docs: reorganize observability docs and update titles

docs/concepts/event-listener.mdx

@@ -13,7 +13,7 @@ CrewAI provides a powerful event system that allows you to listen for and react
 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. **BaseEvent**: Base class for all events in the system
+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.
@@ -234,7 +234,7 @@ 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 `BaseEvent` and include:
+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

docs/concepts/memory.mdx

@@ -164,10 +164,7 @@ crew = Crew(
 
 [Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
 
-
-### Using Mem0 API platform
-
-To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences. In this case `user_memory` is set to `MemoryClient` from mem0.
+To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
 
 
 ```python Code
@@ -178,7 +175,18 @@ from mem0 import MemoryClient
 # Set environment variables for Mem0
 os.environ["MEM0_API_KEY"] = "m0-xx"
 
-# Step 1: Create a Crew with User Memory
+# Step 1: Record preferences based on past conversation or user input
+client = MemoryClient()
+messages = [
+    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
+    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
+    {"role": "user", "content": "I am more of a beach person than a mountain person."},
+    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
+    {"role": "user", "content": "I like Airbnb more."},
+]
+client.add(messages, user_id="john")
+
+# Step 2: Create a Crew with User Memory
 
 crew = Crew(
     agents=[...],
@@ -189,12 +197,11 @@ crew = Crew(
     memory_config={
         "provider": "mem0",
         "config": {"user_id": "john"},
-        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
     },
 )
 ```
 
-#### Additional Memory Configuration Options
+## Memory Configuration Options
 If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
 
 ```python Code
@@ -208,74 +215,10 @@ crew = Crew(
     memory_config={
         "provider": "mem0",
         "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
     },
 )
 ```
 
-### Using Local Mem0 memory
-If you want to use local mem0 memory, with a custom configuration, you can set a parameter `local_mem0_config` in the config itself.
-If both os environment key is set and local_mem0_config is given, the API platform takes higher priority over the local configuration.
-Check [this](https://docs.mem0.ai/open-source/python-quickstart#run-mem0-locally) mem0 local configuration docs for more understanding.
-In this case `user_memory` is set to `Memory` from mem0.
-
-
-```python Code
-from crewai import Crew
-
-
-#local mem0 config
-config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "host": "localhost",
-            "port": 6333
-        }
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "api_key": "your-api-key",
-            "model": "gpt-4"
-        }
-    },
-    "embedder": {
-        "provider": "openai",
-        "config": {
-            "api_key": "your-api-key",
-            "model": "text-embedding-3-small"
-        }
-    },
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {
-            "url": "neo4j+s://your-instance",
-            "username": "neo4j",
-            "password": "password"
-        }
-    },
-    "history_db_path": "/path/to/history.db",
-    "version": "v1.1",
-    "custom_fact_extraction_prompt": "Optional custom prompt for fact extraction for memory",
-    "custom_update_memory_prompt": "Optional custom prompt for update memory"
-}
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", 'local_mem0_config': config},
-        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
-    },
-)
-```
-
-
-
 ## Additional Embedding Providers
 
 ### Using OpenAI embeddings (already default)

docs/custom_llm.md

@@ -1,642 +0,0 @@
-# Custom LLM Implementations
-
-CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
-
-## Using Custom LLM Implementations
-
-To create a custom LLM implementation, you need to:
-
-1. Inherit from the `BaseLLM` abstract base class
-2. Implement the required methods:
-   - `call()`: The main method to call the LLM with messages
-   - `supports_function_calling()`: Whether the LLM supports function calling
-   - `supports_stop_words()`: Whether the LLM supports stop words
-   - `get_context_window_size()`: The context window size of the LLM
-
-## Example: Basic Custom LLM
-
-```python
-from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
-
-class CustomLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not api_key or not isinstance(api_key, str):
-            raise ValueError("Invalid API key: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with the given messages.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM
-        # For example, using requests:
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint, 
-                headers=headers, 
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        # Return True if your LLM supports function calling
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        # Return True if your LLM supports stop words
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        # Return the context window size of your LLM
-        return 8192
-```
-
-## Error Handling Best Practices
-
-When implementing custom LLMs, it's important to handle errors properly to ensure robustness and reliability. Here are some best practices:
-
-### 1. Implement Try-Except Blocks for API Calls
-
-Always wrap API calls in try-except blocks to handle different types of errors:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        # API call implementation
-        response = requests.post(
-            self.endpoint,
-            headers=self.headers,
-            json=self.prepare_payload(messages),
-            timeout=30  # Set a reasonable timeout
-        )
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        return response.json()["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-### 2. Implement Retry Logic for Transient Failures
-
-For transient failures like network issues or rate limiting, implement retry logic with exponential backoff:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import time
-    
-    max_retries = 3
-    retry_delay = 1  # seconds
-    
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(
-                self.endpoint,
-                headers=self.headers,
-                json=self.prepare_payload(messages),
-                timeout=30
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except (requests.Timeout, requests.ConnectionError) as e:
-            if attempt < max_retries - 1:
-                time.sleep(retry_delay * (2 ** attempt))  # Exponential backoff
-                continue
-            raise TimeoutError(f"LLM request failed after {max_retries} attempts: {str(e)}")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-```
-
-### 3. Validate Input Parameters
-
-Always validate input parameters to prevent runtime errors:
-
-```python
-def __init__(self, api_key: str, endpoint: str):
-    super().__init__()
-    if not api_key or not isinstance(api_key, str):
-        raise ValueError("Invalid API key: must be a non-empty string")
-    if not endpoint or not isinstance(endpoint, str):
-        raise ValueError("Invalid endpoint URL: must be a non-empty string")
-    self.api_key = api_key
-    self.endpoint = endpoint
-```
-
-### 4. Handle Authentication Errors Gracefully
-
-Provide clear error messages for authentication failures:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        response = requests.post(self.endpoint, headers=self.headers, json=data)
-        if response.status_code == 401:
-            raise ValueError("Authentication failed: Invalid API key or token")
-        elif response.status_code == 403:
-            raise ValueError("Authorization failed: Insufficient permissions")
-        response.raise_for_status()
-        # Process response
-    except Exception as e:
-        # Handle error
-        raise
-```
-
-## Example: JWT-based Authentication
-
-For services that use JWT-based authentication instead of API keys, you can implement a custom LLM like this:
-
-```python
-from crewai import BaseLLM, Agent, Task
-from typing import Any, Dict, List, Optional, Union
-
-class JWTAuthLLM(BaseLLM):
-    def __init__(self, jwt_token: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not jwt_token or not isinstance(jwt_token, str):
-            raise ValueError("Invalid JWT token: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.jwt_token = jwt_token
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with JWT authentication.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM with JWT authentication
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.jwt_token}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint,
-                headers=headers,
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            
-            if response.status_code == 401:
-                raise ValueError("Authentication failed: Invalid JWT token")
-            elif response.status_code == 403:
-                raise ValueError("Authorization failed: Insufficient permissions")
-                
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        return 8192
-```
-
-## Troubleshooting
-
-Here are some common issues you might encounter when implementing custom LLMs and how to resolve them:
-
-### 1. Authentication Failures
-
-**Symptoms**: 401 Unauthorized or 403 Forbidden errors
-
-**Solutions**:
-- Verify that your API key or JWT token is valid and not expired
-- Check that you're using the correct authentication header format
-- Ensure that your token has the necessary permissions
-
-### 2. Timeout Issues
-
-**Symptoms**: Requests taking too long or timing out
-
-**Solutions**:
-- Implement timeout handling as shown in the examples
-- Use retry logic with exponential backoff
-- Consider using a more reliable network connection
-
-### 3. Response Parsing Errors
-
-**Symptoms**: KeyError, IndexError, or ValueError when processing responses
-
-**Solutions**:
-- Validate the response format before accessing nested fields
-- Implement proper error handling for malformed responses
-- Check the API documentation for the expected response format
-
-### 4. Rate Limiting
-
-**Symptoms**: 429 Too Many Requests errors
-
-**Solutions**:
-- Implement rate limiting in your custom LLM
-- Add exponential backoff for retries
-- Consider using a token bucket algorithm for more precise rate control
-
-## Advanced Features
-
-### Logging
-
-Adding logging to your custom LLM can help with debugging and monitoring:
-
-```python
-import logging
-from typing import Any, Dict, List, Optional, Union
-
-class LoggingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.logger = logging.getLogger("crewai.llm.custom")
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self.logger.info(f"Calling LLM with {len(messages) if isinstance(messages, list) else 1} messages")
-        try:
-            # API call implementation
-            response = self._make_api_call(messages, tools)
-            self.logger.debug(f"LLM response received: {response[:100]}...")
-            return response
-        except Exception as e:
-            self.logger.error(f"LLM call failed: {str(e)}")
-            raise
-```
-
-### Rate Limiting
-
-Implementing rate limiting can help avoid overwhelming the LLM API:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class RateLimitedLLM(BaseLLM):
-    def __init__(
-        self, 
-        api_key: str, 
-        endpoint: str, 
-        requests_per_minute: int = 60
-    ):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.requests_per_minute = requests_per_minute
-        self.request_times: List[float] = []
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self._enforce_rate_limit()
-        # Record this request time
-        self.request_times.append(time.time())
-        # Make the actual API call
-        return self._make_api_call(messages, tools)
-        
-    def _enforce_rate_limit(self) -> None:
-        """Enforce the rate limit by waiting if necessary."""
-        now = time.time()
-        # Remove request times older than 1 minute
-        self.request_times = [t for t in self.request_times if now - t < 60]
-        
-        if len(self.request_times) >= self.requests_per_minute:
-            # Calculate how long to wait
-            oldest_request = min(self.request_times)
-            wait_time = 60 - (now - oldest_request)
-            if wait_time > 0:
-                time.sleep(wait_time)
-```
-
-### Metrics Collection
-
-Collecting metrics can help you monitor your LLM usage:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class MetricsCollectingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.metrics: Dict[str, Any] = {
-            "total_calls": 0,
-            "total_tokens": 0,
-            "errors": 0,
-            "latency": []
-        }
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        start_time = time.time()
-        self.metrics["total_calls"] += 1
-        
-        try:
-            response = self._make_api_call(messages, tools)
-            # Estimate tokens (simplified)
-            if isinstance(messages, str):
-                token_estimate = len(messages) // 4
-            else:
-                token_estimate = sum(len(m.get("content", "")) // 4 for m in messages)
-            self.metrics["total_tokens"] += token_estimate
-            return response
-        except Exception as e:
-            self.metrics["errors"] += 1
-            raise
-        finally:
-            latency = time.time() - start_time
-            self.metrics["latency"].append(latency)
-            
-    def get_metrics(self) -> Dict[str, Any]:
-        """Return the collected metrics."""
-        avg_latency = sum(self.metrics["latency"]) / len(self.metrics["latency"]) if self.metrics["latency"] else 0
-        return {
-            **self.metrics,
-            "avg_latency": avg_latency
-        }
-```
-
-## Advanced Usage: Function Calling
-
-If your LLM supports function calling, you can implement the function calling logic in your custom LLM:
-
-```python
-import json
-from typing import Any, Dict, List, Optional, Union
-
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import requests
-    
-    try:
-        headers = {
-            "Authorization": f"Bearer {self.jwt_token}",
-            "Content-Type": "application/json"
-        }
-        
-        # Convert string message to proper format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        
-        data = {
-            "messages": messages,
-            "tools": tools
-        }
-        
-        response = requests.post(
-            self.endpoint,
-            headers=headers,
-            json=data,
-            timeout=30
-        )
-        response.raise_for_status()
-        response_data = response.json()
-        
-        # Check if the LLM wants to call a function
-        if response_data["choices"][0]["message"].get("tool_calls"):
-            tool_calls = response_data["choices"][0]["message"]["tool_calls"]
-            
-            # Process each tool call
-            for tool_call in tool_calls:
-                function_name = tool_call["function"]["name"]
-                function_args = json.loads(tool_call["function"]["arguments"])
-                
-                if available_functions and function_name in available_functions:
-                    function_to_call = available_functions[function_name]
-                    function_response = function_to_call(**function_args)
-                    
-                    # Add the function response to the messages
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "name": function_name,
-                        "content": str(function_response)
-                    })
-            
-            # Call the LLM again with the updated messages
-            return self.call(messages, tools, callbacks, available_functions)
-        
-        # Return the text response if no function call
-        return response_data["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-## Using Your Custom LLM with CrewAI
-
-Once you've implemented your custom LLM, you can use it with CrewAI agents and crews:
-
-```python
-from crewai import Agent, Task, Crew
-from typing import Dict, Any
-
-# Create your custom LLM instance
-jwt_llm = JWTAuthLLM(
-    jwt_token="your.jwt.token", 
-    endpoint="https://your-llm-endpoint.com/v1/chat/completions"
-)
-
-# Use it with an agent
-agent = Agent(
-    role="Research Assistant",
-    goal="Find information on a topic",
-    backstory="You are a research assistant tasked with finding information.",
-    llm=jwt_llm,
-)
-
-# Create a task for the agent
-task = Task(
-    description="Research the benefits of exercise",
-    agent=agent,
-    expected_output="A summary of the benefits of exercise",
-)
-
-# Execute the task
-result = agent.execute_task(task)
-print(result)
-
-# Or use it with a crew
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    manager_llm=jwt_llm,  # Use your custom LLM for the manager
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Implementing Your Own Authentication Mechanism
-
-The `BaseLLM` class allows you to implement any authentication mechanism you need, not just JWT or API keys. You can use:
-
-- OAuth tokens
-- Client certificates
-- Custom headers
-- Session-based authentication
-- Any other authentication method required by your LLM provider
-
-Simply implement the appropriate authentication logic in your custom LLM class.

docs/docs.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "mint",
+  "theme": "palm",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",
@@ -109,7 +109,6 @@
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
-              "how-to/opik-observability",
               "how-to/portkey-observability"
             ]
           },
@@ -229,4 +228,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/how-to/opik-observability.mdx

