Merge branch 'main' into brandon/gemini-google-docs

.gitignore

@@ -21,5 +21,4 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
-test_flow.html
+agentops.log

docs/concepts/cli.mdx

@@ -136,21 +136,17 @@ crewai test -n 5 -m gpt-3.5-turbo
 
 ### 8. Run
 
-Run the crew or flow.
+Run the crew.
 
 ```shell Terminal
 crewai run
 ```
-
-<Note>
-Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
-</Note>
-
 <Note>
 Make sure to run these commands from the directory where your CrewAI project is set up. 
 Some commands may require additional configuration or setup within your project structure.
 </Note>
 
+
 ### 9. Chat
 
 Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
@@ -179,6 +175,7 @@ def crew(self) -> Crew:
 ```
 </Note>
 
+
 ### 10. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.

docs/concepts/event-listner.mdx

@@ -1,349 +0,0 @@
----
-title: 'Event Listeners'
-description: 'Tap into CrewAI events to build custom integrations and monitoring'
----
-
-# Event Listeners
-
-CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
-
-## How It Works
-
-CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
-
-1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **CrewEvent**: Base class for all events in the system
-3. **BaseEventListener**: Abstract base class for creating custom event listeners
-
-When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
-
-## Creating a Custom Event Listener
-
-To create a custom event listener, you need to:
-
-1. Create a class that inherits from `BaseEventListener`
-2. Implement the `setup_listeners` method
-3. Register handlers for the events you're interested in
-4. Create an instance of your listener in the appropriate file
-
-Here's a simple example of a custom event listener class:
-
-```python
-from crewai.utilities.events import (
-    CrewKickoffStartedEvent,
-    CrewKickoffCompletedEvent,
-    AgentExecutionCompletedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-
-class MyCustomListener(BaseEventListener):
-    def __init__(self):
-        super().__init__()
-    
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
-            print(f"Crew '{event.crew_name}' has started execution!")
-        
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
-            print(f"Crew '{event.crew_name}' has completed execution!")
-            print(f"Output: {event.output}")
-        
-        @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed task")
-            print(f"Output: {event.output}")
-```
-
-## Properly Registering Your Listener
-
-Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
-
-1. The event handlers are registered with the event bus
-2. The listener instance remains in memory (not garbage collected)
-3. The listener is active when events are emitted
-
-### Option 1: Import and Instantiate in Your Crew or Flow Implementation
-
-The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
-
-#### For Crew-based Applications
-
-Create and import your listener at the top of your Crew implementation file:
-
-```python
-# In your crew.py file
-from crewai import Agent, Crew, Task
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomCrew:
-    # Your crew implementation...
-    
-    def crew(self):
-        return Crew(
-            agents=[...],
-            tasks=[...],
-            # ...
-        )
-```
-
-#### For Flow-based Applications
-
-Create and import your listener at the top of your Flow implementation file:
-
-```python
-# In your main.py or flow.py file
-from crewai.flow import Flow, listen, start
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomFlow(Flow):
-    # Your flow implementation...
-    
-    @start()
-    def first_step(self):
-        # ...
-```
-
-This ensures that your listener is loaded and active when your Crew or Flow is executed.
-
-### Option 2: Create a Package for Your Listeners
-
-For a more structured approach, especially if you have multiple listeners:
-
-1. Create a package for your listeners:
-
-```
-my_project/
-  ├── listeners/
-  │   ├── __init__.py
-  │   ├── my_custom_listener.py
-  │   └── another_listener.py
-```
-
-2. In `my_custom_listener.py`, define your listener class and create an instance:
-
-```python
-# my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
-# ... import events ...
-
-class MyCustomListener(BaseEventListener):
-    # ... implementation ...
-
-# Create an instance of your listener
-my_custom_listener = MyCustomListener()
-```
-
-3. In `__init__.py`, import the listener instances to ensure they're loaded:
-
-```python
-# __init__.py
-from .my_custom_listener import my_custom_listener
-from .another_listener import another_listener
-
-# Optionally export them if you need to access them elsewhere
-__all__ = ['my_custom_listener', 'another_listener']
-```
-
-4. Import your listeners package in your Crew or Flow file:
-
-```python
-# In your crew.py or flow.py file
-import my_project.listeners  # This loads all your listeners
-
-class MyCustomCrew:
-    # Your crew implementation...
-```
-
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Available Event Types
-
-CrewAI provides a wide range of events that you can listen for:
-
-### Crew Events
-
-- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
-- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
-- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
-- **CrewTestStartedEvent**: Emitted when a Crew starts testing
-- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
-- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
-- **CrewTrainStartedEvent**: Emitted when a Crew starts training
-- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
-- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
-
-### Agent Events
-
-- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
-- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
-- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
-
-### Task Events
-
-- **TaskStartedEvent**: Emitted when a Task starts execution
-- **TaskCompletedEvent**: Emitted when a Task completes execution
-- **TaskFailedEvent**: Emitted when a Task fails to complete execution
-- **TaskEvaluationEvent**: Emitted when a Task is evaluated
-
-### Tool Usage Events
-
-- **ToolUsageStartedEvent**: Emitted when a tool execution is started
-- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
-- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
-- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
-
-### Flow Events
-
-- **FlowCreatedEvent**: Emitted when a Flow is created
-- **FlowStartedEvent**: Emitted when a Flow starts execution
-- **FlowFinishedEvent**: Emitted when a Flow completes execution
-- **FlowPlotEvent**: Emitted when a Flow is plotted
-- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
-- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
-- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
-
-### LLM Events
-
-- **LLMCallStartedEvent**: Emitted when an LLM call starts
-- **LLMCallCompletedEvent**: Emitted when an LLM call completes
-- **LLMCallFailedEvent**: Emitted when an LLM call fails
-
-## Event Handler Structure
-
-Each event handler receives two parameters:
-
-1. **source**: The object that emitted the event
-2. **event**: The event instance, containing event-specific data
-
-The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
-
-- **timestamp**: The time when the event was emitted
-- **type**: A string identifier for the event type
-
-Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
-
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Advanced Usage: Scoped Handlers
-
-For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
-
-```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
-
-with crewai_event_bus.scoped_handlers():
-    @crewai_event_bus.on(CrewKickoffStartedEvent)
-    def temp_handler(source, event):
-        print("This handler only exists within this context")
-    
-    # Do something that emits events
-    
-# Outside the context, the temporary handler is removed
-```
-
-## Use Cases
-
-Event listeners can be used for a variety of purposes:
-
-1. **Logging and Monitoring**: Track the execution of your Crew and log important events
-2. **Analytics**: Collect data about your Crew's performance and behavior
-3. **Debugging**: Set up temporary listeners to debug specific issues
-4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
-5. **Custom Behavior**: Trigger custom actions based on specific events
-
-## Best Practices
-
-1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
-2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
-3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
-4. **Selective Listening**: Only listen for events you actually need to handle
-5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
-
-By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

docs/concepts/flows.mdx

@@ -150,12 +150,12 @@ final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-```
+````
 
 ```text Output
 ---- Final Output ----
 Second method received: Output from first_method
-```
+````
 
 </CodeGroup>
 
@@ -738,34 +738,3 @@ Also, check out our YouTube video on how to use flows in CrewAI below!
   referrerpolicy="strict-origin-when-cross-origin"
   allowfullscreen
 ></iframe>
-
-## Running Flows
-
-There are two ways to run a flow:
-
-### Using the Flow API
-
-You can run a flow programmatically by creating an instance of your flow class and calling the `kickoff()` method:
-
-```python
-flow = ExampleFlow()
-result = flow.kickoff()
-```
-
-### Using the CLI
-
-Starting from version 0.103.0, you can run flows using the `crewai run` command:
-
-```shell
-crewai run
-```
-
-This command automatically detects if your project is a flow (based on the `type = "flow"` setting in your pyproject.toml) and runs it accordingly. This is the recommended way to run flows from the command line.
-
-For backward compatibility, you can also use:
-
-```shell
-crewai flow kickoff
-```
-
-However, the `crewai run` command is now the preferred method as it works for both crews and flows.

docs/concepts/memory.mdx

@@ -447,38 +447,6 @@ my_crew = Crew(
 )
 ```
 
-### Using Amazon Bedrock embeddings
-
-```python Code
-# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
-
-import os
-import boto3
-from crewai import Crew, Agent, Task, Process
-
-boto3_session = boto3.Session(
-    region_name=os.environ.get("AWS_REGION_NAME"),
-    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
-    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
-)
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    embedder={
-    "provider": "bedrock",
-        "config":{
-            "session": boto3_session,
-            "model": "amazon.titan-embed-text-v2:0",
-            "vector_dimension": 1024
-        }
-    }
-    verbose=True
-)
-```
-
 ### Adding Custom Embedding Function
 
 ```python Code
@@ -506,7 +474,7 @@ my_crew = Crew(
 )
 ```
 
-### Resetting Memory via cli
+### Resetting Memory
 
 ```shell
 crewai reset-memories [OPTIONS]
@@ -520,46 +488,8 @@ crewai reset-memories [OPTIONS]
 | `-s`, `--short`    | Reset SHORT TERM memory.         | Flag (boolean) | False   |
 | `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
 | `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
-| `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False   |
 | `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |
 
-Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
-
-
-
-
-### Resetting Memory via crew object
-
-```python
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-
-my_crew.reset_memories(command_type = 'all') # Resets all the memory
-```
-
-#### Resetting Memory Options
-
-| Command Type       | Description                      |
-| :----------------- | :------------------------------- |
-| `long`             | Reset LONG TERM memory.          | 
-| `short`            | Reset SHORT TERM memory.         | 
-| `entities`         | Reset ENTITIES memory.           | 
-| `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
-| `knowledge`        | Reset KNOWLEDGE memory.          |
-| `all`              | Reset ALL memories.              |
-
 
 ## Benefits of Using CrewAI's Memory System
 

docs/how-to/hierarchical-process.mdx

@@ -48,6 +48,7 @@ Define a crew with a designated manager and establish a clear chain of command.
 </Tip>
 
 ```python Code
+from langchain_openai import ChatOpenAI
 from crewai import Crew, Process, Agent
 
 # Agents are defined with attributes for backstory, cache, and verbose mode
@@ -55,51 +56,38 @@ researcher = Agent(
     role='Researcher',
     goal='Conduct in-depth analysis',
     backstory='Experienced data analyst with a knack for uncovering hidden trends.',
+    cache=True,
+    verbose=False,
+    # tools=[]  # This can be optionally specified; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 writer = Agent(
     role='Writer',
     goal='Create engaging content',
     backstory='Creative writer passionate about storytelling in technical domains.',
+    cache=True,
+    verbose=False,
+    # tools=[]  # Optionally specify tools; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 
 # Establishing the crew with a hierarchical process and additional configurations
 project_crew = Crew(
     tasks=[...],  # Tasks to be delegated and executed under the manager's supervision
     agents=[researcher, writer],
-    manager_llm="gpt-4o",  # Specify which LLM the manager should use
-    process=Process.hierarchical,  
-    planning=True, 
+    manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory if manager_agent is not set
+    process=Process.hierarchical,  # Specifies the hierarchical management approach
+    respect_context_window=True,  # Enable respect of the context window for tasks
+    memory=True,  # Enable memory usage for enhanced task execution
+    manager_agent=None,  # Optional: explicitly set a specific agent as manager instead of the manager_llm
+    planning=True,  # Enable planning feature for pre-execution strategy
 )
 ```
 
-### Using a Custom Manager Agent
-
-Alternatively, you can create a custom manager agent with specific attributes tailored to your project's management needs. This gives you more control over the manager's behavior and capabilities.
-
-```python
-# Define a custom manager agent
-manager = Agent(
-    role="Project Manager",
-    goal="Efficiently manage the crew and ensure high-quality task completion",
-    backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success.",
-    allow_delegation=True,
-)
-
-# Use the custom manager in your crew
-project_crew = Crew(
-    tasks=[...],
-    agents=[researcher, writer],
-    manager_agent=manager,  # Use your custom manager agent
-    process=Process.hierarchical,
-    planning=True,
-)
-```
-
-<Tip>
-    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent).
-</Tip>
-
-
 ### Workflow in Action
 
 1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
@@ -109,4 +97,4 @@ project_crew = Crew(
 ## Conclusion
 
 Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management. 
-Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.
+Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.

docs/how-to/kickoff-async.mdx

@@ -54,8 +54,7 @@ coding_agent = Agent(
 # Create a task that requires code execution
 data_analysis_task = Task(
     description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent,
-    expected_output="The average age of the participants."
+    agent=coding_agent
 )
 
 # Create a crew and add the task
@@ -117,4 +116,4 @@ async def async_multiple_crews():
 
 # Run the async function
 asyncio.run(async_multiple_crews())
-```
+```

docs/how-to/langfuse-observability.mdx

@@ -1,100 +0,0 @@
----
-title: Agent Monitoring with Langfuse
-description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: magnifying-glass-chart
----
-
-# Integrate Langfuse with CrewAI
-
-This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
-
-[![Langfuse Overview Video](https://github.com/user-attachments/assets/3926b288-ff61-4b95-8aa1-45d041c70866)](https://langfuse.com/watch-demo)
-
-## Get Started
-
-We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
-
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openlit crewai crewai_tools
-```
-
-### Step 2: Set Up Environment Variables
-
-Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
-
-
-```python
-import os
-import base64
-
-LANGFUSE_PUBLIC_KEY="pk-lf-..."
-LANGFUSE_SECRET_KEY="sk-lf-..."
-LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
-
-os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
-# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
-os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
-
-# your openai key
-os.environ["OPENAI_API_KEY"] = "sk-..."
-```
-
-### Step 3: Initialize OpenLit
-
-Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
-
-
-```python
-import openlit
-
-openlit.init()
-```
-
-### Step 4: Create a Simple CrewAI Application
-
-We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
-
-
-```python
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-        role="Writer",
-        goal="You make math engaging and understandable for young children through poetry",
-        backstory="You're an expert in writing haikus but you know nothing of math.",
-        tools=[web_rag_tool],  
-    )
-
-task = Task(description=("What is {multiplication}?"),
-            expected_output=("Compose a haiku that includes the answer."),
-            agent=writer)
-
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
-  share_crew=False
-)
-```
-
-### Step 5: See Traces in Langfuse
-
-After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
-
-![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
-
-_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
-
-## References
-
-- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)

docs/how-to/portkey-observability-and-guardrails.mdx

@@ -0,0 +1,211 @@
+# Portkey Integration with CrewAI
+<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
+
+
+[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
+
+Portkey adds 4 core production capabilities to any CrewAI agent:
+1. Routing to **200+ LLMs**
+2. Making each LLM call more robust
+3. Full-stack tracing & cost, performance analytics
+4. Real-time guardrails to enforce behavior
+
+
+
+
+
+## Getting Started
+
+1. **Install Required Packages:**
+
+```bash
+pip install -qU crewai portkey-ai
+```
+
+2. **Configure the LLM Client:**
+
+To build CrewAI Agents with Portkey, you'll need two keys:
+- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
+- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
+
+```python
+from crewai import LLM
+from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+
+gpt_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy", # We are using Virtual key
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
+    )
+)
+```
+
+3. **Create and Run Your First Agent:**
+
+```python
+from crewai import Agent, Task, Crew
+
+# Define your agents with roles and goals
+coder = Agent(
+    role='Software developer',
+    goal='Write clear, concise code on demand',
+    backstory='An expert coder with a keen eye for software trends.',
+    llm=gpt_llm
+)
+
+# Create tasks for your agents
+task1 = Task(
+    description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
+    expected_output="A clear and concise HTML code",
+    agent=coder
+)
+
+# Instantiate your crew
+crew = Crew(
+    agents=[coder],
+    tasks=[task1],
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+
+## Key Features
+
+| Feature | Description |
+|---------|-------------|
+| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
+| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
+| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
+| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
+| 🚧 Security Controls | Set budget limits and implement role-based access control |
+| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
+| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
+
+
+## Production Features with Portkey Configs
+
+All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
+
+<Frame>
+    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
+</Frame>
+
+
+### 1. Use 250+ LLMs
+Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
+
+
+Easily switch between different LLM providers:
+
+```python
+# Anthropic Configuration
+anthropic_llm = LLM(
+    model="claude-3-5-sonnet-latest",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="anthropic_agent"
+    )
+)
+
+# Azure OpenAI Configuration
+azure_llm = LLM(
+    model="gpt-4",
+    base_url=PORTKEY_GATEWAY_URL,
+    api_key="dummy",
+    extra_headers=createHeaders(
+        api_key="YOUR_PORTKEY_API_KEY",
+        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
+        trace_id="azure_agent"
+    )
+)
+```
+
+
+### 2. Caching
+Improve response times and reduce costs with two powerful caching modes:
+- **Simple Cache**: Perfect for exact matches
+- **Semantic Cache**: Matches responses for requests that are semantically similar
+[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
+
+```py
+config = {
+    "cache": {
+        "mode": "semantic",  # or "simple" for exact matching
+    }
+}
+```
+
+### 3. Production Reliability
+Portkey provides comprehensive reliability features:
+- **Automatic Retries**: Handle temporary failures gracefully
+- **Request Timeouts**: Prevent hanging operations
+- **Conditional Routing**: Route requests based on specific conditions
+- **Fallbacks**: Set up automatic provider failovers
+- **Load Balancing**: Distribute requests efficiently
+
+[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
+
+
+
+### 4. Metrics
+
+Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
+
+
+- Cost per agent interaction
+- Response times and latency
+- Token usage and efficiency
+- Success/failure rates
+- Cache hit rates
+
+<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
+
+### 5. Detailed Logging
+Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
+
+
+Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
+
+<details>
+  <summary><b>Traces</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
+</details>
+
+<details>
+  <summary><b>Logs</b></summary>
+  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
+</details>
+
+### 6. Enterprise Security Features
+- Set budget limit and rate limts per Virtual Key (disposable API keys)
+- Implement role-based access control
+- Track system changes with audit logs
+- Configure data retention policies
+
+
+
+For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
+
+## Resources
+
+- [📘 Portkey Documentation](https://docs.portkey.ai)
+- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
+- [🐦 Twitter](https://twitter.com/portkeyai)
+- [💬 Discord Community](https://discord.gg/DD7vgKK299)
+
+
+
+
+
+
+
+
+

docs/how-to/portkey-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with Portkey
+title: Portkey Observability and Guardrails
 description: How to use Portkey with CrewAI
 icon: key
 ---

docs/mint.json

@@ -103,8 +103,7 @@
         "how-to/langtrace-observability",
         "how-to/mlflow-observability",
         "how-to/openlit-observability",
-        "how-to/portkey-observability",
-        "how-to/langfuse-observability"
+        "how-to/portkey-observability"
       ]
     },
     {
@@ -139,7 +138,6 @@
         "tools/nl2sqltool",
         "tools/pdfsearchtool",
         "tools/pgsearchtool",
-        "tools/qdrantvectorsearchtool",
         "tools/scrapewebsitetool",
         "tools/seleniumscrapingtool",
         "tools/spidertool",

docs/tools/qdrantvectorsearchtool.mdx

@@ -1,271 +0,0 @@
----
-title: 'Qdrant Vector Search Tool'
-description: 'Semantic search capabilities for CrewAI agents using Qdrant vector database'
-icon: magnifying-glass-plus
----
-
-# `QdrantVectorSearchTool`
-
-The Qdrant Vector Search Tool enables semantic search capabilities in your CrewAI agents by leveraging [Qdrant](https://qdrant.tech/), a vector similarity search engine. This tool allows your agents to search through documents stored in a Qdrant collection using semantic similarity.
-
-## Installation
-
-Install the required packages:
-
-```bash
-uv pip install 'crewai[tools] qdrant-client'
-```
-
-## Basic Usage
-
-Here's a minimal example of how to use the tool:
-
-```python
-from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
-
-# Initialize the tool
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url="your_qdrant_url",
-    qdrant_api_key="your_qdrant_api_key",
-    collection_name="your_collection"
-)
-
-# Create an agent that uses the tool
-agent = Agent(
-    role="Research Assistant",
-    goal="Find relevant information in documents",
-    tools=[qdrant_tool]
-)
-
-# The tool will automatically use OpenAI embeddings
-# and return the 3 most relevant results with scores > 0.35
-```
-
-## Complete Working Example
-
-Here's a complete example showing how to:
-1. Extract text from a PDF
-2. Generate embeddings using OpenAI
-3. Store in Qdrant
-4. Create a CrewAI agentic RAG workflow for semantic search
-
-```python
-import os
-import uuid
-import pdfplumber
-from openai import OpenAI
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai_tools import QdrantVectorSearchTool
-from qdrant_client import QdrantClient
-from qdrant_client.models import PointStruct, Distance, VectorParams
-
-# Load environment variables
-load_dotenv()
-
-# Initialize OpenAI client
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# Extract text from PDF
-def extract_text_from_pdf(pdf_path):
-    text = []
-    with pdfplumber.open(pdf_path) as pdf:
-        for page in pdf.pages:
-            page_text = page.extract_text()
-            if page_text:
-                text.append(page_text.strip())
-    return text
-
-# Generate OpenAI embeddings
-def get_openai_embedding(text):
-    response = client.embeddings.create(
-        input=text,
-        model="text-embedding-3-small"
-    )
-    return response.data[0].embedding
-
-# Store text and embeddings in Qdrant
-def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
-    # Extract text from PDF
-    text_chunks = extract_text_from_pdf(pdf_path)
-    
-    # Create Qdrant collection
-    if qdrant.collection_exists(collection_name):
-        qdrant.delete_collection(collection_name)
-    qdrant.create_collection(
-        collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
-    )
-
-    # Store embeddings
-    points = []
-    for chunk in text_chunks:
-        embedding = get_openai_embedding(chunk)
-        points.append(PointStruct(
-            id=str(uuid.uuid4()),
-            vector=embedding,
-            payload={"text": chunk}
-        ))
-    qdrant.upsert(collection_name=collection_name, points=points)
-
-# Initialize Qdrant client and load data
-qdrant = QdrantClient(
-    url=os.getenv("QDRANT_URL"),
-    api_key=os.getenv("QDRANT_API_KEY")
-)
-collection_name = "example_collection"
-pdf_path = "path/to/your/document.pdf"
-load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
-
-# Initialize Qdrant search tool
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url=os.getenv("QDRANT_URL"),
-    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-    collection_name=collection_name,
-    limit=3,
-    score_threshold=0.35
-)
-
-# Create CrewAI agents
-search_agent = Agent(
-    role="Senior Semantic Search Agent",
-    goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant 
-    information using semantic search in a Qdrant database.""",
-    tools=[qdrant_tool],
-    verbose=True
-)
-
-answer_agent = Agent(
-    role="Senior Answer Assistant",
-    goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate 
-    answers to questions based on the context provided.""",
-    tools=[qdrant_tool],
-    verbose=True
-)
-
-# Define tasks
-search_task = Task(
-    description="""Search for relevant documents about the {query}.
-    Your final answer should include:
-    - The relevant information found
-    - The similarity scores of the results
-    - The metadata of the relevant documents""",
-    agent=search_agent
-)
-
-answer_task = Task(
-    description="""Given the context and metadata of relevant documents,
-    generate a final answer based on the context.""",
-    agent=answer_agent
-)
-
-# Run CrewAI workflow
-crew = Crew(
-    agents=[search_agent, answer_agent],
-    tasks=[search_task, answer_task],
-    process=Process.sequential,
-    verbose=True
-)
-
-result = crew.kickoff(
-    inputs={"query": "What is the role of X in the document?"}
-)
-print(result)
-```
-
-## Tool Parameters
-
-### Required Parameters
-- `qdrant_url` (str): The URL of your Qdrant server
-- `qdrant_api_key` (str): API key for authentication with Qdrant
-- `collection_name` (str): Name of the Qdrant collection to search
-
-### Optional Parameters
-- `limit` (int): Maximum number of results to return (default: 3)
-- `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
-- `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
-
-## Search Parameters
-
-The tool accepts these parameters in its schema:
-- `query` (str): The search query to find similar documents
-- `filter_by` (str, optional): Metadata field to filter on
-- `filter_value` (str, optional): Value to filter by
-
-## Return Format
-
-The tool returns results in JSON format:
-
-```json
-[
-  {
-    "metadata": {
-      // Any metadata stored with the document
-    },
-    "context": "The actual text content of the document",
-    "distance": 0.95  // Similarity score
-  }
-]
-```
-
-## Default Embedding
-
-By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
-- OpenAI API key set in environment: `OPENAI_API_KEY`
-
-## Custom Embeddings
-
-Instead of using the default embedding model, you might want to use your own embedding function in cases where you:
-
-1. Want to use a different embedding model (e.g., Cohere, HuggingFace, Ollama models)
-2. Need to reduce costs by using open-source embedding models
-3. Have specific requirements for vector dimensions or embedding quality
-4. Want to use domain-specific embeddings (e.g., for medical or legal text)
-
-Here's an example using a HuggingFace model:
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-
-def custom_embeddings(text: str) -> list[float]:
-    # Tokenize and get model outputs
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-    outputs = model(**inputs)
-    
-    # Use mean pooling to get text embedding
-    embeddings = outputs.last_hidden_state.mean(dim=1)
-    
-    # Convert to list of floats and return
-    return embeddings[0].tolist()
-
-# Use custom embeddings with the tool
-tool = QdrantVectorSearchTool(
-    qdrant_url="your_url",
-    qdrant_api_key="your_key",
-    collection_name="your_collection",
-    custom_embedding_fn=custom_embeddings  # Pass your custom function
-)
-```
-
-## Error Handling
-
-The tool handles these specific errors:
-- Raises ImportError if `qdrant-client` is not installed (with option to auto-install)
-- Raises ValueError if `QDRANT_URL` is not set
-- Prompts to install `qdrant-client` if missing using `uv add qdrant-client`
-
-## Environment Variables
-
-Required environment variables:
-```bash
-export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
-export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
-export OPENAI_API_KEY="your_openai_key"  # If using default embeddings

