5sePfa5bQE7Z
+        5R3AqpcPPsc/dr/+syHldZTwxTUdna+s2YGhLx0IrsgA1iNKISxuqYEtwygp9ZN4hBV8eLP8ssOc
+        U9LQgPfbaGE7KR7RJp1gBsVaw/jXv4Teoxwa13BPTNtnKD0vpgW//YKYzg7U26p5Jchy+YUd7T27
+        W8ScZHgXyYQPDrsBUpvGBymAC3xweAvDkjSUgWqq2sTScnYYn3ergNCra6yUmgG6fWuNsNfRHmsJ
+        bocxTIUGurHnY73cYL4KdbaD4pjl2OgWEG3ceZnBSUehz/SFAoRdGtxAaU+Mv+59nG965ckwSY45
+        3h/bg07TTNfgiTFz4l3SKBqPN1mG3/3jo8vdqdepyy9wcvoTvjI3Xd+ap1Eg3TWtWXqfq5o4682C
+        P/xWucB0V+2maMjMth4rx02rqQrdAnz5LPE/XE15OXM5cF8PMva+/erLx2K4g7xOcpu9glVmmwal
+        x0wknmOu+uIPBx8azmzMgFeXYcnmewAJE8lzVXBIn/h9CZHUwaNPv/yHUl5TIDZIhn20eNGmFbIF
+        hWL2SGF9NMDF3LUBR0BOWBnQIxr3aEph0VWIaAneDyLvPxj45ddYN1A18N/6ABJzGckpOpnRp48/
+        H/BA7YXEIg/oYk27BbLbZhHnWy/bpDcbkKW0IiY9Ny6lB/kGW++5+EIwvyhFGW8AjYgIe4dlAtuO
+        czIQvBQFG5B9Unra9xZcz16MtR//0DfdQnrQAGwB1hom7zEp4ld/4ILBr3olMK2gYqUy+eJZtHRv
+        rkTRAQTzuHPWnFTJJ5Q/p1OAo9me3G0Z1RSJrMWRE2nznBbSpYTE77C/ozHj0krchbCl8tN/fiTe
+        pYPuWZCkCiEqCHH+l39XHcywS986EPFx2v3wjajmNaDUViQBRAmq8b67nPO16WwLWufwQKytPwN6
+        8x4ylPDrRnSsJjml1fUD17Mfkz24n8Ema8Um33Ln4bP2vYwIuekmzFty8yGq7mAZgPeRt2OhY0yT
+        k7s8WtNDi13t/cWmTL49pNVB5m1OCO6gQLcx0rK/63kQBzPi2yJM0cevBKJ2o+OKSxwH0BHwx8f3
+        xokWr3mOcLmFxQy1i5pz4s0YYZSwNdHh7jPQlzFe5L11iYj22KJhRb3qoZ4bNWKzr2bYnkoxwp9+
+        dHVggjHtzRle9KAm9s4s9VWx5w6Syk389dBs0ao0+wVmtuNjLdizdKlz1gLh6f38y182836V4avM
+        FGJURqKv2s3SQJ+SAqtMvKN0OwcmfFung/8RGsFduJIdwa/fhbk55aQwQgfxfXH370KmDIK7cstP
+        /3/5rZ0vij+EaE3CF1bhB+p/9fbHFpZZsH0GrBLWArhe4eQvXRtSWt2vJox9hyVKUob5+tlPH6j3
+        ajbD+lyCGT7WDaWhyfgySZ715giBgJLjBvz2FjfDmi+7Ator6OfGPhmAujCZkfq4Jthi1c1d0vvR
+        kkvWVWf0eERgTeTiBr/93Aenh0b5eNQsqD30k//c3Vd3lZ9yD5YAtt9+zUZ0n2gFnMbTm7j7iA4L
+        OEADVnSvkLC1jVqsTa8Hv3qxqRzU/Ea4EerFcMR7/sDRjZlN88cXccrpn4HmAWV+fggxiPzU1+XZ
+        Z7BqtYno9Hmtmzrgux+f8OvZPuqcfD7NcP8QtpmxU9vl11BbfuP1pZ2p6FzC5pYMdkyB9T7cR2Ld
+        h1885u9fP4ZEa7YeMsgUTw7/9N3MSocP/PJbsvekEmz9K9J+9Yi1NHvpi3odBai8sDTvcuXtLsLw
+        ScEbzQ52Vs8eWowUBe3aS0OKkjnTPrluGRSSWCF7Snp3SXt/hmXM2MS/NCrlLTDe4Pmo0fmcSSul
+        Ly/35EU6LsTa4yqnL6nagGZXD2wonZwvMXftYHZlZGKsDOcupzz8ID05qVjVhqe7fPo4RCn0Drio
+        44wuBytQwEWpnPmHR1wEH6nsPsoD9uZD5m58t9dgSUWLHK7zki+fezjDVuhl7BkA19MYBAF8luOA
+        v/pm2N5nlMEEjz2+6XVFV8XuOugxqoado1vWW5CgFPqf8jlLs27R8RoMqby1BGPr4jqA57ddBdfP
+        eiVuKuJoaygUwHc+ZvC8Pun2iIMLlLeZ4OTrtyzLx9ygU847f6MSqse8YD9QPZ114pE1paOlPD3U
+        toJPfAYcKfdMrj0cbsc9SbqLmH/58QbnfXcgztdP2uYrkSEnvY/YcIBab9MCU4BkSydnpFO9t31U
+        yq1uNzN4U5Gu19wIERVcceaTbdPJIiENfvf/LDpsCFbreQwAy5iqL2+CmfNCxAdwq6BA8rbz6DoE
+        L09esnglSpudcuHH9758mRy9E4iW0gkMRJiTPLMdTMEWe10AldN0wPuHXdHxI0oy/KDUJC6vBoOw
+        Sy8FPO6YHXbM8x1sX/8AnqAzk798BcCtgIV0SokvzxX97p8FXaf9ed6ZWU2n7iqE6Lf+VwHK7pQ+
+        2FCuVjvCh+zoDmI3v31kcfj69YMKd/t+L5wVWcUPHFvu9sXjH1/0F3mEdDq4jiXPTngkzqL2Ovn6
+        g1CoL5EvslfBXTzW2oHGOs44OjrH4YcHkL0+PsSYVtvl34kewtu02/kgdG1X+PI5+F0PX2JfRk3T
+        zFVg7Twf2H1BTLf3dmvgmgQvkgmNoK/Pep9Co4jsmVU2ns5+ZflwP4E9/uHRegObgn7+S14Hdr08
+        tWeILNS25CBVfkSX+BZCI68Wn/vuh0mYbwX0CCTYuLs6FYKET8FapzJWZcrp22V378D+aFyxfb3X
+        Na3EJfyL79HdGAequG8LRmyoYGULTSA2TLJAO+9sf9XPr3zj0LWA9zLysRGrjNtqai/AmHY6sSPr
+        pnetiBt4udCEaBJ5g00yZQHeKx9gm7Xs/ItnzU8f4kMTVHRs1DMDS+W84eNEUp0WRmjBe2z15HBA
+        dvTXj+mh85w3BDnw8Sd1+eE3xkWlAJF1lRI59H4idyMtB75dnREoPjMRraNqtI1KpkBkTAq25nyk
+        24uoArrYckocUV8jgpRkB90y5fzlcM916ju5A5Pi9SKHixHVqzLADkLvWc/Mo3N1EWx2hV6i8MDf