@@ -1,129 +0,0 @@
----
-title: Opik Integration
-description: Learn how to use Comet Opik to debug, evaluate, and monitor your CrewAI applications with comprehensive tracing, automated evaluations, and production-ready dashboards. 
-icon: meteor
----
-
-# Opik Overview
-
-With [Comet Opik](https://www.comet.com/docs/opik/), debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
-
-<Frame caption="Opik Agent Dashboard">
-  <img src="/images/opik-crewai-dashboard.png" alt="Opik agent monitoring example with CrewAI" />
-</Frame>
-
-Opik provides comprehensive support for every stage of your CrewAI application development:
-
-- **Log Traces and Spans**: Automatically track LLM calls and application logic to debug and analyze development and production systems. Manually or programmatically annotate, view, and compare responses across projects.
-- **Evaluate Your LLM Application's Performance**: Evaluate against a custom test set and run built-in evaluation metrics or define your own metrics in the SDK or UI.
-- **Test Within Your CI/CD Pipeline**: Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Run online evaluations for continuous monitoring in production.
-- **Monitor & Analyze Production Data**: Understand your models' performance on unseen data in production and generate datasets for new dev iterations.
-
-## Setup
-Comet provides a hosted version of the Opik platform, or you can run the platform locally. 
-
-To use the hosted version, simply [create a free Comet account](https://www.comet.com/signup?utm_medium=github&utm_source=crewai_docs) and grab you API Key.
-
-To run the Opik platform locally, see our [installation guide](https://www.comet.com/docs/opik/self-host/overview/) for more information.
-
-For this guide we will use CrewAI’s quickstart example.
-
-<Steps>
-    <Step title="Install required packages">
-      ```shell
-      pip install crewai crewai-tools opik --upgrade
-      ```
-    </Step>
-    <Step title="Configure Opik">
-      ```python
-      import opik
-      opik.configure(use_local=False)
-      ```
-    </Step>
-    <Step title="Prepare environment">
-      First, we set up our API keys for our LLM-provider as environment variables:
-  
-      ```python
-      import os
-      import getpass
-
-      if "OPENAI_API_KEY" not in os.environ:
-      os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
-      ```
-    </Step>
-    <Step title="Using CrewAI">
-      The first step is to create our project. We will use an example from CrewAI’s documentation:
-
-      ```python
-      from crewai import Agent, Crew, Task, Process
-
-
-      class YourCrewName:
-          def agent_one(self) -> Agent:
-              return Agent(
-                  role="Data Analyst",
-                  goal="Analyze data trends in the market",
-                  backstory="An experienced data analyst with a background in economics",
-                  verbose=True,
-              )
-
-          def agent_two(self) -> Agent:
-              return Agent(
-                  role="Market Researcher",
-                  goal="Gather information on market dynamics",
-                  backstory="A diligent researcher with a keen eye for detail",
-                  verbose=True,
-              )
-
-          def task_one(self) -> Task:
-              return Task(
-                  name="Collect Data Task",
-                  description="Collect recent market data and identify trends.",
-                  expected_output="A report summarizing key trends in the market.",
-                  agent=self.agent_one(),
-              )
-
-          def task_two(self) -> Task:
-              return Task(
-                  name="Market Research Task",
-                  description="Research factors affecting market dynamics.",
-                  expected_output="An analysis of factors influencing the market.",
-                  agent=self.agent_two(),
-              )
-
-          def crew(self) -> Crew:
-              return Crew(
-                  agents=[self.agent_one(), self.agent_two()],
-                  tasks=[self.task_one(), self.task_two()],
-                  process=Process.sequential,
-                  verbose=True,
-              )
-
-      ```
-
-      Now we can import Opik’s tracker and run our crew:
-  
-      ```python
-      from opik.integrations.crewai import track_crewai
-
-      track_crewai(project_name="crewai-integration-demo")
-
-      my_crew = YourCrewName().crew()
-      result = my_crew.kickoff()
-
-      print(result)
-      ```
-      After running your CrewAI application, visit the Opik app to view:
-      - LLM traces, spans, and their metadata
-      - Agent interactions and task execution flow
-      - Performance metrics like latency and token usage
-      - Evaluation metrics (built-in or custom)
-    </Step>
-</Steps>
-
-## Resources
-
-- [🦉 Opik Documentation](https://www.comet.com/docs/opik/)
-- [👉 Opik + CrewAI Colab](https://colab.research.google.com/github/comet-ml/opik/blob/main/apps/opik-documentation/documentation/docs/cookbook/crewai.ipynb)
-- [🐦 X](https://x.com/cometml)
-- [💬 Slack](https://slack.comet.com/)

pyproject.toml

@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.38.0"]
+tools = ["crewai-tools>=0.37.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]
@@ -64,9 +64,6 @@ mem0 = ["mem0ai>=0.1.29"]
 docling = [
     "docling>=2.12.0",
 ]
-aisuite = [
-    "aisuite>=0.1.10",
-]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/__init__.py

@@ -5,7 +5,6 @@ from crewai.crew import Crew
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
 from crewai.process import Process
 from crewai.task import Task
 
@@ -22,7 +21,6 @@ __all__ = [
     "Process",
     "Task",
     "LLM",
-    "BaseLLM",
     "Flow",
     "Knowledge",
 ]

src/crewai/agent.py

@@ -11,7 +11,7 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
-from crewai.llm import BaseLLM
+from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
 from crewai.task import Task
@@ -71,10 +71,10 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -118,9 +118,7 @@ class Agent(BaseAgent):
         self.agent_ops_agent_name = self.role
 
         self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(
-            self.function_calling_llm, BaseLLM
-        ):
+        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
             self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
@@ -142,13 +140,15 @@ class Agent(BaseAgent):
                 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)}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
                     self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
                         embedder=self.embedder,
-                        collection_name=self.role,
+                        collection_name=knowledge_agent_name,
                         storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:

src/crewai/agents/crew_agent_executor.py

@@ -13,13 +13,14 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.llm import BaseLLM
+from crewai.llm import LLM
 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
@@ -60,7 +61,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm: BaseLLM = llm
+        self.llm: LLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -86,14 +87,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        existing_stop = self.llm.stop or []
-        self.llm.stop = list(
-            set(
-                existing_stop + self.stop
-                if isinstance(existing_stop, list)
-                else self.stop
-            )
-        )
+        self.stop = stop_words
+        self.llm.stop = list(set(self.llm.stop + self.stop))
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -152,21 +147,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
-                    # Extract agent fingerprint if available
-                    fingerprint_context = {}
-                    if (
-                        self.agent
-                        and hasattr(self.agent, "security_config")
-                        and hasattr(self.agent.security_config, "fingerprint")
-                    ):
-                        fingerprint_context = {
-                            "agent_fingerprint": str(
-                                self.agent.security_config.fingerprint
-                            )
-                        }
-
                     tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer, fingerprint_context=fingerprint_context
+                        formatted_answer
                     )
                     formatted_answer = self._handle_agent_action(
                         formatted_answer, tool_result
@@ -372,35 +354,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
                 )
 
-    def _execute_tool_and_check_finality(
-        self,
-        agent_action: AgentAction,
-        fingerprint_context: Optional[Dict[str, str]] = None,
-    ) -> ToolResult:
+    def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
         try:
-            fingerprint_context = fingerprint_context or {}
-
             if self.agent:
-                # Create tool usage event with fingerprint information
-                event_data = {
-                    "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,
-                    "agent": self.agent,  # Pass the agent object for fingerprint extraction
-                }
-
-                # Include fingerprint context
-                if fingerprint_context:
-                    event_data.update(fingerprint_context)
-
-                # Emit the tool usage started event with agent information
                 crewai_event_bus.emit(
                     self,
-                    event=ToolUsageStartedEvent(**event_data),
+                    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,
@@ -411,7 +377,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 task=self.task,  # type: ignore[arg-type]
                 agent=self.agent,
                 action=agent_action,
-                fingerprint_context=fingerprint_context,  # Pass fingerprint context
             )
             tool_calling = tool_usage.parse_tool_calling(agent_action.text)
 
@@ -440,23 +405,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         except Exception as e:
             # TODO: drop
             if self.agent:
-                error_event_data = {
-                    "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),
-                    "agent": self.agent,  # Pass the agent object for fingerprint extraction
-                }
-
-                # Include fingerprint context
-                if fingerprint_context:
-                    error_event_data.update(fingerprint_context)
-
                 crewai_event_bus.emit(
                     self,
-                    event=ToolUsageErrorEvent(**error_event_data),
+                    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),
+                    ),
                 )
             raise e
 

src/crewai/cli/create_crew.py

@@ -93,66 +93,50 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
     env_vars = load_env_vars(folder_path)
     if not skip_provider:
+        if not provider:
+            provider_models = get_provider_data()
+            if not provider_models:
+                return
+
+        existing_provider = None
+        for provider, env_keys in ENV_VARS.items():
+            if any(
+                "key_name" in details and details["key_name"] in env_vars
+                for details in env_keys
+            ):
+                existing_provider = provider
+                break
+
+        if existing_provider:
+            if not click.confirm(
+                f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?"
+            ):
+                click.secho("Keeping existing provider configuration.", fg="yellow")
+                return
+
         provider_models = get_provider_data()
         if not provider_models:
-            click.secho("Could not retrieve provider data.", fg="red")
             return
 
-        selected_provider = None
-
-        if provider: 
-            provider = provider.lower()
-            if provider in provider_models:
-                selected_provider = provider
-                click.secho(f"Using specified provider: {selected_provider.capitalize()}", fg="green")
-            else:
-                click.secho(f"Warning: Specified provider '{provider}' is not recognized. Please select one.", fg="yellow")
-
-        if not selected_provider:
-            existing_provider = None
-            for p, env_keys in ENV_VARS.items():
-                if any(
-                    "key_name" in details and details["key_name"] in env_vars
-                    for details in env_keys
-                ):
-                    existing_provider = p
-                    break
-
-            if existing_provider:
-                if not click.confirm(
-                    f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?"
-                ):
-                    click.secho("Keeping existing provider configuration. Exiting provider setup.", fg="yellow")
-                    copy_template_files(folder_path, name, class_name, parent_folder)
-                    click.secho(f"Crew '{name}' created successfully!", fg="green")
-                    click.secho(f"To run your crew, cd into '{folder_name}' and run 'crewai run'", fg="cyan")
-                    return
-                else:
-                    pass
-
-            while True:
-                selected_provider = select_provider(provider_models)
-                if selected_provider is None:  
-                    click.secho("Exiting...", fg="yellow")
-                    sys.exit(0)
-                if selected_provider:  
-                    break
-                click.secho(
-                    "No provider selected. Please try again or press 'q' to exit.", fg="red"
-                )
-
-        if not selected_provider:
-             click.secho("Provider selection failed. Exiting.", fg="red")
-             sys.exit(1)
-
+        while True:
+            selected_provider = select_provider(provider_models)
+            if selected_provider is None:  # User typed 'q'
+                click.secho("Exiting...", fg="yellow")
+                sys.exit(0)
+            if selected_provider:  # Valid selection
+                break
+            click.secho(
+                "No provider selected. Please try again or press 'q' to exit.", fg="red"
+            )
 
+        # Check if the selected provider has predefined models
         if selected_provider in MODELS and MODELS[selected_provider]:
             while True:
                 selected_model = select_model(selected_provider, provider_models)
-                if selected_model is None:  
+                if selected_model is None:  # User typed 'q'
                     click.secho("Exiting...", fg="yellow")
                     sys.exit(0)
-                if selected_model:  
+                if selected_model:  # Valid selection
                     break
                 click.secho(
                     "No model selected. Please try again or press 'q' to exit.",
@@ -160,14 +144,17 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
                 )
             env_vars["MODEL"] = selected_model
 
+        # Check if the selected provider requires API keys
         if selected_provider in ENV_VARS:
             provider_env_vars = ENV_VARS[selected_provider]
             for details in provider_env_vars:
                 if details.get("default", False):
+                    # Automatically add default key-value pairs
                     for key, value in details.items():
                         if key not in ["prompt", "key_name", "default"]:
                             env_vars[key] = value
                 elif "key_name" in details:
+                    # Prompt for non-default key-value pairs
                     prompt = details["prompt"]
                     key_name = details["key_name"]
                     api_key_value = click.prompt(prompt, default="", show_default=False)
@@ -180,12 +167,41 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
             click.secho("API keys and model saved to .env file", fg="green")
         else:
             click.secho(
-                "No API keys provided or required by provider. Skipping .env file creation.", fg="yellow"
+                "No API keys provided. Skipping .env file creation.", fg="yellow"
             )
 
         click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
 
-    copy_template_files(folder_path, name, class_name, parent_folder)
+    package_dir = Path(__file__).parent
+    templates_dir = package_dir / "templates" / "crew"
 
-    click.secho(f"Crew '{name}' created successfully!", fg="green")
-    click.secho(f"To run your crew, cd into '{folder_name}' and run 'crewai run'", fg="cyan")
+    root_template_files = (
+        [".gitignore", "pyproject.toml", "README.md", "knowledge/user_preference.txt"]
+        if not parent_folder
+        else []
+    )
+    tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
+    config_template_files = ["config/agents.yaml", "config/tasks.yaml"]
+    src_template_files = (
+        ["__init__.py", "main.py", "crew.py"] if not parent_folder else ["crew.py"]
+    )
+
+    for file_name in root_template_files:
+        src_file = templates_dir / file_name
+        dst_file = folder_path / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    src_folder = folder_path / "src" / folder_name if not parent_folder else folder_path
+
+    for file_name in src_template_files:
+        src_file = templates_dir / file_name
+        dst_file = src_folder / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    if not parent_folder:
+        for file_name in tools_template_files + config_template_files:
+            src_file = templates_dir / file_name
+            dst_file = src_folder / file_name
+            copy_template(src_file, dst_file, name, class_name, folder_name)
+
+    click.secho(f"Crew {name} created successfully!", fg="green", bold=True)

src/crewai/cli/crew_chat.py

@@ -14,7 +14,7 @@ from packaging import version
 from crewai.cli.utils import read_toml
 from crewai.cli.version import get_crewai_version
 from crewai.crew import Crew
-from crewai.llm import LLM, BaseLLM
+from crewai.llm import LLM
 from crewai.types.crew_chat import ChatInputField, ChatInputs
 from crewai.utilities.llm_utils import create_llm
 
@@ -116,7 +116,7 @@ def show_loading(event: threading.Event):
     print()
 
 
-def initialize_chat_llm(crew: Crew) -> Optional[LLM | BaseLLM]:
+def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
     """Initializes the chat LLM and handles exceptions."""
     try:
         return create_llm(crew.chat_llm)

src/crewai/crew.py

@@ -6,7 +6,7 @@ import warnings
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union, cast
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -26,7 +26,7 @@ from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.llm import LLM, BaseLLM
+from crewai.llm import LLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -37,7 +37,7 @@ from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import BaseTool, Tool
+from crewai.tools.base_tool import Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -153,7 +153,7 @@ class Crew(BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    manager_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     manager_agent: Optional[BaseAgent] = Field(
@@ -187,7 +187,7 @@ class Crew(BaseModel):
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
     )
-    prompt_file: Optional[str] = Field(
+    prompt_file: str = Field(
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
@@ -199,7 +199,7 @@ class Crew(BaseModel):
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    planning_llm: Optional[Any] = Field(
         default=None,
         description="Language model that will run the AgentPlanner if planning is True.",
     )
@@ -215,7 +215,7 @@ class Crew(BaseModel):
         default=None,
         description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
-    chat_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
+    chat_llm: Optional[Any] = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
@@ -290,17 +290,23 @@ class Crew(BaseModel):
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
             if (
-                self.memory_config
-                and "user_memory" in self.memory_config
-                and self.memory_config.get("provider") == "mem0"
+                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)
+                    self._user_memory = UserMemory(
+                        crew=self, **user_memory_config
+                    )  # Initialize with config
                 else:
-                    raise TypeError("user_memory must be a configuration dictionary")
+                    raise TypeError(
+                        "user_memory must be a UserMemory instance or a configuration dictionary"
+                    )
             else:
                 self._user_memory = None  # No user memory if not in config
         return self
@@ -483,7 +489,7 @@ class Crew(BaseModel):
             task.key for task in self.tasks
         ]
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
-
+        
     @property
     def fingerprint(self) -> Fingerprint:
         """
@@ -813,12 +819,7 @@ class Crew(BaseModel):
 
             # Determine which tools to use - task tools take precedence over agent tools
             tools_for_task = task.tools or agent_to_use.tools or []
-            # Prepare tools and ensure they're compatible with task execution
-            tools_for_task = self._prepare_tools(
-                agent_to_use,
-                task,
-                cast(Union[List[Tool], List[BaseTool]], tools_for_task),
-            )
+            tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
 
             self._log_task_start(task, agent_to_use.role)
 