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.102.0"
+version = "0.100.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.36.0"]
+tools = ["crewai-tools>=0.32.1"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.102.0"
+__version__ = "0.100.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -19,17 +19,25 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.utilities.events.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+agentops = None
 
+try:
+    import agentops  # type: ignore # Name "agentops" is already defined
+    from agentops import track_agent  # type: ignore
+except ImportError:
+
+    def track_agent():
+        def noop(f):
+            return f
+
+        return noop
+
+
+@track_agent()
 class Agent(BaseAgent):
     """Represents an agent in a system.
 
@@ -114,6 +122,7 @@ class Agent(BaseAgent):
 
     @model_validator(mode="after")
     def post_init_setup(self):
+        self._set_knowledge()
         self.agent_ops_agent_name = self.role
 
         self.llm = create_llm(self.llm)
@@ -133,11 +142,8 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def _set_knowledge(self):
         try:
-            if self.embedder is None and crew_embedder:
-                self.embedder = crew_embedder
-
             if self.knowledge_sources:
                 full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
                 knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
@@ -234,15 +240,6 @@ class Agent(BaseAgent):
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
         try:
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionStartedEvent(
-                    agent=self,
-                    tools=self.tools,
-                    task_prompt=task_prompt,
-                    task=task,
-                ),
-            )
             result = self.agent_executor.invoke(
                 {
                     "input": task_prompt,
@@ -254,25 +251,9 @@ class Agent(BaseAgent):
         except Exception as e:
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
-                crewai_event_bus.emit(
-                    self,
-                    event=AgentExecutionErrorEvent(
-                        agent=self,
-                        task=task,
-                        error=str(e),
-                    ),
-                )
                 raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
-                crewai_event_bus.emit(
-                    self,
-                    event=AgentExecutionErrorEvent(
-                        agent=self,
-                        task=task,
-                        error=str(e),
-                    ),
-                )
                 raise e
             result = self.execute_task(task, context, tools)
 
@@ -285,10 +266,7 @@ class Agent(BaseAgent):
         for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
             if tool_result.get("result_as_answer", False):
                 result = tool_result["result"]
-        crewai_event_bus.emit(
-            self,
-            event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
-        )
+
         return result
 
     def create_agent_executor(

src/crewai/agents/agent_builder/base_agent.py

@@ -20,7 +20,8 @@ from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.tools.base_tool import BaseTool, Tool
+from crewai.tools import BaseTool
+from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
@@ -111,7 +112,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[BaseTool]] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(
@@ -351,6 +352,3 @@ class BaseAgent(ABC, BaseModel):
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
-
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
-        pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -114,15 +114,10 @@ class CrewAgentExecutorMixin:
             prompt = (
                 "\n\n=====\n"
                 "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
-                "Please follow these guidelines:\n"
-                " - If you are happy with the result, simply hit Enter without typing anything.\n"
-                " - Otherwise, provide specific improvement requests.\n"
-                " - You can provide multiple rounds of feedback until satisfied.\n"
+                "Respond with 'looks good' to accept or provide specific improvement requests.\n"
+                "You can provide multiple rounds of feedback until satisfied.\n"
                 "=====\n"
             )
 
         self._printer.print(content=prompt, color="bold_yellow")
-        response = input()
-        if response.strip() != "":
-            self._printer.print(content="\nProcessing your feedback...", color="cyan")
-        return response
+        return input()

src/crewai/agents/agent_builder/utilities/base_output_converter.py

@@ -31,11 +31,11 @@ class OutputConverter(BaseModel, ABC):
     )
 
     @abstractmethod
-    def to_pydantic(self, current_attempt=1) -> BaseModel:
+    def to_pydantic(self, current_attempt=1):
         """Convert text to pydantic."""
         pass
 
     @abstractmethod
-    def to_json(self, current_attempt=1) -> dict:
+    def to_json(self, current_attempt=1):
         """Convert text to json."""
         pass

src/crewai/agents/crew_agent_executor.py

@@ -18,12 +18,6 @@ from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
 from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
-from crewai.utilities.events import (
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-    crewai_event_bus,
-)
-from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -113,11 +107,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
             raise
         except Exception as e:
-            self._handle_unknown_error(e)
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
                 raise e
             else:
+                self._handle_unknown_error(e)
                 raise e
 
         if self.ask_for_human_input:
@@ -355,68 +349,40 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 )
 
     def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
-        try:
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageStartedEvent(
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                    ),
-                )
-            tool_usage = ToolUsage(
-                tools_handler=self.tools_handler,
-                tools=self.tools,
-                original_tools=self.original_tools,
-                tools_description=self.tools_description,
-                tools_names=self.tools_names,
-                function_calling_llm=self.function_calling_llm,
-                task=self.task,  # type: ignore[arg-type]
-                agent=self.agent,
-                action=agent_action,
-            )
-            tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+        tool_usage = ToolUsage(
+            tools_handler=self.tools_handler,
+            tools=self.tools,
+            original_tools=self.original_tools,
+            tools_description=self.tools_description,
+            tools_names=self.tools_names,
+            function_calling_llm=self.function_calling_llm,
+            task=self.task,  # type: ignore[arg-type]
+            agent=self.agent,
+            action=agent_action,
+        )
+        tool_calling = tool_usage.parse_tool_calling(agent_action.text)
 
-            if isinstance(tool_calling, ToolUsageErrorException):
-                tool_result = tool_calling.message
-                return ToolResult(result=tool_result, result_as_answer=False)
-            else:
-                if tool_calling.tool_name.casefold().strip() in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ]:
-                    tool_result = tool_usage.use(tool_calling, agent_action.text)
-                    tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
-                    if tool:
-                        return ToolResult(
-                            result=tool_result, result_as_answer=tool.result_as_answer
-                        )
-                else:
-                    tool_result = self._i18n.errors("wrong_tool_name").format(
-                        tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
+        if isinstance(tool_calling, ToolUsageErrorException):
+            tool_result = tool_calling.message
+            return ToolResult(result=tool_result, result_as_answer=False)
+        else:
+            if tool_calling.tool_name.casefold().strip() in [
+                name.casefold().strip() for name in self.tool_name_to_tool_map
+            ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                name.casefold().strip() for name in self.tool_name_to_tool_map
+            ]:
+                tool_result = tool_usage.use(tool_calling, agent_action.text)
+                tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
+                if tool:
+                    return ToolResult(
+                        result=tool_result, result_as_answer=tool.result_as_answer
                     )
-                return ToolResult(result=tool_result, result_as_answer=False)
-
-        except Exception as e:
-            # TODO: drop
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageErrorEvent(  # validation error
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                        error=str(e),
-                    ),
+            else:
+                tool_result = self._i18n.errors("wrong_tool_name").format(
+                    tool=tool_calling.tool_name,
+                    tools=", ".join([tool.name.casefold() for tool in self.tools]),
                 )
-            raise e
+        return ToolResult(result=tool_result, result_as_answer=False)
 
     def _summarize_messages(self) -> None:
         messages_groups = []
@@ -548,6 +514,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self, initial_answer: AgentFinish, feedback: str
     ) -> AgentFinish:
         """Process feedback for training scenarios with single iteration."""
+        self._printer.print(
+            content="\nProcessing training feedback.\n",
+            color="yellow",
+        )
         self._handle_crew_training_output(initial_answer, feedback)
         self.messages.append(
             self._format_msg(
@@ -567,8 +537,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         answer = current_answer
 
         while self.ask_for_human_input:
-            # If the user provides a blank response, assume they are happy with the result
-            if feedback.strip() == "":
+            response = self._get_llm_feedback_response(feedback)
+
+            if not self._feedback_requires_changes(response):
                 self.ask_for_human_input = False
             else:
                 answer = self._process_feedback_iteration(feedback)
@@ -576,6 +547,27 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         return answer
 
+    def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
+        """Get LLM classification of whether feedback requires changes."""
+        prompt = self._i18n.slice("human_feedback_classification").format(
+            feedback=feedback
+        )
+        message = self._format_msg(prompt, role="system")
+
+        for retry in range(MAX_LLM_RETRY):
+            try:
+                response = self.llm.call([message], callbacks=self.callbacks)
+                return response.strip().lower() if response else None
+            except Exception as error:
+                self._log_feedback_error(retry, error)
+
+        self._log_max_retries_exceeded()
+        return None
+
+    def _feedback_requires_changes(self, response: Optional[str]) -> bool:
+        """Determine if feedback response indicates need for changes."""
+        return response == "true" if response else False
+
     def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
         """Process a single feedback iteration."""
         self.messages.append(

src/crewai/agents/parser.py

@@ -94,13 +94,6 @@ class CrewAgentParser:
 
         elif includes_answer:
             final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
-            # Check whether the final answer ends with triple backticks.
-            if final_answer.endswith("```"):
-                # Count occurrences of triple backticks in the final answer.
-                count = final_answer.count("```")
-                # If count is odd then it's an unmatched trailing set; remove it.
-                if count % 2 != 0:
-                    final_answer = final_answer[:-3].rstrip()
             return AgentFinish(thought, final_answer, text)
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
@@ -124,15 +117,11 @@ class CrewAgentParser:
             )
 
     def _extract_thought(self, text: str) -> str:
-        thought_index = text.find("\n\nAction")
-        if thought_index == -1:
-            thought_index = text.find("\n\nFinal Answer")
-        if thought_index == -1:
-            return ""
-        thought = text[:thought_index].strip()
-        # Remove any triple backticks from the thought string
-        thought = thought.replace("```", "").strip()
-        return thought
+        regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
+        thought_match = re.search(regex, text, re.DOTALL)
+        if thought_match:
+            return thought_match.group(1).strip()
+        return ""
 
     def _clean_action(self, text: str) -> str:
         """Clean action string by removing non-essential formatting characters."""

src/crewai/cli/cli.py

@@ -203,6 +203,7 @@ def install(context):
 @crewai.command()
 def run():
     """Run the Crew."""
+    click.echo("Running the Crew")
     run_crew()
 
 

src/crewai/cli/constants.py

@@ -216,43 +216,10 @@ MODELS = {
         "watsonx/ibm/granite-3-8b-instruct",
     ],
     "bedrock": [
-        "bedrock/us.amazon.nova-pro-v1:0",
-        "bedrock/us.amazon.nova-micro-v1:0",
-        "bedrock/us.amazon.nova-lite-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        "bedrock/us.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-opus-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/us.meta.llama3-2-11b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-90b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-8b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-3-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-405b-instruct-v1:0",
-        "bedrock/eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/eu.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/eu.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/eu.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/eu.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/apac.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/apac.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/amazon.nova-pro-v1:0",
-        "bedrock/amazon.nova-micro-v1:0",
-        "bedrock/amazon.nova-lite-v1:0",
         "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0",
         "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
         "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
         "bedrock/anthropic.claude-v2:1",
         "bedrock/anthropic.claude-v2",
         "bedrock/anthropic.claude-instant-v1",
@@ -267,6 +234,8 @@ MODELS = {
         "bedrock/ai21.j2-mid-v1",
         "bedrock/ai21.j2-ultra-v1",
         "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
         "bedrock/mistral.mistral-7b-instruct-v0:2",
         "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
     ],

src/crewai/cli/run_crew.py

@@ -1,6 +1,4 @@
 import subprocess
-from enum import Enum
-from typing import List, Optional
 
 import click
 from packaging import version
@@ -9,24 +7,16 @@ from crewai.cli.utils import read_toml
 from crewai.cli.version import get_crewai_version
 
 
-class CrewType(Enum):
-    STANDARD = "standard"
-    FLOW = "flow"
-
-
 def run_crew() -> None:
     """
-    Run the crew or flow by running a command in the UV environment.
-
-    Starting from version 0.103.0, this command can be used to run both
-    standard crews and flows. For flows, it detects the type from pyproject.toml
-    and automatically runs the appropriate command.
+    Run the crew by running a command in the UV environment.
     """
+    command = ["uv", "run", "run_crew"]
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"
+
     pyproject_data = read_toml()
 
-    # Check for legacy poetry configuration
     if pyproject_data.get("tool", {}).get("poetry") and (
         version.parse(crewai_version) < version.parse(min_required_version)
     ):
@@ -36,54 +26,18 @@ def run_crew() -> None:
             fg="red",
         )
 
-    # Determine crew type
-    is_flow = pyproject_data.get("tool", {}).get("crewai", {}).get("type") == "flow"
-    crew_type = CrewType.FLOW if is_flow else CrewType.STANDARD
-
-    # Display appropriate message
-    click.echo(f"Running the {'Flow' if is_flow else 'Crew'}")
-
-    # Execute the appropriate command
-    execute_command(crew_type)
-
-
-def execute_command(crew_type: CrewType) -> None:
-    """
-    Execute the appropriate command based on crew type.
-
-    Args:
-        crew_type: The type of crew to run
-    """
-    command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
-
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)
 
     except subprocess.CalledProcessError as e:
-        handle_error(e, crew_type)
+        click.echo(f"An error occurred while running the crew: {e}", err=True)
+        click.echo(e.output, err=True, nl=True)
+
+        if pyproject_data.get("tool", {}).get("poetry"):
+            click.secho(
+                "It's possible that you are using an old version of crewAI that uses poetry, please run `crewai update` to update your pyproject.toml to use uv.",
+                fg="yellow",
+            )
 
     except Exception as e:
         click.echo(f"An unexpected error occurred: {e}", err=True)
-
-
-def handle_error(error: subprocess.CalledProcessError, crew_type: CrewType) -> None:
-    """
-    Handle subprocess errors with appropriate messaging.
-
-    Args:
-        error: The subprocess error that occurred
-        crew_type: The type of crew that was being run
-    """
-    entity_type = "flow" if crew_type == CrewType.FLOW else "crew"
-    click.echo(f"An error occurred while running the {entity_type}: {error}", err=True)
-
-    if error.output:
-        click.echo(error.output, err=True, nl=True)
-
-    pyproject_data = read_toml()
-    if pyproject_data.get("tool", {}).get("poetry"):
-        click.secho(
-            "It's possible that you are using an old version of crewAI that uses poetry, "
-            "please run `crewai update` to update your pyproject.toml to use uv.",
-            fg="yellow",
-        )

src/crewai/cli/templates/crew/main.py

@@ -56,8 +56,7 @@ def test():
     Test the crew execution and returns the results.
     """
     inputs = {
-        "topic": "AI LLMs",
-        "current_year": str(datetime.now().year)
+        "topic": "AI LLMs"
     }
     try:
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0"
+    "crewai[tools]>=0.100.1,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/README.md

@@ -30,13 +30,13 @@ crewai install
 
 ## Running the Project
 
-To kickstart your flow and begin execution, run this from the root folder of your project:
+To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
 
 ```bash
 crewai run
 ```
 
-This command initializes the {{name}} Flow as defined in your configuration.
+This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
 
 This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
 

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0,<1.0.0",
+    "crewai[tools]>=0.100.1,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.102.0"
+    "crewai[tools]>=0.100.1"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -257,11 +257,11 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
         import os
 
         for root, _, files in os.walk("."):
-            if crew_path in files:
-                crew_os_path = os.path.join(root, crew_path)
+            if "crew.py" in files:
+                crew_path = os.path.join(root, "crew.py")
                 try:
                     spec = importlib.util.spec_from_file_location(
-                        "crew_module", crew_os_path
+                        "crew_module", crew_path
                     )
                     if not spec or not spec.loader:
                         continue
@@ -273,11 +273,9 @@ def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
                         for attr_name in dir(module):
                             attr = getattr(module, attr_name)
                             try:
-                                if isinstance(attr, Crew) and hasattr(attr, "kickoff"):
-                                    print(
-                                        f"Found valid crew object in attribute '{attr_name}' at {crew_os_path}."
-                                    )
-                                    return attr
+                                if callable(attr) and hasattr(attr, "crew"):
+                                    crew_instance = attr().crew()
+                                    return crew_instance
 
                             except Exception as e:
                                 print(f"Error processing attribute {attr_name}: {e}")

src/crewai/crew.py

@@ -35,6 +35,7 @@ from crewai.process import Process
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
+from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import Tool
 from crewai.types.usage_metrics import UsageMetrics
@@ -42,18 +43,6 @@ from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.events.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
@@ -63,6 +52,12 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
+
+
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 
@@ -256,6 +251,8 @@ class Crew(BaseModel):
         if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
             self.function_calling_llm = create_llm(self.function_calling_llm)
 
+        self._telemetry = Telemetry()
+        self._telemetry.set_tracer()
         return self
 
     @model_validator(mode="after")
@@ -278,26 +275,12 @@ class Crew(BaseModel):
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            if (
-                self.memory_config and "user_memory" in self.memory_config
-            ):  # Check for user_memory in config
-                user_memory_config = self.memory_config["user_memory"]
-                if isinstance(
-                    user_memory_config, UserMemory
-                ):  # Check if it is already an instance
-                    self._user_memory = user_memory_config
-                elif isinstance(
-                    user_memory_config, dict
-                ):  # Check if it's a configuration dict
-                    self._user_memory = UserMemory(
-                        crew=self, **user_memory_config
-                    )  # Initialize with config
-                else:
-                    raise TypeError(
-                        "user_memory must be a UserMemory instance or a configuration dictionary"
-                    )
+            if hasattr(self, "memory_config") and self.memory_config is not None:
+                self._user_memory = (
+                    self.user_memory if self.user_memory else UserMemory(crew=self)
+                )
             else:
-                self._user_memory = None  # No user memory if not in config
+                self._user_memory = None
         return self
 
     @model_validator(mode="after")
@@ -472,6 +455,8 @@ class Crew(BaseModel):
                         )
         return self
 
+
+
     @property
     def key(self) -> str:
         source = [agent.key for agent in self.agents] + [
@@ -524,19 +509,10 @@ class Crew(BaseModel):
         self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
     ) -> None:
         """Trains the crew for a given number of iterations."""
-        try:
-            crewai_event_bus.emit(
-                self,
-                CrewTrainStartedEvent(
-                    crew_name=self.name or "crew",
-                    n_iterations=n_iterations,
-                    filename=filename,
-                    inputs=inputs,
-                ),
-            )
-            train_crew = self.copy()
-            train_crew._setup_for_training(filename)
+        train_crew = self.copy()
+        train_crew._setup_for_training(filename)
 
+        try:
             for n_iteration in range(n_iterations):
                 train_crew._train_iteration = n_iteration
                 train_crew.kickoff(inputs=inputs)
@@ -551,20 +527,7 @@ class Crew(BaseModel):
                     CrewTrainingHandler(filename).save_trained_data(
                         agent_id=str(agent.role), trained_data=result.model_dump()
                     )
-
-            crewai_event_bus.emit(
-                self,
-                CrewTrainCompletedEvent(
-                    crew_name=self.name or "crew",
-                    n_iterations=n_iterations,
-                    filename=filename,
-                ),
-            )
         except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                CrewTrainFailedEvent(error=str(e), crew_name=self.name or "crew"),
-            )
             self._logger.log("error", f"Training failed: {e}", color="red")
             CrewTrainingHandler(TRAINING_DATA_FILE).clear()
             CrewTrainingHandler(filename).clear()
@@ -574,71 +537,60 @@ class Crew(BaseModel):
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
-        try:
-            for before_callback in self.before_kickoff_callbacks:
-                if inputs is None:
-                    inputs = {}
-                inputs = before_callback(inputs)
+        for before_callback in self.before_kickoff_callbacks:
+            if inputs is None:
+                inputs = {}
+            inputs = before_callback(inputs)
 