+        eohG77TGkF69duaFda0HsVwzSAVbxEaRtnnnrtwGv37+X/7YxM+n9+sfWO/XsuaOt02WoL06RFPe
+        SsTJD6VE4PSase4NYUR2w8aA5vQCvkDPhiuqupxCn/VexGiySzSeZl0B/q7jfFnq2Ho6uJqD5ONp
+        mCV0etZrNZvyb/1n2E6Nu4gzb8DKQoJPqiCo/+oJG0QcthL7mU+i8eh//jUxM5OL3v1pqeBkaAV2
+        Xvyu3sZCZ2B29p5Ey81jLhRqqSHNLbxZiLE3rOftMkNcbidybBNFF8W+9/++T/j6n18+6MGv/0k8
+        7qbWwu4ZapBNpxNRqsuBcsZR15CldSpWs1IG09dvAdxUvWZuOXiRsMrJR37Kpoitey3mK1cqFir6
+        4oOVwNrTTX6JJRCfZkX0nWfkfOM+GaQRHvnCR6ER1djUk7nKm7HKyxqg1aIWsMguZ59juVIn1f1q
+        wB0U9bkf10XfUqMsIR5NB2f3dQ+EeneR5bprUuxs58FdzkdSQVm88NgVhmO0SazOQEFAl6nfFY27
+        MIlUwFOp2cTcvKoeT1YVwv1pKYlyig75IlxWBvJducyfl73lE5szKfjxt68eGsghf0HwKi8Kvjiv
+        mZKUrTe5Pp522JRyUZ++fhr4fY9xKjClRfHdj23i+rzqjWCV0tWAbzQ6836R33RNH2IA9k6skKg0
+        BX29s8YGv/rb38ncwd0eL2X54T+O1/BIJ92rMvTFI7wX1nUgB+FgwXuD4h+e1mPB1QJUbx/oj/aL
+        B30tFCnMjMLy2XvziebDEQZwfeZHf33PtJ4uKAugchdHst/2bkQOAnbAnBvST9+7f/OHr7828w+7
+        Akv3hhWstxGS6L6EYJ0U+QPe8XUiZiK9o02Nlgaxwb7wly3sAK37rP+bh6hSvw3k1okG1LrHivUC
+        zzr96jUQWlFMXHCmw5e/hvDOnEufZvM7+upT55eHEFuAmU5PgyXDRxIf8bnrWroJbiugrtwMoqVS
+        lW9rzMmwnOMEK8Ppqm8//tWIGuNvMLzodMcMKbxN8u4vv/zxY5TnZf3XH1msaVnA1x/Ahn1qKL/1
+        xx18UetKUrHB+toWWQzZvdwSS+Ze7pYlSw855hwR9T3TYVCLa/n7Pl9Ol4u+cefdCPdOqmC1ffYD
+        yY5vH0pTo+AjME1Ad/bIgMG+jfgcKmW97pVzBddczjDeTYhuatcz8NFZ0szXvP3Ne7oPrJ36QX7+
+        lsDxnx7gtUHkm/eA33z88hGi//IE75ULsqLI+Tc/eLo9B9IdGNzsOoP4Oear8YljmAXdPLPf943d
+        WqfgqjjmzNi8ka+H3WmEIogyojF5oG8P791B+/q5EWXpHZ2G+RBC7O87fOqOM6Bm6WnwxxevfHjP
+        iS1pEOXtdCN7N2CH583/FFDRe25+G1eGLtUeKn+fr8Mr/vWTHlZVEJMom+2cj/yLB8/654UPt1Mx
+        iGPf7ODPr01Nj4/GV7WfwTffnCX3+IzoVy+DhYg1Oe6fmruZ99MO1VJKiNfoh3x1T8GMUNSy+OtX
+        UP5b31AQ2AvWXXag6yFcGPjrR3pppvo3HzDgvnw/iJEMGV2DyPWB22oycU0U5OskuRukH/30zUt1
+        /ZOcCQe//rDPH3Su3h6S9MXvQMF5rK0uOeQEwi+ekK9eBIvXvEeIFEXGipGv4Kefdm3L+SR9HiqX
+        fusFFATYM6vdtoFKZD+DcMsK7HviEfz8U/CCZYuPsKDD9ticFFro1RKFQn0QGfdRgJ2vUp+5kjoi
+        CVYLOFXgjhWtsQHHmhGHDJw55Jt/uHzHPjT446uLucbg5y/L89X2iFs1dbTot7KH0e2VYpsP75GY
+        7bcL2thDPkP+VbvjL5+IJ52ZX9i7Uxq/2QZ88QmrHynRxZQdNjhPfIjP2LuDbx5Wwnqh2B9+ei+b
+        kxCc40dElLDoAUnSXQCXhyZi9+svU+3ZhGhbed5HYxMP4i8P/PldjP/81PTDSin0vSf65gH7YWSW
+        ywf+8qDzaz3k27UXGWgOrxg7wfHjbubiBCBJcD5T+21ROg1sDxLb4IluIK0WBHcSwD+/UwH/9a8/
+        f/7H74RB19+L9nswYCrW6T/+z1GB/xD/Y+yytv17DGEes7L459//+wTCP++h797T/5z6pniN//z7
+        Dy/9PWvwz9RPWfv/Xv/X91X/9a//BQAA//8DAK+bEofgIAAA
+    headers:
+      CF-RAY:
+      - 94640d492fdbb6c9-LAX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 27 May 2025 08:13:15 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-allow-origin:
+      - '*'
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-model:
+      - text-embedding-3-small
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '393'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      via:
+      - envoy-router-78456c78d9-689qp
+      x-envoy-upstream-service-time:
+      - '395'
+      x-ratelimit-limit-requests:
+      - '10000'
+      x-ratelimit-limit-tokens:
+      - '10000000'
+      x-ratelimit-remaining-requests:
+      - '9999'
+      x-ratelimit-remaining-tokens:
+      - '9999979'
+      x-ratelimit-reset-requests:
+      - 6ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_c9c729639c1a9714296bd221d8edd696
+    status:
+      code: 200
+      message: OK
 version: 1