@@ -837,7 +838,7 @@ class Crew(BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -849,7 +850,7 @@ class Crew(BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(List[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -887,12 +888,10 @@ class Crew(BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+        self, agent: BaseAgent, task: Task, tools: List[Tool]
+    ) -> List[Tool]:
         # Add delegation tools if agent allows delegation
-        if hasattr(agent, "allow_delegation") and getattr(
-            agent, "allow_delegation", False
-        ):
+        if agent.allow_delegation:
             if self.process == Process.hierarchical:
                 if self.manager_agent:
                     tools = self._update_manager_tools(task, tools)
@@ -901,24 +900,17 @@ class Crew(BaseModel):
                         "Manager agent is required for hierarchical process."
                     )
 
-            elif agent:
+            elif agent and agent.allow_delegation:
                 tools = self._add_delegation_tools(task, tools)
 
         # Add code execution tools if agent allows code execution
-        if hasattr(agent, "allow_code_execution") and getattr(
-            agent, "allow_code_execution", False
-        ):
+        if agent.allow_code_execution:
             tools = self._add_code_execution_tools(agent, tools)
 
-        if (
-            agent
-            and hasattr(agent, "multimodal")
-            and getattr(agent, "multimodal", False)
-        ):
+        if agent and agent.multimodal:
             tools = self._add_multimodal_tools(agent, tools)
 
-        # Return a List[BaseTool] which is compatible with both Task.execute_sync and Task.execute_async
-        return cast(List[BaseTool], tools)
+        return tools
 
     def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
@@ -926,13 +918,11 @@ class Crew(BaseModel):
         return task.agent
 
     def _merge_tools(
-        self,
-        existing_tools: Union[List[Tool], List[BaseTool]],
-        new_tools: Union[List[Tool], List[BaseTool]],
-    ) -> List[BaseTool]:
+        self, existing_tools: List[Tool], new_tools: List[Tool]
+    ) -> List[Tool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
-            return cast(List[BaseTool], existing_tools)
+            return existing_tools
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -943,41 +933,23 @@ class Crew(BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(List[BaseTool], tools)
+        return tools
 
     def _inject_delegation_tools(
-        self,
-        tools: Union[List[Tool], List[BaseTool]],
-        task_agent: BaseAgent,
-        agents: List[BaseAgent],
-    ) -> List[BaseTool]:
-        if hasattr(task_agent, "get_delegation_tools"):
-            delegation_tools = task_agent.get_delegation_tools(agents)
-            # Cast delegation_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], delegation_tools))
-        return cast(List[BaseTool], tools)
+        self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]
+    ):
+        delegation_tools = task_agent.get_delegation_tools(agents)
+        return self._merge_tools(tools, delegation_tools)
 
-    def _add_multimodal_tools(
-        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
-        if hasattr(agent, "get_multimodal_tools"):
-            multimodal_tools = agent.get_multimodal_tools()
-            # Cast multimodal_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], multimodal_tools))
-        return cast(List[BaseTool], tools)
+    def _add_multimodal_tools(self, agent: BaseAgent, tools: List[Tool]):
+        multimodal_tools = agent.get_multimodal_tools()
+        return self._merge_tools(tools, multimodal_tools)
 
-    def _add_code_execution_tools(
-        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
-        if hasattr(agent, "get_code_execution_tools"):
-            code_tools = agent.get_code_execution_tools()
-            # Cast code_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(List[BaseTool], code_tools))
-        return cast(List[BaseTool], tools)
+    def _add_code_execution_tools(self, agent: BaseAgent, tools: List[Tool]):
+        code_tools = agent.get_code_execution_tools()
+        return self._merge_tools(tools, code_tools)
 
-    def _add_delegation_tools(
-        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+    def _add_delegation_tools(self, task: Task, tools: List[Tool]):
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
@@ -985,7 +957,7 @@ class Crew(BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return cast(List[BaseTool], tools)
+        return tools
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
@@ -993,9 +965,7 @@ class Crew(BaseModel):
                 task_name=task.name, task=task.description, agent=role, status="started"
             )
 
-    def _update_manager_tools(
-        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
-    ) -> List[BaseTool]:
+    def _update_manager_tools(self, task: Task, tools: List[Tool]):
         if self.manager_agent:
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
@@ -1003,7 +973,7 @@ class Crew(BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return cast(List[BaseTool], tools)
+        return tools
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
         context = (
@@ -1150,12 +1120,7 @@ class Crew(BaseModel):
         return required_inputs
 
     def copy(self):
-        """
-        Creates a deep copy of the Crew instance.
-
-        Returns:
-            Crew: A new instance with copied components
-        """
+        """Create a deep copy of the Crew."""
 
         exclude = {
             "id",
@@ -1167,18 +1132,13 @@ class Crew(BaseModel):
             "_short_term_memory",
             "_long_term_memory",
             "_entity_memory",
-            "_telemetry",
             "agents",
             "tasks",
             "knowledge_sources",
             "knowledge",
-            "manager_agent",
-            "manager_llm",
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]
-        manager_agent = self.manager_agent.copy() if self.manager_agent else None
-        manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
 
         task_mapping = {}
 
@@ -1211,8 +1171,6 @@ class Crew(BaseModel):
             tasks=cloned_tasks,
             knowledge_sources=existing_knowledge_sources,
             knowledge=existing_knowledge,
-            manager_agent=manager_agent,
-            manager_llm=manager_llm,
         )
 
         return copied_crew
@@ -1256,14 +1214,13 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: Union[str, InstanceOf[BaseLLM]],
+        eval_llm: Union[str, InstanceOf[LLM]],
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
-            # Create LLM instance and ensure it's of type LLM for CrewEvaluator
-            llm_instance = create_llm(eval_llm)
-            if not llm_instance:
+            eval_llm = create_llm(eval_llm)
+            if not eval_llm:
                 raise ValueError("Failed to create LLM instance.")
 
             crewai_event_bus.emit(
@@ -1271,12 +1228,12 @@ class Crew(BaseModel):
                 CrewTestStartedEvent(
                     crew_name=self.name or "crew",
                     n_iterations=n_iterations,
-                    eval_llm=llm_instance,
+                    eval_llm=eval_llm,
                     inputs=inputs,
                 ),
             )
             test_crew = self.copy()
-            evaluator = CrewEvaluator(test_crew, llm_instance)
+            evaluator = CrewEvaluator(test_crew, eval_llm)  # type: ignore[arg-type]
 
             for i in range(1, n_iterations + 1):
                 evaluator.set_iteration(i)

src/crewai/flow/state_utils.py

@@ -1,21 +1,38 @@
 import json
-import uuid
 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,
-    exclude: set[str] | None = None,
-    max_depth: int = 5,
-    _current_depth: int = 0,
+    obj: Any, max_depth: int = 5, _current_depth: int = 0
 ) -> Serializable:
     """Converts a Python object into a JSON-compatible representation.
 
@@ -25,7 +42,6 @@ def to_serializable(
 
     Args:
         obj (Any): Object to transform.
-        exclude (set[str], optional): Set of keys to exclude from the result.
         max_depth (int, optional): Maximum recursion depth. Defaults to 5.
 
     Returns:
@@ -34,39 +50,21 @@ def to_serializable(
     if _current_depth >= max_depth:
         return repr(obj)
 
-    if exclude is None:
-        exclude = set()
-
     if isinstance(obj, (str, int, float, bool, type(None))):
         return obj
-    elif isinstance(obj, uuid.UUID):
-        return str(obj)
     elif isinstance(obj, (date, datetime)):
         return obj.isoformat()
     elif isinstance(obj, (list, tuple, set)):
-        return [
-            to_serializable(
-                item, max_depth=max_depth, _current_depth=_current_depth + 1
-            )
-            for item in obj
-        ]
+        return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
     elif isinstance(obj, dict):
         return {
             _to_serializable_key(key): to_serializable(
-                obj=value,
-                exclude=exclude,
-                max_depth=max_depth,
-                _current_depth=_current_depth + 1,
+                value, max_depth, _current_depth + 1
             )
             for key, value in obj.items()
-            if key not in exclude
         }
     elif isinstance(obj, BaseModel):
-        return to_serializable(
-            obj=obj.model_dump(exclude=exclude),
-            max_depth=max_depth,
-            _current_depth=_current_depth + 1,
-        )
+        return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
     else:
         return repr(obj)
 

src/crewai/knowledge/storage/knowledge_storage.py

@@ -14,7 +14,6 @@ from chromadb.config import Settings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.chromadb import sanitize_collection_name
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
 from crewai.utilities.logger import Logger
 from crewai.utilities.paths import db_storage_path
@@ -100,8 +99,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             )
             if self.app:
                 self.collection = self.app.get_or_create_collection(
-                    name=sanitize_collection_name(collection_name),
-                    embedding_function=self.embedder,
+                    name=collection_name, embedding_function=self.embedder
                 )
             else:
                 raise Exception("Vector Database Client not initialized")

src/crewai/llm.py

@@ -40,7 +40,6 @@ with warnings.catch_warnings():
     from litellm.utils import supports_response_schema
 
 
-from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
@@ -219,7 +218,7 @@ class StreamingChoices(TypedDict):
     finish_reason: Optional[str]
 
 
-class LLM(BaseLLM):
+class LLM:
     def __init__(
         self,
         model: str,

src/crewai/llms/base_llm.py

@@ -1,91 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, Union
-
-
-class BaseLLM(ABC):
-    """Abstract base class for LLM implementations.
-
-    This class defines the interface that all LLM implementations must follow.
-    Users can extend this class to create custom LLM implementations that don't
-    rely on litellm's authentication mechanism.
-
-    Custom LLM implementations should handle error cases gracefully, including
-    timeouts, authentication failures, and malformed responses. They should also
-    implement proper validation for input parameters and provide clear error
-    messages when things go wrong.
-
-    Attributes:
-        stop (list): A list of stop sequences that the LLM should use to stop generation.
-            This is used by the CrewAgentExecutor and other components.
-    """
-
-    model: str
-    temperature: Optional[float] = None
-    stop: Optional[List[str]] = None
-
-    def __init__(
-        self,
-        model: str,
-        temperature: Optional[float] = None,
-    ):
-        """Initialize the BaseLLM with default attributes.
-
-        This constructor sets default values for attributes that are expected
-        by the CrewAgentExecutor and other components.
-
-        All custom LLM implementations should call super().__init__() to ensure
-        that these default attributes are properly initialized.
-        """
-        self.model = model
-        self.temperature = temperature
-        self.stop = []
-
-    @abstractmethod
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with the given messages.
-
-        Args:
-            messages: Input messages for the LLM.
-                     Can be a string or list of message dictionaries.
-                     If string, it will be converted to a single user message.
-                     If list, each dict must have 'role' and 'content' keys.
-            tools: Optional list of tool schemas for function calling.
-                  Each tool should define its name, description, and parameters.
-            callbacks: Optional list of callback functions to be executed
-                      during and after the LLM call.
-            available_functions: Optional dict mapping function names to callables
-                               that can be invoked by the LLM.
-
-        Returns:
-            Either a text response from the LLM (str) or
-            the result of a tool function call (Any).
-
-        Raises:
-            ValueError: If the messages format is invalid.
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-        """
-        pass
-
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-
-        Returns:
-            bool: True if the LLM supports stop words, False otherwise.
-        """
-        return True  # Default implementation assumes support for stop words
-
-    def get_context_window_size(self) -> int:
-        """Get the context window size for the LLM.
-
-        Returns:
-            int: The number of tokens/characters the model can handle.
-        """
-        # Default implementation - subclasses should override with model-specific values
-        return 4096

src/crewai/llms/third_party/ai_suite.py

@@ -1,38 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-
-import aisuite as ai
-
-from crewai.llms.base_llm import BaseLLM
-
-
-class AISuiteLLM(BaseLLM):
-    def __init__(self, model: str, temperature: Optional[float] = None, **kwargs):
-        super().__init__(model, temperature, **kwargs)
-        self.client = ai.Client()
-
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        completion_params = self._prepare_completion_params(messages, tools)
-        response = self.client.chat.completions.create(**completion_params)
-
-        return response.choices[0].message.content
-
-    def _prepare_completion_params(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-    ) -> Dict[str, Any]:
-        return {
-            "model": self.model,
-            "messages": messages,
-            "temperature": self.temperature,
-            "tools": tools,
-        }
-
-    def supports_function_calling(self) -> bool:
-        return False

src/crewai/memory/contextual/contextual_memory.py

@@ -94,10 +94,6 @@ class ContextualMemory:
         Returns:
             str: Formatted user memories as bullet points, or an empty string if none found.
         """
-
-        if self.um is None:
-            return ""
-
         user_memories = self.um.search(query)
         if not user_memories:
             return ""

src/crewai/memory/storage/mem0_storage.py

@@ -31,7 +31,6 @@ class Mem0Storage(Storage):
         mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
         mem0_org_id = config.get("org_id")
         mem0_project_id = config.get("project_id")
-        mem0_local_config = config.get("local_mem0_config")
 
         # Initialize MemoryClient or Memory based on the presence of the mem0_api_key
         if mem0_api_key:
@@ -42,10 +41,7 @@ class Mem0Storage(Storage):
             else:
                 self.memory = MemoryClient(api_key=mem0_api_key)
         else:
-            if mem0_local_config and len(mem0_local_config):
-                self.memory = Memory.from_config(config)
-            else:
-                self.memory = Memory()
+            self.memory = Memory()  # Fallback to Memory if no Mem0 API key is provided
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -118,7 +114,3 @@ class Mem0Storage(Storage):
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
         return agents
-
-    def reset(self):
-        if self.memory:
-            self.memory.reset()

src/crewai/memory/user/user_memory.py

@@ -43,11 +43,3 @@ class UserMemory(Memory):
             score_threshold=score_threshold,
         )
         return results
-
-    def reset(self) -> None:
-        try:
-            self.storage.reset()
-        except Exception as e:
-            raise Exception(
-                f"An error occurred while resetting the user memory: {e}"
-            )

src/crewai/task.py

@@ -388,7 +388,7 @@ class Task(BaseModel):
             tools = tools or self.tools or []
 
             self.processed_by_agents.add(agent.role)
-            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context))
             result = agent.execute_task(
                 task=self,
                 context=context,
@@ -464,11 +464,11 @@ class Task(BaseModel):
                     )
                 )
                 self._save_file(content)
-            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output, task=self))
+            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), task=self))
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e)))
             raise e  # Re-raise the exception after emitting the event
 
     def prompt(self) -> str:
@@ -572,15 +572,7 @@ class Task(BaseModel):
     def copy(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Creates a deep copy of the Task while preserving its original class type.
-
-        Args:
-            agents: List of agents available for the task.
-            task_mapping: Dictionary mapping task IDs to Task instances.
-
-        Returns:
-            A copy of the task with the same class type as the original.
-        """
+        """Create a deep copy of the Task."""
         exclude = {
             "id",
             "agent",
@@ -603,7 +595,7 @@ class Task(BaseModel):
         cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
         cloned_tools = copy(self.tools) if self.tools else []
 