-            crewai_event_bus.emit(
-                self,
-                CrewKickoffStartedEvent(crew_name=self.name or "crew", inputs=inputs),
+        """Starts the crew to work on its assigned tasks."""
+        self._execution_span = self._telemetry.crew_execution_span(self, inputs)
+        self._task_output_handler.reset()
+        self._logging_color = "bold_purple"
+
+        if inputs is not None:
+            self._inputs = inputs
+            self._interpolate_inputs(inputs)
+        self._set_tasks_callbacks()
+
+        i18n = I18N(prompt_file=self.prompt_file)
+
+        for agent in self.agents:
+            agent.i18n = i18n
+            # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
+            agent.crew = self  # type: ignore[attr-defined]
+            # TODO: Create an AgentFunctionCalling protocol for future refactoring
+            if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+                agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+
+            if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
+                agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
+
+            agent.create_agent_executor()
+
+        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:
+            result = self._run_hierarchical_process()
+        else:
+            raise NotImplementedError(
+                f"The process '{self.process}' is not implemented yet."
             )
 
-            # Starts the crew to work on its assigned tasks.
-            self._task_output_handler.reset()
-            self._logging_color = "bold_purple"
+        for after_callback in self.after_kickoff_callbacks:
+            result = after_callback(result)
 
-            if inputs is not None:
-                self._inputs = inputs
-                self._interpolate_inputs(inputs)
-            self._set_tasks_callbacks()
+        metrics += [agent._token_process.get_summary() for agent in self.agents]
 
-            i18n = I18N(prompt_file=self.prompt_file)
+        self.usage_metrics = UsageMetrics()
+        for metric in metrics:
+            self.usage_metrics.add_usage_metrics(metric)
 
-            for agent in self.agents:
-                agent.i18n = i18n
-                # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-                agent.crew = self  # type: ignore[attr-defined]
-                agent.set_knowledge(crew_embedder=self.embedder)
-                # TODO: Create an AgentFunctionCalling protocol for future refactoring
-                if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-                    agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-
-                if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
-                    agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
-
-                agent.create_agent_executor()
-
-            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:
-                result = self._run_hierarchical_process()
-            else:
-                raise NotImplementedError(
-                    f"The process '{self.process}' is not implemented yet."
-                )
-
-            for after_callback in self.after_kickoff_callbacks:
-                result = after_callback(result)
-
-            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(
-                self,
-                CrewKickoffFailedEvent(error=str(e), crew_name=self.name or "crew"),
-            )
-            raise
+        return result
 
     def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""
@@ -976,22 +928,17 @@ class Crew(BaseModel):
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
         if not task_outputs:
             raise ValueError("No task outputs available to create crew output.")
-
+            
         # Filter out empty outputs and get the last valid one as the main output
         valid_outputs = [t for t in task_outputs if t.raw]
         if not valid_outputs:
             raise ValueError("No valid task outputs available to create crew output.")
         final_task_output = valid_outputs[-1]
-
+            
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         token_usage = self.calculate_usage_metrics()
-        crewai_event_bus.emit(
-            self,
-            CrewKickoffCompletedEvent(
-                crew_name=self.name or "crew", output=final_task_output
-            ),
-        )
+
         return CrewOutput(
             raw=final_task_output.raw,
             pydantic=final_task_output.pydantic,
@@ -1111,6 +1058,7 @@ class Crew(BaseModel):
             "_short_term_memory",
             "_long_term_memory",
             "_entity_memory",
+            "_telemetry",
             "agents",
             "tasks",
             "knowledge_sources",
@@ -1176,6 +1124,13 @@ class Crew(BaseModel):
     def _finish_execution(self, final_string_output: str) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
+        if agentops:
+            agentops.end_session(
+                end_state="Success",
+                end_state_reason="Finished Execution",
+                is_auto_end=True,
+            )
+        self._telemetry.end_crew(self, final_string_output)
 
     def calculate_usage_metrics(self) -> UsageMetrics:
         """Calculates and returns the usage metrics."""
@@ -1193,45 +1148,25 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: Union[str, InstanceOf[LLM]],
+        openai_model_name: Optional[str] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
-        try:
-            eval_llm = create_llm(eval_llm)
-            if not eval_llm:
-                raise ValueError("Failed to create LLM instance.")
+        test_crew = self.copy()
 
-            crewai_event_bus.emit(
-                self,
-                CrewTestStartedEvent(
-                    crew_name=self.name or "crew",
-                    n_iterations=n_iterations,
-                    eval_llm=eval_llm,
-                    inputs=inputs,
-                ),
-            )
-            test_crew = self.copy()
-            evaluator = CrewEvaluator(test_crew, eval_llm)  # type: ignore[arg-type]
+        self._test_execution_span = test_crew._telemetry.test_execution_span(
+            test_crew,
+            n_iterations,
+            inputs,
+            openai_model_name,  # type: ignore[arg-type]
+        )  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
 
-            for i in range(1, n_iterations + 1):
-                evaluator.set_iteration(i)
-                test_crew.kickoff(inputs=inputs)
+        for i in range(1, n_iterations + 1):
+            evaluator.set_iteration(i)
+            test_crew.kickoff(inputs=inputs)
 
-            evaluator.print_crew_evaluation_result()
-
-            crewai_event_bus.emit(
-                self,
-                CrewTestCompletedEvent(
-                    crew_name=self.name or "crew",
-                ),
-            )
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                CrewTestFailedEvent(error=str(e), crew_name=self.name or "crew"),
-            )
-            raise
+        evaluator.print_crew_evaluation_result()
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
@@ -1273,11 +1208,11 @@ class Crew(BaseModel):
     def _reset_all_memories(self) -> None:
         """Reset all available memory systems."""
         memory_systems = [
-            ("short term", getattr(self, "_short_term_memory", None)),
-            ("entity", getattr(self, "_entity_memory", None)),
-            ("long term", getattr(self, "_long_term_memory", None)),
-            ("task output", getattr(self, "_task_output_handler", None)),
-            ("knowledge", getattr(self, "knowledge", None)),
+            ("short term", self._short_term_memory),
+            ("entity", self._entity_memory),
+            ("long term", self._long_term_memory),
+            ("task output", self._task_output_handler),
+            ("knowledge", self.knowledge),
         ]
 
         for name, system in memory_systems:

src/crewai/flow/flow.py

@@ -1,5 +1,4 @@
 import asyncio
-import copy
 import inspect
 import logging
 from typing import (
@@ -17,21 +16,19 @@ from typing import (
 )
 from uuid import uuid4
 
+from blinker import Signal
 from pydantic import BaseModel, Field, ValidationError
 
-from crewai.flow.flow_visualizer import plot_flow
-from crewai.flow.persistence.base import FlowPersistence
-from crewai.flow.utils import get_possible_return_constants
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.flow_events import (
-    FlowCreatedEvent,
+from crewai.flow.flow_events import (
     FlowFinishedEvent,
-    FlowPlotEvent,
     FlowStartedEvent,
-    MethodExecutionFailedEvent,
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
+from crewai.flow.flow_visualizer import plot_flow
+from crewai.flow.persistence.base import FlowPersistence
+from crewai.flow.utils import get_possible_return_constants
+from crewai.telemetry import Telemetry
 from crewai.utilities.printer import Printer
 
 logger = logging.getLogger(__name__)
@@ -397,6 +394,7 @@ class FlowMeta(type):
                 or hasattr(attr_value, "__trigger_methods__")
                 or hasattr(attr_value, "__is_router__")
             ):
+
                 # Register start methods
                 if hasattr(attr_value, "__is_start_method__"):
                     start_methods.append(attr_name)
@@ -429,6 +427,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     Type parameter T must be either Dict[str, Any] or a subclass of BaseModel."""
 
+    _telemetry = Telemetry()
     _printer = Printer()
 
     _start_methods: List[str] = []
@@ -436,6 +435,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     _routers: Set[str] = set()
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
+    event_emitter = Signal("event_emitter")
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -469,13 +469,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if kwargs:
             self._initialize_state(kwargs)
 
-        crewai_event_bus.emit(
-            self,
-            FlowCreatedEvent(
-                type="flow_created",
-                flow_name=self.__class__.__name__,
-            ),
-        )
+        self._telemetry.flow_creation_span(self.__class__.__name__)
 
         # Register all flow-related methods
         for method_name in dir(self):
@@ -575,9 +569,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
         )
 
-    def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
-
     @property
     def state(self) -> T:
         return self._state
@@ -709,34 +700,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
             raise TypeError(f"State must be dict or BaseModel, got {type(self._state)}")
 
     def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
-        """
-        Start the flow execution in a synchronous context.
-
-        This method wraps kickoff_async so that all state initialization and event
-        emission is handled in the asynchronous method.
-        """
-
-        async def run_flow():
-            return await self.kickoff_async(inputs)
-
-        return asyncio.run(run_flow())
-
-    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
-        """
-        Start the flow execution asynchronously.
-
-        This method performs state restoration (if an 'id' is provided and persistence is available)
-        and updates the flow state with any additional inputs. It then emits the FlowStartedEvent,
-        logs the flow startup, and executes all start methods. Once completed, it emits the
-        FlowFinishedEvent and returns the final output.
+        """Start the flow execution.
 
         Args:
-            inputs: Optional dictionary containing input values and/or a state ID for restoration.
-
-        Returns:
-            The final output from the flow, which is the result of the last executed method.
+            inputs: Optional dictionary containing input values and potentially a state ID to restore
         """
-        if inputs:
+        # Handle state restoration if ID is provided in inputs
+        if inputs and "id" in inputs and self._persistence is not None:
+            restore_uuid = inputs["id"]
+            stored_state = self._persistence.load_state(restore_uuid)
+
             # Override the id in the state if it exists in inputs
             if "id" in inputs:
                 if isinstance(self._state, dict):
@@ -744,33 +717,29 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 elif isinstance(self._state, BaseModel):
                     setattr(self._state, "id", inputs["id"])
 
-            # If persistence is enabled, attempt to restore the stored state using the provided id.
-            if "id" in inputs and self._persistence is not None:
-                restore_uuid = inputs["id"]
-                stored_state = self._persistence.load_state(restore_uuid)
-                if stored_state:
-                    self._log_flow_event(
-                        f"Loading flow state from memory for UUID: {restore_uuid}",
-                        color="yellow",
-                    )
-                    self._restore_state(stored_state)
-                else:
-                    self._log_flow_event(
-                        f"No flow state found for UUID: {restore_uuid}", color="red"
-                    )
+            if stored_state:
+                self._log_flow_event(
+                    f"Loading flow state from memory for UUID: {restore_uuid}",
+                    color="yellow",
+                )
+                # Restore the state
+                self._restore_state(stored_state)
+            else:
+                self._log_flow_event(
+                    f"No flow state found for UUID: {restore_uuid}", color="red"
+                )
 
-            # Update state with any additional inputs (ignoring the 'id' key)
+            # Apply any additional inputs after restoration
             filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
             if filtered_inputs:
                 self._initialize_state(filtered_inputs)
 
-        # Emit FlowStartedEvent and log the start of the flow.
-        crewai_event_bus.emit(
+        # Start flow execution
+        self.event_emitter.send(
             self,
-            FlowStartedEvent(
+            event=FlowStartedEvent(
                 type="flow_started",
                 flow_name=self.__class__.__name__,
-                inputs=inputs,
             ),
         )
         self._log_flow_event(
@@ -780,6 +749,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if inputs is not None and "id" not in inputs:
             self._initialize_state(inputs)
 
+        return asyncio.run(self.kickoff_async())
+
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        if not self._start_methods:
+            raise ValueError("No start method defined")
+
+        self._telemetry.flow_execution_span(
+            self.__class__.__name__, list(self._methods.keys())
+        )
+
         tasks = [
             self._execute_start_method(start_method)
             for start_method in self._start_methods
@@ -788,15 +767,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         final_output = self._method_outputs[-1] if self._method_outputs else None
 
-        crewai_event_bus.emit(
+        self.event_emitter.send(
             self,
-            FlowFinishedEvent(
+            event=FlowFinishedEvent(
                 type="flow_finished",
                 flow_name=self.__class__.__name__,
                 result=final_output,
             ),
         )
-
         return final_output
 
     async def _execute_start_method(self, start_method_name: str) -> None:
@@ -825,55 +803,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:
-        try:
-            dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (
-                kwargs or {}
-            )
-            crewai_event_bus.emit(
-                self,
-                MethodExecutionStartedEvent(
-                    type="method_execution_started",
-                    method_name=method_name,
-                    flow_name=self.__class__.__name__,
-                    params=dumped_params,
-                    state=self._copy_state(),
-                ),
-            )
-
-            result = (
-                await method(*args, **kwargs)
-                if asyncio.iscoroutinefunction(method)
-                else method(*args, **kwargs)
-            )
-
-            self._method_outputs.append(result)
-            self._method_execution_counts[method_name] = (
-                self._method_execution_counts.get(method_name, 0) + 1
-            )
-
-            crewai_event_bus.emit(
-                self,
-                MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=method_name,
-                    flow_name=self.__class__.__name__,
-                    state=self._copy_state(),
-                    result=result,
-                ),
-            )
-
-            return result
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                MethodExecutionFailedEvent(
-                    type="method_execution_failed",
-                    method_name=method_name,
-                    flow_name=self.__class__.__name__,
-                    error=e,
-                ),
-            )
-            raise e
+        result = (
+            await method(*args, **kwargs)
+            if asyncio.iscoroutinefunction(method)
+            else method(*args, **kwargs)
+        )
+        self._method_outputs.append(result)
+        self._method_execution_counts[method_name] = (
+            self._method_execution_counts.get(method_name, 0) + 1
+        )
+        return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
         """
@@ -894,45 +833,35 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Notes
         -----
         - Routers are executed sequentially to maintain flow control
-        - Each router's result becomes a new trigger_method
+        - Each router's result becomes the new trigger_method
         - Normal listeners are executed in parallel for efficiency
         - Listeners can receive the trigger method's result as a parameter
         """
         # First, handle routers repeatedly until no router triggers anymore
-        router_results = []
-        current_trigger = trigger_method
-
         while True:
             routers_triggered = self._find_triggered_methods(
-                current_trigger, router_only=True
+                trigger_method, router_only=True
             )
             if not routers_triggered:
                 break
-
             for router_name in routers_triggered:
                 await self._execute_single_listener(router_name, result)
                 # After executing router, the router's result is the path
-                router_result = self._method_outputs[-1]
-                if router_result:  # Only add non-None results
-                    router_results.append(router_result)
-                current_trigger = (
-                    router_result  # Update for next iteration of router chain
-                )
+                # The last router executed sets the trigger_method
+                # The router result is the last element in self._method_outputs
+                trigger_method = self._method_outputs[-1]
 
-        # Now execute normal listeners for all router results and the original trigger
-        all_triggers = [trigger_method] + router_results
-
-        for current_trigger in all_triggers:
-            if current_trigger:  # Skip None results
-                listeners_triggered = self._find_triggered_methods(
-                    current_trigger, router_only=False
-                )
-                if listeners_triggered:
-                    tasks = [
-                        self._execute_single_listener(listener_name, result)
-                        for listener_name in listeners_triggered
-                    ]
-                    await asyncio.gather(*tasks)
+        # Now that no more routers are triggered by current trigger_method,
+        # execute normal listeners
+        listeners_triggered = self._find_triggered_methods(
+            trigger_method, router_only=False
+        )
+        if listeners_triggered:
+            tasks = [
+                self._execute_single_listener(listener_name, result)
+                for listener_name in listeners_triggered
+            ]
+            await asyncio.gather(*tasks)
 
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
@@ -1022,6 +951,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
         try:
             method = self._methods[listener_name]
 
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionStartedEvent(
+                    type="method_execution_started",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
             method_params = [p for p in params if p.name != "self"]
@@ -1033,6 +971,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
             else:
                 listener_result = await self._execute_method(listener_name, method)
 
+            self.event_emitter.send(
+                self,
+                event=MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=listener_name,
+                    flow_name=self.__class__.__name__,
+                ),
+            )
+
             # Execute listeners (and possibly routers) of this listener
             await self._execute_listeners(listener_name, listener_result)
 
@@ -1071,11 +1018,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             logger.warning(message)
 
     def plot(self, filename: str = "crewai_flow") -> None:
-        crewai_event_bus.emit(
-            self,
-            FlowPlotEvent(
-                type="flow_plot",
-                flow_name=self.__class__.__name__,
-            ),
+        self._telemetry.flow_plotting_span(
+            self.__class__.__name__, list(self._methods.keys())
         )
         plot_flow(self, filename)

src/crewai/flow/flow_events.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any, Optional
+
+
+@dataclass
+class Event:
+    type: str
+    flow_name: str
+    timestamp: datetime = field(init=False)
+
+    def __post_init__(self):
+        self.timestamp = datetime.now()
+
+
+@dataclass
+class FlowStartedEvent(Event):
+    pass
+
+
+@dataclass
+class MethodExecutionStartedEvent(Event):
+    method_name: str
+
+
+@dataclass
+class MethodExecutionFinishedEvent(Event):
+    method_name: str
+
+
+@dataclass
+class FlowFinishedEvent(Event):
+    result: Optional[Any] = None

src/crewai/flow/persistence/decorators.py

@@ -58,7 +58,7 @@ class PersistenceDecorator:
     _printer = Printer()  # Class-level printer instance
 
     @classmethod
-    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence, verbose: bool = False) -> None:
+    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence) -> None:
         """Persist flow state with proper error handling and logging.
 
         This method handles the persistence of flow state data, including proper
@@ -68,7 +68,6 @@ class PersistenceDecorator:
             flow_instance: The flow instance whose state to persist
             method_name: Name of the method that triggered persistence
             persistence_instance: The persistence backend to use
-            verbose: Whether to log persistence operations
 
         Raises:
             ValueError: If flow has no state or state lacks an ID
@@ -89,10 +88,9 @@ class PersistenceDecorator:
             if not flow_uuid:
                 raise ValueError("Flow state must have an 'id' field for persistence")
 
-            # Log state saving only if verbose is True
-            if verbose:
-                cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
-                logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
+            # Log state saving with consistent message
+            cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
+            logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
 
             try:
                 persistence_instance.save_state(
@@ -117,7 +115,7 @@ class PersistenceDecorator:
             raise ValueError(error_msg) from e
 
 
-def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False):
+def persist(persistence: Optional[FlowPersistence] = None):
     """Decorator to persist flow state.
 
     This decorator can be applied at either the class level or method level.
@@ -128,7 +126,6 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
     Args:
         persistence: Optional FlowPersistence implementation to use.
                     If not provided, uses SQLiteFlowPersistence.
-        verbose: Whether to log persistence operations. Defaults to False.
 
     Returns:
         A decorator that can be applied to either a class or method
@@ -138,12 +135,13 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
         RuntimeError: If state persistence fails
 
     Example:
-        @persist(verbose=True)  # Class-level persistence with logging
+        @persist  # Class-level persistence with default SQLite
         class MyFlow(Flow[MyState]):
             @start()
             def begin(self):
                 pass
     """
+
     def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
         """Decorator that handles both class and method decoration."""
         actual_persistence = persistence or SQLiteFlowPersistence()
@@ -181,7 +179,7 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
                         @functools.wraps(original_method)
                         async def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = await original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence)
                             return result
                         return method_wrapper
 
@@ -201,7 +199,7 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
                         @functools.wraps(original_method)
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence)
                             return result
                         return method_wrapper
 
@@ -230,7 +228,7 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
                         result = await method_coro
                     else:
                         result = method_coro
-                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
                     return result
 
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
@@ -242,7 +240,7 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
                 @functools.wraps(method)
                 def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
                     result = method(flow_instance, *args, **kwargs)
-                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
                     return result
 
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:

src/crewai/flow/persistence/sqlite.py

@@ -4,7 +4,7 @@ SQLite-based implementation of flow state persistence.
 
 import json
 import sqlite3
-from datetime import datetime, timezone
+from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, Optional, Union
 
@@ -34,7 +34,6 @@ class SQLiteFlowPersistence(FlowPersistence):
             ValueError: If db_path is invalid
         """
         from crewai.utilities.paths import db_storage_path
-
         # Get path from argument or default location
         path = db_path or str(Path(db_storage_path()) / "flow_states.db")
 