tests/cli/organization/test_main.py

@@ -33,9 +33,9 @@ def mock_settings():
 def test_org_list_command(mock_org_command_class, runner):
     mock_org_instance = MagicMock()
     mock_org_command_class.return_value = mock_org_instance
-
+    
     result = runner.invoke(list)
-
+    
     assert result.exit_code == 0
     mock_org_command_class.assert_called_once()
     mock_org_instance.list.assert_called_once()
@@ -45,9 +45,9 @@ def test_org_list_command(mock_org_command_class, runner):
 def test_org_switch_command(mock_org_command_class, runner):
     mock_org_instance = MagicMock()
     mock_org_command_class.return_value = mock_org_instance
-
+    
     result = runner.invoke(switch, ['test-id'])
-
+    
     assert result.exit_code == 0
     mock_org_command_class.assert_called_once()
     mock_org_instance.switch.assert_called_once_with('test-id')
@@ -57,9 +57,9 @@ def test_org_switch_command(mock_org_command_class, runner):
 def test_org_current_command(mock_org_command_class, runner):
     mock_org_instance = MagicMock()
     mock_org_command_class.return_value = mock_org_instance
-
+    
     result = runner.invoke(current)
-
+    
     assert result.exit_code == 0
     mock_org_command_class.assert_called_once()
     mock_org_instance.current.assert_called_once()
@@ -70,7 +70,7 @@ class TestOrganizationCommand(unittest.TestCase):
         with patch.object(OrganizationCommand, '__init__', return_value=None):
             self.org_command = OrganizationCommand()
             self.org_command.plus_api_client = MagicMock()
-
+        
     @patch('crewai.cli.organization.main.console')
     @patch('crewai.cli.organization.main.Table')
     def test_list_organizations_success(self, mock_table, mock_console):
@@ -82,11 +82,11 @@ class TestOrganizationCommand(unittest.TestCase):
         ]
         self.org_command.plus_api_client = MagicMock()
         self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
+        
         mock_console.print = MagicMock()
-
+        
         self.org_command.list()
-
+        
         self.org_command.plus_api_client.get_organizations.assert_called_once()
         mock_table.assert_called_once_with(title="Your Organizations")
         mock_table.return_value.add_column.assert_has_calls([
@@ -105,12 +105,12 @@ class TestOrganizationCommand(unittest.TestCase):
         mock_response.json.return_value = []
         self.org_command.plus_api_client = MagicMock()
         self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
+        
         self.org_command.list()
-
+        
         self.org_command.plus_api_client.get_organizations.assert_called_once()
         mock_console.print.assert_called_once_with(
-            "You don't belong to any organizations yet.",
+            "You don't belong to any organizations yet.", 
             style="yellow"
         )
 
@@ -118,14 +118,14 @@ class TestOrganizationCommand(unittest.TestCase):
     def test_list_organizations_api_error(self, mock_console):
         self.org_command.plus_api_client = MagicMock()
         self.org_command.plus_api_client.get_organizations.side_effect = requests.exceptions.RequestException("API Error")
-
+        
         with pytest.raises(SystemExit):
             self.org_command.list()
-
+        
 
         self.org_command.plus_api_client.get_organizations.assert_called_once()
         mock_console.print.assert_called_once_with(
-            "Failed to retrieve organization list: API Error",
+            "Failed to retrieve organization list: API Error", 
             style="bold red"
         )
 
@@ -140,12 +140,12 @@ class TestOrganizationCommand(unittest.TestCase):
         ]
         self.org_command.plus_api_client = MagicMock()
         self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
+        
         mock_settings_instance = MagicMock()
         mock_settings_class.return_value = mock_settings_instance
-
+        
         self.org_command.switch("test-id")
-
+        
         self.org_command.plus_api_client.get_organizations.assert_called_once()
         mock_settings_instance.dump.assert_called_once()
         assert mock_settings_instance.org_name == "Test Org"
@@ -165,9 +165,9 @@ class TestOrganizationCommand(unittest.TestCase):
         ]
         self.org_command.plus_api_client = MagicMock()
         self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
+        
         self.org_command.switch("non-existent-id")
-
+        
         self.org_command.plus_api_client.get_organizations.assert_called_once()
         mock_console.print.assert_called_once_with(
             "Organization with id 'non-existent-id' not found.",
@@ -181,9 +181,9 @@ class TestOrganizationCommand(unittest.TestCase):
         mock_settings_instance.org_name = "Test Org"
         mock_settings_instance.org_uuid = "test-id"
         mock_settings_class.return_value = mock_settings_instance
-
+        
         self.org_command.current()
-
+        
         self.org_command.plus_api_client.get_organizations.assert_not_called()
         mock_console.print.assert_called_once_with(
             "Currently logged in to organization Test Org (test-id)",
@@ -196,49 +196,11 @@ class TestOrganizationCommand(unittest.TestCase):
         mock_settings_instance = MagicMock()
         mock_settings_instance.org_uuid = None
         mock_settings_class.return_value = mock_settings_instance
-
+        
         self.org_command.current()
-
+        
         assert mock_console.print.call_count == 3
         mock_console.print.assert_any_call(
-            "You're not currently logged in to any organization.",
+            "You're not currently logged in to any organization.", 
             style="yellow"
         )
-
-    @patch('crewai.cli.organization.main.console')
-    def test_list_organizations_unauthorized(self, mock_console):
-        mock_response = MagicMock()
-        mock_http_error = requests.exceptions.HTTPError(
-            "401 Client Error: Unauthorized",
-            response=MagicMock(status_code=401)
-        )
-
-        mock_response.raise_for_status.side_effect = mock_http_error
-        self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
-        self.org_command.list()
-
-        self.org_command.plus_api_client.get_organizations.assert_called_once()
-        mock_console.print.assert_called_once_with(
-            "You are not logged in to any organization. Use 'crewai login' to login.",
-            style="bold red"
-        )
-
-    @patch('crewai.cli.organization.main.console')
-    def test_switch_organization_unauthorized(self, mock_console):
-        mock_response = MagicMock()
-        mock_http_error = requests.exceptions.HTTPError(
-            "401 Client Error: Unauthorized",
-            response=MagicMock(status_code=401)
-        )
-
-        mock_response.raise_for_status.side_effect = mock_http_error
-        self.org_command.plus_api_client.get_organizations.return_value = mock_response
-
-        self.org_command.switch("test-id")
-
-        self.org_command.plus_api_client.get_organizations.assert_called_once()
-        mock_console.print.assert_called_once_with(
-            "You are not logged in to any organization. Use 'crewai login' to login.",
-            style="bold red"
-        )

tests/cli/tools/test_main.py

@@ -56,8 +56,7 @@ def test_create_success(mock_subprocess, capsys, tool_command):
 