-        copied_task = self.__class__(
+        copied_task = Task(
             **copied_data,
             context=cloned_context,
             agent=cloned_agent,

src/crewai/telemetry/telemetry.py

@@ -112,23 +112,6 @@ class Telemetry:
             self._add_attribute(span, "crew_memory", crew.memory)
             self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
             self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
-
-            # Add fingerprint data
-            if hasattr(crew, "fingerprint") and crew.fingerprint:
-                self._add_attribute(span, "crew_fingerprint", crew.fingerprint.uuid_str)
-                self._add_attribute(
-                    span,
-                    "crew_fingerprint_created_at",
-                    crew.fingerprint.created_at.isoformat(),
-                )
-                # Add fingerprint metadata if it exists
-                if hasattr(crew.fingerprint, "metadata") and crew.fingerprint.metadata:
-                    self._add_attribute(
-                        span,
-                        "crew_fingerprint_metadata",
-                        json.dumps(crew.fingerprint.metadata),
-                    )
-
             if crew.share_crew:
                 self._add_attribute(
                     span,
@@ -146,43 +129,17 @@ class Telemetry:
                                 "max_rpm": agent.max_rpm,
                                 "i18n": agent.i18n.prompt_file,
                                 "function_calling_llm": (
-                                    getattr(
-                                        getattr(agent, "function_calling_llm", None),
-                                        "model",
-                                        "",
-                                    )
-                                    if getattr(agent, "function_calling_llm", None)
+                                    agent.function_calling_llm.model
+                                    if agent.function_calling_llm
                                     else ""
                                 ),
                                 "llm": agent.llm.model,
                                 "delegation_enabled?": agent.allow_delegation,
-                                "allow_code_execution?": getattr(
-                                    agent, "allow_code_execution", False
-                                ),
-                                "max_retry_limit": getattr(agent, "max_retry_limit", 3),
+                                "allow_code_execution?": agent.allow_code_execution,
+                                "max_retry_limit": agent.max_retry_limit,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []
                                 ],
-                                # Add agent fingerprint data if sharing crew details
-                                "fingerprint": (
-                                    getattr(
-                                        getattr(agent, "fingerprint", None),
-                                        "uuid_str",
-                                        None,
-                                    )
-                                ),
-                                "fingerprint_created_at": (
-                                    created_at.isoformat()
-                                    if (
-                                        created_at := getattr(
-                                            getattr(agent, "fingerprint", None),
-                                            "created_at",
-                                            None,
-                                        )
-                                    )
-                                    is not None
-                                    else None
-                                ),
                             }
                             for agent in crew.agents
                         ]
@@ -212,17 +169,6 @@ class Telemetry:
                                 "tools_names": [
                                     tool.name.casefold() for tool in task.tools or []
                                 ],
-                                # Add task fingerprint data if sharing crew details
-                                "fingerprint": (
-                                    task.fingerprint.uuid_str
-                                    if hasattr(task, "fingerprint") and task.fingerprint
-                                    else None
-                                ),
-                                "fingerprint_created_at": (
-                                    task.fingerprint.created_at.isoformat()
-                                    if hasattr(task, "fingerprint") and task.fingerprint
-                                    else None
-                                ),
                             }
                             for task in crew.tasks
                         ]
@@ -250,20 +196,14 @@ class Telemetry:
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
                                 "function_calling_llm": (
-                                    getattr(
-                                        getattr(agent, "function_calling_llm", None),
-                                        "model",
-                                        "",
-                                    )
-                                    if getattr(agent, "function_calling_llm", None)
+                                    agent.function_calling_llm.model
+                                    if agent.function_calling_llm
                                     else ""
                                 ),
                                 "llm": agent.llm.model,
                                 "delegation_enabled?": agent.allow_delegation,
-                                "allow_code_execution?": getattr(
-                                    agent, "allow_code_execution", False
-                                ),
-                                "max_retry_limit": getattr(agent, "max_retry_limit", 3),
+                                "allow_code_execution?": agent.allow_code_execution,
+                                "max_retry_limit": agent.max_retry_limit,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []
                                 ],
@@ -312,39 +252,6 @@ class Telemetry:
             self._add_attribute(created_span, "task_key", task.key)
             self._add_attribute(created_span, "task_id", str(task.id))
 
-            # Add fingerprint data
-            if hasattr(crew, "fingerprint") and crew.fingerprint:
-                self._add_attribute(
-                    created_span, "crew_fingerprint", crew.fingerprint.uuid_str
-                )
-
-            if hasattr(task, "fingerprint") and task.fingerprint:
-                self._add_attribute(
-                    created_span, "task_fingerprint", task.fingerprint.uuid_str
-                )
-                self._add_attribute(
-                    created_span,
-                    "task_fingerprint_created_at",
-                    task.fingerprint.created_at.isoformat(),
-                )
-                # Add fingerprint metadata if it exists
-                if hasattr(task.fingerprint, "metadata") and task.fingerprint.metadata:
-                    self._add_attribute(
-                        created_span,
-                        "task_fingerprint_metadata",
-                        json.dumps(task.fingerprint.metadata),
-                    )
-
-            # Add agent fingerprint if task has an assigned agent
-            if hasattr(task, "agent") and task.agent:
-                agent_fingerprint = getattr(
-                    getattr(task.agent, "fingerprint", None), "uuid_str", None
-                )
-                if agent_fingerprint:
-                    self._add_attribute(
-                        created_span, "agent_fingerprint", agent_fingerprint
-                    )
-
             if crew.share_crew:
                 self._add_attribute(
                     created_span, "formatted_description", task.description
@@ -363,21 +270,6 @@ class Telemetry:
             self._add_attribute(span, "task_key", task.key)
             self._add_attribute(span, "task_id", str(task.id))
 
-            # Add fingerprint data to execution span
-            if hasattr(crew, "fingerprint") and crew.fingerprint:
-                self._add_attribute(span, "crew_fingerprint", crew.fingerprint.uuid_str)
-
-            if hasattr(task, "fingerprint") and task.fingerprint:
-                self._add_attribute(span, "task_fingerprint", task.fingerprint.uuid_str)
-
-            # Add agent fingerprint if task has an assigned agent
-            if hasattr(task, "agent") and task.agent:
-                agent_fingerprint = getattr(
-                    getattr(task.agent, "fingerprint", None), "uuid_str", None
-                )
-                if agent_fingerprint:
-                    self._add_attribute(span, "agent_fingerprint", agent_fingerprint)
-
             if crew.share_crew:
                 self._add_attribute(span, "formatted_description", task.description)
                 self._add_attribute(
@@ -399,12 +291,7 @@ class Telemetry:
         Note:
             If share_crew is enabled, this will also record the task output
         """
-
         def operation():
-            # Ensure fingerprint data is present on completion span
-            if hasattr(task, "fingerprint") and task.fingerprint:
-                self._add_attribute(span, "task_fingerprint", task.fingerprint.uuid_str)
-
             if crew.share_crew:
                 self._add_attribute(
                     span,
@@ -425,7 +312,6 @@ class Telemetry:
             tool_name (str): Name of the tool being repeatedly used
             attempts (int): Number of attempts made with this tool
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Repeated Usage")
@@ -443,16 +329,14 @@ class Telemetry:
 
         self._safe_telemetry_operation(operation)
 
-    def tool_usage(self, llm: Any, tool_name: str, attempts: int, agent: Any = None):
+    def tool_usage(self, llm: Any, tool_name: str, attempts: int):
         """Records the usage of a tool by an agent.
 
         Args:
             llm (Any): The language model being used
             tool_name (str): Name of the tool being used
             attempts (int): Number of attempts made with this tool
-            agent (Any, optional): The agent using the tool
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage")
@@ -465,31 +349,17 @@ class Telemetry:
             self._add_attribute(span, "attempts", attempts)
             if llm:
                 self._add_attribute(span, "llm", llm.model)
-
-            # Add agent fingerprint data if available
-            if agent and hasattr(agent, "fingerprint") and agent.fingerprint:
-                self._add_attribute(
-                    span, "agent_fingerprint", agent.fingerprint.uuid_str
-                )
-                if hasattr(agent, "role"):
-                    self._add_attribute(span, "agent_role", agent.role)
-
             span.set_status(Status(StatusCode.OK))
             span.end()
 
         self._safe_telemetry_operation(operation)
 
-    def tool_usage_error(
-        self, llm: Any, agent: Any = None, tool_name: Optional[str] = None
-    ):
+    def tool_usage_error(self, llm: Any):
         """Records when a tool usage results in an error.
 
         Args:
             llm (Any): The language model being used when the error occurred
-            agent (Any, optional): The agent using the tool
-            tool_name (str, optional): Name of the tool that caused the error
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Tool Usage Error")
@@ -500,18 +370,6 @@ class Telemetry:
             )
             if llm:
                 self._add_attribute(span, "llm", llm.model)
-
-            if tool_name:
-                self._add_attribute(span, "tool_name", tool_name)
-
-            # Add agent fingerprint data if available
-            if agent and hasattr(agent, "fingerprint") and agent.fingerprint:
-                self._add_attribute(
-                    span, "agent_fingerprint", agent.fingerprint.uuid_str
-                )
-                if hasattr(agent, "role"):
-                    self._add_attribute(span, "agent_role", agent.role)
-
             span.set_status(Status(StatusCode.OK))
             span.end()
 
@@ -528,7 +386,6 @@ class Telemetry:
             exec_time (int): Execution time in seconds
             model_name (str): Name of the model used
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Individual Test Result")
@@ -563,7 +420,6 @@ class Telemetry:
             inputs (dict[str, Any] | None): Input parameters for the test
             model_name (str): Name of the model used in testing
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Crew Test Execution")
@@ -590,7 +446,6 @@ class Telemetry:
 
     def deploy_signup_error_span(self):
         """Records when an error occurs during the deployment signup process."""
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Deploy Signup Error")
@@ -605,7 +460,6 @@ class Telemetry:
         Args:
             uuid (Optional[str]): Unique identifier for the deployment
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Start Deployment")
@@ -618,7 +472,6 @@ class Telemetry:
 
     def create_crew_deployment_span(self):
         """Records the creation of a new crew deployment."""
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Create Crew Deployment")
@@ -634,7 +487,6 @@ class Telemetry:
             uuid (Optional[str]): Unique identifier for the crew
             log_type (str, optional): Type of logs being retrieved. Defaults to "deployment".
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Get Crew Logs")
@@ -652,7 +504,6 @@ class Telemetry:
         Args:
             uuid (Optional[str]): Unique identifier for the crew being removed
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Remove Crew")
@@ -783,7 +634,6 @@ class Telemetry:
         Args:
             flow_name (str): Name of the flow being created
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Creation")
@@ -800,7 +650,6 @@ class Telemetry:
             flow_name (str): Name of the flow being plotted
             node_names (list[str]): List of node names in the flow
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Plotting")
@@ -818,7 +667,6 @@ class Telemetry:
             flow_name (str): Name of the flow being executed
             node_names (list[str]): List of nodes being executed in the flow
         """
-
         def operation():
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Execution")

src/crewai/tools/base_tool.py

@@ -7,27 +7,29 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
+    PydanticDeprecatedSince20,
     create_model,
-    field_validator,
+    validator,
 )
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
+# Ignore all "PydanticDeprecatedSince20" warnings globally
+warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
+
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = ConfigDict()
 
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[PydanticBaseModel] = Field(
-        default_factory=_ArgsSchemaPlaceholder, validate_default=True
-    )
+    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
     """Flag to check if the description has been updated."""
@@ -36,8 +38,7 @@ class BaseTool(BaseModel, ABC):
     result_as_answer: bool = False
     """Flag to check if the tool should be the final agent answer."""
 
-    @field_validator("args_schema", mode="before")
-    @classmethod
+    @validator("args_schema", always=True, pre=True)
     def _default_args_schema(
         cls, v: Type[PydanticBaseModel]
     ) -> Type[PydanticBaseModel]:

src/crewai/tools/tool_usage.py

@@ -22,7 +22,6 @@ from crewai.utilities.events.tool_usage_events import (
     ToolSelectionErrorEvent,
     ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
     ToolValidateInputErrorEvent,
 )
 
@@ -70,7 +69,6 @@ class ToolUsage:
         function_calling_llm: Any,
         agent: Any,
         action: Any,
-        fingerprint_context: Optional[Dict[str, str]] = None,
     ) -> None:
         self._i18n: I18N = agent.i18n
         self._printer: Printer = Printer()
@@ -87,7 +85,6 @@ class ToolUsage:
         self.task = task
         self.action = action
         self.function_calling_llm = function_calling_llm
-        self.fingerprint_context = fingerprint_context or {}
 
         # Set the maximum parsing attempts for bigger models
         if (
@@ -120,10 +117,7 @@ class ToolUsage:
                 self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
 
-        if (
-            isinstance(tool, CrewStructuredTool)
-            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
-        ):
+        if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]:  # type: ignore
             try:
                 result = self._use(tool_string=tool_string, tool=tool, calling=calling)
                 return result
@@ -187,26 +181,18 @@ class ToolUsage:
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.model_json_schema()[
-                            "properties"
-                        ].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
                             if k in acceptable_args
                         }
-                        # Add fingerprint metadata if available
-                        arguments = self._add_fingerprint_metadata(arguments)
                         result = tool.invoke(input=arguments)
                     except Exception:
                         arguments = calling.arguments
-                        # Add fingerprint metadata if available
-                        arguments = self._add_fingerprint_metadata(arguments)
                         result = tool.invoke(input=arguments)
                 else:
-                    # Add fingerprint metadata even to empty arguments
-                    arguments = self._add_fingerprint_metadata({})
-                    result = tool.invoke(input=arguments)
+                    result = tool.invoke(input={})
             except Exception as e:
                 self.on_tool_error(tool=tool, tool_calling=calling, e=e)
                 self._run_attempts += 1
@@ -216,7 +202,7 @@ class ToolUsage:
                         error=e, tool=tool.name, tool_inputs=tool.description
                     )
                     error = ToolUsageErrorException(
-                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                        f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
                     ).message
                     self.task.increment_tools_errors()
                     if self.agent.verbose:
@@ -258,7 +244,6 @@ class ToolUsage:
             tool_calling=calling,
             from_cache=from_cache,
             started_at=started_at,
-            result=result,
         )
 
         if (
@@ -395,7 +380,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f"{self._i18n.errors('tool_arguments_error')}"
+                    f'{self._i18n.errors("tool_arguments_error")}'
                 )
 
         if not isinstance(arguments, dict):
@@ -403,7 +388,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f"{self._i18n.errors('tool_arguments_error')}"
+                    f'{self._i18n.errors("tool_arguments_error")}'
                 )
 
         return ToolCalling(
@@ -431,7 +416,7 @@ class ToolUsage:
                 if self.agent.verbose:
                     self._printer.print(content=f"\n\n{e}\n", color="red")
                 return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                    f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                    f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
                 )
             return self._tool_calling(tool_string)
 
@@ -495,13 +480,8 @@ class ToolUsage:
             "tool_name": self.action.tool,
             "tool_args": str(self.action.tool_input),
             "tool_class": self.__class__.__name__,
-            "agent": self.agent,  # Adding agent for fingerprint extraction
         }
 