@@ -47,8 +46,7 @@ class SQLiteFlowPersistence(FlowPersistence):
     def init_db(self) -> None:
         """Create the necessary tables if they don't exist."""
         with sqlite3.connect(self.db_path) as conn:
-            conn.execute(
-                """
+            conn.execute("""
             CREATE TABLE IF NOT EXISTS flow_states (
                 id INTEGER PRIMARY KEY AUTOINCREMENT,
                 flow_uuid TEXT NOT NULL,
@@ -56,15 +54,12 @@ class SQLiteFlowPersistence(FlowPersistence):
                 timestamp DATETIME NOT NULL,
                 state_json TEXT NOT NULL
             )
-            """
-            )
+            """)
             # Add index for faster UUID lookups
-            conn.execute(
-                """
+            conn.execute("""
             CREATE INDEX IF NOT EXISTS idx_flow_states_uuid
             ON flow_states(flow_uuid)
-            """
-            )
+            """)
 
     def save_state(
         self,
@@ -90,22 +85,19 @@ class SQLiteFlowPersistence(FlowPersistence):
             )
 
         with sqlite3.connect(self.db_path) as conn:
-            conn.execute(
-                """
+            conn.execute("""
             INSERT INTO flow_states (
                 flow_uuid,
                 method_name,
                 timestamp,
                 state_json
             ) VALUES (?, ?, ?, ?)
-            """,
-                (
-                    flow_uuid,
-                    method_name,
-                    datetime.now(timezone.utc).isoformat(),
-                    json.dumps(state_dict),
-                ),
-            )
+            """, (
+                flow_uuid,
+                method_name,
+                datetime.utcnow().isoformat(),
+                json.dumps(state_dict),
+            ))
 
     def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.
@@ -117,16 +109,13 @@ class SQLiteFlowPersistence(FlowPersistence):
             The most recent state as a dictionary, or None if no state exists
         """
         with sqlite3.connect(self.db_path) as conn:
-            cursor = conn.execute(
-                """
+            cursor = conn.execute("""
             SELECT state_json
             FROM flow_states
             WHERE flow_uuid = ?
             ORDER BY id DESC
             LIMIT 1
-            """,
-                (flow_uuid,),
-            )
+            """, (flow_uuid,))
             row = cursor.fetchone()
 
         if row:

src/crewai/flow/state_utils.py

@@ -1,91 +0,0 @@
-import json
-from datetime import date, datetime
-from typing import Any, Dict, List, Union
-
-from pydantic import BaseModel
-
-from crewai.flow import Flow
-
-SerializablePrimitive = Union[str, int, float, bool, None]
-Serializable = Union[
-    SerializablePrimitive, List["Serializable"], Dict[str, "Serializable"]
-]
-
-
-def export_state(flow: Flow) -> dict[str, Serializable]:
-    """Exports the Flow's internal state as JSON-compatible data structures.
-
-    Performs a one-way transformation of a Flow's state into basic Python types
-    that can be safely serialized to JSON. To prevent infinite recursion with
-    circular references, the conversion is limited to a depth of 5 levels.
-
-    Args:
-        flow: The Flow object whose state needs to be exported
-
-    Returns:
-        dict[str, Any]: The transformed state using JSON-compatible Python
-            types.
-    """
-    result = to_serializable(flow._state)
-    assert isinstance(result, dict)
-    return result
-
-
-def to_serializable(
-    obj: Any, max_depth: int = 5, _current_depth: int = 0
-) -> Serializable:
-    """Converts a Python object into a JSON-compatible representation.
-
-    Supports primitives, datetime objects, collections, dictionaries, and
-    Pydantic models. Recursion depth is limited to prevent infinite nesting.
-    Non-convertible objects default to their string representations.
-
-    Args:
-        obj (Any): Object to transform.
-        max_depth (int, optional): Maximum recursion depth. Defaults to 5.
-
-    Returns:
-        Serializable: A JSON-compatible structure.
-    """
-    if _current_depth >= max_depth:
-        return repr(obj)
-
-    if isinstance(obj, (str, int, float, bool, type(None))):
-        return obj
-    elif isinstance(obj, (date, datetime)):
-        return obj.isoformat()
-    elif isinstance(obj, (list, tuple, set)):
-        return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
-    elif isinstance(obj, dict):
-        return {
-            _to_serializable_key(key): to_serializable(
-                value, max_depth, _current_depth + 1
-            )
-            for key, value in obj.items()
-        }
-    elif isinstance(obj, BaseModel):
-        return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
-    else:
-        return repr(obj)
-
-
-def _to_serializable_key(key: Any) -> str:
-    if isinstance(key, (str, int)):
-        return str(key)
-    return f"key_{id(key)}_{repr(key)}"
-
-
-def to_string(obj: Any) -> str | None:
-    """Serializes an object into a JSON string.
-
-    Args:
-        obj (Any): Object to serialize.
-
-    Returns:
-        str | None: A JSON-formatted string or `None` if empty.
-    """
-    serializable = to_serializable(obj)
-    if serializable is None:
-        return None
-    else:
-        return json.dumps(serializable)

src/crewai/flow/utils.py

@@ -16,8 +16,7 @@ Example
 import ast
 import inspect
 import textwrap
-from collections import defaultdict, deque
-from typing import Any, Deque, Dict, List, Optional, Set, Union
+from typing import Any, Dict, List, Optional, Set, Union
 
 
 def get_possible_return_constants(function: Any) -> Optional[List[str]]:
@@ -119,7 +118,7 @@ def calculate_node_levels(flow: Any) -> Dict[str, int]:
     - Processes router paths separately
     """
     levels: Dict[str, int] = {}
-    queue: Deque[str] = deque()
+    queue: List[str] = []
     visited: Set[str] = set()
     pending_and_listeners: Dict[str, Set[str]] = {}
 
@@ -129,35 +128,28 @@ def calculate_node_levels(flow: Any) -> Dict[str, int]:
             levels[method_name] = 0
             queue.append(method_name)
 
-    # Precompute listener dependencies
-    or_listeners = defaultdict(list)
-    and_listeners = defaultdict(set)
-    for listener_name, (condition_type, trigger_methods) in flow._listeners.items():
-        if condition_type == "OR":
-            for method in trigger_methods:
-                or_listeners[method].append(listener_name)
-        elif condition_type == "AND":
-            and_listeners[listener_name] = set(trigger_methods)
-
     # Breadth-first traversal to assign levels
     while queue:
-        current = queue.popleft()
+        current = queue.pop(0)
         current_level = levels[current]
         visited.add(current)
 
-        for listener_name in or_listeners[current]:
-            if listener_name not in levels or levels[listener_name] > current_level + 1:
-                levels[listener_name] = current_level + 1
-                if listener_name not in visited:
-                    queue.append(listener_name)
-
-        for listener_name, required_methods in and_listeners.items():
-            if current in required_methods:
+        for listener_name, (condition_type, trigger_methods) in flow._listeners.items():
+            if condition_type == "OR":
+                if current in trigger_methods:
+                    if (
+                        listener_name not in levels
+                        or levels[listener_name] > current_level + 1
+                    ):
+                        levels[listener_name] = current_level + 1
+                        if listener_name not in visited:
+                            queue.append(listener_name)
+            elif condition_type == "AND":
                 if listener_name not in pending_and_listeners:
                     pending_and_listeners[listener_name] = set()
-                pending_and_listeners[listener_name].add(current)
-
-                if required_methods == pending_and_listeners[listener_name]:
+                if current in trigger_methods:
+                    pending_and_listeners[listener_name].add(current)
+                if set(trigger_methods) == pending_and_listeners[listener_name]:
                     if (
                         listener_name not in levels
                         or levels[listener_name] > current_level + 1
@@ -167,7 +159,22 @@ def calculate_node_levels(flow: Any) -> Dict[str, int]:
                             queue.append(listener_name)
 
         # Handle router connections
-        process_router_paths(flow, current, current_level, levels, queue)
+        if current in flow._routers:
+            router_method_name = current
+            paths = flow._router_paths.get(router_method_name, [])
+            for path in paths:
+                for listener_name, (
+                    condition_type,
+                    trigger_methods,
+                ) in flow._listeners.items():
+                    if path in trigger_methods:
+                        if (
+                            listener_name not in levels
+                            or levels[listener_name] > current_level + 1
+                        ):
+                            levels[listener_name] = current_level + 1
+                            if listener_name not in visited:
+                                queue.append(listener_name)
 
     return levels
 
@@ -220,7 +227,10 @@ def build_ancestor_dict(flow: Any) -> Dict[str, Set[str]]:
 
 
 def dfs_ancestors(
-    node: str, ancestors: Dict[str, Set[str]], visited: Set[str], flow: Any
+    node: str,
+    ancestors: Dict[str, Set[str]],
+    visited: Set[str],
+    flow: Any
 ) -> None:
     """
     Perform depth-first search to build ancestor relationships.
@@ -264,9 +274,7 @@ def dfs_ancestors(
                     dfs_ancestors(listener_name, ancestors, visited, flow)
 
 
-def is_ancestor(
-    node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]
-) -> bool:
+def is_ancestor(node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]) -> bool:
     """
     Check if one node is an ancestor of another.
 
@@ -331,9 +339,7 @@ def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]:
     return parent_children
 
 
-def get_child_index(
-    parent: str, child: str, parent_children: Dict[str, List[str]]
-) -> int:
+def get_child_index(parent: str, child: str, parent_children: Dict[str, List[str]]) -> int:
     """
     Get the index of a child node in its parent's sorted children list.
 
@@ -354,23 +360,3 @@ def get_child_index(
     children = parent_children.get(parent, [])
     children.sort()
     return children.index(child)
-
-
-def process_router_paths(flow, current, current_level, levels, queue):
-    """
-    Handle the router connections for the current node.
-    """
-    if current in flow._routers:
-        paths = flow._router_paths.get(current, [])
-        for path in paths:
-            for listener_name, (
-                condition_type,
-                trigger_methods,
-            ) in flow._listeners.items():
-                if path in trigger_methods:
-                    if (
-                        listener_name not in levels
-                        or levels[listener_name] > current_level + 1
-                    ):
-                        levels[listener_name] = current_level + 1
-                        queue.append(listener_name)

src/crewai/knowledge/storage/knowledge_storage.py

@@ -76,7 +76,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                         "context": fetched["documents"][0][i],  # type: ignore
                         "score": fetched["distances"][0][i],  # type: ignore
                     }
-                    if result["score"] >= score_threshold:
+                    if result["score"] >= score_threshold:  # type: ignore
                         results.append(result)
                 return results
             else:

src/crewai/llm.py

@@ -10,23 +10,14 @@ from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
 from dotenv import load_dotenv
 from pydantic import BaseModel
 
-from crewai.utilities.events.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMCallType,
-)
-from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
-
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
-    from litellm import Choices
+    from litellm import Choices, get_supported_openai_params
     from litellm.types.utils import ModelResponse
-    from litellm.utils import get_supported_openai_params, supports_response_schema
+    from litellm.utils import supports_response_schema
 
 
-from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -64,7 +55,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4-turbo": 128000,
     "o1-preview": 128000,
     "o1-mini": 128000,
-    "o3-mini": 200000,  # Based on official o3-mini specifications
     # gemini
     "gemini-2.0-flash": 1048576,
     "gemini-1.5-pro": 2097152,
@@ -191,14 +181,14 @@ class LLM:
 
     def _is_anthropic_model(self, model: str) -> bool:
         """Determine if the model is from Anthropic provider.
-
+        
         Args:
             model: The model identifier string.
-
+            
         Returns:
             bool: True if the model is from Anthropic, False otherwise.
         """
-        ANTHROPIC_PREFIXES = ("anthropic/", "claude-", "claude/")
+        ANTHROPIC_PREFIXES = ('anthropic/', 'claude-', 'claude/')
         return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
 
     def call(
@@ -209,7 +199,7 @@ class LLM:
         available_functions: Optional[Dict[str, Any]] = None,
     ) -> Union[str, Any]:
         """High-level LLM call method.
-
+        
         Args:
             messages: Input messages for the LLM.
                      Can be a string or list of message dictionaries.
@@ -221,22 +211,22 @@ class LLM:
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
-
+        
         Returns:
             Union[str, Any]: Either a text response from the LLM (str) or
                            the result of a tool function call (Any).
-
+        
         Raises:
             TypeError: If messages format is invalid
             ValueError: If response format is not supported
             LLMContextLengthExceededException: If input exceeds model's context limit
-
+        
         Examples:
             # Example 1: Simple string input
             >>> response = llm.call("Return the name of a random city.")
             >>> print(response)
             "Paris"
-
+            
             # Example 2: Message list with system and user messages
             >>> messages = [
             ...     {"role": "system", "content": "You are a geography expert"},
@@ -246,15 +236,6 @@ class LLM:
             >>> print(response)
             "The capital of France is Paris."
         """
-        crewai_event_bus.emit(
-            self,
-            event=LLMCallStartedEvent(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-            ),
-        )
         # Validate parameters before proceeding with the call.
         self._validate_call_params()
 
@@ -329,7 +310,6 @@ class LLM:
 
                 # --- 4) If no tool calls, return the text response
                 if not tool_calls or not available_functions:
-                    self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
                     return text_response
 
                 # --- 5) Handle the tool call
@@ -347,28 +327,12 @@ class LLM:
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
-                        self._handle_emit_call_events(result, LLMCallType.TOOL_CALL)
                         return result
 
                     except Exception as e:
                         logging.error(
                             f"Error executing function '{function_name}': {e}"
                         )
-                        crewai_event_bus.emit(
-                            self,
-                            event=ToolExecutionErrorEvent(
-                                tool_name=function_name,
-                                tool_args=function_args,
-                                tool_class=fn,
-                                error=str(e),
-                            ),
-                        )
-                        crewai_event_bus.emit(
-                            self,
-                            event=LLMCallFailedEvent(
-                                error=f"Tool execution error: {str(e)}"
-                            ),
-                        )
                         return text_response
 
                 else:
@@ -378,62 +342,42 @@ class LLM:
                     return text_response
 
             except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=LLMCallFailedEvent(error=str(e)),
-                )
                 if not LLMContextLengthExceededException(
                     str(e)
                 )._is_context_limit_error(str(e)):
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):
-        """Handle the events for the LLM call.
-
-        Args:
-            response (str): The response from the LLM call.
-            call_type (str): The type of call, either "tool_call" or "llm_call".
-        """
-        crewai_event_bus.emit(
-            self,
-            event=LLMCallCompletedEvent(response=response, call_type=call_type),
-        )
-
-    def _format_messages_for_provider(
-        self, messages: List[Dict[str, str]]
-    ) -> List[Dict[str, str]]:
+    def _format_messages_for_provider(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
         """Format messages according to provider requirements.
-
+        
         Args:
             messages: List of message dictionaries with 'role' and 'content' keys.
                      Can be empty or None.
-
+        
         Returns:
             List of formatted messages according to provider requirements.
             For Anthropic models, ensures first message has 'user' role.
-
+        
         Raises:
             TypeError: If messages is None or contains invalid message format.
         """
         if messages is None:
             raise TypeError("Messages cannot be None")
-
+            
         # Validate message format first
         for msg in messages:
             if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
-                raise TypeError(
-                    "Invalid message format. Each message must be a dict with 'role' and 'content' keys"
-                )
-
+                raise TypeError("Invalid message format. Each message must be a dict with 'role' and 'content' keys")
+            
         if not self.is_anthropic:
             return messages
-
+            
         # Anthropic requires messages to start with 'user' role
         if not messages or messages[0]["role"] == "system":
             # If first message is system or empty, add a placeholder user message
             return [{"role": "user", "content": "."}, *messages]
-
+                
         return messages
 
     def _get_custom_llm_provider(self) -> str:
@@ -469,7 +413,7 @@ class LLM:
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return params is not None and "tools" in params
+            return "response_format" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
@@ -477,7 +421,7 @@ class LLM:
     def supports_stop_words(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return params is not None and "stop" in params
+            return "stop" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
@@ -486,23 +430,10 @@ class LLM:
         """
         Returns the context window size, using 75% of the maximum to avoid
         cutting off messages mid-thread.
-
-        Raises:
-            ValueError: If a model's context window size is outside valid bounds (1024-2097152)
         """
         if self.context_window_size != 0:
             return self.context_window_size
 
-        MIN_CONTEXT = 1024
-        MAX_CONTEXT = 2097152  # Current max from gemini-1.5-pro
-
-        # Validate all context window sizes
-        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < MIN_CONTEXT or value > MAX_CONTEXT:
-                raise ValueError(
-                    f"Context window for {key} must be between {MIN_CONTEXT} and {MAX_CONTEXT}"
-                )
-
         self.context_window_size = int(
             DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
         )

src/crewai/task.py

@@ -21,6 +21,7 @@ from typing import (
     Union,
 )
 
+from opentelemetry.trace import Span
 from pydantic import (
     UUID4,
     BaseModel,
@@ -35,15 +36,10 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
+from crewai.telemetry.telemetry import Telemetry
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
-from crewai.utilities.events import (
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
 
@@ -187,6 +183,8 @@ class Task(BaseModel):
                     )
         return v
 
+    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
+    _execution_span: Optional[Span] = PrivateAttr(default=None)
     _original_description: Optional[str] = PrivateAttr(default=None)
     _original_expected_output: Optional[str] = PrivateAttr(default=None)
     _original_output_file: Optional[str] = PrivateAttr(default=None)
@@ -350,103 +348,100 @@ class Task(BaseModel):
         tools: Optional[List[Any]],
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
-        try:
-            agent = agent or self.agent
-            self.agent = agent
-            if not agent:
-                raise Exception(
-                    f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
-                )
-
-            self.start_time = datetime.datetime.now()
-
-            self.prompt_context = context
-            tools = tools or self.tools or []
-
-            self.processed_by_agents.add(agent.role)
-            crewai_event_bus.emit(self, TaskStartedEvent(context=context))
-            result = agent.execute_task(
-                task=self,
-                context=context,
-                tools=tools,
+        agent = agent or self.agent
+        self.agent = agent
+        if not agent:
+            raise Exception(
+                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
-            pydantic_output, json_output = self._export_output(result)
-            task_output = TaskOutput(
-                key=self.key,
-                name=self.name,
-                description=self.description,
-                expected_output=self.expected_output,
-                raw=result,
-                pydantic=pydantic_output,
-                json_dict=json_output,
-                agent=agent.role,
-                output_format=self._get_output_format(),
-            )
+        self.start_time = datetime.datetime.now()
+        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
 
-            if self.guardrail:
-                guardrail_result = GuardrailResult.from_tuple(
-                    self.guardrail(task_output)
-                )
-                if not guardrail_result.success:
-                    if self.retry_count >= self.max_retries:
-                        raise Exception(
-                            f"Task failed guardrail validation after {self.max_retries} retries. "
-                            f"Last error: {guardrail_result.error}"
-                        )
+        self.prompt_context = context
+        tools = tools or self.tools or []
 
-                    self.retry_count += 1
-                    context = self.i18n.errors("validation_error").format(
-                        guardrail_result_error=guardrail_result.error,
-                        task_output=task_output.raw,
-                    )
-                    printer = Printer()
-                    printer.print(
-                        content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
-                        color="yellow",
-                    )
-                    return self._execute_core(agent, context, tools)
+        self.processed_by_agents.add(agent.role)
 
-                if guardrail_result.result is None:
+        result = agent.execute_task(
+            task=self,
+            context=context,
+            tools=tools,
+        )
+
+        pydantic_output, json_output = self._export_output(result)
+        task_output = TaskOutput(
+            name=self.name,
+            description=self.description,
+            expected_output=self.expected_output,
+            raw=result,
+            pydantic=pydantic_output,
+            json_dict=json_output,
+            agent=agent.role,
+            output_format=self._get_output_format(),
+        )
+
+        if self.guardrail:
+            guardrail_result = GuardrailResult.from_tuple(self.guardrail(task_output))
+            if not guardrail_result.success:
+                if self.retry_count >= self.max_retries:
                     raise Exception(
-                        "Task guardrail returned None as result. This is not allowed."
+                        f"Task failed guardrail validation after {self.max_retries} retries. "
+                        f"Last error: {guardrail_result.error}"
                     )
 
-                if isinstance(guardrail_result.result, str):
-                    task_output.raw = guardrail_result.result
-                    pydantic_output, json_output = self._export_output(
-                        guardrail_result.result
-                    )
-                    task_output.pydantic = pydantic_output
-                    task_output.json_dict = json_output
-                elif isinstance(guardrail_result.result, TaskOutput):
-                    task_output = guardrail_result.result
-
-            self.output = task_output
-            self.end_time = datetime.datetime.now()
-
-            if self.callback:
-                self.callback(self.output)
-
-            crew = self.agent.crew  # type: ignore[union-attr]
-            if crew and crew.task_callback and crew.task_callback != self.callback:
-                crew.task_callback(self.output)
-
-            if self.output_file:
-                content = (
-                    json_output
-                    if json_output
-                    else pydantic_output.model_dump_json()
-                    if pydantic_output
-                    else result
+                self.retry_count += 1
+                context = self.i18n.errors("validation_error").format(
+                    guardrail_result_error=guardrail_result.error,
+                    task_output=task_output.raw,
                 )
-                self._save_file(content)
-            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output))
-            return task_output
-        except Exception as e:
-            self.end_time = datetime.datetime.now()
-            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e)))
-            raise e  # Re-raise the exception after emitting the event
+                printer = Printer()
+                printer.print(
+                    content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
+                    color="yellow",
+                )
+                return self._execute_core(agent, context, tools)
+
+            if guardrail_result.result is None:
+                raise Exception(
+                    "Task guardrail returned None as result. This is not allowed."
+                )
+
+            if isinstance(guardrail_result.result, str):
+                task_output.raw = guardrail_result.result
+                pydantic_output, json_output = self._export_output(
+                    guardrail_result.result
+                )
+                task_output.pydantic = pydantic_output
+                task_output.json_dict = json_output
+            elif isinstance(guardrail_result.result, TaskOutput):
+                task_output = guardrail_result.result
+
+        self.output = task_output
+        self.end_time = datetime.datetime.now()
+
+        if self.callback:
+            self.callback(self.output)
+
+        crew = self.agent.crew  # type: ignore[union-attr]
+        if crew and crew.task_callback and crew.task_callback != self.callback:
+            crew.task_callback(self.output)
+
+        if self._execution_span:
+            self._telemetry.task_ended(self._execution_span, self, agent.crew)
+            self._execution_span = None
+
+        if self.output_file:
+            content = (
+                json_output
+                if json_output
+                else pydantic_output.model_dump_json()
+                if pydantic_output
+                else result
+            )
+            self._save_file(content)
+
+        return task_output
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -721,9 +716,10 @@ class Task(BaseModel):
                     file.write(str(result))
         except (OSError, IOError) as e:
             raise RuntimeError(
-                "\n".join(
-                    [f"Failed to save output file: {e}", FILEWRITER_RECOMMENDATION]
-                )
+                "\n".join([
+                    f"Failed to save output file: {e}",
+                    FILEWRITER_RECOMMENDATION
+                ])
             )
         return None
 

src/crewai/tasks/task_output.py

@@ -9,7 +9,6 @@ from crewai.tasks.output_format import OutputFormat
 class TaskOutput(BaseModel):
     """Class that represents the result of a task."""
 
