Merge branch 'main' into bugfix/flow-persist-nested-models

docs/changelog.mdx

@@ -4,7 +4,7 @@ description: View the latest updates and changes to CrewAI
 icon: timeline
 ---
 
-<Update label="2025-03-17" description="v0.108.0">
+<Update label="2024-03-17" description="v0.108.0">
   **Features**
   - Converted tabs to spaces in `crew.py` template
   - Enhanced LLM Streaming Response Handling and Event System
@@ -24,7 +24,7 @@ icon: timeline
   - Added documentation for `ApifyActorsTool`
 </Update>
 
-<Update label="2025-03-10" description="v0.105.0">
+<Update label="2024-03-10" description="v0.105.0">
   **Core Improvements & Fixes**
   - Fixed issues with missing template variables and user memory configuration
   - Improved async flow support and addressed agent response formatting
@@ -45,7 +45,7 @@ icon: timeline
   - Fixed typos in prompts and updated Amazon Bedrock model listings
 </Update>
 
-<Update label="2025-02-12" description="v0.102.0">
+<Update label="2024-02-12" description="v0.102.0">
   **Core Improvements & Fixes**
   - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
   - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
@@ -65,7 +65,7 @@ icon: timeline
   - Fixed Various Typos & Formatting Issues
 </Update>
 
-<Update label="2025-01-28" description="v0.100.0">
+<Update label="2024-01-28" description="v0.100.0">
   **Features**
   - Add Composio docs
   - Add SageMaker as a LLM provider
@@ -80,7 +80,7 @@ icon: timeline
   - Improve formatting and clarity in CLI and Composio Tool docs
 </Update>
 
-<Update label="2025-01-20" description="v0.98.0">
+<Update label="2024-01-20" description="v0.98.0">
   **Features**
   - Conversation crew v1
   - Add unique ID to flow states
@@ -101,7 +101,7 @@ icon: timeline
   - Fixed typos, nested pydantic model issue, and docling issues
 </Update>
 
-<Update label="2025-01-04" description="v0.95.0">
+<Update label="2024-01-04" description="v0.95.0">
   **New Features**
   - Adding Multimodal Abilities to Crew
   - Programatic Guardrails
@@ -131,7 +131,7 @@ icon: timeline
   - Suppressed userWarnings from litellm pydantic issues
 </Update>
 
-<Update label="2024-12-05" description="v0.86.0">
+<Update label="2023-12-05" description="v0.86.0">
   **Changes**
   - Remove all references to pipeline and pipeline router
   - Add Nvidia NIM as provider in Custom LLM
@@ -141,7 +141,7 @@ icon: timeline
   - Simplify template crew
 </Update>
 
-<Update label="2024-12-04" description="v0.85.0">
+<Update label="2023-12-04" description="v0.85.0">
   **Features**
   - Added knowledge to agent level
   - Feat/remove langchain
@@ -161,7 +161,7 @@ icon: timeline
   - Improvements to LLM Configuration and Usage
 </Update>
 
-<Update label="2024-11-25" description="v0.83.0">
+<Update label="2023-11-25" description="v0.83.0">
   **New Features**
   - New before_kickoff and after_kickoff crew callbacks
   - Support to pre-seed agents with Knowledge
@@ -178,7 +178,7 @@ icon: timeline
   - Update Docs
 </Update>
 
-<Update label="2024-11-13" description="v0.80.0">
+<Update label="2023-11-13" description="v0.80.0">
   **Fixes**
   - Fixing Tokens callback replacement bug
   - Fixing Step callback issue

docs/concepts/event-listener.mdx

@@ -1,7 +1,6 @@
 ---
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
-icon: spinner
 ---
 
 # Event Listeners

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",
@@ -97,20 +97,13 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks"
-            ]
-          },
-          {
-            "group": "Agent Monitoring & Observability",
-            "pages": [
-              "how-to/weave-integration",
+              "how-to/conditional-tasks",
               "how-to/agentops-observability",
-              "how-to/langfuse-observability",
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
-              "how-to/opik-observability",
-              "how-to/portkey-observability"
+              "how-to/portkey-observability",
+              "how-to/langfuse-observability"
             ]
           },
           {
@@ -118,8 +111,6 @@
             "pages": [
               "tools/aimindtool",
               "tools/apifyactorstool",
-              "tools/bedrockinvokeagenttool",
-              "tools/bedrockkbretriever",
               "tools/bravesearchtool",
               "tools/browserbaseloadtool",
               "tools/codedocssearchtool",
@@ -229,4 +220,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/how-to/agentops-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: AgentOps Integration
+title: Agent Monitoring with AgentOps
 description: Understanding and logging your agent performance with AgentOps.
 icon: paperclip
 ---

docs/how-to/kickoff-for-each.mdx

@@ -39,7 +39,8 @@ analysis_crew = Crew(
     agents=[coding_agent],
     tasks=[data_analysis_task],
     verbose=True,
-    memory=False
+    memory=False,
+    respect_context_window=True  # enable by default
 )
 
 datasets = [

docs/how-to/langfuse-observability.mdx

@@ -1,7 +1,7 @@
 ---
-title: Langfuse Integration
+title: Agent Monitoring with Langfuse
 description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: vials
+icon: magnifying-glass-chart
 ---
 