-        # Include fingerprint context if available
-        if self.fingerprint_context:
-            tool_selection_data.update(self.fingerprint_context)
-
         crewai_event_bus.emit(
             self,
             ToolValidateInputErrorEvent(**tool_selection_data, error=final_error),
@@ -512,12 +492,7 @@ class ToolUsage:
         crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
 
     def on_tool_use_finished(
-        self,
-        tool: Any,
-        tool_calling: ToolCalling,
-        from_cache: bool,
-        started_at: float,
-        result: Any,
+        self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
     ) -> None:
         finished_at = time.time()
         event_data = self._prepare_event_data(tool, tool_calling)
@@ -526,13 +501,12 @@ class ToolUsage:
                 "started_at": datetime.datetime.fromtimestamp(started_at),
                 "finished_at": datetime.datetime.fromtimestamp(finished_at),
                 "from_cache": from_cache,
-                "output": result,
             }
         )
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
     def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
-        event_data = {
+        return {
             "agent_key": self.agent.key,
             "agent_role": (self.agent._original_role or self.agent.role),
             "run_attempts": self._run_attempts,
@@ -540,43 +514,4 @@ class ToolUsage:
             "tool_name": tool.name,
             "tool_args": tool_calling.arguments,
             "tool_class": tool.__class__.__name__,
-            "agent": self.agent,  # Adding agent for fingerprint extraction
         }
-
-        # Include fingerprint context if available
-        if self.fingerprint_context:
-            event_data.update(self.fingerprint_context)
-
-        return event_data
-
-    def _add_fingerprint_metadata(self, arguments: dict) -> dict:
-        """Add fingerprint metadata to tool arguments if available.
-
-        Args:
-            arguments: The original tool arguments
-
-        Returns:
-            Updated arguments dictionary with fingerprint metadata
-        """
-        # Create a shallow copy to avoid modifying the original
-        arguments = arguments.copy()
-
-        # Add security metadata under a designated key
-        if not "security_context" in arguments:
-            arguments["security_context"] = {}
-
-        security_context = arguments["security_context"]
-
-        # Add agent fingerprint if available
-        if hasattr(self, "agent") and hasattr(self.agent, "security_config"):
-            security_context["agent_fingerprint"] = self.agent.security_config.fingerprint.to_dict()
-
-        # Add task fingerprint if available
-        if hasattr(self, "task") and hasattr(self.task, "security_config"):
-            security_context["task_fingerprint"] = self.task.security_config.fingerprint.to_dict()
-
-        # Add crew fingerprint if available
-        if hasattr(self, "crew") and hasattr(self.crew, "security_config"):
-            security_context["crew_fingerprint"] = self.crew.security_config.fingerprint.to_dict()
-
-        return arguments

src/crewai/utilities/chromadb.py

@@ -1,62 +0,0 @@
-import re
-from typing import Optional
-
-MIN_COLLECTION_LENGTH = 3
-MAX_COLLECTION_LENGTH = 63
-DEFAULT_COLLECTION = "default_collection"
-
-# Compiled regex patterns for better performance
-INVALID_CHARS_PATTERN = re.compile(r"[^a-zA-Z0-9_-]")
-IPV4_PATTERN = re.compile(r"^(\d{1,3}\.){3}\d{1,3}$")
-
-
-def is_ipv4_pattern(name: str) -> bool:
-    """
-    Check if a string matches an IPv4 address pattern.
-
-    Args:
-        name: The string to check
-
-    Returns:
-        True if the string matches an IPv4 pattern, False otherwise
-    """
-    return bool(IPV4_PATTERN.match(name))
-
-
-def sanitize_collection_name(name: Optional[str]) -> str:
-    """
-    Sanitize a collection name to meet ChromaDB requirements:
-    1. 3-63 characters long
-    2. Starts and ends with alphanumeric character
-    3. Contains only alphanumeric characters, underscores, or hyphens
-    4. No consecutive periods
-    5. Not a valid IPv4 address
-
-    Args:
-        name: The original collection name to sanitize
-
-    Returns:
-        A sanitized collection name that meets ChromaDB requirements
-    """
-    if not name:
-        return DEFAULT_COLLECTION
-
-    if is_ipv4_pattern(name):
-        name = f"ip_{name}"
-
-    sanitized = INVALID_CHARS_PATTERN.sub("_", name)
-
-    if not sanitized[0].isalnum():
-        sanitized = "a" + sanitized
-
-    if not sanitized[-1].isalnum():
-        sanitized = sanitized[:-1] + "z"
-
-    if len(sanitized) < MIN_COLLECTION_LENGTH:
-        sanitized = sanitized + "x" * (MIN_COLLECTION_LENGTH - len(sanitized))
-    if len(sanitized) > MAX_COLLECTION_LENGTH:
-        sanitized = sanitized[:MAX_COLLECTION_LENGTH]
-        if not sanitized[-1].isalnum():
-            sanitized = sanitized[:-1] + "z"
-
-    return sanitized

src/crewai/utilities/converter.py

@@ -287,9 +287,8 @@ def generate_model_description(model: Type[BaseModel]) -> str:
         else:
             return str(field_type)
 
-    fields = model.model_fields
+    fields = model.__annotations__
     field_descriptions = [
-        f'"{name}": {describe_field(field.annotation)}'
-        for name, field in fields.items()
+        f'"{name}": {describe_field(type_)}' for name, type_ in fields.items()
     ]
     return "{\n  " + ",\n  ".join(field_descriptions) + "\n}"

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -6,7 +6,7 @@ from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
-from crewai.llm import BaseLLM
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -24,7 +24,7 @@ class CrewEvaluator:
 
     Attributes:
         crew (Crew): The crew of agents to evaluate.
-        eval_llm (BaseLLM): Language model instance to use for evaluations
+        eval_llm (LLM): Language model instance to use for evaluations
         tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
         iteration (int): The current iteration of the evaluation.
     """
@@ -33,7 +33,7 @@ class CrewEvaluator:
     run_execution_times: defaultdict = defaultdict(list)
     iteration: int = 0
 
-    def __init__(self, crew, eval_llm: InstanceOf[BaseLLM]):
+    def __init__(self, crew, eval_llm: InstanceOf[LLM]):
         self.crew = crew
         self.llm = eval_llm
         self._telemetry = Telemetry()

src/crewai/utilities/evaluators/task_evaluator.py

@@ -45,7 +45,7 @@ class TaskEvaluator:
 
     def evaluate(self, task, output) -> TaskEvaluation:
         crewai_event_bus.emit(
-            self, TaskEvaluationEvent(evaluation_type="task_evaluation", task=task)
+            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"

src/crewai/utilities/events/agent_events.py

@@ -4,13 +4,13 @@ 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 BaseEvent
+from .base_events import CrewEvent
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
-class AgentExecutionStartedEvent(BaseEvent):
+class AgentExecutionStartedEvent(CrewEvent):
     """Event emitted when an agent starts executing a task"""
 
     agent: BaseAgent
@@ -21,20 +21,8 @@ class AgentExecutionStartedEvent(BaseEvent):
 
     model_config = {"arbitrary_types_allowed": True}
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
-        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
-            self.source_fingerprint = self.agent.fingerprint.uuid_str
-            self.source_type = "agent"
-            if (
-                hasattr(self.agent.fingerprint, "metadata")
-                and self.agent.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.agent.fingerprint.metadata
 
-
-class AgentExecutionCompletedEvent(BaseEvent):
+class AgentExecutionCompletedEvent(CrewEvent):
     """Event emitted when an agent completes executing a task"""
 
     agent: BaseAgent
@@ -42,35 +30,11 @@ class AgentExecutionCompletedEvent(BaseEvent):
     output: str
     type: str = "agent_execution_completed"
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
-        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
-            self.source_fingerprint = self.agent.fingerprint.uuid_str
-            self.source_type = "agent"
-            if (
-                hasattr(self.agent.fingerprint, "metadata")
-                and self.agent.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.agent.fingerprint.metadata
 
-
-class AgentExecutionErrorEvent(BaseEvent):
+class AgentExecutionErrorEvent(CrewEvent):
     """Event emitted when an agent encounters an error during execution"""
 
     agent: BaseAgent
     task: Any
     error: str
     type: str = "agent_execution_error"
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
-        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
-            self.source_fingerprint = self.agent.fingerprint.uuid_str
-            self.source_type = "agent"
-            if (
-                hasattr(self.agent.fingerprint, "metadata")
-                and self.agent.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.agent.fingerprint.metadata

src/crewai/utilities/events/base_events.py

@@ -1,28 +1,10 @@
 from datetime import datetime
-from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.serialization import to_serializable
 
-
-class BaseEvent(BaseModel):
-    """Base class for all events"""
+class CrewEvent(BaseModel):
+    """Base class for all crew events"""
 
     timestamp: datetime = Field(default_factory=datetime.now)
     type: str
-    source_fingerprint: Optional[str] = None  # UUID string of the source entity
-    source_type: Optional[str] = None  # "agent", "task", "crew"
-    fingerprint_metadata: Optional[Dict[str, Any]] = None  # Any relevant metadata
-
-    def to_json(self, exclude: set[str] | None = None):
-        """
-        Converts the event to a JSON-serializable dictionary.
-
-        Args:
-            exclude (set[str], optional): Set of keys to exclude from the result. Defaults to None.
-
-        Returns:
-            dict: A JSON-serializable dictionary.
-        """
-        return to_serializable(self, exclude=exclude)

src/crewai/utilities/events/crew_events.py

@@ -1,102 +1,81 @@
-from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+from typing import Any, Dict, Optional, Union
 
-from crewai.utilities.events.base_events import BaseEvent
+from pydantic import InstanceOf
 
-if TYPE_CHECKING:
-    from crewai.crew import Crew
-else:
-    Crew = Any
+from crewai.utilities.events.base_events import CrewEvent
 
 
-class CrewBaseEvent(BaseEvent):
-    """Base class for crew events with fingerprint handling"""
-
-    crew_name: Optional[str]
-    crew: Optional[Crew] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self.set_crew_fingerprint()
-
-    def set_crew_fingerprint(self) -> None:
-        if self.crew and hasattr(self.crew, "fingerprint") and self.crew.fingerprint:
-            self.source_fingerprint = self.crew.fingerprint.uuid_str
-            self.source_type = "crew"
-            if (
-                hasattr(self.crew.fingerprint, "metadata")
-                and self.crew.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.crew.fingerprint.metadata
-
-    def to_json(self, exclude: set[str] | None = None):
-        if exclude is None:
-            exclude = set()
-        exclude.add("crew")
-        return super().to_json(exclude=exclude)
-
-
-class CrewKickoffStartedEvent(CrewBaseEvent):
+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(CrewBaseEvent):
+class CrewKickoffCompletedEvent(CrewEvent):
     """Event emitted when a crew completes execution"""
 
+    crew_name: Optional[str]
     output: Any
     type: str = "crew_kickoff_completed"
 
 
-class CrewKickoffFailedEvent(CrewBaseEvent):
+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(CrewBaseEvent):
+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(CrewBaseEvent):
+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(CrewBaseEvent):
+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(CrewBaseEvent):
+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(CrewBaseEvent):
+class CrewTestCompletedEvent(CrewEvent):
     """Event emitted when a crew completes testing"""
 
+    crew_name: Optional[str]
     type: str = "crew_test_completed"
 
 
-class CrewTestFailedEvent(CrewBaseEvent):
+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

@@ -4,10 +4,10 @@ from typing import Any, Callable, Dict, List, Type, TypeVar, cast
 
 from blinker import Signal
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.utilities.events.base_events import CrewEvent
 from crewai.utilities.events.event_types import EventTypes
 
-EventT = TypeVar("EventT", bound=BaseEvent)
+EventT = TypeVar("EventT", bound=CrewEvent)
 
 
 class CrewAIEventsBus:
@@ -30,7 +30,7 @@ class CrewAIEventsBus:
     def _initialize(self) -> None:
         """Initialize the event bus internal state"""
         self._signal = Signal("crewai_event_bus")
-        self._handlers: Dict[Type[BaseEvent], List[Callable]] = {}
+        self._handlers: Dict[Type[CrewEvent], List[Callable]] = {}
 
     def on(
         self, event_type: Type[EventT]
@@ -59,7 +59,7 @@ class CrewAIEventsBus:
 
         return decorator
 
-    def emit(self, source: Any, event: BaseEvent) -> None:
+    def emit(self, source: Any, event: CrewEvent) -> None:
         """
         Emit an event to all registered handlers
 

src/crewai/utilities/events/flow_events.py

@@ -2,10 +2,10 @@ from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
 
-from .base_events import BaseEvent
+from .base_events import CrewEvent
 
 
-class FlowEvent(BaseEvent):
+class FlowEvent(CrewEvent):
     """Base class for all flow events"""
 
     type: str

src/crewai/utilities/events/llm_events.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any, Dict, List, Optional, Union
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.utilities.events.base_events import CrewEvent
 
 
 class LLMCallType(Enum):
@@ -11,22 +11,17 @@ class LLMCallType(Enum):
     LLM_CALL = "llm_call"
 
 
-class LLMCallStartedEvent(BaseEvent):
-    """Event emitted when a LLM call starts
-
-    Attributes:
-        messages: Content can be either a string or a list of dictionaries that support
-            multimodal content (text, images, etc.)
-    """
+class LLMCallStartedEvent(CrewEvent):
+    """Event emitted when a LLM call starts"""
 
     type: str = "llm_call_started"
-    messages: Union[str, List[Dict[str, Any]]]
+    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(BaseEvent):
+class LLMCallCompletedEvent(CrewEvent):
     """Event emitted when a LLM call completes"""
 
     type: str = "llm_call_completed"
@@ -34,14 +29,14 @@ class LLMCallCompletedEvent(BaseEvent):
     call_type: LLMCallType
 
 
-class LLMCallFailedEvent(BaseEvent):
+class LLMCallFailedEvent(CrewEvent):
     """Event emitted when a LLM call fails"""
 
     error: str
     type: str = "llm_call_failed"
 
 
-class LLMStreamChunkEvent(BaseEvent):
+class LLMStreamChunkEvent(CrewEvent):
     """Event emitted when a streaming chunk is received"""
 
     type: str = "llm_stream_chunk"

src/crewai/utilities/events/task_events.py

@@ -1,84 +1,32 @@
-from typing import Any, Optional
+from typing import Optional
 
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.utilities.events.base_events import CrewEvent
 
 
-class TaskStartedEvent(BaseEvent):
+class TaskStartedEvent(CrewEvent):
     """Event emitted when a task starts"""
 
     type: str = "task_started"
     context: Optional[str]
-    task: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
-            self.source_fingerprint = self.task.fingerprint.uuid_str
-            self.source_type = "task"
-            if (
-                hasattr(self.task.fingerprint, "metadata")
-                and self.task.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.task.fingerprint.metadata
 
 
-class TaskCompletedEvent(BaseEvent):
+class TaskCompletedEvent(CrewEvent):
     """Event emitted when a task completes"""
 
     output: TaskOutput
     type: str = "task_completed"
-    task: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
-            self.source_fingerprint = self.task.fingerprint.uuid_str
-            self.source_type = "task"
-            if (
-                hasattr(self.task.fingerprint, "metadata")
-                and self.task.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.task.fingerprint.metadata
 
 
-class TaskFailedEvent(BaseEvent):
+class TaskFailedEvent(CrewEvent):
     """Event emitted when a task fails"""
 
     error: str
     type: str = "task_failed"
-    task: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
-            self.source_fingerprint = self.task.fingerprint.uuid_str
-            self.source_type = "task"
-            if (
-                hasattr(self.task.fingerprint, "metadata")
-                and self.task.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.task.fingerprint.metadata
 
 
-class TaskEvaluationEvent(BaseEvent):
+class TaskEvaluationEvent(CrewEvent):
     """Event emitted when a task evaluation is completed"""
 
     type: str = "task_evaluation"
     evaluation_type: str
-    task: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the task
-        if hasattr(self.task, "fingerprint") and self.task.fingerprint:
-            self.source_fingerprint = self.task.fingerprint.uuid_str
-            self.source_type = "task"
-            if (
-                hasattr(self.task.fingerprint, "metadata")
-                and self.task.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.task.fingerprint.metadata

src/crewai/utilities/events/tool_usage_events.py

@@ -1,10 +1,10 @@
 from datetime import datetime
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Dict
 