-    key: Optional[str] = Field(description="Key of the task", default=None)
     description: str = Field(description="Description of the task")
     name: Optional[str] = Field(description="Name of the task", default=None)
     expected_output: Optional[str] = Field(

src/crewai/tools/tool_usage.py

@@ -10,21 +10,20 @@ from typing import Any, Dict, List, Optional, Union
 import json5
 from json_repair import repair_json
 
+import crewai.utilities.events as events
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
+from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.tool_usage_events import (
-    ToolSelectionErrorEvent,
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolValidateInputErrorEvent,
-)
 
+try:
+    import agentops  # type: ignore
+except ImportError:
+    agentops = None
 OPENAI_BIGGER_MODELS = [
     "gpt-4",
     "gpt-4o",
@@ -137,6 +136,7 @@ class ToolUsage:
         tool: Any,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:  # TODO: Fix this return type
+        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None  # type: ignore
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
@@ -212,6 +212,10 @@ class ToolUsage:
                     return error  # type: ignore # No return value expected
 
                 self.task.increment_tools_errors()
+                if agentops:
+                    agentops.record(
+                        agentops.ErrorEvent(exception=e, trigger_event=tool_event)
+                    )
                 return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
 
             if self.tools_handler:
@@ -227,6 +231,9 @@ class ToolUsage:
                 self.tools_handler.on_tool_use(
                     calling=calling, output=result, should_cache=should_cache
                 )
+
+        if agentops:
+            agentops.record(tool_event)
         self._telemetry.tool_usage(
             llm=self.function_calling_llm,
             tool_name=tool.name,
@@ -301,33 +308,14 @@ class ToolUsage:
             ):
                 return tool
         self.task.increment_tools_errors()
-        tool_selection_data = {
-            "agent_key": self.agent.key,
-            "agent_role": self.agent.role,
-            "tool_name": tool_name,
-            "tool_args": {},
-            "tool_class": self.tools_description,
-        }
         if tool_name and tool_name != "":
-            error = f"Action '{tool_name}' don't exist, these are the only available Actions:\n{self.tools_description}"
-            crewai_event_bus.emit(
-                self,
-                ToolSelectionErrorEvent(
-                    **tool_selection_data,
-                    error=error,
-                ),
+            raise Exception(
+                f"Action '{tool_name}' don't exist, these are the only available Actions:\n{self.tools_description}"
             )
-            raise Exception(error)
         else:
-            error = f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
-            crewai_event_bus.emit(
-                self,
-                ToolSelectionErrorEvent(
-                    **tool_selection_data,
-                    error=error,
-                ),
+            raise Exception(
+                f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
             )
-            raise Exception(error)
 
     def _render(self) -> str:
         """Render the tool name and description in plain text."""
@@ -463,33 +451,18 @@ class ToolUsage:
             if isinstance(arguments, dict):
                 return arguments
         except Exception as e:
-            error = f"Failed to repair JSON: {e}"
-            self._printer.print(content=error, color="red")
+            self._printer.print(content=f"Failed to repair JSON: {e}", color="red")
 
-        error_message = (
-            "Tool input must be a valid dictionary in JSON or Python literal format"
-        )
-        self._emit_validate_input_error(error_message)
         # If all parsing attempts fail, raise an error
-        raise Exception(error_message)
-
-    def _emit_validate_input_error(self, final_error: str):
-        tool_selection_data = {
-            "agent_key": self.agent.key,
-            "agent_role": self.agent.role,
-            "tool_name": self.action.tool,
-            "tool_args": str(self.action.tool_input),
-            "tool_class": self.__class__.__name__,
-        }
-
-        crewai_event_bus.emit(
-            self,
-            ToolValidateInputErrorEvent(**tool_selection_data, error=final_error),
+        raise Exception(
+            "Tool input must be a valid dictionary in JSON or Python literal format"
         )
 
     def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)
-        crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
+        events.emit(
+            source=self, event=ToolUsageError(**{**event_data, "error": str(e)})
+        )
 
     def on_tool_use_finished(
         self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
@@ -503,7 +476,7 @@ class ToolUsage:
                 "from_cache": from_cache,
             }
         )
-        crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
+        events.emit(source=self, event=ToolUsageFinished(**event_data))
 
     def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
         return {

src/crewai/tools/tool_usage_events.py

@@ -0,0 +1,24 @@
+from datetime import datetime
+from typing import Any, Dict
+
+from pydantic import BaseModel
+
+
+class ToolUsageEvent(BaseModel):
+    agent_key: str
+    agent_role: str
+    tool_name: str
+    tool_args: Dict[str, Any]
+    tool_class: str
+    run_attempts: int | None = None
+    delegations: int | None = None
+
+
+class ToolUsageFinished(ToolUsageEvent):
+    started_at: datetime
+    finished_at: datetime
+    from_cache: bool = False
+
+
+class ToolUsageError(ToolUsageEvent):
+    error: str

src/crewai/translations/en.json

@@ -23,6 +23,7 @@
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "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.",
+    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
     "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."
   },
@@ -39,8 +40,8 @@
     "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
   },
   "tools": {
-    "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don't reference things but instead explain them.",
-    "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don't reference things but instead explain them.",
+    "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
+    "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them.",
     "add_image": {
       "name": "Add image to content",
       "description": "See image to understand its content, you can optionally ask a question about the image",

src/crewai/utilities/constants.py

@@ -4,4 +4,3 @@ DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
 MAX_FILE_NAME_LENGTH = 255
-EMITTER_COLOR = "bold_blue"

src/crewai/utilities/converter.py

@@ -20,11 +20,11 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
-    def to_pydantic(self, current_attempt=1) -> BaseModel:
+    def to_pydantic(self, current_attempt=1):
         """Convert text to pydantic."""
         try:
             if self.llm.supports_function_calling():
-                result = self._create_instructor().to_pydantic()
+                return self._create_instructor().to_pydantic()
             else:
                 response = self.llm.call(
                     [
@@ -32,40 +32,18 @@ class Converter(OutputConverter):
                         {"role": "user", "content": self.text},
                     ]
                 )
-                try:
-                    # Try to directly validate the response JSON
-                    result = self.model.model_validate_json(response)
-                except ValidationError:
-                    # If direct validation fails, attempt to extract valid JSON
-                    result = handle_partial_json(response, self.model, False, None)
-                    # Ensure result is a BaseModel instance
-                    if not isinstance(result, BaseModel):
-                        if isinstance(result, dict):
-                            result = self.model.parse_obj(result)
-                        elif isinstance(result, str):
-                            try:
-                                parsed = json.loads(result)
-                                result = self.model.parse_obj(parsed)
-                            except Exception as parse_err:
-                                raise ConverterError(
-                                    f"Failed to convert partial JSON result into Pydantic: {parse_err}"
-                                )
-                        else:
-                            raise ConverterError(
-                                "handle_partial_json returned an unexpected type."
-                            )
-            return result
+                return self.model.model_validate_json(response)
         except ValidationError as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to validation error: {e}"
+                f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
             )
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to error: {e}"
+                f"Failed to convert text into a Pydantic model due to the following error: {e}"
             )
 
     def to_json(self, current_attempt=1):
@@ -219,15 +197,11 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
         instructions += (
-            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
-            f"The JSON must follow this schema exactly:\n```json\n{model_schema}\n```"
+            f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
         )
     else:
         model_description = generate_model_description(model)
-        instructions += (
-            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
-            f"The JSON must follow this format exactly:\n{model_description}"
-        )
+        instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
     return instructions
 
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,12 +1,11 @@
 from collections import defaultdict
 
-from pydantic import BaseModel, Field, InstanceOf
+from pydantic import BaseModel, Field
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
-from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -24,7 +23,7 @@ class CrewEvaluator:
 
     Attributes:
         crew (Crew): The crew of agents to evaluate.
-        eval_llm (LLM): Language model instance to use for evaluations
+        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
         tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
         iteration (int): The current iteration of the evaluation.
     """
@@ -33,9 +32,9 @@ class CrewEvaluator:
     run_execution_times: defaultdict = defaultdict(list)
     iteration: int = 0
 
-    def __init__(self, crew, eval_llm: InstanceOf[LLM]):
+    def __init__(self, crew, openai_model_name: str):
         self.crew = crew
-        self.llm = eval_llm
+        self.openai_model_name = openai_model_name
         self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
@@ -52,7 +51,7 @@ class CrewEvaluator:
             ),
             backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
             verbose=False,
-            llm=self.llm,
+            llm=self.openai_model_name,
         )
 
     def _evaluation_task(
@@ -182,7 +181,7 @@ class CrewEvaluator:
                 self.crew,
                 evaluation_result.pydantic.quality,
                 current_task.execution_duration,
-                self.llm.model,
+                self.openai_model_name,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

src/crewai/utilities/evaluators/task_evaluator.py

@@ -3,9 +3,19 @@ from typing import List
 from pydantic import BaseModel, Field
 
 from crewai.utilities import Converter
-from crewai.utilities.events import TaskEvaluationEvent, crewai_event_bus
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
+agentops = None
+try:
+    from agentops import track_agent  # type: ignore
+except ImportError:
+
+    def track_agent(name):
+        def noop(f):
+            return f
+
+        return noop
+
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")
@@ -38,15 +48,12 @@ class TrainingTaskEvaluation(BaseModel):
     )
 
 
+@track_agent(name="Task Evaluator")
 class TaskEvaluator:
     def __init__(self, original_agent):
         self.llm = original_agent.llm
-        self.original_agent = original_agent
 
     def evaluate(self, task, output) -> TaskEvaluation:
-        crewai_event_bus.emit(
-            self, TaskEvaluationEvent(evaluation_type="task_evaluation")
-        )
         evaluation_query = (
             f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"
             f"Task Description:\n{task.description}\n\n"
@@ -83,9 +90,6 @@ class TaskEvaluator:
             - training_data (dict): The training data to be evaluated.
             - agent_id (str): The ID of the agent.
         """
-        crewai_event_bus.emit(
-            self, TaskEvaluationEvent(evaluation_type="training_data_evaluation")
-        )
 
         output_training_data = training_data[agent_id]
         final_aggregated_data = ""

src/crewai/utilities/events.py

@@ -0,0 +1,44 @@
+from functools import wraps
+from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
+
+from pydantic import BaseModel
+
+T = TypeVar("T")
+EVT = TypeVar("EVT", bound=BaseModel)
+
+
+class Emitter(Generic[T, EVT]):
+    _listeners: Dict[Type[EVT], List[Callable]] = {}
+
+    def on(self, event_type: Type[EVT]):
+        def decorator(func: Callable):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                return func(*args, **kwargs)
+
+            self._listeners.setdefault(event_type, []).append(wrapper)
+            return wrapper
+
+        return decorator
+
+    def emit(self, source: T, event: EVT) -> None:
+        event_type = type(event)
+        for func in self._listeners.get(event_type, []):
+            func(source, event)
+
+
+default_emitter = Emitter[Any, BaseModel]()
+
+
+def emit(source: Any, event: BaseModel, raise_on_error: bool = False) -> None:
+    try:
+        default_emitter.emit(source, event)
+    except Exception as e:
+        if raise_on_error:
+            raise e
+        else:
+            print(f"Error emitting event: {e}")
+
+
+def on(event_type: Type[BaseModel]) -> Callable:
+    return default_emitter.on(event_type)

src/crewai/utilities/events/__init__.py

@@ -1,41 +0,0 @@
-from .crew_events import (
-    CrewKickoffStartedEvent,
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewTrainStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTestStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-)
-from .agent_events import (
-    AgentExecutionStartedEvent,
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-)
-from .task_events import TaskStartedEvent, TaskCompletedEvent, TaskFailedEvent, TaskEvaluationEvent
-from .flow_events import (
-    FlowCreatedEvent,
-    FlowStartedEvent,
-    FlowFinishedEvent,
-    FlowPlotEvent,
-    MethodExecutionStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionFailedEvent,
-)
-from .crewai_event_bus import CrewAIEventsBus, crewai_event_bus
-from .tool_usage_events import (
-    ToolUsageFinishedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-    ToolExecutionErrorEvent,
-    ToolSelectionErrorEvent,
-    ToolUsageEvent,
-    ToolValidateInputErrorEvent,
-)
-from .llm_events import LLMCallCompletedEvent, LLMCallFailedEvent, LLMCallStartedEvent
-
-# events
-from .event_listener import EventListener
-from .third_party.agentops_listener import agentops_listener

src/crewai/utilities/events/agent_events.py

@@ -1,40 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Union
-
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tools.base_tool import BaseTool
-from crewai.tools.structured_tool import CrewStructuredTool
-
-from .base_events import CrewEvent
-
-if TYPE_CHECKING:
-    from crewai.agents.agent_builder.base_agent import BaseAgent
-
-
-class AgentExecutionStartedEvent(CrewEvent):
-    """Event emitted when an agent starts executing a task"""
-
-    agent: BaseAgent
-    task: Any
-    tools: Optional[Sequence[Union[BaseTool, CrewStructuredTool]]]
-    task_prompt: str
-    type: str = "agent_execution_started"
-
-    model_config = {"arbitrary_types_allowed": True}
-
-
-class AgentExecutionCompletedEvent(CrewEvent):
-    """Event emitted when an agent completes executing a task"""
-
-    agent: BaseAgent
-    task: Any
-    output: str
-    type: str = "agent_execution_completed"
-
-
-class AgentExecutionErrorEvent(CrewEvent):
-    """Event emitted when an agent encounters an error during execution"""
-
-    agent: BaseAgent
-    task: Any
-    error: str
-    type: str = "agent_execution_error"

src/crewai/utilities/events/base_event_listener.py

@@ -1,14 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-
-from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus, crewai_event_bus
-
-
-class BaseEventListener(ABC):
-    def __init__(self):
-        super().__init__()
-        self.setup_listeners(crewai_event_bus)
-
-    @abstractmethod
-    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus):
-        pass

src/crewai/utilities/events/base_events.py

@@ -1,10 +0,0 @@
-from datetime import datetime
-
-from pydantic import BaseModel, Field
-
-
-class CrewEvent(BaseModel):
-    """Base class for all crew events"""
-
-    timestamp: datetime = Field(default_factory=datetime.now)
-    type: str

src/crewai/utilities/events/crew_events.py

@@ -1,81 +0,0 @@
-from typing import Any, Dict, Optional, Union
-
-from pydantic import InstanceOf
-
-from crewai.utilities.events.base_events import CrewEvent
-
-
-class CrewKickoffStartedEvent(CrewEvent):
-    """Event emitted when a crew starts execution"""
-
-    crew_name: Optional[str]
-    inputs: Optional[Dict[str, Any]]
-    type: str = "crew_kickoff_started"
-
-
-class CrewKickoffCompletedEvent(CrewEvent):
-    """Event emitted when a crew completes execution"""
-
-    crew_name: Optional[str]
-    output: Any
-    type: str = "crew_kickoff_completed"
-
-
-class CrewKickoffFailedEvent(CrewEvent):
-    """Event emitted when a crew fails to complete execution"""
-
-    error: str
-    crew_name: Optional[str]
-    type: str = "crew_kickoff_failed"
-
-
-class CrewTrainStartedEvent(CrewEvent):
-    """Event emitted when a crew starts training"""
-
-    crew_name: Optional[str]
-    n_iterations: int
-    filename: str
-    inputs: Optional[Dict[str, Any]]
-    type: str = "crew_train_started"
-
-
-class CrewTrainCompletedEvent(CrewEvent):
-    """Event emitted when a crew completes training"""
-
-    crew_name: Optional[str]
-    n_iterations: int
-    filename: str
-    type: str = "crew_train_completed"
-
-
-class CrewTrainFailedEvent(CrewEvent):
-    """Event emitted when a crew fails to complete training"""
-
-    error: str
-    crew_name: Optional[str]
-    type: str = "crew_train_failed"
-
-
-class CrewTestStartedEvent(CrewEvent):
-    """Event emitted when a crew starts testing"""
-
-    crew_name: Optional[str]
-    n_iterations: int
-    eval_llm: Optional[Union[str, Any]]
-    inputs: Optional[Dict[str, Any]]
-    type: str = "crew_test_started"
-
-
-class CrewTestCompletedEvent(CrewEvent):
-    """Event emitted when a crew completes testing"""
-
-    crew_name: Optional[str]
-    type: str = "crew_test_completed"
-
-
-class CrewTestFailedEvent(CrewEvent):
-    """Event emitted when a crew fails to complete testing"""
-
-    error: str
-    crew_name: Optional[str]
-    type: str = "crew_test_failed"

src/crewai/utilities/events/crewai_event_bus.py

@@ -1,113 +0,0 @@
-import threading
-from contextlib import contextmanager
-from typing import Any, Callable, Dict, List, Type, TypeVar, cast
-
-from blinker import Signal
-
-from crewai.utilities.events.base_events import CrewEvent
-from crewai.utilities.events.event_types import EventTypes
-
-EventT = TypeVar("EventT", bound=CrewEvent)
-
-
-class CrewAIEventsBus:
-    """
-    A singleton event bus that uses blinker signals for event handling.
-    Allows both internal (Flow/Crew) and external event handling.
-    """
-
-    _instance = None
-    _lock = threading.Lock()
-
-    def __new__(cls):
-        if cls._instance is None:
-            with cls._lock:
-                if cls._instance is None:  # prevent race condition
-                    cls._instance = super(CrewAIEventsBus, cls).__new__(cls)
-                    cls._instance._initialize()
-        return cls._instance
-
-    def _initialize(self) -> None:
-        """Initialize the event bus internal state"""
-        self._signal = Signal("crewai_event_bus")
-        self._handlers: Dict[Type[CrewEvent], List[Callable]] = {}
-
-    def on(
-        self, event_type: Type[EventT]
-    ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]:
-        """
-        Decorator to register an event handler for a specific event type.
-
-        Usage:
-            @crewai_event_bus.on(AgentExecutionCompletedEvent)
-            def on_agent_execution_completed(
-                source: Any, event: AgentExecutionCompletedEvent
-            ):
-                print(f"👍 Agent '{event.agent}' completed task")
-                print(f"   Output: {event.output}")
-        """
-
-        def decorator(
-            handler: Callable[[Any, EventT], None],
-        ) -> Callable[[Any, EventT], None]:
-            if event_type not in self._handlers:
-                self._handlers[event_type] = []
-            self._handlers[event_type].append(
-                cast(Callable[[Any, EventT], None], handler)
-            )
-            return handler
-
-        return decorator
-
-    def emit(self, source: Any, event: CrewEvent) -> None:
-        """
-        Emit an event to all registered handlers
-
-        Args:
-            source: The object emitting the event
-            event: The event instance to emit
-        """
-        event_type = type(event)
-        if event_type in self._handlers:
-            for handler in self._handlers[event_type]:
-                handler(source, event)
-        self._signal.send(source, event=event)
-
-    def clear_handlers(self) -> None:
-        """Clear all registered event handlers - useful for testing"""
-        self._handlers.clear()
-
-    def register_handler(
-        self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]
-    ) -> None:
-        """Register an event handler for a specific event type"""
-        if event_type not in self._handlers:
-            self._handlers[event_type] = []
-        self._handlers[event_type].append(
-            cast(Callable[[Any, EventTypes], None], handler)
-        )
-
-    @contextmanager
-    def scoped_handlers(self):
-        """
-        Context manager for temporary event handling scope.
-        Useful for testing or temporary event handling.
-
-        Usage:
-            with crewai_event_bus.scoped_handlers():
-                @crewai_event_bus.on(CrewKickoffStarted)
-                def temp_handler(source, event):
-                    print("Temporary handler")
-                # Do stuff...
-            # Handlers are cleared after the context
-        """
-        previous_handlers = self._handlers.copy()
-        self._handlers.clear()
-        try:
-            yield
-        finally:
-            self._handlers = previous_handlers
-
-
-# Global instance
-crewai_event_bus = CrewAIEventsBus()

src/crewai/utilities/events/event_listener.py

@@ -1,288 +0,0 @@
-from typing import Any, Dict
-
-from pydantic import Field, PrivateAttr
-
-from crewai.task import Task
-from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities import Logger
-from crewai.utilities.constants import EMITTER_COLOR
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-)
-
-from .agent_events import AgentExecutionCompletedEvent, AgentExecutionStartedEvent
-from .crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
-from .flow_events import (
-    FlowCreatedEvent,
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
-from .task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent
-from .tool_usage_events import (
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
-
-
-class EventListener(BaseEventListener):
-    _instance = None
-    _telemetry: Telemetry = PrivateAttr(default_factory=lambda: Telemetry())
-    logger = Logger(verbose=True, default_color=EMITTER_COLOR)
-    execution_spans: Dict[Task, Any] = Field(default_factory=dict)
-
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-            cls._instance._initialized = False
-        return cls._instance
-
-    def __init__(self):
-        if not hasattr(self, "_initialized") or not self._initialized:
-            super().__init__()
-            self._telemetry = Telemetry()
-            self._telemetry.set_tracer()
-            self.execution_spans = {}
-            self._initialized = True
-
-    # ----------- CREW EVENTS -----------
-
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event: CrewKickoffStartedEvent):
-            self.logger.log(
-                f"🚀 Crew '{event.crew_name}' started, {source.id}",
-                event.timestamp,
-            )
-            self._telemetry.crew_execution_span(source, event.inputs)
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event: CrewKickoffCompletedEvent):
-            final_string_output = event.output.raw
-            self._telemetry.end_crew(source, final_string_output)
-            self.logger.log(
-                f"✅ Crew '{event.crew_name}' completed, {source.id}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewKickoffFailedEvent)
-        def on_crew_failed(source, event: CrewKickoffFailedEvent):
-            self.logger.log(
-                f"❌ Crew '{event.crew_name}' failed, {source.id}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTestStartedEvent)
-        def on_crew_test_started(source, event: CrewTestStartedEvent):
-            cloned_crew = source.copy()
-            self._telemetry.test_execution_span(
-                cloned_crew,
-                event.n_iterations,
-                event.inputs,
-                event.eval_llm or "",
-            )
-            self.logger.log(
-                f"🚀 Crew '{event.crew_name}' started test, {source.id}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTestCompletedEvent)
-        def on_crew_test_completed(source, event: CrewTestCompletedEvent):
-            self.logger.log(
-                f"✅ Crew '{event.crew_name}' completed test",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTestFailedEvent)
-        def on_crew_test_failed(source, event: CrewTestFailedEvent):
-            self.logger.log(
-                f"❌ Crew '{event.crew_name}' failed test",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTrainStartedEvent)
-        def on_crew_train_started(source, event: CrewTrainStartedEvent):
-            self.logger.log(
-                f"📋 Crew '{event.crew_name}' started train",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTrainCompletedEvent)
-        def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
-            self.logger.log(
-                f"✅ Crew '{event.crew_name}' completed train",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(CrewTrainFailedEvent)
-        def on_crew_train_failed(source, event: CrewTrainFailedEvent):
-            self.logger.log(
-                f"❌ Crew '{event.crew_name}' failed train",
-                event.timestamp,
-            )
-
-        # ----------- TASK EVENTS -----------
-
-        @crewai_event_bus.on(TaskStartedEvent)
-        def on_task_started(source, event: TaskStartedEvent):
-            span = self._telemetry.task_started(crew=source.agent.crew, task=source)
-            self.execution_spans[source] = span
-
-            self.logger.log(
-                f"📋 Task started: {source.description}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(TaskCompletedEvent)
-        def on_task_completed(source, event: TaskCompletedEvent):
-            span = self.execution_spans.get(source)
-            if span:
-                self._telemetry.task_ended(span, source, source.agent.crew)
-            self.logger.log(
-                f"✅ Task completed: {source.description}",
-                event.timestamp,
-            )
-            self.execution_spans[source] = None
-
-        @crewai_event_bus.on(TaskFailedEvent)
-        def on_task_failed(source, event: TaskFailedEvent):
-            span = self.execution_spans.get(source)
-            if span:
-                if source.agent and source.agent.crew:
-                    self._telemetry.task_ended(span, source, source.agent.crew)
-                self.execution_spans[source] = None
-            self.logger.log(
-                f"❌ Task failed: {source.description}",
-                event.timestamp,
-            )
-
-        # ----------- AGENT EVENTS -----------
-
-        @crewai_event_bus.on(AgentExecutionStartedEvent)
-        def on_agent_execution_started(source, event: AgentExecutionStartedEvent):
-            self.logger.log(
-                f"🤖 Agent '{event.agent.role}' started task",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event: AgentExecutionCompletedEvent):
-            self.logger.log(
-                f"✅ Agent '{event.agent.role}' completed task",
-                event.timestamp,
-            )
-
-        # ----------- FLOW EVENTS -----------
-
-        @crewai_event_bus.on(FlowCreatedEvent)
-        def on_flow_created(source, event: FlowCreatedEvent):
-            self._telemetry.flow_creation_span(event.flow_name)
-            self.logger.log(
-                f"🌊 Flow Created: '{event.flow_name}'",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(FlowStartedEvent)
-        def on_flow_started(source, event: FlowStartedEvent):
-            self._telemetry.flow_execution_span(
-                event.flow_name, list(source._methods.keys())
-            )
-            self.logger.log(
-                f"🤖 Flow Started: '{event.flow_name}', {source.flow_id}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(FlowFinishedEvent)
-        def on_flow_finished(source, event: FlowFinishedEvent):
-            self.logger.log(
-                f"👍 Flow Finished: '{event.flow_name}', {source.flow_id}",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(MethodExecutionStartedEvent)
-        def on_method_execution_started(source, event: MethodExecutionStartedEvent):
-            self.logger.log(
-                f"🤖 Flow Method Started: '{event.method_name}'",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(MethodExecutionFailedEvent)
-        def on_method_execution_failed(source, event: MethodExecutionFailedEvent):
-            self.logger.log(
-                f"❌ Flow Method Failed: '{event.method_name}'",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(MethodExecutionFinishedEvent)
-        def on_method_execution_finished(source, event: MethodExecutionFinishedEvent):
-            self.logger.log(
-                f"👍 Flow Method Finished: '{event.method_name}'",
-                event.timestamp,
-            )
-
-        # ----------- TOOL USAGE EVENTS -----------
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.logger.log(
-                f"🤖 Tool Usage Started: '{event.tool_name}'",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(ToolUsageFinishedEvent)
-        def on_tool_usage_finished(source, event: ToolUsageFinishedEvent):
-            self.logger.log(
-                f"✅ Tool Usage Finished: '{event.tool_name}'",
-                event.timestamp,
-                #
-            )
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            self.logger.log(
-                f"❌ Tool Usage Error: '{event.tool_name}'",
-                event.timestamp,
-                #
-            )
-
-        # ----------- LLM EVENTS -----------
-
-        @crewai_event_bus.on(LLMCallStartedEvent)
-        def on_llm_call_started(source, event: LLMCallStartedEvent):
-            self.logger.log(
-                f"🤖 LLM Call Started",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(LLMCallCompletedEvent)
-        def on_llm_call_completed(source, event: LLMCallCompletedEvent):
-            self.logger.log(
-                f"✅ LLM Call Completed",
-                event.timestamp,
-            )
-
-        @crewai_event_bus.on(LLMCallFailedEvent)
-        def on_llm_call_failed(source, event: LLMCallFailedEvent):
-            self.logger.log(
-                f"❌ LLM Call Failed: '{event.error}'",
-                event.timestamp,
-            )
-
-
-event_listener = EventListener()