 @patch("crewai.cli.tools.main.subprocess.run")
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")
-@patch("crewai.cli.tools.main.ToolCommand._print_current_organization")
-def test_install_success(mock_print_org, mock_get, mock_subprocess_run, capsys, tool_command):
+def test_install_success(mock_get, mock_subprocess_run, capsys, tool_command):
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200
     mock_get_response.json.return_value = {
@@ -86,9 +85,6 @@ def test_install_success(mock_print_org, mock_get, mock_subprocess_run, capsys,
         env=unittest.mock.ANY,
     )
 
-    # Verify _print_current_organization was called
-    mock_print_org.assert_called_once()
-
 @patch("crewai.cli.tools.main.subprocess.run")
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")
 def test_install_success_from_pypi(mock_get, mock_subprocess_run, capsys, tool_command):
@@ -170,9 +166,7 @@ def test_publish_when_not_in_sync(mock_is_synced, capsys, tool_command):
 @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
 @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
 @patch("crewai.cli.tools.main.extract_available_exports", return_value=[{"name": "SampleTool"}])
-@patch("crewai.cli.tools.main.ToolCommand._print_current_organization")
 def test_publish_when_not_in_sync_and_force(
-    mock_print_org,
     mock_available_exports,
     mock_is_synced,
     mock_publish,
@@ -208,7 +202,6 @@ def test_publish_when_not_in_sync_and_force(
         encoded_file=unittest.mock.ANY,
         available_exports=[{"name": "SampleTool"}],
     )
-    mock_print_org.assert_called_once()
 
 
 @patch("crewai.cli.tools.main.get_project_name", return_value="sample-tool")
@@ -336,27 +329,3 @@ def test_publish_api_error(
     assert "Request to Enterprise API failed" in output
 
     mock_publish.assert_called_once()
-
-
-
-@patch("crewai.cli.tools.main.Settings")
-def test_print_current_organization_with_org(mock_settings, capsys, tool_command):
-    mock_settings_instance = MagicMock()
-    mock_settings_instance.org_uuid = "test-org-uuid"
-    mock_settings_instance.org_name = "Test Organization"
-    mock_settings.return_value = mock_settings_instance
-    tool_command._print_current_organization()
-    output = capsys.readouterr().out
-    assert "Current organization: Test Organization (test-org-uuid)" in output
-
-
-@patch("crewai.cli.tools.main.Settings")
-def test_print_current_organization_without_org(mock_settings, capsys, tool_command):
-    mock_settings_instance = MagicMock()
-    mock_settings_instance.org_uuid = None
-    mock_settings_instance.org_name = None
-    mock_settings.return_value = mock_settings_instance
-    tool_command._print_current_organization()
-    output = capsys.readouterr().out
-    assert "No organization currently set" in output
-    assert "org switch <org_id>" in output

tests/crew_test.py

@@ -1765,50 +1765,6 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     )
 
 
-def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
-    """Ensure Crew.kickoff() sums UsageMetrics from both regular and manager agents."""
-
-    # ── 1.  Build the manager and a simple task ──────────────────────────────────
-    manager = Agent(
-        role="Manager",
-        goal="Coordinate everything.",
-        backstory="Keeps the project on track.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Say hello",
-        expected_output="Hello",
-        agent=researcher,  # *regular* agent
-    )
-
-    # ── 2.  Stub out each agent’s _token_process.get_summary() ───────────────────
-    researcher_metrics = UsageMetrics(total_tokens=120, prompt_tokens=80, completion_tokens=40, successful_requests=2)
-    manager_metrics = UsageMetrics(total_tokens=30, prompt_tokens=20, completion_tokens=10, successful_requests=1)
-
-    # Replace the internal _token_process objects with simple mocks
-    researcher._token_process = MagicMock(get_summary=MagicMock(return_value=researcher_metrics))
-    manager._token_process = MagicMock(get_summary=MagicMock(return_value=manager_metrics))
-
-    # ── 3.  Create the crew (hierarchical!) and kick it off ──────────────────────
-    crew = Crew(
-        agents=[researcher],  # regular agents
-        manager_agent=manager,  # manager to be included
-        tasks=[task],
-        process=Process.hierarchical,
-    )
-
-    # We don’t care about LLM output here; patch execute_sync to avoid network
-    with patch.object(Task, "execute_sync", return_value=TaskOutput(description="dummy", raw="Hello", agent=researcher.role)):
-        crew.kickoff()
-
-    # ── 4.  Assert the aggregated numbers are the *sum* of both agents ───────────
-    assert crew.usage_metrics.total_tokens == researcher_metrics.total_tokens + manager_metrics.total_tokens
-    assert crew.usage_metrics.prompt_tokens == researcher_metrics.prompt_tokens + manager_metrics.prompt_tokens
-    assert crew.usage_metrics.completion_tokens == researcher_metrics.completion_tokens + manager_metrics.completion_tokens
-    assert crew.usage_metrics.successful_requests == researcher_metrics.successful_requests + manager_metrics.successful_requests
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_crew_creation_tasks_with_agents(researcher, writer):
     """
@@ -4462,7 +4418,7 @@ def test_reset_knowledge_with_no_crew_knowledge(researcher,writer):
 
     with pytest.raises(RuntimeError) as excinfo:
         crew.reset_memories(command_type='knowledge')
-        
+
         # Optionally, you can also check the error message
     assert "Crew Knowledge and Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
 
@@ -4541,7 +4497,7 @@ def test_reset_agent_knowledge_with_no_agent_knowledge(researcher,writer):
 
     with pytest.raises(RuntimeError) as excinfo:
         crew.reset_memories(command_type='agent_knowledge')
-    
+
     # Optionally, you can also check the error message
     assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
 
@@ -4561,7 +4517,7 @@ def test_reset_agent_knowledge_with_only_crew_knowledge(researcher,writer):
 
     with pytest.raises(RuntimeError) as excinfo:
         crew.reset_memories(command_type='agent_knowledge')
-    
+
     # Optionally, you can also check the error message
     assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
 