-from .base_events import BaseEvent
+from .base_events import CrewEvent
 
 
-class ToolUsageEvent(BaseEvent):
+class ToolUsageEvent(CrewEvent):
     """Base event for tool usage tracking"""
 
     agent_key: str
@@ -14,22 +14,9 @@ class ToolUsageEvent(BaseEvent):
     tool_class: str
     run_attempts: int | None = None
     delegations: int | None = None
-    agent: Optional[Any] = None
 
     model_config = {"arbitrary_types_allowed": True}
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
-        if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
-            self.source_fingerprint = self.agent.fingerprint.uuid_str
-            self.source_type = "agent"
-            if (
-                hasattr(self.agent.fingerprint, "metadata")
-                and self.agent.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.agent.fingerprint.metadata
-
 
 class ToolUsageStartedEvent(ToolUsageEvent):
     """Event emitted when a tool execution is started"""
@@ -43,7 +30,6 @@ class ToolUsageFinishedEvent(ToolUsageEvent):
     started_at: datetime
     finished_at: datetime
     from_cache: bool = False
-    output: Any
     type: str = "tool_usage_finished"
 
 
@@ -68,7 +54,7 @@ class ToolSelectionErrorEvent(ToolUsageEvent):
     type: str = "tool_selection_error"
 
 
-class ToolExecutionErrorEvent(BaseEvent):
+class ToolExecutionErrorEvent(CrewEvent):
     """Event emitted when a tool execution encounters an error"""
 
     error: Any
@@ -76,16 +62,3 @@ class ToolExecutionErrorEvent(BaseEvent):
     tool_name: str
     tool_args: Dict[str, Any]
     tool_class: Callable
-    agent: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        # Set fingerprint data from the agent
-        if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
-            self.source_fingerprint = self.agent.fingerprint.uuid_str
-            self.source_type = "agent"
-            if (
-                hasattr(self.agent.fingerprint, "metadata")
-                and self.agent.fingerprint.metadata
-            ):
-                self.fingerprint_metadata = self.agent.fingerprint.metadata

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

@@ -507,10 +507,9 @@ class ConsoleFormatter:
 
         # Remove the thinking status node when complete
         if "Thinking" in str(tool_branch.label):
-            if tool_branch in agent_branch.children:
-                agent_branch.children.remove(tool_branch)
-                self.print(crew_tree)
-                self.print()
+            agent_branch.children.remove(tool_branch)
+            self.print(crew_tree)
+            self.print()
 
     def handle_llm_call_failed(
         self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -588,7 +587,6 @@ class ConsoleFormatter:
             for child in flow_tree.children:
                 if "Running tests" in str(child.label):
                     child.label = Text("✅ Tests completed successfully", style="green")
-                    break
 
             self.print(flow_tree)
             self.print()

src/crewai/utilities/llm_utils.py

@@ -2,28 +2,28 @@ import os
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
-from crewai.llm import LLM, BaseLLM
+from crewai.llm import LLM
 
 
 def create_llm(
     llm_value: Union[str, LLM, Any, None] = None,
-) -> Optional[LLM | BaseLLM]:
+) -> Optional[LLM]:
     """
     Creates or returns an LLM instance based on the given llm_value.
 
     Args:
-        llm_value (str | BaseLLM | Any | None):
+        llm_value (str | LLM | Any | None):
             - str: The model name (e.g., "gpt-4").
-            - BaseLLM: Already instantiated BaseLLM (including LLM), returned as-is.
+            - LLM: Already instantiated LLM, returned as-is.
             - Any: Attempt to extract known attributes like model_name, temperature, etc.
             - None: Use environment-based or fallback default model.
 
     Returns:
-        A BaseLLM instance if successful, or None if something fails.
+        An LLM instance if successful, or None if something fails.
     """
 
-    # 1) If llm_value is already a BaseLLM or LLM object, return it directly
-    if isinstance(llm_value, LLM) or isinstance(llm_value, BaseLLM):
+    # 1) If llm_value is already an LLM object, return it directly
+    if isinstance(llm_value, LLM):
         return llm_value
 
     # 2) If llm_value is a string (model name)

tests/agent_test.py

@@ -1621,38 +1621,6 @@ def test_agent_with_knowledge_sources():
         assert "red" in result.raw.lower()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_extensive_role():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-
-        agent = Agent(
-            role="Information Agent with extensive role description that is longer than 80 characters",
-            goal="Provide information based on knowledge sources",
-            backstory="You have access to specific knowledge sources.",
-            llm=LLM(model="gpt-4o-mini"),
-            knowledge_sources=[string_source],
-        )
-
-        task = Task(
-            description="What is Brandon's favorite color?",
-            expected_output="Brandon's favorite color.",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert "red" in result.raw.lower()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources_works_with_copy():
     content = "Brandon's favorite color is red and he likes Mexican food."

tests/cassettes/test_before_kickoff_callback.yaml

@@ -710,117 +710,4 @@ interactions:
       - req_4ceac9bc8ae57f631959b91d2ab63c4d
     http_version: HTTP/1.1
     status_code: 200
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test agent
-      backstory\nYour personal goal is: Test agent goal\nTo give my best complete
-      final answer to the task respond using the exact following format:\n\nThought:
-      I now can give a great answer\nFinal Answer: Your final answer must be the great
-      and the most complete as possible, it must be outcome described.\n\nI MUST use
-      these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent
-      Task: Test task description\n\nThis is the expected criteria for your final
-      answer: Test expected output\nyou MUST return the actual complete content as
-      the final answer, not a summary.\n\nBegin! This is VERY important to you, use
-      the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],
-      "model": "gpt-4o", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '840'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.61.0
-      x-stainless-arch:
-      - x64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.61.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-BExKOliqPgvHyozZaBu5oN50CHtsa\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1742904348,\n  \"model\": \"gpt-4o-2024-08-06\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
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-import pytest
-from click.testing import CliRunner
-from unittest.mock import patch, MagicMock
-from pathlib import Path
-import sys
-
-# Ensure the src directory is in the Python path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'src'))
-
-from crewai.cli.cli import crewai
-from crewai.cli import create_crew
-from crewai.cli.constants import MODELS, ENV_VARS
-
-# Mock provider data for testing
-MOCK_PROVIDER_DATA = {
-    'openai': {'models': ['gpt-4', 'gpt-3.5-turbo']}, 
-    'google': {'models': ['gemini-pro']}, 
-    'anthropic': {'models': ['claude-3-opus']}
-}
-
-MOCK_VALID_PROVIDERS = list(MOCK_PROVIDER_DATA.keys())
-
-@pytest.fixture
-def runner():
-    return CliRunner()
-
-@pytest.fixture(autouse=True)
-def isolate_fs(monkeypatch):
-    # Prevent tests from interacting with the actual filesystem or real env vars
-    monkeypatch.setattr(Path, 'mkdir', lambda *args, **kwargs: None)
-    monkeypatch.setattr(Path, 'exists', lambda *args: False) # Assume folders don't exist initially
-    monkeypatch.setattr(create_crew, 'load_env_vars', lambda *args: {}) # Start with empty env vars
-    monkeypatch.setattr(create_crew, 'write_env_file', lambda *args, **kwargs: None)
-    monkeypatch.setattr(create_crew, 'copy_template_files', lambda *args, **kwargs: None)
-
-@patch('crewai.cli.create_crew.get_provider_data', return_value=MOCK_PROVIDER_DATA)
-@patch('crewai.cli.create_crew.select_provider')
-@patch('crewai.cli.create_crew.select_model')
-@patch('click.prompt')
-@patch('click.confirm', return_value=True) # Default to confirming prompts
-def test_create_crew_with_valid_provider(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, runner):
-    """Test `crewai create crew <name> --provider <valid_provider>`"""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew', '--provider', 'openai'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    assert "Using specified provider: Openai" in result.output
-    mock_select_provider.assert_not_called() # Should not ask interactively
-    # Depending on whether openai needs models/keys, check select_model/prompt calls
-    assert "Crew 'testcrew' created successfully!" in result.output
-
-@patch('crewai.cli.create_crew.get_provider_data', return_value=MOCK_PROVIDER_DATA)
-@patch('crewai.cli.create_crew.select_provider', return_value='google') # Simulate user selecting google
-@patch('crewai.cli.create_crew.select_model', return_value='gemini-pro')
-@patch('click.prompt')
-@patch('click.confirm', return_value=True)
-def test_create_crew_with_invalid_provider(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, runner):
-    """Test `crewai create crew <name> --provider <invalid_provider>`"""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew', '--provider', 'invalidprovider'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    assert "Warning: Specified provider 'invalidprovider' is not recognized." in result.output
-    mock_select_provider.assert_called_once() # Should ask interactively
-    # Check if subsequent steps for the selected provider (google) ran
-    mock_select_model.assert_called_once()
-    assert "Crew 'testcrew' created successfully!" in result.output
-
-@patch('crewai.cli.create_crew.get_provider_data', return_value=MOCK_PROVIDER_DATA)
-@patch('crewai.cli.create_crew.select_provider', return_value='anthropic') # Simulate user selecting anthropic
-@patch('crewai.cli.create_crew.select_model', return_value='claude-3-opus')
-@patch('click.prompt', return_value='sk-abc') # Simulate API key entry
-@patch('click.confirm', return_value=True)
-def test_create_crew_no_provider(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, runner):
-    """Test `crewai create crew <name>`"""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    assert "Using specified provider:" not in result.output # Should not mention specified provider
-    mock_select_provider.assert_called_once() # Should ask interactively
-    mock_select_model.assert_called_once()
-    # Check if prompt for API key was called (assuming anthropic needs one)
-    if 'anthropic' in ENV_VARS and any('key_name' in d for d in ENV_VARS['anthropic']):
-         mock_prompt.assert_called()
-    assert "Crew 'testcrew' created successfully!" in result.output
-
-@patch('crewai.cli.create_crew.get_provider_data')
-@patch('crewai.cli.create_crew.select_provider')
-@patch('crewai.cli.create_crew.select_model')
-@patch('click.prompt')
-@patch('click.confirm')
-def test_create_crew_skip_provider(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, runner):
-    """Test `crewai create crew <name> --skip_provider`"""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew', '--skip_provider'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    mock_get_data.assert_not_called()
-    mock_select_provider.assert_not_called()
-    mock_select_model.assert_not_called()
-    mock_prompt.assert_not_called()
-    mock_confirm.assert_not_called()
-    assert "Crew 'testcrew' created successfully!" in result.output
-
-@patch('crewai.cli.create_crew.load_env_vars', return_value={'OPENAI_API_KEY': 'existing_key'}) # Simulate existing env
-@patch('crewai.cli.create_crew.get_provider_data', return_value=MOCK_PROVIDER_DATA)
-@patch('crewai.cli.create_crew.select_provider', return_value='google') # Simulate selecting new provider
-@patch('crewai.cli.create_crew.select_model', return_value='gemini-pro')
-@patch('click.prompt')
-@patch('click.confirm', return_value=True) # User confirms override
-def test_create_crew_existing_override(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, mock_load_env, runner):
-    """Test `crewai create crew <name>` with existing config and user overrides."""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    mock_confirm.assert_called_once_with(
-        'Found existing environment variable configuration for Openai. Do you want to override it?'
-    )
-    mock_select_provider.assert_called_once() # Should ask for new provider after confirming override
-    assert "Crew 'testcrew' created successfully!" in result.output
-
-@patch('crewai.cli.create_crew.load_env_vars', return_value={'OPENAI_API_KEY': 'existing_key'}) # Simulate existing env
-@patch('crewai.cli.create_crew.get_provider_data', return_value=MOCK_PROVIDER_DATA)
-@patch('crewai.cli.create_crew.select_provider')
-@patch('crewai.cli.create_crew.select_model')
-@patch('click.prompt')
-@patch('click.confirm', return_value=False) # User denies override
-def test_create_crew_existing_keep(mock_confirm, mock_prompt, mock_select_model, mock_select_provider, mock_get_data, mock_load_env, runner):
-    """Test `crewai create crew <name>` with existing config and user keeps it."""
-    result = runner.invoke(crewai, ['create', 'crew', 'testcrew'])
-    
-    print(f"CLI Output:\n{result.output}") # Debug output
-    assert result.exit_code == 0, f"CLI exited with code {result.exit_code}\nOutput: {result.output}"
-    mock_confirm.assert_called_once_with(
-        'Found existing environment variable configuration for Openai. Do you want to override it?'
-    )
-    assert "Keeping existing provider configuration. Exiting provider setup." in result.output
-    mock_select_provider.assert_not_called() # Should NOT ask for new provider
-    assert "Crew 'testcrew' created successfully!" in result.output
-

tests/crew_test.py

@@ -11,9 +11,7 @@ import pydantic_core
 import pytest
 
 from crewai.agent import Agent
-from crewai.agents import CacheHandler
 from crewai.agents.cache import CacheHandler
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -3733,44 +3731,6 @@ def test_multimodal_agent_image_tool_handling():
         assert result["content"][1]["type"] == "image_url"
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_multimodal_agent_describing_image_successfully():
-    """
-    Test that a multimodal agent can process images without validation errors.
-    This test reproduces the scenario from issue #2475.
-    """
-    llm = LLM(model="openai/gpt-4o", temperature=0.7)  # model with vision capabilities
-
-    expert_analyst = Agent(
-        role="Visual Quality Inspector",
-        goal="Perform detailed quality analysis of product images",
-        backstory="Senior quality control expert with expertise in visual inspection",
-        llm=llm,
-        verbose=True,
-        allow_delegation=False,
-        multimodal=True,
-    )
-
-    inspection_task = Task(
-        description="""
-        Analyze the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244 with focus on:
-        1. Quality of materials
-        2. Manufacturing defects
-        3. Compliance with standards
-        Provide a detailed report highlighting any issues found.
-        """,
-        expected_output="A detailed report highlighting any issues found",
-        agent=expert_analyst,
-    )
-
-    crew = Crew(agents=[expert_analyst], tasks=[inspection_task])
-    result = crew.kickoff()
-
-    task_output = result.tasks_output[0]
-    assert isinstance(task_output, TaskOutput)
-    assert task_output.raw == result.raw
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_live_image_analysis():
     """
@@ -4065,52 +4025,3 @@ def test_crew_with_knowledge_sources_works_with_copy():
     assert len(crew_copy.tasks) == len(crew.tasks)
 
     assert len(crew_copy.tasks) == len(crew.tasks)
-
-
-def test_crew_kickoff_for_each_works_with_manager_agent_copy():
-    researcher = Agent(
-        role="Researcher",
-        goal="Conduct thorough research and analysis on AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
-        allow_delegation=False
-    )
-
-    writer = Agent(
-        role="Senior Writer",
-        goal="Create compelling content about AI and AI agents",
-        backstory="You're a senior writer, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently writing content for a new client.",
-        allow_delegation=False
-    )
-
-    # Define task
-    task = Task(
-        description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
-        expected_output="5 bullet points, each with a paragraph and accompanying notes.",
-    )
-
-    # Define 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. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
-        allow_delegation=True
-    )
-
-    # Instantiate crew with a custom manager
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[task],
-        manager_agent=manager,
-        process=Process.hierarchical,
-        verbose=True
-    )
-
-    crew_copy = crew.copy()
-    assert crew_copy.manager_agent is not None
-    assert crew_copy.manager_agent.id != crew.manager_agent.id
-    assert crew_copy.manager_agent.role == crew.manager_agent.role
-    assert crew_copy.manager_agent.goal == crew.manager_agent.goal
-    assert crew_copy.manager_agent.backstory == crew.manager_agent.backstory
-    assert isinstance(crew_copy.manager_agent.agent_executor, CrewAgentExecutor)
-    assert isinstance(crew_copy.manager_agent.cache_handler, CacheHandler)
-