src/crewai/utilities/events/event_types.py

@@ -1,61 +0,0 @@
-from typing import Union
-
-from .agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from .crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
-from .flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
-from .task_events import (
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
-)
-from .tool_usage_events import (
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
-
-EventTypes = Union[
-    CrewKickoffStartedEvent,
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewTestStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTrainStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    AgentExecutionStartedEvent,
-    AgentExecutionCompletedEvent,
-    TaskStartedEvent,
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    FlowStartedEvent,
-    FlowFinishedEvent,
-    MethodExecutionStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionFailedEvent,
-    AgentExecutionErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-]

src/crewai/utilities/events/flow_events.py

@@ -1,71 +0,0 @@
-from typing import Any, Dict, Optional, Union
-
-from pydantic import BaseModel
-
-from .base_events import CrewEvent
-
-
-class FlowEvent(CrewEvent):
-    """Base class for all flow events"""
-
-    type: str
-    flow_name: str
-
-
-class FlowStartedEvent(FlowEvent):
-    """Event emitted when a flow starts execution"""
-
-    flow_name: str
-    inputs: Optional[Dict[str, Any]] = None
-    type: str = "flow_started"
-
-
-class FlowCreatedEvent(FlowEvent):
-    """Event emitted when a flow is created"""
-
-    flow_name: str
-    type: str = "flow_created"
-
-
-class MethodExecutionStartedEvent(FlowEvent):
-    """Event emitted when a flow method starts execution"""
-
-    flow_name: str
-    method_name: str
-    state: Union[Dict[str, Any], BaseModel]
-    params: Optional[Dict[str, Any]] = None
-    type: str = "method_execution_started"
-
-
-class MethodExecutionFinishedEvent(FlowEvent):
-    """Event emitted when a flow method completes execution"""
-
-    flow_name: str
-    method_name: str
-    result: Any = None
-    state: Union[Dict[str, Any], BaseModel]
-    type: str = "method_execution_finished"
-
-
-class MethodExecutionFailedEvent(FlowEvent):
-    """Event emitted when a flow method fails execution"""
-
-    flow_name: str
-    method_name: str
-    error: Any
-    type: str = "method_execution_failed"
-
-
-class FlowFinishedEvent(FlowEvent):
-    """Event emitted when a flow completes execution"""
-
-    flow_name: str
-    result: Optional[Any] = None
-    type: str = "flow_finished"
-
-
-class FlowPlotEvent(FlowEvent):
-    """Event emitted when a flow plot is created"""
-
-    flow_name: str
-    type: str = "flow_plot"

src/crewai/utilities/events/llm_events.py

@@ -1,36 +0,0 @@
-from enum import Enum
-from typing import Any, Dict, List, Optional, Union
-
-from crewai.utilities.events.base_events import CrewEvent
-
-
-class LLMCallType(Enum):
-    """Type of LLM call being made"""
-
-    TOOL_CALL = "tool_call"
-    LLM_CALL = "llm_call"
-
-
-class LLMCallStartedEvent(CrewEvent):
-    """Event emitted when a LLM call starts"""
-
-    type: str = "llm_call_started"
-    messages: Union[str, List[Dict[str, str]]]
-    tools: Optional[List[dict]] = None
-    callbacks: Optional[List[Any]] = None
-    available_functions: Optional[Dict[str, Any]] = None
-
-
-class LLMCallCompletedEvent(CrewEvent):
-    """Event emitted when a LLM call completes"""
-
-    type: str = "llm_call_completed"
-    response: Any
-    call_type: LLMCallType
-
-
-class LLMCallFailedEvent(CrewEvent):
-    """Event emitted when a LLM call fails"""
-
-    error: str
-    type: str = "llm_call_failed"

src/crewai/utilities/events/task_events.py

@@ -1,32 +0,0 @@
-from typing import Optional
-
-from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.events.base_events import CrewEvent
-
-
-class TaskStartedEvent(CrewEvent):
-    """Event emitted when a task starts"""
-
-    type: str = "task_started"
-    context: Optional[str]
-
-
-class TaskCompletedEvent(CrewEvent):
-    """Event emitted when a task completes"""
-
-    output: TaskOutput
-    type: str = "task_completed"
-
-
-class TaskFailedEvent(CrewEvent):
-    """Event emitted when a task fails"""
-
-    error: str
-    type: str = "task_failed"
-
-
-class TaskEvaluationEvent(CrewEvent):
-    """Event emitted when a task evaluation is completed"""
-
-    type: str = "task_evaluation"
-    evaluation_type: str

src/crewai/utilities/events/third_party/__init__.py

@@ -1 +0,0 @@
-from .agentops_listener import agentops_listener

src/crewai/utilities/events/third_party/agentops_listener.py

@@ -1,67 +0,0 @@
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-
-        @crewai_event_bus.on(TaskEvaluationEvent)
-        def on_task_evaluation(source, event: TaskEvaluationEvent):
-            if self.session:
-                self.session.create_agent(
-                    name="Task Evaluator", agent_id=str(source.original_agent.id)
-                )
-
-
-agentops_listener = AgentOpsListener()

src/crewai/utilities/events/tool_usage_events.py

@@ -1,64 +0,0 @@
-from datetime import datetime
-from typing import Any, Callable, Dict
-
-from .base_events import CrewEvent
-
-
-class ToolUsageEvent(CrewEvent):
-    """Base event for tool usage tracking"""
-
-    agent_key: str
-    agent_role: str
-    tool_name: str
-    tool_args: Dict[str, Any] | str
-    tool_class: str
-    run_attempts: int | None = None
-    delegations: int | None = None
-
-    model_config = {"arbitrary_types_allowed": True}
-
-
-class ToolUsageStartedEvent(ToolUsageEvent):
-    """Event emitted when a tool execution is started"""
-
-    type: str = "tool_usage_started"
-
-
-class ToolUsageFinishedEvent(ToolUsageEvent):
-    """Event emitted when a tool execution is completed"""
-
-    started_at: datetime
-    finished_at: datetime
-    from_cache: bool = False
-    type: str = "tool_usage_finished"
-
-
-class ToolUsageErrorEvent(ToolUsageEvent):
-    """Event emitted when a tool execution encounters an error"""
-
-    error: Any
-    type: str = "tool_usage_error"
-
-
-class ToolValidateInputErrorEvent(ToolUsageEvent):
-    """Event emitted when a tool input validation encounters an error"""
-
-    error: Any
-    type: str = "tool_validate_input_error"
-
-
-class ToolSelectionErrorEvent(ToolUsageEvent):
-    """Event emitted when a tool selection encounters an error"""
-
-    error: Any
-    type: str = "tool_selection_error"
-
-
-class ToolExecutionErrorEvent(CrewEvent):
-    """Event emitted when a tool execution encounters an error"""
-
-    error: Any
-    type: str = "tool_execution_error"
-    tool_name: str
-    tool_args: Dict[str, Any]
-    tool_class: Callable

src/crewai/utilities/llm_utils.py

@@ -44,7 +44,6 @@ def create_llm(
         # Extract attributes with explicit types
         model = (
             getattr(llm_value, "model_name", None)
-            or getattr(llm_value, "model", None)
             or getattr(llm_value, "deployment_name", None)
             or str(llm_value)
         )

src/crewai/utilities/logger.py

@@ -8,11 +8,8 @@ from crewai.utilities.printer import Printer
 class Logger(BaseModel):
     verbose: bool = Field(default=False)
     _printer: Printer = PrivateAttr(default_factory=Printer)
-    default_color: str = Field(default="bold_yellow")
 
-    def log(self, level, message, color=None):
-        if color is None:
-            color = self.default_color
+    def log(self, level, message, color="bold_yellow"):
         if self.verbose:
             timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
             self._printer.print(

src/crewai/utilities/token_counter_callback.py

@@ -30,14 +30,8 @@ class TokenCalcHandler(CustomLogger):
                     if hasattr(usage, "prompt_tokens"):
                         self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
                     if hasattr(usage, "completion_tokens"):
-                        self.token_cost_process.sum_completion_tokens(
-                            usage.completion_tokens
-                        )
-                    if (
-                        hasattr(usage, "prompt_tokens_details")
-                        and usage.prompt_tokens_details
-                        and usage.prompt_tokens_details.cached_tokens
-                    ):
+                        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+                    if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
                         self.token_cost_process.sum_cached_prompt_tokens(
                             usage.prompt_tokens_details.cached_tokens
                         )

tests/agent_test.py

@@ -8,7 +8,7 @@ import pytest
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
-from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -16,9 +16,9 @@ from crewai.llm import LLM
 from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
+from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
+from crewai.utilities.events import Emitter
 
 
 def test_agent_llm_creation_with_env_vars():
@@ -154,19 +154,15 @@ def test_agent_execution_with_tools():
         agent=agent,
         expected_output="The result of the multiplication.",
     )
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
-
-    output = agent.execute_task(task)
-    assert output == "The result of the multiplication is 12."
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0], ToolUsageFinishedEvent)
-    assert received_events[0].tool_name == "multiplier"
-    assert received_events[0].tool_args == {"first_number": 3, "second_number": 4}
+    with patch.object(Emitter, "emit") as emit:
+        output = agent.execute_task(task)
+        assert output == "The result of the multiplication is 12."
+        assert emit.call_count == 1
+        args, _ = emit.call_args
+        assert isinstance(args[1], ToolUsageFinished)
+        assert not args[1].from_cache
+        assert args[1].tool_name == "multiplier"
+        assert args[1].tool_args == {"first_number": 3, "second_number": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -253,14 +249,10 @@ def test_cache_hitting():
         "multiplier-{'first_number': 3, 'second_number': 3}": 9,
         "multiplier-{'first_number': 12, 'second_number': 3}": 36,
     }
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
 
     with (
         patch.object(CacheHandler, "read") as read,
+        patch.object(Emitter, "emit") as emit,
     ):
         read.return_value = "0"
         task = Task(
@@ -273,9 +265,10 @@ def test_cache_hitting():
         read.assert_called_with(
             tool="multiplier", input={"first_number": 2, "second_number": 6}
         )
-        assert len(received_events) == 1
-        assert isinstance(received_events[0], ToolUsageFinishedEvent)
-        assert received_events[0].from_cache
+        assert emit.call_count == 1
+        args, _ = emit.call_args
+        assert isinstance(args[1], ToolUsageFinished)
+        assert args[1].from_cache
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -990,35 +983,23 @@ def test_agent_human_input():
     # Side effect function for _ask_human_input to simulate multiple feedback iterations
     feedback_responses = iter(
         [
-            "Don't say hi, say Hello instead!",  # First feedback: instruct change
-            "",  # Second feedback: empty string signals acceptance
+            "Don't say hi, say Hello instead!",  # First feedback
+            "looks good",  # Second feedback to exit loop
         ]
     )
 
     def ask_human_input_side_effect(*args, **kwargs):
         return next(feedback_responses)
 
-    # Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
-    with (
-        patch.object(
-            CrewAgentExecutor,
-            "_ask_human_input",
-            side_effect=ask_human_input_side_effect,
-        ) as mock_human_input,
-        patch.object(
-            CrewAgentExecutor,
-            "_invoke_loop",
-            return_value=AgentFinish(output="Hello", thought="", text=""),
-        ) as mock_invoke_loop,
-    ):
+    with patch.object(
+        CrewAgentExecutor, "_ask_human_input", side_effect=ask_human_input_side_effect
+    ) as mock_human_input:
         # Execute the task
         output = agent.execute_task(task)
 
-        # Assertions to ensure the agent behaves correctly.
-        # It should have requested feedback twice.
-        assert mock_human_input.call_count == 2
-        # The final result should be processed to "Hello"
-        assert output.strip().lower() == "hello"
+        # Assertions to ensure the agent behaves correctly
+        assert mock_human_input.call_count == 2  # Should have asked for feedback twice
+        assert output.strip().lower() == "hello"  # Final output should be 'Hello'
 
 
 def test_interpolate_inputs():

tests/cassettes/test_agent_human_input.yaml

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tests/crew_test.py

@@ -6,6 +6,7 @@ from concurrent.futures import Future
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
+import instructor
 import pydantic_core
 import pytest
 
@@ -14,24 +15,15 @@ from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
+from crewai.project import crew
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import Logger
-from crewai.utilities.events import (
-    CrewTrainCompletedEvent,
-    CrewTrainStartedEvent,
-    crewai_event_bus,
-)
-from crewai.utilities.events.crew_events import (
-    CrewTestCompletedEvent,
-    CrewTestStartedEvent,
-)
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
@@ -833,12 +825,6 @@ def test_crew_verbose_output(capsys):
 
     crew.kickoff()
     captured = capsys.readouterr()
-
-    # Filter out event listener logs (lines starting with '[')
-    filtered_output = "\n".join(
-        line for line in captured.out.split("\n") if not line.startswith("[")
-    )
-
     expected_strings = [
         "\x1b[1m\x1b[95m# Agent:\x1b[00m \x1b[1m\x1b[92mResearcher",
         "\x1b[00m\n\x1b[95m## Task:\x1b[00m \x1b[92mResearch AI advancements.",
@@ -851,19 +837,14 @@ def test_crew_verbose_output(capsys):
     ]
 
     for expected_string in expected_strings:
-        assert expected_string in filtered_output
+        assert expected_string in captured.out
 
     # Now test with verbose set to False
     crew.verbose = False
     crew._logger = Logger(verbose=False)
     crew.kickoff()
     captured = capsys.readouterr()
-    filtered_output = "\n".join(
-        line
-        for line in captured.out.split("\n")
-        if not line.startswith("[") and line.strip() and not line.startswith("\x1b")
-    )
-    assert filtered_output == ""
+    assert captured.out == ""
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1301,9 +1282,9 @@ def test_kickoff_for_each_invalid_input():
 
     crew = Crew(agents=[agent], tasks=[task])
 
-    with pytest.raises(pydantic_core._pydantic_core.ValidationError):
+    with pytest.raises(TypeError):
         # Pass a string instead of a list
-        crew.kickoff_for_each(["invalid input"])
+        crew.kickoff_for_each("invalid input")
 
 
 def test_kickoff_for_each_error_handling():
@@ -2587,16 +2568,6 @@ def test_crew_train_success(
     # Create a mock for the copied crew
     copy_mock.return_value = crew
 
-    received_events = []
-
-    @crewai_event_bus.on(CrewTrainStartedEvent)
-    def on_crew_train_started(source, event: CrewTrainStartedEvent):
-        received_events.append(event)
-
-    @crewai_event_bus.on(CrewTrainCompletedEvent)
-    def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
-        received_events.append(event)
-
     crew.train(
         n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl"
     )
@@ -2642,10 +2613,6 @@ def test_crew_train_success(
         ]
     )
 
-    assert len(received_events) == 2
-    assert isinstance(received_events[0], CrewTrainStartedEvent)
-    assert isinstance(received_events[1], CrewTrainCompletedEvent)
-
 
 def test_crew_train_error():
     task = Task(
@@ -3374,19 +3341,7 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
     copy_mock.return_value = crew
 
     n_iterations = 2
-    llm_instance = LLM("gpt-4o-mini")
-
-    received_events = []
-
-    @crewai_event_bus.on(CrewTestStartedEvent)
-    def on_crew_test_started(source, event: CrewTestStartedEvent):
-        received_events.append(event)
-
-    @crewai_event_bus.on(CrewTestCompletedEvent)
-    def on_crew_test_completed(source, event: CrewTestCompletedEvent):
-        received_events.append(event)
-
-    crew.test(n_iterations, llm_instance, inputs={"topic": "AI"})
+    crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
 
     # Ensure kickoff is called on the copied crew
     kickoff_mock.assert_has_calls(
@@ -3395,17 +3350,13 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
 
     crew_evaluator.assert_has_calls(
         [
-            mock.call(crew, llm_instance),
+            mock.call(crew, "gpt-4o-mini"),
             mock.call().set_iteration(1),
             mock.call().set_iteration(2),
             mock.call().print_crew_evaluation_result(),
         ]
     )
 
-    assert len(received_events) == 2
-    assert isinstance(received_events[0], CrewTestStartedEvent)
-    assert isinstance(received_events[1], CrewTestCompletedEvent)
-
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_verbose_manager_agent():

tests/flow/test_state_utils.py

@@ -1,150 +0,0 @@
-from datetime import date, datetime
-from typing import List
-from unittest.mock import Mock
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.flow import Flow
-from crewai.flow.state_utils import export_state, to_string
-
-
-class Address(BaseModel):
-    street: str
-    city: str
-    country: str
-
-
-class Person(BaseModel):
-    name: str
-    age: int
-    address: Address
-    birthday: date
-    skills: List[str]
-
-
-@pytest.fixture
-def mock_flow():
-    def create_flow(state):
-        flow = Mock(spec=Flow)
-        flow._state = state
-        return flow
-
-    return create_flow
-
-
-@pytest.mark.parametrize(
-    "test_input,expected",
-    [
-        ({"text": "hello world"}, {"text": "hello world"}),
-        ({"number": 42}, {"number": 42}),
-        ({"decimal": 3.14}, {"decimal": 3.14}),
-        ({"flag": True}, {"flag": True}),
-        ({"empty": None}, {"empty": None}),
-        ({"list": [1, 2, 3]}, {"list": [1, 2, 3]}),
-        ({"tuple": (1, 2, 3)}, {"tuple": [1, 2, 3]}),
-        ({"set": {1, 2, 3}}, {"set": [1, 2, 3]}),
-        ({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
-    ],
-)
-def test_basic_serialization(mock_flow, test_input, expected):
-    flow = mock_flow(test_input)
-    result = export_state(flow)
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "input_date,expected",
-    [
-        (date(2024, 1, 1), "2024-01-01"),
-        (datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
-    ],
-)
-def test_temporal_serialization(mock_flow, input_date, expected):
-    flow = mock_flow({"date": input_date})
-    result = export_state(flow)
-    assert result["date"] == expected
-
-
-@pytest.mark.parametrize(
-    "key,value,expected_key_type",
-    [
-        (("tuple", "key"), "value", str),
-        (None, "value", str),
-        (123, "value", str),
-        ("normal", "value", str),
-    ],
-)
-def test_dictionary_key_serialization(mock_flow, key, value, expected_key_type):
-    flow = mock_flow({key: value})
-    result = export_state(flow)
-    assert len(result) == 1
-    result_key = next(iter(result.keys()))
-    assert isinstance(result_key, expected_key_type)
-    assert result[result_key] == value
-
-
-@pytest.mark.parametrize(
-    "callable_obj,expected_in_result",
-    [
-        (lambda x: x * 2, "lambda"),
-        (str.upper, "upper"),
-    ],
-)
-def test_callable_serialization(mock_flow, callable_obj, expected_in_result):
-    flow = mock_flow({"func": callable_obj})
-    result = export_state(flow)
-    assert isinstance(result["func"], str)
-    assert expected_in_result in result["func"].lower()
-
-
-def test_pydantic_model_serialization(mock_flow):
-    address = Address(street="123 Main St", city="Tech City", country="Pythonia")
-
-    person = Person(
-        name="John Doe",
-        age=30,
-        address=address,
-        birthday=date(1994, 1, 1),
-        skills=["Python", "Testing"],
-    )
-
-    flow = mock_flow(
-        {
-            "single_model": address,
-            "nested_model": person,
-            "model_list": [address, address],
-            "model_dict": {"home": address},
-        }
-    )
-
-    result = export_state(flow)
-    assert (
-        to_string(result)
-        == '{"single_model": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "nested_model": {"name": "John Doe", "age": 30, "address": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "birthday": "1994-01-01", "skills": ["Python", "Testing"]}, "model_list": [{"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}], "model_dict": {"home": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}}}'
-    )
-
-
-def test_depth_limit(mock_flow):
-    """Test max depth handling with a deeply nested structure"""
-
-    def create_nested(depth):
-        if depth == 0:
-            return "value"
-        return {"next": create_nested(depth - 1)}
-
-    deep_structure = create_nested(10)
-    flow = mock_flow(deep_structure)
-    result = export_state(flow)
-
-    assert result == {
-        "next": {
-            "next": {
-                "next": {
-                    "next": {
-                        "next": "{'next': {'next': {'next': {'next': {'next': 'value'}}}}}"
-                    }
-                }
-            }
-        }
-    }

tests/flow_test.py

@@ -1,20 +1,11 @@
 """Test Flow creation and execution basic functionality."""
 