@@ -4608,3 +4564,5 @@ def test_reset_agent_knowledge_with_only_agent_knowledge(researcher,writer):
 
         crew.reset_memories(command_type='agent_knowledge')
         mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
+
+

tests/reasoning_interval_test.py

@@ -0,0 +1,258 @@
+"""Tests for reasoning interval and adaptive reasoning in agents."""
+
+import pytest
+from unittest.mock import patch, MagicMock
+
+from crewai import Agent, Task
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.utilities.reasoning_handler import AgentReasoning
+
+
+def test_agent_with_reasoning_interval():
+    """Ensure that the agent triggers mid-execution reasoning based on the fixed interval."""
+
+    # Use a mock LLM to avoid real network calls
+    llm = MagicMock()
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning interval feature",
+        backstory="I am a test agent created to verify the reasoning interval feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        reasoning_interval=2,  # Reason every 2 steps
+        verbose=True,
+    )
+
+    task = Task(
+        description="Multi-step task that requires periodic reasoning.",
+        expected_output="The task should be completed with periodic reasoning.",
+        agent=agent,
+    )
+
+    # Create a mock executor that will be injected into the agent
+    mock_executor = MagicMock()
+    mock_executor.steps_since_reasoning = 0
+    
+    def mock_invoke(*args, **kwargs):
+        return mock_executor._invoke_loop()
+    
+    def mock_invoke_loop():
+        assert not mock_executor._should_trigger_reasoning()
+        mock_executor.steps_since_reasoning += 1
+        
+        mock_executor.steps_since_reasoning = 2
+        assert mock_executor._should_trigger_reasoning()
+        mock_executor._handle_mid_execution_reasoning()
+        
+        return {"output": "Task completed successfully."}
+    
+    mock_executor.invoke = MagicMock(side_effect=mock_invoke)
+    mock_executor._invoke_loop = MagicMock(side_effect=mock_invoke_loop)
+    mock_executor._should_trigger_reasoning = MagicMock(side_effect=lambda: mock_executor.steps_since_reasoning >= 2)
+    mock_executor._handle_mid_execution_reasoning = MagicMock()
+
+    # Monkey-patch create_agent_executor so that it sets our mock_executor
+    def _fake_create_agent_executor(self, tools=None, task=None):  # noqa: D401,E501
+        """Replace the real executor with the mock while preserving behaviour."""
+        self.agent_executor = mock_executor
+        return mock_executor
+
+    with patch.object(Agent, "create_agent_executor", _fake_create_agent_executor):
+        result = agent.execute_task(task)
+
+    # Validate results and that reasoning happened when expected
+    assert result == "Task completed successfully."
+    mock_executor._invoke_loop.assert_called_once()
+    mock_executor._handle_mid_execution_reasoning.assert_called_once()
+
+
+def test_agent_with_adaptive_reasoning():
+    """Test agent with adaptive reasoning."""
+    # Create a mock agent with adaptive reasoning
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = None
+    agent.adaptive_reasoning = True
+    agent.role = "Test Agent"
+    
+    # Create a mock task
+    task = MagicMock()
+    
+    executor = CrewAgentExecutor(
+        llm=MagicMock(),
+        task=task,
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=10,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock()
+    )
+    
+    def mock_invoke_loop():
+        assert executor._should_adaptive_reason()
+        executor._handle_mid_execution_reasoning()
+        return {"output": "Task completed with adaptive reasoning."}
+    
+    executor._invoke_loop = MagicMock(side_effect=mock_invoke_loop)
+    executor._should_adaptive_reason = MagicMock(return_value=True)
+    executor._handle_mid_execution_reasoning = MagicMock()
+    
+    result = executor._invoke_loop()
+    
+    assert result["output"] == "Task completed with adaptive reasoning."
+    executor._should_adaptive_reason.assert_called_once()
+    executor._handle_mid_execution_reasoning.assert_called_once()
+
+
+def test_mid_execution_reasoning_handler():
+    """Test the mid-execution reasoning handler."""
+    llm = MagicMock()
+    llm.call.return_value = "Based on progress, I'll adjust my approach.\n\nREADY: I am ready to continue executing the task."
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the mid-execution reasoning handler",
+        backstory="I am a test agent created to verify the mid-execution reasoning handler works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True
+    )
+
+    task = Task(
+        description="Task to test mid-execution reasoning handler.",
+        expected_output="The mid-execution reasoning handler should work correctly.",
+        agent=agent
+    )
+
+    agent.llm.call = MagicMock(return_value="Based on progress, I'll adjust my approach.\n\nREADY: I am ready to continue executing the task.")
+
+    reasoning_handler = AgentReasoning(task=task, agent=agent)
+
+    result = reasoning_handler.handle_mid_execution_reasoning(
+        current_steps=3,
+        tools_used=["search_tool", "calculator_tool"],
+        current_progress="Made progress on steps 1-3",
+        iteration_messages=[
+            {"role": "assistant", "content": "I'll search for information."},
+            {"role": "system", "content": "Search results: ..."},
+            {"role": "assistant", "content": "I'll calculate the answer."},
+            {"role": "system", "content": "Calculation result: 42"}
+        ]
+    )
+
+    assert result is not None
+    assert hasattr(result, 'plan')
+    assert hasattr(result.plan, 'plan')
+    assert hasattr(result.plan, 'ready')
+    assert result.plan.ready is True
+
+
+def test_should_trigger_reasoning_interval():
+    """Test the _should_trigger_reasoning method with interval-based reasoning."""
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = 3
+    agent.adaptive_reasoning = False
+
+    executor = CrewAgentExecutor(
+        llm=MagicMock(),
+        task=MagicMock(),
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=10,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock()
+    )
+
+    executor.steps_since_reasoning = 0
+    assert executor._should_trigger_reasoning() is False
+
+    executor.steps_since_reasoning = 2
+    assert executor._should_trigger_reasoning() is False
+
+    executor.steps_since_reasoning = 3
+    assert executor._should_trigger_reasoning() is True
+
+    executor.steps_since_reasoning = 4
+    assert executor._should_trigger_reasoning() is True
+
+
+def test_should_trigger_adaptive_reasoning():
+    """Test the _should_adaptive_reason method."""
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = None
+    agent.adaptive_reasoning = True
+
+    executor = CrewAgentExecutor(
+        llm=MagicMock(),
+        task=MagicMock(),
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=10,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock()
+    )
+
+    with patch('crewai.utilities.reasoning_handler.AgentReasoning.should_adaptive_reason_llm', return_value=True):
+        assert executor._should_adaptive_reason() is True
+    
+    executor.messages = [
+        {"role": "assistant", "content": "I'll try this approach."},
+        {"role": "system", "content": "Error: Failed to execute the command."},
+        {"role": "assistant", "content": "Let me try something else."}
+    ]
+    assert executor._should_adaptive_reason() is True
+    
+    executor.messages = [
+        {"role": "assistant", "content": "I'll try this approach."},
+        {"role": "system", "content": "Command executed successfully."},
+        {"role": "assistant", "content": "Let me continue with the next step."}
+    ]
+    with patch('crewai.utilities.reasoning_handler.AgentReasoning.should_adaptive_reason_llm', return_value=False):
+        assert executor._should_adaptive_reason() is False
+
+
+@pytest.mark.parametrize("interval,steps,should_reason", [
+    (None, 5, False),
+    (3, 2, False),
+    (3, 3, True),
+    (1, 1, True),
+    (5, 10, True),
+])
+def test_reasoning_interval_scenarios(interval, steps, should_reason):
+    """Test various reasoning interval scenarios."""
+    agent = MagicMock()
+    agent.reasoning = True
+    agent.reasoning_interval = interval
+    agent.adaptive_reasoning = False
+
+    executor = CrewAgentExecutor(
+        llm=MagicMock(),
+        task=MagicMock(),
+        crew=MagicMock(),
+        agent=agent,
+        prompt={},
+        max_iter=10,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=MagicMock()
+    )
+
+    executor.steps_since_reasoning = steps
+    assert executor._should_trigger_reasoning() is should_reason

tests/telemetry/test_telemetry.py

@@ -9,14 +9,6 @@ from crewai.telemetry import Telemetry
 from opentelemetry import trace
 