tests/custom_llm_test.py

@@ -1,359 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-from unittest.mock import Mock
-
-import pytest
-
-from crewai import Agent, Crew, Process, Task
-from crewai.llms.base_llm import BaseLLM
-from crewai.utilities.llm_utils import create_llm
-
-
-class CustomLLM(BaseLLM):
-    """Custom LLM implementation for testing.
-
-    This is a simple implementation of the BaseLLM abstract base class
-    that returns a predefined response for testing purposes.
-    """
-
-    def __init__(self, response="Default response", model="test-model"):
-        """Initialize the CustomLLM with a predefined response.
-
-        Args:
-            response: The predefined response to return from call().
-        """
-        super().__init__(model=model)
-        self.response = response
-        self.call_count = 0
-
-    def call(
-        self,
-        messages,
-        tools=None,
-        callbacks=None,
-        available_functions=None,
-    ):
-        """
-        Mock LLM call that returns a predefined response.
-        Properly formats messages to match OpenAI's expected structure.
-        """
-        self.call_count += 1
-
-        # If input is a string, convert to proper message format
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-
-        # Ensure each message has properly formatted content
-        for message in messages:
-            if isinstance(message["content"], str):
-                message["content"] = [{"type": "text", "text": message["content"]}]
-
-        # Return predefined response in expected format
-        if "Thought:" in str(messages):
-            return f"Thought: I will say hi\nFinal Answer: {self.response}"
-        return self.response
-
-    def supports_function_calling(self) -> bool:
-        """Return False to indicate that function calling is not supported.
-
-        Returns:
-            False, indicating that this LLM does not support function calling.
-        """
-        return False
-
-    def supports_stop_words(self) -> bool:
-        """Return False to indicate that stop words are not supported.
-
-        Returns:
-            False, indicating that this LLM does not support stop words.
-        """
-        return False
-
-    def get_context_window_size(self) -> int:
-        """Return a default context window size.
-
-        Returns:
-            4096, a typical context window size for modern LLMs.
-        """
-        return 4096
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_custom_llm_implementation():
-    """Test that a custom LLM implementation works with create_llm."""
-    custom_llm = CustomLLM(response="The answer is 42")
-
-    # Test that create_llm returns the custom LLM instance directly
-    result_llm = create_llm(custom_llm)
-
-    assert result_llm is custom_llm
-
-    # Test calling the custom LLM
-    response = result_llm.call(
-        "What is the answer to life, the universe, and everything?"
-    )
-
-    # Verify that the response from the custom LLM was used
-    assert "42" in response
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_custom_llm_within_crew():
-    """Test that a custom LLM implementation works with create_llm."""
-    custom_llm = CustomLLM(response="Hello! Nice to meet you!", model="test-model")
-
-    agent = Agent(
-        role="Say Hi",
-        goal="Say hi to the user",
-        backstory="""You just say hi to the user""",
-        llm=custom_llm,
-    )
-
-    task = Task(
-        description="Say hi to the user",
-        expected_output="A greeting to the user",
-        agent=agent,
-    )
-
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        process=Process.sequential,
-    )
-
-    result = crew.kickoff()
-
-    # Assert the LLM was called
-    assert custom_llm.call_count > 0
-    # Assert we got a response
-    assert "Hello!" in result.raw
-
-
-def test_custom_llm_message_formatting():
-    """Test that the custom LLM properly formats messages"""
-    custom_llm = CustomLLM(response="Test response", model="test-model")
-
-    # Test with string input
-    result = custom_llm.call("Test message")
-    assert result == "Test response"
-
-    # Test with message list
-    messages = [
-        {"role": "system", "content": "System message"},
-        {"role": "user", "content": "User message"},
-    ]
-    result = custom_llm.call(messages)
-    assert result == "Test response"
-
-
-class JWTAuthLLM(BaseLLM):
-    """Custom LLM implementation with JWT authentication."""
-
-    def __init__(self, jwt_token: str):
-        super().__init__(model="test-model")
-        if not jwt_token or not isinstance(jwt_token, str):
-            raise ValueError("Invalid JWT token")
-        self.jwt_token = jwt_token
-        self.calls = []
-        self.stop = []
-
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Record the call and return a predefined response."""
-        self.calls.append(
-            {
-                "messages": messages,
-                "tools": tools,
-                "callbacks": callbacks,
-                "available_functions": available_functions,
-            }
-        )
-        # In a real implementation, this would use the JWT token to authenticate
-        # with an external service
-        return "Response from JWT-authenticated LLM"
-
-    def supports_function_calling(self) -> bool:
-        """Return True to indicate that function calling is supported."""
-        return True
-
-    def supports_stop_words(self) -> bool:
-        """Return True to indicate that stop words are supported."""
-        return True
-
-    def get_context_window_size(self) -> int:
-        """Return a default context window size."""
-        return 8192
-
-
-def test_custom_llm_with_jwt_auth():
-    """Test a custom LLM implementation with JWT authentication."""
-    jwt_llm = JWTAuthLLM(jwt_token="example.jwt.token")
-
-    # Test that create_llm returns the JWT-authenticated LLM instance directly
-    result_llm = create_llm(jwt_llm)
-
-    assert result_llm is jwt_llm
-
-    # Test calling the JWT-authenticated LLM
-    response = result_llm.call("Test message")
-
-    # Verify that the JWT-authenticated LLM was called
-    assert len(jwt_llm.calls) > 0
-    # Verify that the response from the JWT-authenticated LLM was used
-    assert response == "Response from JWT-authenticated LLM"
-
-
-def test_jwt_auth_llm_validation():
-    """Test that JWT token validation works correctly."""
-    # Test with invalid JWT token (empty string)
-    with pytest.raises(ValueError, match="Invalid JWT token"):
-        JWTAuthLLM(jwt_token="")
-
-    # Test with invalid JWT token (non-string)
-    with pytest.raises(ValueError, match="Invalid JWT token"):
-        JWTAuthLLM(jwt_token=None)
-
-
-class TimeoutHandlingLLM(BaseLLM):
-    """Custom LLM implementation with timeout handling and retry logic."""
-
-    def __init__(self, max_retries: int = 3, timeout: int = 30):
-        """Initialize the TimeoutHandlingLLM with retry and timeout settings.
-
-        Args:
-            max_retries: Maximum number of retry attempts.
-            timeout: Timeout in seconds for each API call.
-        """
-        super().__init__(model="test-model")
-        self.max_retries = max_retries
-        self.timeout = timeout
-        self.calls = []
-        self.stop = []
-        self.fail_count = 0  # Number of times to simulate failure
-
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Simulate API calls with timeout handling and retry logic.
-
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-
-        Returns:
-            A response string based on whether this is the first attempt or a retry.
-
-        Raises:
-            TimeoutError: If all retry attempts fail.
-        """
-        # Record the initial call
-        self.calls.append(
-            {
-                "messages": messages,
-                "tools": tools,
-                "callbacks": callbacks,
-                "available_functions": available_functions,
-                "attempt": 0,
-            }
-        )
-
-        # Simulate retry logic
-        for attempt in range(self.max_retries):
-            # Skip the first attempt recording since we already did that above
-            if attempt == 0:
-                # Simulate a failure if fail_count > 0
-                if self.fail_count > 0:
-                    self.fail_count -= 1
-                    # If we've used all retries, raise an error
-                    if attempt == self.max_retries - 1:
-                        raise TimeoutError(
-                            f"LLM request failed after {self.max_retries} attempts"
-                        )
-                    # Otherwise, continue to the next attempt (simulating backoff)
-                    continue
-                else:
-                    # Success on first attempt
-                    return "First attempt response"
-            else:
-                # This is a retry attempt (attempt > 0)
-                # Always record retry attempts
-                self.calls.append(
-                    {
-                        "retry_attempt": attempt,
-                        "messages": messages,
-                        "tools": tools,
-                        "callbacks": callbacks,
-                        "available_functions": available_functions,
-                    }
-                )
-
-                # Simulate a failure if fail_count > 0
-                if self.fail_count > 0:
-                    self.fail_count -= 1
-                    # If we've used all retries, raise an error
-                    if attempt == self.max_retries - 1:
-                        raise TimeoutError(
-                            f"LLM request failed after {self.max_retries} attempts"
-                        )
-                    # Otherwise, continue to the next attempt (simulating backoff)
-                    continue
-                else:
-                    # Success on retry
-                    return "Response after retry"
-
-    def supports_function_calling(self) -> bool:
-        """Return True to indicate that function calling is supported.
-
-        Returns:
-            True, indicating that this LLM supports function calling.
-        """
-        return True
-
-    def supports_stop_words(self) -> bool:
-        """Return True to indicate that stop words are supported.
-
-        Returns:
-            True, indicating that this LLM supports stop words.
-        """
-        return True
-
-    def get_context_window_size(self) -> int:
-        """Return a default context window size.
-
-        Returns:
-            8192, a typical context window size for modern LLMs.
-        """
-        return 8192
-
-
-def test_timeout_handling_llm():
-    """Test a custom LLM implementation with timeout handling and retry logic."""
-    # Test successful first attempt
-    llm = TimeoutHandlingLLM()
-    response = llm.call("Test message")
-    assert response == "First attempt response"
-    assert len(llm.calls) == 1
-
-    # Test successful retry
-    llm = TimeoutHandlingLLM()
-    llm.fail_count = 1  # Fail once, then succeed
-    response = llm.call("Test message")
-    assert response == "Response after retry"
-    assert len(llm.calls) == 2  # Initial call + successful retry call
-
-    # Test failure after all retries
-    llm = TimeoutHandlingLLM(max_retries=2)
-    llm.fail_count = 2  # Fail twice, which is all retries
-    with pytest.raises(TimeoutError, match="LLM request failed after 2 attempts"):
-        llm.call("Test message")
-    assert len(llm.calls) == 2  # Initial call + failed retry attempt

tests/flow/test_state_utils.py

@@ -5,7 +5,8 @@ from unittest.mock import Mock
 import pytest
 from pydantic import BaseModel
 
-from crewai.utilities.serialization import to_serializable, to_string
+from crewai.flow import Flow
+from crewai.flow.state_utils import export_state, to_string
 
 
 class Address(BaseModel):
@@ -22,6 +23,16 @@ class Person(BaseModel):
     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",
     [
@@ -36,8 +47,9 @@ class Person(BaseModel):
         ({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
     ],
 )
-def test_basic_serialization(test_input, expected):
-    result = to_serializable(test_input)
+def test_basic_serialization(mock_flow, test_input, expected):
+    flow = mock_flow(test_input)
+    result = export_state(flow)
     assert result == expected
 
 
@@ -48,8 +60,9 @@ def test_basic_serialization(test_input, expected):
         (datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
     ],
 )
-def test_temporal_serialization(input_date, expected):
-    result = to_serializable({"date": input_date})
+def test_temporal_serialization(mock_flow, input_date, expected):
+    flow = mock_flow({"date": input_date})
+    result = export_state(flow)
     assert result["date"] == expected
 
 
@@ -62,8 +75,9 @@ def test_temporal_serialization(input_date, expected):
         ("normal", "value", str),
     ],
 )
-def test_dictionary_key_serialization(key, value, expected_key_type):
-    result = to_serializable({key: value})
+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)
@@ -77,13 +91,14 @@ def test_dictionary_key_serialization(key, value, expected_key_type):
         (str.upper, "upper"),
     ],
 )
-def test_callable_serialization(callable_obj, expected_in_result):
-    result = to_serializable({"func": callable_obj})
+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():
+def test_pydantic_model_serialization(mock_flow):
     address = Address(street="123 Main St", city="Tech City", country="Pythonia")
 
     person = Person(
@@ -94,21 +109,23 @@ def test_pydantic_model_serialization():
         skills=["Python", "Testing"],
     )
 
-    data = {
-        "single_model": address,
-        "nested_model": person,
-        "model_list": [address, address],
-        "model_dict": {"home": address},
-    }
+    flow = mock_flow(
+        {
+            "single_model": address,
+            "nested_model": person,
+            "model_list": [address, address],
+            "model_dict": {"home": address},
+        }
+    )
 
-    result = to_serializable(data)
+    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():
+def test_depth_limit(mock_flow):
     """Test max depth handling with a deeply nested structure"""
 
     def create_nested(depth):
@@ -117,7 +134,8 @@ def test_depth_limit():
         return {"next": create_nested(depth - 1)}
 
     deep_structure = create_nested(10)
-    result = to_serializable(deep_structure)
+    flow = mock_flow(deep_structure)
+    result = export_state(flow)
 
     assert result == {
         "next": {
@@ -130,23 +148,3 @@ def test_depth_limit():
             }
         }
     }
-
-
-def test_exclude_keys():
-    result = to_serializable({"key1": "value1", "key2": "value2"}, exclude={"key1"})
-    assert result == {"key2": "value2"}
-
-    model = Person(
-        name="John Doe",
-        age=30,
-        address=Address(street="123 Main St", city="Tech City", country="Pythonia"),
-        birthday=date(1994, 1, 1),
-        skills=["Python", "Testing"],
-    )
-    result = to_serializable(model, exclude={"address"})
-    assert result == {
-        "name": "John Doe",
-        "age": 30,
-        "birthday": "1994-01-01",
-        "skills": ["Python", "Testing"],
-    }

tests/memory/user_memory_test.py

@@ -1,68 +0,0 @@
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-from mem0.memory.main import Memory
-
-from crewai.memory.user.user_memory import UserMemory
-from crewai.memory.user.user_memory_item import UserMemoryItem
-
-
-class MockCrew:
-    def __init__(self, memory_config):
-        self.memory_config = memory_config
-
-@pytest.fixture
-def user_memory():
-    """Fixture to create a UserMemory instance"""
-    crew = MockCrew(
-        memory_config={
-            "provider": "mem0",
-            "config": {"user_id": "john"},
-            "user_memory" : {}
-        }
-    )
-
-    user_memory = MagicMock(spec=UserMemory)
-
-    with patch.object(Memory,'__new__',return_value=user_memory):
-        user_memory_instance = UserMemory(crew=crew)
-    
-    return user_memory_instance
-
-def test_save_and_search(user_memory):
-    memory = UserMemoryItem(
-        data="""test value test value test value test value test value test value
-        test value test value test value test value test value test value
-        test value test value test value test value test value test value""",
-        user="test_user",
-        metadata={"task": "test_task"},
-    )
-
-    with patch.object(UserMemory, "save") as mock_save:
-        user_memory.save(
-            value=memory.data,
-            metadata=memory.metadata,
-            user=memory.user
-        )
-
-        mock_save.assert_called_once_with(
-            value=memory.data,
-            metadata=memory.metadata,
-            user=memory.user
-        )
-
-    expected_result = [
-        {
-            "context": memory.data,
-            "metadata": {"agent": "test_agent"},
-            "score": 0.95,
-        }
-    ]
-    expected_result = ["mocked_result"]
-
-    # Use patch.object to mock UserMemory's search method
-    with patch.object(UserMemory, 'search', return_value=expected_result) as mock_search:
-        find = UserMemory.search("test value", score_threshold=0.01)[0]
-        mock_search.assert_called_once_with("test value", score_threshold=0.01)
-        assert find == expected_result[0]