 import asyncio
-from datetime import datetime
 
 import pytest
 from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
-from crewai.utilities.events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-    crewai_event_bus,
-)
-from crewai.utilities.events.flow_events import FlowPlotEvent
 
 
 def test_simple_sequential_flow():
@@ -407,351 +398,3 @@ def test_router_with_multiple_conditions():
 
     # final_step should run after router_and
     assert execution_order.index("log_final_step") > execution_order.index("router_and")
-
-
-def test_unstructured_flow_event_emission():
-    """Test that the correct events are emitted during unstructured flow
-    execution with all fields validated."""
-
-    class PoemFlow(Flow):
-        @start()
-        def prepare_flower(self):
-            self.state["flower"] = "roses"
-            return "foo"
-
-        @start()
-        def prepare_color(self):
-            self.state["color"] = "red"
-            return "bar"
-
-        @listen(prepare_color)
-        def write_first_sentence(self):
-            return f"{self.state['flower']} are {self.state['color']}"
-
-        @listen(write_first_sentence)
-        def finish_poem(self, first_sentence):
-            separator = self.state.get("separator", "\n")
-            return separator.join([first_sentence, "violets are blue"])
-
-        @listen(finish_poem)
-        def save_poem_to_database(self):
-            # A method without args/kwargs to ensure events are sent correctly
-            return "roses are red\nviolets are blue"
-
-    flow = PoemFlow()
-    received_events = []
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(MethodExecutionStartedEvent)
-    def handle_method_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(FlowFinishedEvent)
-    def handle_flow_end(source, event):
-        received_events.append(event)
-
-    flow.kickoff(inputs={"separator": ", "})
-    assert isinstance(received_events[0], FlowStartedEvent)
-    assert received_events[0].flow_name == "PoemFlow"
-    assert received_events[0].inputs == {"separator": ", "}
-    assert isinstance(received_events[0].timestamp, datetime)
-
-    # All subsequent events are MethodExecutionStartedEvent
-    for event in received_events[1:-1]:
-        assert isinstance(event, MethodExecutionStartedEvent)
-        assert event.flow_name == "PoemFlow"
-        assert isinstance(event.state, dict)
-        assert isinstance(event.state["id"], str)
-        assert event.state["separator"] == ", "
-
-    assert received_events[1].method_name == "prepare_flower"
-    assert received_events[1].params == {}
-    assert "flower" not in received_events[1].state
-
-    assert received_events[2].method_name == "prepare_color"
-    assert received_events[2].params == {}
-    print("received_events[2]", received_events[2])
-    assert "flower" in received_events[2].state
-
-    assert received_events[3].method_name == "write_first_sentence"
-    assert received_events[3].params == {}
-    assert received_events[3].state["flower"] == "roses"
-    assert received_events[3].state["color"] == "red"
-
-    assert received_events[4].method_name == "finish_poem"
-    assert received_events[4].params == {"_0": "roses are red"}
-    assert received_events[4].state["flower"] == "roses"
-    assert received_events[4].state["color"] == "red"
-
-    assert received_events[5].method_name == "save_poem_to_database"
-    assert received_events[5].params == {}
-    assert received_events[5].state["flower"] == "roses"
-    assert received_events[5].state["color"] == "red"
-
-    assert isinstance(received_events[6], FlowFinishedEvent)
-    assert received_events[6].flow_name == "PoemFlow"
-    assert received_events[6].result == "roses are red\nviolets are blue"
-    assert isinstance(received_events[6].timestamp, datetime)
-
-
-def test_structured_flow_event_emission():
-    """Test that the correct events are emitted during structured flow
-    execution with all fields validated."""
-
-    class OnboardingState(BaseModel):
-        name: str = ""
-        sent: bool = False
-
-    class OnboardingFlow(Flow[OnboardingState]):
-        @start()
-        def user_signs_up(self):
-            self.state.sent = False
-
-        @listen(user_signs_up)
-        def send_welcome_message(self):
-            self.state.sent = True
-            return f"Welcome, {self.state.name}!"
-
-    flow = OnboardingFlow()
-    flow.kickoff(inputs={"name": "Anakin"})
-
-    received_events = []
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(MethodExecutionStartedEvent)
-    def handle_method_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(MethodExecutionFinishedEvent)
-    def handle_method_end(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(FlowFinishedEvent)
-    def handle_flow_end(source, event):
-        received_events.append(event)
-
-    flow.kickoff(inputs={"name": "Anakin"})
-
-    assert isinstance(received_events[0], FlowStartedEvent)
-    assert received_events[0].flow_name == "OnboardingFlow"
-    assert received_events[0].inputs == {"name": "Anakin"}
-    assert isinstance(received_events[0].timestamp, datetime)
-
-    assert isinstance(received_events[1], MethodExecutionStartedEvent)
-    assert received_events[1].method_name == "user_signs_up"
-
-    assert isinstance(received_events[2], MethodExecutionFinishedEvent)
-    assert received_events[2].method_name == "user_signs_up"
-
-    assert isinstance(received_events[3], MethodExecutionStartedEvent)
-    assert received_events[3].method_name == "send_welcome_message"
-    assert received_events[3].params == {}
-    assert getattr(received_events[3].state, "sent") is False
-
-    assert isinstance(received_events[4], MethodExecutionFinishedEvent)
-    assert received_events[4].method_name == "send_welcome_message"
-    assert getattr(received_events[4].state, "sent") is True
-    assert received_events[4].result == "Welcome, Anakin!"
-
-    assert isinstance(received_events[5], FlowFinishedEvent)
-    assert received_events[5].flow_name == "OnboardingFlow"
-    assert received_events[5].result == "Welcome, Anakin!"
-    assert isinstance(received_events[5].timestamp, datetime)
-
-
-def test_stateless_flow_event_emission():
-    """Test that the correct events are emitted stateless during flow execution
-    with all fields validated."""
-
-    class StatelessFlow(Flow):
-        @start()
-        def init(self):
-            pass
-
-        @listen(init)
-        def process(self):
-            return "Deeds will not be less valiant because they are unpraised."
-
-    event_log = []
-
-    def handle_event(_, event):
-        event_log.append(event)
-
-    flow = StatelessFlow()
-    received_events = []
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(MethodExecutionStartedEvent)
-    def handle_method_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(MethodExecutionFinishedEvent)
-    def handle_method_end(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(FlowFinishedEvent)
-    def handle_flow_end(source, event):
-        received_events.append(event)
-
-    flow.kickoff()
-
-    assert isinstance(received_events[0], FlowStartedEvent)
-    assert received_events[0].flow_name == "StatelessFlow"
-    assert received_events[0].inputs is None
-    assert isinstance(received_events[0].timestamp, datetime)
-
-    assert isinstance(received_events[1], MethodExecutionStartedEvent)
-    assert received_events[1].method_name == "init"
-
-    assert isinstance(received_events[2], MethodExecutionFinishedEvent)
-    assert received_events[2].method_name == "init"
-
-    assert isinstance(received_events[3], MethodExecutionStartedEvent)
-    assert received_events[3].method_name == "process"
-
-    assert isinstance(received_events[4], MethodExecutionFinishedEvent)
-    assert received_events[4].method_name == "process"
-
-    assert isinstance(received_events[5], FlowFinishedEvent)
-    assert received_events[5].flow_name == "StatelessFlow"
-    assert (
-        received_events[5].result
-        == "Deeds will not be less valiant because they are unpraised."
-    )
-    assert isinstance(received_events[5].timestamp, datetime)
-
-
-def test_flow_plotting():
-    class StatelessFlow(Flow):
-        @start()
-        def init(self):
-            return "Initializing flow..."
-
-        @listen(init)
-        def process(self):
-            return "Deeds will not be less valiant because they are unpraised."
-
-    flow = StatelessFlow()
-    flow.kickoff()
-    received_events = []
-
-    @crewai_event_bus.on(FlowPlotEvent)
-    def handle_flow_plot(source, event):
-        received_events.append(event)
-
-    flow.plot("test_flow")
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0], FlowPlotEvent)
-    assert received_events[0].flow_name == "StatelessFlow"
-    assert isinstance(received_events[0].timestamp, datetime)
-
-
-def test_multiple_routers_from_same_trigger():
-    """Test that multiple routers triggered by the same method all activate their listeners."""
-    execution_order = []
-
-    class MultiRouterFlow(Flow):
-        def __init__(self):
-            super().__init__()
-            # Set diagnosed conditions to trigger all routers
-            self.state["diagnosed_conditions"] = "DHA"  # Contains D, H, and A
-
-        @start()
-        def scan_medical(self):
-            execution_order.append("scan_medical")
-            return "scan_complete"
-
-        @router(scan_medical)
-        def diagnose_conditions(self):
-            execution_order.append("diagnose_conditions")
-            return "diagnosis_complete"
-
-        @router(diagnose_conditions)
-        def diabetes_router(self):
-            execution_order.append("diabetes_router")
-            if "D" in self.state["diagnosed_conditions"]:
-                return "diabetes"
-            return None
-
-        @listen("diabetes")
-        def diabetes_analysis(self):
-            execution_order.append("diabetes_analysis")
-            return "diabetes_analysis_complete"
-
-        @router(diagnose_conditions)
-        def hypertension_router(self):
-            execution_order.append("hypertension_router")
-            if "H" in self.state["diagnosed_conditions"]:
-                return "hypertension"
-            return None
-
-        @listen("hypertension")
-        def hypertension_analysis(self):
-            execution_order.append("hypertension_analysis")
-            return "hypertension_analysis_complete"
-
-        @router(diagnose_conditions)
-        def anemia_router(self):
-            execution_order.append("anemia_router")
-            if "A" in self.state["diagnosed_conditions"]:
-                return "anemia"
-            return None
-
-        @listen("anemia")
-        def anemia_analysis(self):
-            execution_order.append("anemia_analysis")
-            return "anemia_analysis_complete"
-
-    flow = MultiRouterFlow()
-    flow.kickoff()
-
-    # Verify all methods were called
-    assert "scan_medical" in execution_order
-    assert "diagnose_conditions" in execution_order
-
-    # Verify all routers were called
-    assert "diabetes_router" in execution_order
-    assert "hypertension_router" in execution_order
-    assert "anemia_router" in execution_order
-
-    # Verify all listeners were called - this is the key test for the fix
-    assert "diabetes_analysis" in execution_order
-    assert "hypertension_analysis" in execution_order
-    assert "anemia_analysis" in execution_order
-
-    # Verify execution order constraints
-    assert execution_order.index("diagnose_conditions") > execution_order.index(
-        "scan_medical"
-    )
-
-    # All routers should execute after diagnose_conditions
-    assert execution_order.index("diabetes_router") > execution_order.index(
-        "diagnose_conditions"
-    )
-    assert execution_order.index("hypertension_router") > execution_order.index(
-        "diagnose_conditions"
-    )
-    assert execution_order.index("anemia_router") > execution_order.index(
-        "diagnose_conditions"
-    )
-
-    # All analyses should execute after their respective routers
-    assert execution_order.index("diabetes_analysis") > execution_order.index(
-        "diabetes_router"
-    )
-    assert execution_order.index("hypertension_analysis") > execution_order.index(
-        "hypertension_router"
-    )
-    assert execution_order.index("anemia_analysis") > execution_order.index(
-        "anemia_router"
-    )

tests/llm_test.py

@@ -6,9 +6,8 @@ import pytest
 from pydantic import BaseModel
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
-from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
+from crewai.llm import LLM
+from crewai.tools import tool
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 
 
@@ -285,23 +284,6 @@ def test_o3_mini_reasoning_effort_medium():
     assert isinstance(result, str)
     assert "Paris" in result
 
-def test_context_window_validation():
-    """Test that context window validation works correctly."""
-    # Test valid window size
-    llm = LLM(model="o3-mini")
-    assert llm.get_context_window_size() == int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
-
-    # Test invalid window size
-    with pytest.raises(ValueError) as excinfo:
-        with patch.dict(
-            "crewai.llm.LLM_CONTEXT_WINDOW_SIZES",
-            {"test-model": 500},  # Below minimum
-            clear=True,
-        ):
-            llm = LLM(model="test-model")
-            llm.get_context_window_size()
-    assert "must be between 1024 and 2097152" in str(excinfo.value)
-
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.fixture
@@ -309,36 +291,32 @@ def anthropic_llm():
     """Fixture providing an Anthropic LLM instance."""
     return LLM(model="anthropic/claude-3-sonnet")
 
-
 @pytest.fixture
 def system_message():
     """Fixture providing a system message."""
     return {"role": "system", "content": "test"}
 
-
 @pytest.fixture
 def user_message():
     """Fixture providing a user message."""
     return {"role": "user", "content": "test"}
 
-
 def test_anthropic_message_formatting_edge_cases(anthropic_llm):
     """Test edge cases for Anthropic message formatting."""
     # Test None messages
     with pytest.raises(TypeError, match="Messages cannot be None"):
         anthropic_llm._format_messages_for_provider(None)
-
+        
     # Test empty message list
     formatted = anthropic_llm._format_messages_for_provider([])
     assert len(formatted) == 1
     assert formatted[0]["role"] == "user"
     assert formatted[0]["content"] == "."
-
+    
     # Test invalid message format
     with pytest.raises(TypeError, match="Invalid message format"):
         anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
 
-
 def test_anthropic_model_detection():
     """Test Anthropic model detection with various formats."""
     models = [
@@ -349,12 +327,11 @@ def test_anthropic_model_detection():
         ("", False),
         ("anthropomorphic", False),  # Should not match partial words
     ]
-
+    
     for model, expected in models:
         llm = LLM(model=model)
         assert llm.is_anthropic == expected, f"Failed for model: {model}"
 
-
 def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
     """Test Anthropic message formatting with fixtures."""
     # Test when first message is system
@@ -394,51 +371,3 @@ def test_deepseek_r1_with_open_router():
     result = llm.call("What is the capital of France?")
     assert isinstance(result, str)
     assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tool_execution_error_event():
-    llm = LLM(model="gpt-4o-mini")
-
-    def failing_tool(param: str) -> str:
-        """This tool always fails."""
-        raise Exception("Tool execution failed!")
-
-    tool_schema = {
-        "type": "function",
-        "function": {
-            "name": "failing_tool",
-            "description": "This tool always fails.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "param": {"type": "string", "description": "A test parameter"}
-                },
-                "required": ["param"],
-            },
-        },
-    }
-
-    received_events = []
-
-    @crewai_event_bus.on(ToolExecutionErrorEvent)
-    def event_handler(source, event):
-        received_events.append(event)
-
-    available_functions = {"failing_tool": failing_tool}
-
-    messages = [{"role": "user", "content": "Use the failing tool"}]
-
-    llm.call(
-        messages,
-        tools=[tool_schema],
-        available_functions=available_functions,
-    )
-
-    assert len(received_events) == 1
-    event = received_events[0]
-    assert isinstance(event, ToolExecutionErrorEvent)
-    assert event.tool_name == "failing_tool"
-    assert event.tool_args == {"param": "test"}
-    assert event.tool_class == failing_tool
-    assert "Tool execution failed!" in event.error

tests/task_test.py

@@ -111,7 +111,6 @@ def test_task_callback():
         task.execute_sync(agent=researcher)
         task_completed.assert_called_once_with(task.output)
 
-        assert task.output.key == task.key
         assert task.output.description == task.description
         assert task.output.expected_output == task.expected_output
         assert task.output.name == task.name
@@ -150,7 +149,6 @@ def test_task_callback_returns_task_output():
         assert isinstance(callback_data, str)
         output_dict = json.loads(callback_data)
         expected_output = {
-            "key": task.key,
             "description": task.description,
             "raw": "exported_ok",
             "pydantic": None,

tests/test_flow_persistence.py

@@ -13,12 +13,11 @@ from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
 
 class TestState(FlowState):
     """Test state model with required id field."""
-
     counter: int = 0
     message: str = ""
 
 
-def test_persist_decorator_saves_state(tmp_path, caplog):
+def test_persist_decorator_saves_state(tmp_path):
     """Test that @persist decorator saves state in SQLite."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
@@ -74,6 +73,7 @@ def test_flow_state_restoration(tmp_path):
 
     # First flow execution to create initial state
     class RestorableFlow(Flow[TestState]):
+
         @start()
         @persist(persistence)
         def set_message(self):
@@ -89,7 +89,10 @@ def test_flow_state_restoration(tmp_path):
 
     # Test case 1: Restore using restore_uuid with field override
     flow2 = RestorableFlow(persistence=persistence)
-    flow2.kickoff(inputs={"id": original_uuid, "counter": 43})
+    flow2.kickoff(inputs={
+        "id": original_uuid,
+        "counter": 43
+    })
 
     # Verify state restoration and selective field override
     assert flow2.state.id == original_uuid
@@ -98,7 +101,10 @@ def test_flow_state_restoration(tmp_path):
 
     # Test case 2: Restore using kwargs['id']
     flow3 = RestorableFlow(persistence=persistence)
-    flow3.kickoff(inputs={"id": original_uuid, "message": "Updated message"})
+    flow3.kickoff(inputs={
+        "id": original_uuid,
+        "message": "Updated message"
+    })
 
     # Verify state restoration and selective field override
     assert flow3.state.id == original_uuid
@@ -168,43 +174,3 @@ def test_multiple_method_persistence(tmp_path):
     final_state = flow2.state
     assert final_state.counter == 99999
     assert final_state.message == "Step 99999"
-
-
-def test_persist_decorator_verbose_logging(tmp_path, caplog):
-    """Test that @persist decorator's verbose parameter controls logging."""
-    # Set logging level to ensure we capture all logs
-    caplog.set_level("INFO")
-
-    db_path = os.path.join(tmp_path, "test_flows.db")
-    persistence = SQLiteFlowPersistence(db_path)
-
-    # Test with verbose=False (default)
-    class QuietFlow(Flow[Dict[str, str]]):
-        initial_state = dict()
-
-        @start()
-        @persist(persistence)  # Default verbose=False
-        def init_step(self):
-            self.state["message"] = "Hello, World!"
-            self.state["id"] = "test-uuid-1"
-
-    flow = QuietFlow(persistence=persistence)
-    flow.kickoff()
-    assert "Saving flow state" not in caplog.text
-
-    # Clear the log
-    caplog.clear()
-
-    # Test with verbose=True
-    class VerboseFlow(Flow[Dict[str, str]]):
-        initial_state = dict()
-
-        @start()
-        @persist(persistence, verbose=True)
-        def init_step(self):
-            self.state["message"] = "Hello, World!"
-            self.state["id"] = "test-uuid-2"
-
-    flow = VerboseFlow(persistence=persistence)
-    flow.kickoff()
-    assert "Saving flow state" in caplog.text

tests/tools/test_tool_usage.py

@@ -1,6 +1,6 @@
 import json
 import random
-from unittest.mock import MagicMock, patch
+from unittest.mock import MagicMock
 
 import pytest
 from pydantic import BaseModel, Field
@@ -8,11 +8,6 @@ from pydantic import BaseModel, Field
 from crewai import Agent, Task
 from crewai.tools import BaseTool
 from crewai.tools.tool_usage import ToolUsage
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.tool_usage_events import (
-    ToolSelectionErrorEvent,
-    ToolValidateInputErrorEvent,
-)
 
 
 class RandomNumberToolInput(BaseModel):
@@ -231,7 +226,7 @@ def test_validate_tool_input_with_special_characters():
     )
 
     # Input with special characters
-    tool_input = '{"message": "Hello, world! \u263a", "valid": True}'
+    tool_input = '{"message": "Hello, world! \u263A", "valid": True}'
     expected_arguments = {"message": "Hello, world! ☺", "valid": True}
 
     arguments = tool_usage._validate_tool_input(tool_input)
@@ -336,19 +331,6 @@ def test_validate_tool_input_with_trailing_commas():
 
 
 def test_validate_tool_input_invalid_input():
-    # Create mock agent with proper string values
-    mock_agent = MagicMock()
-    mock_agent.key = "test_agent_key"  # Must be a string
-    mock_agent.role = "test_agent_role"  # Must be a string
-    mock_agent._original_role = "test_agent_role"  # Must be a string
-    mock_agent.i18n = MagicMock()
-    mock_agent.verbose = False
-
-    # Create mock action with proper string value
-    mock_action = MagicMock()
-    mock_action.tool = "test_tool"  # Must be a string
-    mock_action.tool_input = "test_input"  # Must be a string
-
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
@@ -357,8 +339,8 @@ def test_validate_tool_input_invalid_input():
         tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
-        agent=mock_agent,
-        action=mock_action,
+        agent=MagicMock(),
+        action=MagicMock(),
     )
 
     invalid_inputs = [
@@ -378,7 +360,7 @@ def test_validate_tool_input_invalid_input():
 
     # Test for None input separately
     arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}
+    assert arguments == {}  # Expecting an empty dictionary
 
 
 def test_validate_tool_input_complex_structure():
@@ -486,141 +468,18 @@ def test_validate_tool_input_large_json_content():
     assert arguments == expected_arguments
 
 
-def test_tool_selection_error_event_direct():
-    """Test tool selection error event emission directly from ToolUsage class."""
-    mock_agent = MagicMock()
-    mock_agent.key = "test_key"
-    mock_agent.role = "test_role"
-    mock_agent.i18n = MagicMock()
-    mock_agent.verbose = False
-
-    mock_task = MagicMock()
-    mock_tools_handler = MagicMock()
-
-    class TestTool(BaseTool):
-        name: str = "Test Tool"
-        description: str = "A test tool"
-
-        def _run(self, input: dict) -> str:
-            return "test result"
-
-    test_tool = TestTool()
-
+def test_validate_tool_input_none_input():
     tool_usage = ToolUsage(
-        tools_handler=mock_tools_handler,
-        tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
-        task=mock_task,
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
         function_calling_llm=None,
-        agent=mock_agent,
+        agent=MagicMock(),
         action=MagicMock(),
     )
 