 
-@pytest.fixture(autouse=True)
-def cleanup_telemetry():
-    """Automatically clean up Telemetry singleton between tests."""
-    Telemetry._instance = None
-    yield
-    Telemetry._instance = None
-
-
 @pytest.mark.parametrize(
     "env_var,value,expected_ready",
     [

tests/telemetry/test_telemetry_disable.py

@@ -1,19 +1,11 @@
 import os
-from unittest.mock import patch, MagicMock
+from unittest.mock import patch
 
 import pytest
 
 from crewai.telemetry import Telemetry
 
 
-@pytest.fixture(autouse=True)
-def cleanup_telemetry():
-    """Automatically clean up Telemetry singleton between tests."""
-    Telemetry._instance = None
-    yield
-    Telemetry._instance = None
-
-
 @pytest.mark.parametrize("env_var,value,expected_ready", [
     ("OTEL_SDK_DISABLED", "true", False),
     ("OTEL_SDK_DISABLED", "TRUE", False),
@@ -36,59 +28,3 @@ def test_telemetry_enabled_by_default():
         with patch("crewai.telemetry.telemetry.TracerProvider"):
             telemetry = Telemetry()
             assert telemetry.ready is True
-
-
-def test_telemetry_disable_after_singleton_creation():
-    """Test that telemetry operations are disabled when env var is set after singleton creation."""
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.telemetry.telemetry.TracerProvider"):
-            telemetry = Telemetry()
-            assert telemetry.ready is True
-            
-            mock_operation = MagicMock()
-            telemetry._safe_telemetry_operation(mock_operation)
-            mock_operation.assert_called_once()
-            
-            mock_operation.reset_mock()
-            
-            os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
-            
-            telemetry._safe_telemetry_operation(mock_operation)
-            mock_operation.assert_not_called()
-
-
-def test_telemetry_disable_with_multiple_instances():
-    """Test that multiple telemetry instances respect dynamically changed env vars."""
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.telemetry.telemetry.TracerProvider"):
-            telemetry1 = Telemetry()
-            assert telemetry1.ready is True
-            
-            os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
-            
-            telemetry2 = Telemetry()
-            assert telemetry2 is telemetry1
-            assert telemetry2.ready is True
-            
-            mock_operation = MagicMock()
-            telemetry2._safe_telemetry_operation(mock_operation)
-            mock_operation.assert_not_called()
-
-
-def test_telemetry_otel_sdk_disabled_after_creation():
-    """Test that OTEL_SDK_DISABLED also works when set after singleton creation."""
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.telemetry.telemetry.TracerProvider"):
-            telemetry = Telemetry()
-            assert telemetry.ready is True
-            
-            mock_operation = MagicMock()
-            telemetry._safe_telemetry_operation(mock_operation)
-            mock_operation.assert_called_once()
-            
-            mock_operation.reset_mock()
-            
-            os.environ['OTEL_SDK_DISABLED'] = 'true'
-            
-            telemetry._safe_telemetry_operation(mock_operation)
-            mock_operation.assert_not_called()

tests/test_lite_agent.py

@@ -1,4 +1,4 @@
-from collections import defaultdict
+import asyncio
 from typing import cast
 from unittest.mock import Mock
 
@@ -313,108 +313,5 @@ def test_sets_parent_flow_when_inside_flow():
             nonlocal captured_agent
             captured_agent = source
 
-        flow.kickoff()
+        result = flow.kickoff()
         assert captured_agent.parent_flow is flow
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_guardrail_is_called_using_string():
-    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
-    with crewai_event_bus.scoped_handlers():
-        @crewai_event_bus.on(LLMGuardrailStartedEvent)
-        def capture_guardrail_started(source, event):
-            guardrail_events["started"].append(event)
-
-        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
-        def capture_guardrail_completed(source, event):
-            guardrail_events["completed"].append(event)
-
-        agent = Agent(
-            role="Sports Analyst",
-            goal="Gather information about the best soccer players",
-            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-            guardrail="""Only include Brazilian players, both women and men""",
-        )
-
-        result = agent.kickoff(messages="Top 10 best players in the world?")
-
-        assert len(guardrail_events['started']) == 2
-        assert len(guardrail_events['completed']) == 2
-        assert not guardrail_events['completed'][0].success
-        assert guardrail_events['completed'][1].success
-        assert "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" in result.raw
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_guardrail_is_called_using_callable():
-    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
-    with crewai_event_bus.scoped_handlers():
-        @crewai_event_bus.on(LLMGuardrailStartedEvent)
-        def capture_guardrail_started(source, event):
-            guardrail_events["started"].append(event)
-
-        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
-        def capture_guardrail_completed(source, event):
-            guardrail_events["completed"].append(event)
-
-        agent = Agent(
-            role="Sports Analyst",
-            goal="Gather information about the best soccer players",
-            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-            guardrail=lambda output: (True, "Pelé - Santos, 1958"),
-        )
-
-        result = agent.kickoff(messages="Top 1 best players in the world?")
-
-        assert len(guardrail_events['started']) == 1
-        assert len(guardrail_events['completed']) == 1
-        assert guardrail_events['completed'][0].success
-        assert "Pelé - Santos, 1958" in result.raw
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_guardrail_reached_attempt_limit():
-    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
-    with crewai_event_bus.scoped_handlers():
-        @crewai_event_bus.on(LLMGuardrailStartedEvent)
-        def capture_guardrail_started(source, event):
-            guardrail_events["started"].append(event)
-
-        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
-        def capture_guardrail_completed(source, event):
-            guardrail_events["completed"].append(event)
-
-        agent = Agent(
-            role="Sports Analyst",
-            goal="Gather information about the best soccer players",
-            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-            guardrail=lambda output: (False, "You are not allowed to include Brazilian players"),
-            guardrail_max_retries=2,
-        )
-
-        with pytest.raises(Exception, match="Agent's guardrail failed validation after 2 retries"):
-            agent.kickoff(messages="Top 10 best players in the world?")
-
-        assert len(guardrail_events['started']) == 3 # 2 retries + 1 initial call
-        assert len(guardrail_events['completed']) == 3 # 2 retries + 1 initial call
-        assert not guardrail_events['completed'][0].success
-        assert not guardrail_events['completed'][1].success
-        assert not guardrail_events['completed'][2].success
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_output_when_guardrail_returns_base_model():
-    class Player(BaseModel):
-        name: str
-        country: str
-
-    agent = Agent(
-        role="Sports Analyst",
-        goal="Gather information about the best soccer players",
-        backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-        guardrail=lambda output: (True, Player(name="Lionel Messi", country="Argentina")),
-    )
-
-    result = agent.kickoff(messages="Top 10 best players in the world?")
-
-    assert result.pydantic == Player(name="Lionel Messi", country="Argentina")