tests/storage/test_mem0_storage.py

@@ -1,114 +0,0 @@
-import os
-from unittest.mock import MagicMock, patch
-
-import pytest
-from mem0.client.main import MemoryClient
-from mem0.memory.main import Memory
-
-from crewai.agent import Agent
-from crewai.crew import Crew
-from crewai.memory.storage.mem0_storage import Mem0Storage
-from crewai.task import Task
-
-
-# Define the class (if not already defined)
-class MockCrew:
-    def __init__(self, memory_config):
-        self.memory_config = memory_config
-
-
-@pytest.fixture
-def mock_mem0_memory():
-    """Fixture to create a mock Memory instance"""
-    mock_memory = MagicMock(spec=Memory)
-    return mock_memory
-
-
-@pytest.fixture
-def mem0_storage_with_mocked_config(mock_mem0_memory):
-    """Fixture to create a Mem0Storage instance with mocked dependencies"""
-
-    # Patch the Memory class to return our mock
-    with patch('mem0.memory.main.Memory.from_config', return_value=mock_mem0_memory):
-        config = {
-            "vector_store": {
-                "provider": "mock_vector_store",
-                "config": {
-                    "host": "localhost",
-                    "port": 6333
-                }
-            },
-            "llm": {
-                "provider": "mock_llm",
-                "config": {
-                    "api_key": "mock-api-key",
-                    "model": "mock-model"
-                }
-            },
-            "embedder": {
-                "provider": "mock_embedder",
-                "config": {
-                    "api_key": "mock-api-key",
-                    "model": "mock-model"
-                }
-            },
-            "graph_store": {
-                "provider": "mock_graph_store",
-                "config": {
-                    "url": "mock-url",
-                    "username": "mock-user",
-                    "password": "mock-password"
-                }
-            },
-            "history_db_path": "/mock/path",
-            "version": "test-version",
-            "custom_fact_extraction_prompt": "mock prompt 1",
-            "custom_update_memory_prompt": "mock prompt 2"
-        }
-
-        # Instantiate the class with memory_config
-        crew = MockCrew(
-            memory_config={
-                "provider": "mem0",
-                "config": {"user_id": "test_user", "local_mem0_config": config},
-            }
-        )
-
-        mem0_storage = Mem0Storage(type="short_term", crew=crew)
-        return mem0_storage
-
-
-def test_mem0_storage_initialization(mem0_storage_with_mocked_config, mock_mem0_memory):
-    """Test that Mem0Storage initializes correctly with the mocked config"""
-    assert mem0_storage_with_mocked_config.memory_type == "short_term"
-    assert mem0_storage_with_mocked_config.memory is mock_mem0_memory
-
-
-@pytest.fixture
-def mock_mem0_memory_client():
-    """Fixture to create a mock MemoryClient instance"""
-    mock_memory = MagicMock(spec=MemoryClient)
-    return mock_memory
-
-
-@pytest.fixture
-def mem0_storage_with_memory_client(mock_mem0_memory_client):
-    """Fixture to create a Mem0Storage instance with mocked dependencies"""
-
-    # We need to patch the MemoryClient before it's instantiated
-    with patch.object(MemoryClient, '__new__', return_value=mock_mem0_memory_client):
-            crew = MockCrew(
-                memory_config={
-                    "provider": "mem0",
-                    "config": {"user_id": "test_user", "api_key": "ABCDEFGH", "org_id": "my_org_id", "project_id": "my_project_id"},
-                }
-            )
-
-            mem0_storage = Mem0Storage(type="short_term", crew=crew)
-            return mem0_storage
-
-
-def test_mem0_storage_with_memory_client_initialization(mem0_storage_with_memory_client, mock_mem0_memory_client):
-    """Test Mem0Storage initialization with MemoryClient"""
-    assert mem0_storage_with_memory_client.memory_type == "short_term"
-    assert mem0_storage_with_memory_client.memory is mock_mem0_memory_client

tests/task_test.py

@@ -787,25 +787,6 @@ def test_conditional_task_definition_based_on_dict():
     assert task.agent is None
 
 
-def test_conditional_task_copy_preserves_type():
-    task_config = {
-        "description": "Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work', check examples to based your evaluation.",
-        "expected_output": "The score of the title.",
-    }
-    original_task = Task(**task_config)
-    copied_task = original_task.copy(agents=[], task_mapping={})
-    assert isinstance(copied_task, Task)
-
-    original_conditional_config = {
-        "description": "Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'. Check examples to base your evaluation on.",
-        "expected_output": "The score of the title.",
-        "condition": lambda x: True,
-    }
-    original_conditional_task = ConditionalTask(**original_conditional_config)
-    copied_conditional_task = original_conditional_task.copy(agents=[], task_mapping={})
-    assert isinstance(copied_conditional_task, ConditionalTask)
-
-
 def test_interpolate_inputs():
     task = Task(
         description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",

tests/test_multimodal_validation.py

@@ -1,46 +0,0 @@
-import os
-
-import pytest
-
-from crewai import LLM, Agent, Crew, Task
-
-
-@pytest.mark.skip(reason="Only run manually with valid API keys")
-def test_multimodal_agent_with_image_url():
-    """
-    Test that a multimodal agent can process images without validation errors.
-    This test reproduces the scenario from issue #2475.
-    """
-    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-    if not OPENAI_API_KEY:
-        pytest.skip("OPENAI_API_KEY environment variable not set")
-
-    llm = LLM(
-        model="openai/gpt-4o",  # model with vision capabilities
-        api_key=OPENAI_API_KEY,
-        temperature=0.7
-    )
-
-    expert_analyst = Agent(
-        role="Visual Quality Inspector",
-        goal="Perform detailed quality analysis of product images",
-        backstory="Senior quality control expert with expertise in visual inspection",
-        llm=llm,
-        verbose=True,
-        allow_delegation=False,
-        multimodal=True
-    )
-
-    inspection_task = Task(
-        description="""
-        Analyze the product image at https://www.us.maguireshoes.com/collections/spring-25/products/lucena-black-boot with focus on:
-        1. Quality of materials
-        2. Manufacturing defects
-        3. Compliance with standards
-        Provide a detailed report highlighting any issues found.
-        """,
-        expected_output="A detailed report highlighting any issues found",
-        agent=expert_analyst
-    )
-
-    crew = Crew(agents=[expert_analyst], tasks=[inspection_task])

tests/tools/test_tool_usage.py

@@ -1,7 +1,5 @@
-import datetime
 import json
 import random
-import time
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -13,7 +11,6 @@ from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import (
     ToolSelectionErrorEvent,
-    ToolUsageFinishedEvent,
     ToolValidateInputErrorEvent,
 )
 
@@ -627,161 +624,3 @@ def test_tool_validate_input_error_event():
         assert event.agent_role == "test_role"
         assert event.tool_name == "test_tool"
         assert "must be a valid dictionary" in event.error
-
-
-def test_tool_usage_finished_event_with_result():
-    """Test that ToolUsageFinishedEvent is emitted with correct result attributes."""
-    # Create mock agent with proper string values
-    mock_agent = MagicMock()
-    mock_agent.key = "test_agent_key"
-    mock_agent.role = "test_agent_role"
-    mock_agent._original_role = "test_agent_role"
-    mock_agent.i18n = MagicMock()
-    mock_agent.verbose = False
-
-    # Create mock task
-    mock_task = MagicMock()
-    mock_task.delegations = 0
-
-    # Create mock 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 mock tool calling
-    mock_tool_calling = MagicMock()
-    mock_tool_calling.arguments = {"arg1": "value1"}
-
-    # Create ToolUsage instance
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        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(),
-    )
-
-    # Track received events
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def event_handler(source, event):
-        received_events.append(event)
-
-    # Call on_tool_use_finished with test data
-    started_at = time.time()
-    result = "test output result"
-    tool_usage.on_tool_use_finished(
-        tool=test_tool,
-        tool_calling=mock_tool_calling,
-        from_cache=False,
-        started_at=started_at,
-        result=result,
-    )
-
-    # Verify event was emitted
-    assert len(received_events) == 1, "Expected one event to be emitted"
-    event = received_events[0]
-    assert isinstance(event, ToolUsageFinishedEvent)
-
-    # Verify event attributes
-    assert event.agent_key == "test_agent_key"
-    assert event.agent_role == "test_agent_role"
-    assert event.tool_name == "Test Tool"
-    assert event.tool_args == {"arg1": "value1"}
-    assert event.tool_class == "TestTool"
-    assert event.run_attempts == 1  # Default value from ToolUsage
-    assert event.delegations == 0
-    assert event.from_cache is False
-    assert event.output == "test output result"
-    assert isinstance(event.started_at, datetime.datetime)
-    assert isinstance(event.finished_at, datetime.datetime)
-    assert event.type == "tool_usage_finished"
-
-
-def test_tool_usage_finished_event_with_cached_result():
-    """Test that ToolUsageFinishedEvent is emitted with correct result attributes when using cached result."""
-    # Create mock agent with proper string values
-    mock_agent = MagicMock()
-    mock_agent.key = "test_agent_key"
-    mock_agent.role = "test_agent_role"
-    mock_agent._original_role = "test_agent_role"
-    mock_agent.i18n = MagicMock()
-    mock_agent.verbose = False
-
-    # Create mock task
-    mock_task = MagicMock()
-    mock_task.delegations = 0
-
-    # Create mock 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 mock tool calling
-    mock_tool_calling = MagicMock()
-    mock_tool_calling.arguments = {"arg1": "value1"}
-
-    # Create ToolUsage instance
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        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(),
-    )
-
-    # Track received events
-    received_events = []
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def event_handler(source, event):
-        received_events.append(event)
-
-    # Call on_tool_use_finished with test data and from_cache=True
-    started_at = time.time()
-    result = "cached test output result"
-    tool_usage.on_tool_use_finished(
-        tool=test_tool,
-        tool_calling=mock_tool_calling,
-        from_cache=True,
-        started_at=started_at,
-        result=result,
-    )
-
-    # Verify event was emitted
-    assert len(received_events) == 1, "Expected one event to be emitted"
-    event = received_events[0]
-    assert isinstance(event, ToolUsageFinishedEvent)
-
-    # Verify event attributes
-    assert event.agent_key == "test_agent_key"
-    assert event.agent_role == "test_agent_role"
-    assert event.tool_name == "Test Tool"
-    assert event.tool_args == {"arg1": "value1"}
-    assert event.tool_class == "TestTool"
-    assert event.run_attempts == 1  # Default value from ToolUsage
-    assert event.delegations == 0
-    assert event.from_cache is True
-    assert event.output == "cached test output result"
-    assert isinstance(event.started_at, datetime.datetime)
-    assert isinstance(event.finished_at, datetime.datetime)
-    assert event.type == "tool_usage_finished"

tests/utilities/events/test_crewai_event_bus.py

@@ -1,10 +1,10 @@
 from unittest.mock import Mock
 
-from crewai.utilities.events.base_events import BaseEvent
+from crewai.utilities.events.base_events import CrewEvent
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 
 
-class TestEvent(BaseEvent):
+class TestEvent(CrewEvent):
     pass
 
 
@@ -24,7 +24,7 @@ def test_specific_event_handler():
 def test_wildcard_event_handler():
     mock_handler = Mock()
 
-    @crewai_event_bus.on(BaseEvent)
+    @crewai_event_bus.on(CrewEvent)
     def handler(source, event):
         mock_handler(source, event)
 

tests/utilities/test_chromadb_utils.py

@@ -1,81 +0,0 @@
-import unittest
-from typing import Any, Dict, List, Union
-
-import pytest
-
-from crewai.utilities.chromadb import (
-    MAX_COLLECTION_LENGTH,
-    MIN_COLLECTION_LENGTH,
-    is_ipv4_pattern,
-    sanitize_collection_name,
-)
-
-
-class TestChromadbUtils(unittest.TestCase):
-    def test_sanitize_collection_name_long_name(self):
-        """Test sanitizing a very long collection name."""
-        long_name = "This is an extremely long role name that will definitely exceed the ChromaDB collection name limit of 63 characters and cause an error when used as a collection name"
-        sanitized = sanitize_collection_name(long_name)
-        self.assertLessEqual(len(sanitized), MAX_COLLECTION_LENGTH)
-        self.assertTrue(sanitized[0].isalnum())
-        self.assertTrue(sanitized[-1].isalnum())
-        self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
-
-    def test_sanitize_collection_name_special_chars(self):
-        """Test sanitizing a name with special characters."""
-        special_chars = "Agent@123!#$%^&*()"
-        sanitized = sanitize_collection_name(special_chars)
-        self.assertTrue(sanitized[0].isalnum())
-        self.assertTrue(sanitized[-1].isalnum())
-        self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
-
-    def test_sanitize_collection_name_short_name(self):
-        """Test sanitizing a very short name."""
-        short_name = "A"
-        sanitized = sanitize_collection_name(short_name)
-        self.assertGreaterEqual(len(sanitized), MIN_COLLECTION_LENGTH)
-        self.assertTrue(sanitized[0].isalnum())
-        self.assertTrue(sanitized[-1].isalnum())
-
-    def test_sanitize_collection_name_bad_ends(self):
-        """Test sanitizing a name with non-alphanumeric start/end."""
-        bad_ends = "_Agent_"
-        sanitized = sanitize_collection_name(bad_ends)
-        self.assertTrue(sanitized[0].isalnum())
-        self.assertTrue(sanitized[-1].isalnum())
-
-    def test_sanitize_collection_name_none(self):
-        """Test sanitizing a None value."""
-        sanitized = sanitize_collection_name(None)
-        self.assertEqual(sanitized, "default_collection")
-
-    def test_sanitize_collection_name_ipv4_pattern(self):
-        """Test sanitizing an IPv4 address."""
-        ipv4 = "192.168.1.1"
-        sanitized = sanitize_collection_name(ipv4)
-        self.assertTrue(sanitized.startswith("ip_"))
-        self.assertTrue(sanitized[0].isalnum())
-        self.assertTrue(sanitized[-1].isalnum())
-        self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
-
-    def test_is_ipv4_pattern(self):
-        """Test IPv4 pattern detection."""
-        self.assertTrue(is_ipv4_pattern("192.168.1.1"))
-        self.assertFalse(is_ipv4_pattern("not.an.ip.address"))
-
-    def test_sanitize_collection_name_properties(self):
-        """Test that sanitized collection names always meet ChromaDB requirements."""
-        test_cases = [
-            "A" * 100,  # Very long name
-            "_start_with_underscore",
-            "end_with_underscore_",
-            "contains@special#characters",
-            "192.168.1.1",  # IPv4 address
-            "a" * 2,  # Too short
-        ]
-        for test_case in test_cases:
-            sanitized = sanitize_collection_name(test_case)
-            self.assertGreaterEqual(len(sanitized), MIN_COLLECTION_LENGTH)
-            self.assertLessEqual(len(sanitized), MAX_COLLECTION_LENGTH)
-            self.assertTrue(sanitized[0].isalnum())
-            self.assertTrue(sanitized[-1].isalnum())

uv.lock

@@ -1,19 +1,42 @@
 version = 1
-revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
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+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
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+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
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+    "python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
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+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
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+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -43,7 +66,7 @@ wheels = [
 
 [[package]]
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 dependencies = [
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@@ -52,55 +75,56 @@ dependencies = [
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     { name = "frozenlist" },
     { name = "multidict" },
+    { name = "propcache" },
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