-    received_events = []
-
-    @crewai_event_bus.on(ToolSelectionErrorEvent)
-    def event_handler(source, event):
-        received_events.append(event)
-
-    with pytest.raises(Exception) as exc_info:
-        tool_usage._select_tool("Non Existent Tool")
-    assert len(received_events) == 1
-    event = received_events[0]
-    assert isinstance(event, ToolSelectionErrorEvent)
-    assert event.agent_key == "test_key"
-    assert event.agent_role == "test_role"
-    assert event.tool_name == "Non Existent Tool"
-    assert event.tool_args == {}
-    assert event.tool_class == "Test Tool Description"
-    assert "don't exist" in event.error
-
-    received_events.clear()
-    with pytest.raises(Exception) as exc_info:
-        tool_usage._select_tool("")
-
-    assert len(received_events) == 1
-    event = received_events[0]
-    assert isinstance(event, ToolSelectionErrorEvent)
-    assert event.agent_key == "test_key"
-    assert event.agent_role == "test_role"
-    assert event.tool_name == ""
-    assert event.tool_args == {}
-    assert event.tool_class == "Test Tool Description"
-    assert "forgot the Action name" in event.error
-
-
-def test_tool_validate_input_error_event():
-    """Test tool validation input error event emission from ToolUsage class."""
-    # Mock agent and required components
-    mock_agent = MagicMock()
-    mock_agent.key = "test_key"
-    mock_agent.role = "test_role"
-    mock_agent.verbose = False
-    mock_agent._original_role = "test_role"
-
-    # Mock i18n with error message
-    mock_i18n = MagicMock()
-    mock_i18n.errors.return_value = (
-        "Tool input must be a valid dictionary in JSON or Python literal format"
-    )
-    mock_agent.i18n = mock_i18n
-
-    # Mock task and tools handler
-    mock_task = MagicMock()
-    mock_tools_handler = MagicMock()
-
-    # Mock printer
-    mock_printer = MagicMock()
-
-    # Create test tool
-    class TestTool(BaseTool):
-        name: str = "Test Tool"
-        description: str = "A test tool"
-
-        def _run(self, input: dict) -> str:
-            return "test result"
-
-    test_tool = TestTool()
-
-    # Create ToolUsage instance
-    tool_usage = ToolUsage(
-        tools_handler=mock_tools_handler,
-        tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
-        task=mock_task,
-        function_calling_llm=None,
-        agent=mock_agent,
-        action=MagicMock(tool="test_tool"),
-    )
-    tool_usage._printer = mock_printer
-
-    # Mock all parsing attempts to fail
-    with (
-        patch("json.loads", side_effect=json.JSONDecodeError("Test Error", "", 0)),
-        patch("ast.literal_eval", side_effect=ValueError),
-        patch("json5.loads", side_effect=json.JSONDecodeError("Test Error", "", 0)),
-        patch("json_repair.repair_json", side_effect=Exception("Failed to repair")),
-    ):
-        received_events = []
-
-        @crewai_event_bus.on(ToolValidateInputErrorEvent)
-        def event_handler(source, event):
-            received_events.append(event)
-
-        # Test invalid input
-        invalid_input = "invalid json {[}"
-        with pytest.raises(Exception) as exc_info:
-            tool_usage._validate_tool_input(invalid_input)
-
-        # Verify event was emitted
-        assert len(received_events) == 1, "Expected one event to be emitted"
-        event = received_events[0]
-        assert isinstance(event, ToolValidateInputErrorEvent)
-        assert event.agent_key == "test_key"
-        assert event.agent_role == "test_role"
-        assert event.tool_name == "test_tool"
-        assert "must be a valid dictionary" in event.error
+    arguments = tool_usage._validate_tool_input(None)
+    assert arguments == {}  # Expecting an empty dictionary

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tests/utilities/test_converter.py

@@ -1,5 +1,4 @@
 import json
-import os
 from typing import Dict, List, Optional
 from unittest.mock import MagicMock, Mock, patch
 
@@ -221,13 +220,10 @@ def test_get_conversion_instructions_gpt():
         supports_function_calling.return_value = True
         instructions = get_conversion_instructions(SimpleModel, llm)
         model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        expected_instructions = (
-            "Please convert the following text into valid JSON.\n\n"
-            "Output ONLY the valid JSON and nothing else.\n\n"
-            "The JSON must follow this schema exactly:\n```json\n"
-            f"{model_schema}\n```"
+        assert (
+            instructions
+            == f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
         )
-        assert instructions == expected_instructions
 
 
 def test_get_conversion_instructions_non_gpt():
@@ -350,17 +346,12 @@ def test_convert_with_instructions():
     assert output.age == 30
 
 
-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_2_model():
     llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
+
     sample_text = "Name: Alice Llama, Age: 30"
+
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -368,17 +359,19 @@ def test_converter_with_llama3_2_model():
         model=SimpleModel,
         instructions=instructions,
     )
+
     output = converter.to_pydantic()
+
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_1_model():
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
+
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -386,19 +379,14 @@ def test_converter_with_llama3_1_model():
         model=SimpleModel,
         instructions=instructions,
     )
+
     output = converter.to_pydantic()
+
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_nested_model():
     llm = LLM(model="gpt-4o-mini")
@@ -575,7 +563,7 @@ def test_converter_with_ambiguous_input():
     with pytest.raises(ConverterError) as exc_info:
         output = converter.to_pydantic()
 
-    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
+    assert "validation error" in str(exc_info.value).lower()
 
 
 # Tests for function calling support

tests/utilities/test_events.py

@@ -1,617 +0,0 @@
-from datetime import datetime
-from unittest.mock import Mock, patch
-
-import pytest
-from pydantic import Field
-
-from crewai.agent import Agent
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.crew import Crew
-from crewai.flow.flow import Flow, listen, start
-from crewai.llm import LLM
-from crewai.task import Task
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities.events.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.utilities.events.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.event_listener import EventListener
-from crewai.utilities.events.event_types import ToolUsageFinishedEvent
-from crewai.utilities.events.flow_events import (
-    FlowCreatedEvent,
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionStartedEvent,
-)
-from crewai.utilities.events.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMCallType,
-)
-from crewai.utilities.events.task_events import (
-    TaskCompletedEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
-)
-from crewai.utilities.events.tool_usage_events import (
-    ToolUsageErrorEvent,
-)
-
-base_agent = Agent(
-    role="base_agent",
-    llm="gpt-4o-mini",
-    goal="Just say hi",
-    backstory="You are a helpful assistant that just says hi",
-)
-
-base_task = Task(
-    description="Just say hi",
-    expected_output="hi",
-    agent=base_agent,
-)
-event_listener = EventListener()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_kickoff_event():
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(CrewKickoffStartedEvent)
-    def handle_crew_start(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-    with (
-        patch.object(
-            event_listener._telemetry, "crew_execution_span", return_value=mock_span
-        ) as mock_crew_execution_span,
-        patch.object(
-            event_listener._telemetry, "end_crew", return_value=mock_span
-        ) as mock_crew_ended,
-    ):
-        crew.kickoff()
-    mock_crew_execution_span.assert_called_once_with(crew, None)
-    mock_crew_ended.assert_called_once_with(crew, "hi")
-
-    assert len(received_events) == 1
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_kickoff_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_kickoff_event():
-    received_events = []
-
-    @crewai_event_bus.on(CrewKickoffCompletedEvent)
-    def handle_crew_end(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-    crew.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_kickoff_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_test_kickoff_type_event():
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(CrewTestStartedEvent)
-    def handle_crew_end(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(CrewTestCompletedEvent)
-    def handle_crew_test_end(source, event):
-        received_events.append(event)
-
-    eval_llm = LLM(model="gpt-4o-mini")
-    with (
-        patch.object(
-            event_listener._telemetry, "test_execution_span", return_value=mock_span
-        ) as mock_crew_execution_span,
-    ):
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.test(n_iterations=1, eval_llm=eval_llm)
-
-        # Verify the call was made with correct argument types and values
-        assert mock_crew_execution_span.call_count == 1
-        args = mock_crew_execution_span.call_args[0]
-        assert isinstance(args[0], Crew)
-        assert args[1] == 1
-        assert args[2] is None
-        assert args[3] == eval_llm
-
-    assert len(received_events) == 2
-    assert received_events[0].crew_name == "TestCrew"
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "crew_test_started"
-    assert received_events[1].crew_name == "TestCrew"
-    assert isinstance(received_events[1].timestamp, datetime)
-    assert received_events[1].type == "crew_test_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_kickoff_failed_event():
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(CrewKickoffFailedEvent)
-        def handle_crew_failed(source, event):
-            received_events.append(event)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-        with patch.object(Crew, "_execute_tasks") as mock_execute:
-            error_message = "Simulated crew kickoff failure"
-            mock_execute.side_effect = Exception(error_message)
-
-            with pytest.raises(Exception):
-                crew.kickoff()
-
-        assert len(received_events) == 1
-        assert received_events[0].error == error_message
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "crew_kickoff_failed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_task_event():
-    received_events = []
-
-    @crewai_event_bus.on(TaskStartedEvent)
-    def handle_task_start(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-
-    crew.kickoff()
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "task_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_task_event():
-    received_events = []
-
-    @crewai_event_bus.on(TaskCompletedEvent)
-    def handle_task_end(source, event):
-        received_events.append(event)
-
-    mock_span = Mock()
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-    with (
-        patch.object(
-            event_listener._telemetry, "task_started", return_value=mock_span
-        ) as mock_task_started,
-        patch.object(
-            event_listener._telemetry, "task_ended", return_value=mock_span
-        ) as mock_task_ended,
-    ):
-        crew.kickoff()
-
-    mock_task_started.assert_called_once_with(crew=crew, task=base_task)
-    mock_task_ended.assert_called_once_with(mock_span, base_task, crew)
-
-    assert len(received_events) == 1
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "task_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_emits_failed_event_on_execution_error():
-    received_events = []
-    received_sources = []
-
-    @crewai_event_bus.on(TaskFailedEvent)
-    def handle_task_failed(source, event):
-        received_events.append(event)
-        received_sources.append(source)
-
-    with patch.object(
-        Task,
-        "_execute_core",
-    ) as mock_execute:
-        error_message = "Simulated task failure"
-        mock_execute.side_effect = Exception(error_message)
-        agent = Agent(
-            role="base_agent",
-            goal="Just say hi",
-            backstory="You are a helpful assistant that just says hi",
-        )
-        task = Task(
-            description="Just say hi",
-            expected_output="hi",
-            agent=agent,
-        )
-
-        with pytest.raises(Exception):
-            agent.execute_task(task=task)
-
-            assert len(received_events) == 1
-            assert received_sources[0] == task
-            assert received_events[0].error == error_message
-            assert isinstance(received_events[0].timestamp, datetime)
-            assert received_events[0].type == "task_failed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_started_and_completed_events():
-    received_events = []
-
-    @crewai_event_bus.on(AgentExecutionStartedEvent)
-    def handle_agent_start(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(AgentExecutionCompletedEvent)
-    def handle_agent_completed(source, event):
-        received_events.append(event)
-
-    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-    crew.kickoff()
-    assert len(received_events) == 2
-    assert received_events[0].agent == base_agent
-    assert received_events[0].task == base_task
-    assert received_events[0].tools == []
-    assert isinstance(received_events[0].task_prompt, str)
-    assert (
-        received_events[0].task_prompt
-        == "Just say hi\n\nThis is the expected criteria for your final answer: hi\nyou MUST return the actual complete content as the final answer, not a summary."
-    )
-    assert isinstance(received_events[0].timestamp, datetime)
-    assert received_events[0].type == "agent_execution_started"
-    assert isinstance(received_events[1].timestamp, datetime)
-    assert received_events[1].type == "agent_execution_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_error_event():
-    received_events = []
-
-    @crewai_event_bus.on(AgentExecutionErrorEvent)
-    def handle_agent_start(source, event):
-        received_events.append(event)
-
-    error_message = "Error happening while sending prompt to model."
-    base_agent.max_retry_limit = 0
-    with patch.object(
-        CrewAgentExecutor, "invoke", wraps=base_agent.agent_executor.invoke
-    ) as invoke_mock:
-        invoke_mock.side_effect = Exception(error_message)
-
-        with pytest.raises(Exception) as e:
-            base_agent.execute_task(
-                task=base_task,
-            )
-
-        assert len(received_events) == 1
-        assert received_events[0].agent == base_agent
-        assert received_events[0].task == base_task
-        assert received_events[0].error == error_message
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "agent_execution_error"
-
-
-class SayHiTool(BaseTool):
-    name: str = Field(default="say_hi", description="The name of the tool")
-    description: str = Field(
-        default="Say hi", description="The description of the tool"
-    )
-
-    def _run(self) -> str:
-        return "hi"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tools_emits_finished_events():
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
-
-    agent = Agent(
-        role="base_agent",
-        goal="Just say hi",
-        backstory="You are a helpful assistant that just says hi",
-        tools=[SayHiTool()],
-    )
-
-    task = Task(
-        description="Just say hi",
-        expected_output="hi",
-        agent=agent,
-    )
-    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
-    crew.kickoff()
-    assert len(received_events) == 1
-    assert received_events[0].agent_key == agent.key
-    assert received_events[0].agent_role == agent.role
-    assert received_events[0].tool_name == SayHiTool().name
-    assert received_events[0].tool_args == {}
-    assert received_events[0].type == "tool_usage_finished"
-    assert isinstance(received_events[0].timestamp, datetime)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tools_emits_error_events():
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageErrorEvent)
-    def handle_tool_end(source, event):
-        received_events.append(event)
-
-    class ErrorTool(BaseTool):
-        name: str = Field(
-            default="error_tool", description="A tool that raises an error"
-        )
-        description: str = Field(
-            default="This tool always raises an error",
-            description="The description of the tool",
-        )
-
-        def _run(self) -> str:
-            raise Exception("Simulated tool error")
-
-    agent = Agent(
-        role="base_agent",
-        goal="Try to use the error tool",
-        backstory="You are an assistant that tests error handling",
-        tools=[ErrorTool()],
-    )
-
-    task = Task(
-        description="Use the error tool",
-        expected_output="This should error",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
-    crew.kickoff()
-
-    assert len(received_events) == 75
-    assert received_events[0].agent_key == agent.key
-    assert received_events[0].agent_role == agent.role
-    assert received_events[0].tool_name == "error_tool"
-    assert received_events[0].tool_args == {}
-    assert str(received_events[0].error) == "Simulated tool error"
-    assert received_events[0].type == "tool_usage_error"
-    assert isinstance(received_events[0].timestamp, datetime)
-
-
-def test_flow_emits_start_event():
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(FlowStartedEvent)
-    def handle_flow_start(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            return "started"
-
-    with (
-        patch.object(
-            event_listener._telemetry, "flow_execution_span", return_value=mock_span
-        ) as mock_flow_execution_span,
-    ):
-        flow = TestFlow()
-        flow.kickoff()
-
-    mock_flow_execution_span.assert_called_once_with("TestFlow", ["begin"])
-    assert len(received_events) == 1
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "flow_started"
-
-
-def test_flow_emits_finish_event():
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(FlowFinishedEvent)
-        def handle_flow_finish(source, event):
-            received_events.append(event)
-
-        class TestFlow(Flow[dict]):
-            @start()
-            def begin(self):
-                return "completed"
-
-        flow = TestFlow()
-        result = flow.kickoff()
-
-        assert len(received_events) == 1
-        assert received_events[0].flow_name == "TestFlow"
-        assert received_events[0].type == "flow_finished"
-        assert received_events[0].result == "completed"
-        assert result == "completed"
-
-
-def test_flow_emits_method_execution_started_event():
-    received_events = []
-
-    with crewai_event_bus.scoped_handlers():
-
-        @crewai_event_bus.on(MethodExecutionStartedEvent)
-        def handle_method_start(source, event):
-            print("event in method name", event.method_name)
-            received_events.append(event)
-
-        class TestFlow(Flow[dict]):
-            @start()
-            def begin(self):
-                return "started"
-
-            @listen("begin")
-            def second_method(self):
-                return "executed"
-
-        flow = TestFlow()
-        flow.kickoff()
-
-        assert len(received_events) == 2
-
-        assert received_events[0].method_name == "begin"
-        assert received_events[0].flow_name == "TestFlow"
-        assert received_events[0].type == "method_execution_started"
-
-        assert received_events[1].method_name == "second_method"
-        assert received_events[1].flow_name == "TestFlow"
-        assert received_events[1].type == "method_execution_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_register_handler_adds_new_handler():
-    received_events = []
-
-    def custom_handler(source, event):
-        received_events.append(event)
-
-    with crewai_event_bus.scoped_handlers():
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, custom_handler)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-        assert len(received_events) == 1
-        assert isinstance(received_events[0].timestamp, datetime)
-        assert received_events[0].type == "crew_kickoff_started"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_multiple_handlers_for_same_event():
-    received_events_1 = []
-    received_events_2 = []
-
-    def handler_1(source, event):
-        received_events_1.append(event)
-
-    def handler_2(source, event):
-        received_events_2.append(event)
-
-    with crewai_event_bus.scoped_handlers():
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_1)
-        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_2)
-
-        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
-        crew.kickoff()
-
-        assert len(received_events_1) == 1
-        assert len(received_events_2) == 1
-        assert received_events_1[0].type == "crew_kickoff_started"
-        assert received_events_2[0].type == "crew_kickoff_started"
-
-
-def test_flow_emits_created_event():
-    received_events = []
-    mock_span = Mock()
-
-    @crewai_event_bus.on(FlowCreatedEvent)
-    def handle_flow_created(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            return "started"
-
-    with (
-        patch.object(
-            event_listener._telemetry, "flow_creation_span", return_value=mock_span
-        ) as mock_flow_creation_span,
-    ):
-        flow = TestFlow()
-        flow.kickoff()
-
-    mock_flow_creation_span.assert_called_once_with("TestFlow")
-
-    assert len(received_events) == 1
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "flow_created"
-
-
-def test_flow_emits_method_execution_failed_event():
-    received_events = []
-    error = Exception("Simulated method failure")
-
-    @crewai_event_bus.on(MethodExecutionFailedEvent)
-    def handle_method_failed(source, event):
-        received_events.append(event)
-
-    class TestFlow(Flow[dict]):
-        @start()
-        def begin(self):
-            raise error
-
-    flow = TestFlow()
-    with pytest.raises(Exception):
-        flow.kickoff()
-
-    assert len(received_events) == 1
-    assert received_events[0].method_name == "begin"
-    assert received_events[0].flow_name == "TestFlow"
-    assert received_events[0].type == "method_execution_failed"
-    assert received_events[0].error == error
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_call_started_event():
-    received_events = []
-
-    @crewai_event_bus.on(LLMCallStartedEvent)
-    def handle_llm_call_started(source, event):
-        received_events.append(event)
-
-    @crewai_event_bus.on(LLMCallCompletedEvent)
-    def handle_llm_call_completed(source, event):
-        received_events.append(event)
-
-    llm = LLM(model="gpt-4o-mini")
-    llm.call("Hello, how are you?")
-
-    assert len(received_events) == 2
-    assert received_events[0].type == "llm_call_started"
-    assert received_events[1].type == "llm_call_completed"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_emits_call_failed_event():
-    received_events = []
-
-    @crewai_event_bus.on(LLMCallFailedEvent)
-    def handle_llm_call_failed(source, event):
-        received_events.append(event)
-
-    error_message = "Simulated LLM call failure"
-    with patch("crewai.llm.litellm.completion", side_effect=Exception(error_message)):
-        llm = LLM(model="gpt-4o-mini")
-        with pytest.raises(Exception) as exc_info:
-            llm.call("Hello, how are you?")
-
-        assert str(exc_info.value) == error_message
-        assert len(received_events) == 1
-        assert received_events[0].type == "llm_call_failed"
-        assert received_events[0].error == error_message

uv.lock

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+[[package]]
+name = "asn1crypto"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
+wheels = [
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+]
+
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 name = "asttokens"
 version = "2.4.1"
@@ -219,6 +228,15 @@ wheels = [
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+[[package]]
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+version = "5.0"
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 name = "attrs"
 version = "24.2.0"
@@ -244,6 +262,18 @@ wheels = [
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+[[package]]
+name = "authlib"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+]
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+]
+
 [[package]]
 name = "autoflake"
 version = "2.3.1"
@@ -565,14 +595,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.1.8"
+version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
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 [[package]]
@@ -619,7 +649,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.102.0"
+version = "0.100.1"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -703,7 +733,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.36.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -752,24 +782,33 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.36.0"
+version = "0.32.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "beautifulsoup4" },
     { name = "chromadb" },
-    { name = "click" },
     { name = "crewai" },
     { name = "docker" },
+    { name = "docx2txt" },
     { name = "embedchain" },
     { name = "lancedb" },
+    { name = "linkup-sdk" },
     { name = "openai" },
+    { name = "patronus" },
     { name = "pydantic" },
     { name = "pyright" },
     { name = "pytube" },
     { name = "requests" },
+    { name = "scrapegraph-py" },
+    { name = "selenium" },
+    { name = "serpapi" },
+    { name = "snowflake" },
+    { name = "spider-client" },
+    { name = "weaviate-client" },
 ]
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+
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 version = "0.9"
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+[[package]]
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+version = "1.62.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "protobuf" },
+]
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+[[package]]
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 version = "5.1.0"
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 ]
 
+[[package]]
+name = "weaviate-client"
+version = "4.9.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "authlib" },
+    { name = "grpcio" },
+    { name = "grpcio-health-checking" },
+    { name = "grpcio-tools" },
+    { name = "httpx" },
+    { name = "pydantic" },
+    { name = "requests" },
+    { name = "validators" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5d/7d/3894d12065d006743271b0b6bcc3bf911910473e91179d5966966816d694/weaviate_client-4.9.6.tar.gz", hash = "sha256:56d67c40fc94b0d53e81e0aa4477baaebbf3646fbec26551df66e396a72adcb6", size = 696813 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2f/40/e3550e743b92ddd8dc69ebfd69cceb6de45b7d9a1cd439995454b499e9a3/weaviate_client-4.9.6-py3-none-any.whl", hash = "sha256:1d3b551939c0f7314f25e417cbcf4cf34e7adf942627993eef36ae6b4a044673", size = 386998 },
+]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -5504,6 +5893,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ff/21/abdedb4cdf6ff41ebf01a74087740a709e2edb146490e4d9beea054b0b7a/wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1", size = 23362 },
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+[[package]]
+name = "wsproto"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c9/4a/44d3c295350d776427904d73c189e10aeae66d7f555bb2feee16d1e4ba5a/wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065", size = 53425 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/58/e860788190eba3bcce367f74d29c4675466ce8dddfba85f7827588416f01/wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736", size = 24226 },
+]
+
 [[package]]
 name = "xlsxwriter"
 version = "3.2.0"