 # Integrate Langfuse with CrewAI

docs/how-to/langtrace-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Langtrace Integration
+title: Agent Monitoring with Langtrace
 description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
 icon: chart-line
 ---

docs/how-to/mlflow-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: MLflow Integration
+title: Agent Monitoring with MLflow
 description: Quickly start monitoring your Agents with MLflow.
 icon: bars-staggered
 ---

docs/how-to/openlit-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: OpenLIT Integration
+title: Agent Monitoring with OpenLIT
 description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry.
 icon: magnifying-glass-chart
 ---

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/)

docs/how-to/portkey-observability.mdx

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

docs/how-to/weave-integration.mdx

@@ -1,124 +0,0 @@
----
-title: Weave Integration
-description: Learn how to use Weights & Biases (W&B) Weave to track, experiment with, evaluate, and improve your CrewAI applications.
-icon: radar
----
-
-# Weave Overview
-
-[Weights & Biases (W&B) Weave](https://weave-docs.wandb.ai/) is a framework for tracking, experimenting with, evaluating, deploying, and improving LLM-based applications. 
-
-![Overview of W&B Weave CrewAI tracing usage](/images/weave-tracing.gif)
-
-Weave provides comprehensive support for every stage of your CrewAI application development:
-
-- **Tracing & Monitoring**: Automatically track LLM calls and application logic to debug and analyze production systems
-- **Systematic Iteration**: Refine and iterate on prompts, datasets, and models
-- **Evaluation**: Use custom or pre-built scorers to systematically assess and enhance agent performance
-- **Guardrails**: Protect your agents with pre- and post-safeguards for content moderation and prompt safety
-
-Weave automatically captures traces for your CrewAI applications, enabling you to monitor and analyze your agents' performance, interactions, and execution flow. This helps you build better evaluation datasets and optimize your agent workflows.
-
-## Setup Instructions
-
-<Steps>
-    <Step title="Install required packages">
-      ```shell
-      pip install crewai weave
-      ```
-    </Step>
-    <Step title="Set up W&B Account">
-      Sign up for a [Weights & Biases account](https://wandb.ai) if you haven't already. You'll need this to view your traces and metrics.
-    </Step>
-    <Step title="Initialize Weave in Your Application">
-      Add the following code to your application:
-
-      ```python
-      import weave
-
-      # Initialize Weave with your project name
-      weave.init(project_name="crewai_demo")
-      ```
-      
-      After initialization, Weave will provide a URL where you can view your traces and metrics.
-    </Step>
-    <Step title="Create your Crews/Flows">
-      ```python
-      from crewai import Agent, Task, Crew, LLM, Process
-
-      # Create an LLM with a temperature of 0 to ensure deterministic outputs
-      llm = LLM(model="gpt-4o", temperature=0)
-
-      # Create agents
-      researcher = Agent(
-          role='Research Analyst',
-          goal='Find and analyze the best investment opportunities',
-          backstory='Expert in financial analysis and market research',
-          llm=llm,
-          verbose=True,
-          allow_delegation=False,
-      )
-
-      writer = Agent(
-          role='Report Writer',
-          goal='Write clear and concise investment reports',
-          backstory='Experienced in creating detailed financial reports',
-          llm=llm,
-          verbose=True,
-          allow_delegation=False,
-      )
-
-      # Create tasks
-      research_task = Task(
-          description='Deep research on the {topic}',
-          expected_output='Comprehensive market data including key players, market size, and growth trends.',
-          agent=researcher
-      )
-
-      writing_task = Task(
-          description='Write a detailed report based on the research',
-          expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
-          agent=writer
-      )
-
-      # Create a crew
-      crew = Crew(
-          agents=[researcher, writer],
-          tasks=[research_task, writing_task],
-          verbose=True,
-          process=Process.sequential,
-      )
-
-      # Run the crew
-      result = crew.kickoff(inputs={"topic": "AI in material science"})
-      print(result)
-      ```
-    </Step>
-    <Step title="View Traces in Weave">
-      After running your CrewAI application, visit the Weave URL provided during initialization to view:
-      - LLM calls and their metadata
-      - Agent interactions and task execution flow
-      - Performance metrics like latency and token usage
-      - Any errors or issues that occurred during execution
-
-      <Frame caption="Weave Tracing Dashboard">
-        <img src="/images/weave-tracing.png" alt="Weave tracing example with CrewAI" />
-      </Frame>
-    </Step>
-</Steps>
-
-## Features
-
-- Weave automatically captures all CrewAI operations: agent interactions and task executions; LLM calls with metadata and token usage; tool usage and results.
-- The integration supports all CrewAI execution methods: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
-- Automatic tracing of all [crewAI-tools](https://github.com/crewAIInc/crewAI-tools).
-- Flow feature support with decorator patching (`@start`, `@listen`, `@router`, `@or_`, `@and_`).
-- Track custom guardrails passed to CrewAI `Task` with `@weave.op()`.
-
-For detailed information on what's supported, visit the [Weave CrewAI documentation](https://weave-docs.wandb.ai/guides/integrations/crewai/#getting-started-with-flow).
-
-## Resources
-
-- [📘 Weave Documentation](https://weave-docs.wandb.ai)
-- [📊 Example Weave x CrewAI dashboard](https://wandb.ai/ayut/crewai_demo/weave/traces?cols=%7B%22wb_run_id%22%3Afalse%2C%22attributes.weave.client_version%22%3Afalse%2C%22attributes.weave.os_name%22%3Afalse%2C%22attributes.weave.os_release%22%3Afalse%2C%22attributes.weave.os_version%22%3Afalse%2C%22attributes.weave.source%22%3Afalse%2C%22attributes.weave.sys_version%22%3Afalse%7D&peekPath=%2Fayut%2Fcrewai_demo%2Fcalls%2F0195c838-38cb-71a2-8a15-651ecddf9d89)
-- [🐦 X](https://x.com/weave_wb)