tests/tools/test_structured_tool.py

@@ -25,206 +25,122 @@ def schema_class():
     return TestSchema
 
 
-def test_initialization(basic_function, schema_class):
-    """Test basic initialization of CrewStructuredTool"""
-    tool = CrewStructuredTool(
-        name="test_tool",
-        description="Test tool description",
-        func=basic_function,
-        args_schema=schema_class,
-    )
+class InternalCrewStructuredTool:
+    def test_initialization(self, basic_function, schema_class):
+        """Test basic initialization of CrewStructuredTool"""
+        tool = CrewStructuredTool(
+            name="test_tool",
+            description="Test tool description",
+            func=basic_function,
+            args_schema=schema_class,
+        )
 
-    assert tool.name == "test_tool"
-    assert tool.description == "Test tool description"
-    assert tool.func == basic_function
-    assert tool.args_schema == schema_class
+        assert tool.name == "test_tool"
+        assert tool.description == "Test tool description"
+        assert tool.func == basic_function
+        assert tool.args_schema == schema_class
 
-def test_from_function(basic_function):
-    """Test creating tool from function"""
-    tool = CrewStructuredTool.from_function(
-        func=basic_function, name="test_tool", description="Test description"
-    )
+    def test_from_function(self, basic_function):
+        """Test creating tool from function"""
+        tool = CrewStructuredTool.from_function(
+            func=basic_function, name="test_tool", description="Test description"
+        )
 
-    assert tool.name == "test_tool"
-    assert tool.description == "Test description"
-    assert tool.func == basic_function
-    assert isinstance(tool.args_schema, type(BaseModel))
+        assert tool.name == "test_tool"
+        assert tool.description == "Test description"
+        assert tool.func == basic_function
+        assert isinstance(tool.args_schema, type(BaseModel))
 
-def test_validate_function_signature(basic_function, schema_class):
-    """Test function signature validation"""
-    tool = CrewStructuredTool(
-        name="test_tool",
-        description="Test tool",
-        func=basic_function,
-        args_schema=schema_class,
-    )
+    def test_validate_function_signature(self, basic_function, schema_class):
+        """Test function signature validation"""
+        tool = CrewStructuredTool(
+            name="test_tool",
+            description="Test tool",
+            func=basic_function,
+            args_schema=schema_class,
+        )
 
-    # Should not raise any exceptions
-    tool._validate_function_signature()
+        # Should not raise any exceptions
+        tool._validate_function_signature()
 
-@pytest.mark.asyncio
-async def test_ainvoke(basic_function):
-    """Test asynchronous invocation"""
-    tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
+    @pytest.mark.asyncio
+    async def test_ainvoke(self, basic_function):
+        """Test asynchronous invocation"""
+        tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
 
-    result = await tool.ainvoke(input={"param1": "test"})
-    assert result == "test 0"
+        result = await tool.ainvoke(input={"param1": "test"})
+        assert result == "test 0"
 
-def test_parse_args_dict(basic_function):
-    """Test parsing dictionary arguments"""
-    tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
+    def test_parse_args_dict(self, basic_function):
+        """Test parsing dictionary arguments"""
+        tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
 
-    parsed = tool._parse_args({"param1": "test", "param2": 42})
-    assert parsed["param1"] == "test"
-    assert parsed["param2"] == 42
+        parsed = tool._parse_args({"param1": "test", "param2": 42})
+        assert parsed["param1"] == "test"
+        assert parsed["param2"] == 42
 
-def test_parse_args_string(basic_function):
-    """Test parsing string arguments"""
-    tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
+    def test_parse_args_string(self, basic_function):
+        """Test parsing string arguments"""
+        tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool")
 
-    parsed = tool._parse_args('{"param1": "test", "param2": 42}')
-    assert parsed["param1"] == "test"
-    assert parsed["param2"] == 42
+        parsed = tool._parse_args('{"param1": "test", "param2": 42}')
+        assert parsed["param1"] == "test"
+        assert parsed["param2"] == 42
 
-def test_complex_types():
-    """Test handling of complex parameter types"""
+    def test_complex_types(self):
+        """Test handling of complex parameter types"""
 
-    def complex_func(nested: dict, items: list) -> str:
-        """Process complex types."""
-        return f"Processed {len(items)} items with {len(nested)} nested keys"
+        def complex_func(nested: dict, items: list) -> str:
+            """Process complex types."""
+            return f"Processed {len(items)} items with {len(nested)} nested keys"
 
-    tool = CrewStructuredTool.from_function(
-        func=complex_func, name="test_tool", description="Test complex types"
-    )
-    result = tool.invoke({"nested": {"key": "value"}, "items": [1, 2, 3]})
-    assert result == "Processed 3 items with 1 nested keys"
+        tool = CrewStructuredTool.from_function(
+            func=complex_func, name="test_tool", description="Test complex types"
+        )
+        result = tool.invoke({"nested": {"key": "value"}, "items": [1, 2, 3]})
+        assert result == "Processed 3 items with 1 nested keys"
 
-def test_schema_inheritance():
-    """Test tool creation with inherited schema"""
+    def test_schema_inheritance(self):
+        """Test tool creation with inherited schema"""
 
-    def extended_func(base_param: str, extra_param: int) -> str:
-        """Test function with inherited schema."""
-        return f"{base_param} {extra_param}"
+        def extended_func(base_param: str, extra_param: int) -> str:
+            """Test function with inherited schema."""
+            return f"{base_param} {extra_param}"
 
-    class BaseSchema(BaseModel):
-        base_param: str
+        class BaseSchema(BaseModel):
+            base_param: str
 
-    class ExtendedSchema(BaseSchema):
-        extra_param: int
+        class ExtendedSchema(BaseSchema):
+            extra_param: int
 
-    tool = CrewStructuredTool.from_function(
-        func=extended_func, name="test_tool", args_schema=ExtendedSchema
-    )
+        tool = CrewStructuredTool.from_function(
+            func=extended_func, name="test_tool", args_schema=ExtendedSchema
+        )
 
-    result = tool.invoke({"base_param": "test", "extra_param": 42})
-    assert result == "test 42"
+        result = tool.invoke({"base_param": "test", "extra_param": 42})
+        assert result == "test 42"
 
-def test_default_values_in_schema():
-    """Test handling of default values in schema"""
+    def test_default_values_in_schema(self):
+        """Test handling of default values in schema"""
 
-    def default_func(
-        required_param: str,
-        optional_param: str = "default",
-        nullable_param: Optional[int] = None,
-    ) -> str:
-        """Test function with default values."""
-        return f"{required_param} {optional_param} {nullable_param}"
+        def default_func(
+            required_param: str,
+            optional_param: str = "default",
+            nullable_param: Optional[int] = None,
+        ) -> str:
+            """Test function with default values."""
+            return f"{required_param} {optional_param} {nullable_param}"
 
-    tool = CrewStructuredTool.from_function(
-        func=default_func, name="test_tool", description="Test defaults"
-    )
+        tool = CrewStructuredTool.from_function(
+            func=default_func, name="test_tool", description="Test defaults"
+        )
 
-    # Test with minimal parameters
-    result = tool.invoke({"required_param": "test"})
-    assert result == "test default None"
+        # Test with minimal parameters
+        result = tool.invoke({"required_param": "test"})
+        assert result == "test default None"
 
-    # Test with all parameters
-    result = tool.invoke(
-        {"required_param": "test", "optional_param": "custom", "nullable_param": 42}
-    )
-    assert result == "test custom 42"
-
-@pytest.fixture
-def custom_tool_decorator():
-    from crewai.tools import tool
-
-    @tool("custom_tool", result_as_answer=True)
-    async def custom_tool():
-        """This is a tool that does something"""
-        return "Hello World from Custom Tool"
-
-    return custom_tool
-
-@pytest.fixture
-def custom_tool():
-    from crewai.tools import BaseTool
-
-    class CustomTool(BaseTool):
-        name: str = "my_tool"
-        description: str = "This is a tool that does something"
-        result_as_answer: bool = True
-
-        async def _run(self):
-            return "Hello World from Custom Tool"
-
-    return CustomTool()
-
-def build_simple_crew(tool):
-    from crewai import Agent, Task, Crew
-
-    agent1 = Agent(role="Simple role", goal="Simple goal", backstory="Simple backstory", tools=[tool])
-
-    say_hi_task = Task(
-        description="Use the custom tool result as answer.", agent=agent1, expected_output="Use the tool result"
-    )
-
-    crew = Crew(agents=[agent1], tasks=[say_hi_task])
-    return crew
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_async_tool_using_within_isolated_crew(custom_tool):
-    crew = build_simple_crew(custom_tool)
-    result = crew.kickoff()
-
-    assert result.raw == "Hello World from Custom Tool"
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_async_tool_using_decorator_within_isolated_crew(custom_tool_decorator):
-    crew = build_simple_crew(custom_tool_decorator)
-    result = crew.kickoff()
-
-    assert result.raw == "Hello World from Custom Tool"
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_async_tool_within_flow(custom_tool):
-    from crewai.flow.flow import Flow
-
-    class StructuredExampleFlow(Flow):
-        from crewai.flow.flow import start
-
-        @start()
-        async def start(self):
-            crew = build_simple_crew(custom_tool)
-            result = await crew.kickoff_async()
-            return result
-
-    flow = StructuredExampleFlow()
-    result = flow.kickoff()
-    assert result.raw == "Hello World from Custom Tool"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_async_tool_using_decorator_within_flow(custom_tool_decorator):
-    from crewai.flow.flow import Flow
-
-    class StructuredExampleFlow(Flow):
-        from crewai.flow.flow import start
-        @start()
-        async def start(self):
-            crew = build_simple_crew(custom_tool_decorator)
-            result = await crew.kickoff_async()
-            return result
-
-    flow = StructuredExampleFlow()
-    result = flow.kickoff()
-    assert result.raw == "Hello World from Custom Tool"
+        # Test with all parameters
+        result = tool.invoke(
+            {"required_param": "test", "optional_param": "custom", "nullable_param": 42}
+        )
+        assert result == "test custom 42"

uv.lock

@@ -820,7 +820,7 @@ requires-dist = [
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.72.0" },
+    { name = "litellm", specifier = "==1.68.0" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
     { name = "onnxruntime", specifier = "==1.22.0" },
     { name = "openai", specifier = ">=1.13.3" },
@@ -2245,7 +2245,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.72.0"
+version = "1.68.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2260,9 +2260,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/55/d3/f1a8c9c9ffdd3bab1bc410254c8140b1346f05a01b8c6b37c48b56abb4b0/litellm-1.72.0.tar.gz", hash = "sha256:135022b9b8798f712ffa84e71ac419aa4310f1d0a70f79dd2007f7ef3a381e43", size = 8082337 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/22/138545b646303ca3f4841b69613c697b9d696322a1386083bb70bcbba60b/litellm-1.68.0.tar.gz", hash = "sha256:9fb24643db84dfda339b64bafca505a2eef857477afbc6e98fb56512c24dbbfa", size = 7314051 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c2/98/bec08f5a3e504013db6f52b5fd68375bd92b463c91eb454d5a6460e957af/litellm-1.72.0-py3-none-any.whl", hash = "sha256:88360a7ae9aa9c96278ae1bb0a459226f909e711c5d350781296d0640386a824", size = 7979630 },
+    { url = "https://files.pythonhosted.org/packages/10/af/1e344bc8aee41445272e677d802b774b1f8b34bdc3bb5697ba30f0fb5d52/litellm-1.68.0-py3-none-any.whl", hash = "sha256:3bca38848b1a5236b11aa6b70afa4393b60880198c939e582273f51a542d4759", size = 7684460 },
 ]
 
 [[package]]
@@ -3123,7 +3123,7 @@ wheels = [
 
 [[package]]
 name = "openai"
-version = "1.78.0"
+version = "1.68.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -3135,9 +3135,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d1/7c/7c48bac9be52680e41e99ae7649d5da3a0184cd94081e028897f9005aa03/openai-1.78.0.tar.gz", hash = "sha256:254aef4980688468e96cbddb1f348ed01d274d02c64c6c69b0334bf001fb62b3", size = 442652 }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/6b/6b002d5d38794645437ae3ddb42083059d556558493408d39a0fcea608bc/openai-1.68.2.tar.gz", hash = "sha256:b720f0a95a1dbe1429c0d9bb62096a0d98057bcda82516f6e8af10284bdd5b19", size = 413429 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/cc/41/d64a6c56d0ec886b834caff7a07fc4d43e1987895594b144757e7a6b90d7/openai-1.78.0-py3-none-any.whl", hash = "sha256:1ade6a48cd323ad8a7715e7e1669bb97a17e1a5b8a916644261aaef4bf284778", size = 680407 },
+    { url = "https://files.pythonhosted.org/packages/fd/34/cebce15f64eb4a3d609a83ac3568d43005cc9a1cba9d7fde5590fd415423/openai-1.68.2-py3-none-any.whl", hash = "sha256:24484cb5c9a33b58576fdc5acf0e5f92603024a4e39d0b99793dfa1eb14c2b36", size = 606073 },
 ]
 
 [[package]]