docs/tools/bedrockinvokeagenttool.mdx

@@ -1,187 +0,0 @@
----
-title: Bedrock Invoke Agent Tool
-description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
-icon: aws
----
-
-# `BedrockInvokeAgentTool`
-
-The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
-
-## Installation
-
-```bash
-uv pip install 'crewai[tools]'
-```
-
-## Requirements
-
-- AWS credentials configured (either through environment variables or AWS CLI)
-- `boto3` and `python-dotenv` packages
-- Access to Amazon Bedrock Agents
-
-## Usage
-
-Here's how to use the tool with a CrewAI agent:
-
-```python {2, 4-8}
-from crewai import Agent, Task, Crew
-from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
-
-# Initialize the tool
-agent_tool = BedrockInvokeAgentTool(
-    agent_id="your-agent-id",
-    agent_alias_id="your-agent-alias-id"
-)
-
-# Create a CrewAI agent that uses the tool
-aws_expert = Agent(
-    role='AWS Service Expert',
-    goal='Help users understand AWS services and quotas',
-    backstory='I am an expert in AWS services and can provide detailed information about them.',
-    tools=[agent_tool],
-    verbose=True
-)
-
-# Create a task for the agent
-quota_task = Task(
-    description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
-    agent=aws_expert
-)
-
-# Create a crew with the agent
-crew = Crew(
-    agents=[aws_expert],
-    tasks=[quota_task],
-    verbose=2
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Tool Arguments
-
-| Argument | Type | Required | Default | Description |
-|:---------|:-----|:---------|:--------|:------------|
-| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
-| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
-| **session_id** | `str` | No | timestamp | The unique identifier of the session |
-| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
-| **end_session** | `bool` | No | False | Whether to end the session after invocation |
-| **description** | `str` | No | None | Custom description for the tool |
-
-## Environment Variables
-
-```bash
-BEDROCK_AGENT_ID=your-agent-id           # Alternative to passing agent_id
-BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
-AWS_REGION=your-aws-region               # Defaults to us-west-2
-AWS_ACCESS_KEY_ID=your-access-key        # Required for AWS authentication
-AWS_SECRET_ACCESS_KEY=your-secret-key    # Required for AWS authentication
-```
-
-## Advanced Usage
-
-### Multi-Agent Workflow with Session Management
-
-```python {2, 4-22}
-from crewai import Agent, Task, Crew, Process
-from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
-
-# Initialize tools with session management
-initial_tool = BedrockInvokeAgentTool(
-    agent_id="your-agent-id",
-    agent_alias_id="your-agent-alias-id",
-    session_id="custom-session-id"
-)
-
-followup_tool = BedrockInvokeAgentTool(
-    agent_id="your-agent-id",
-    agent_alias_id="your-agent-alias-id",
-    session_id="custom-session-id"
-)
-
-final_tool = BedrockInvokeAgentTool(
-    agent_id="your-agent-id",
-    agent_alias_id="your-agent-alias-id",
-    session_id="custom-session-id",
-    end_session=True
-)
-
-# Create agents for different stages
-researcher = Agent(
-    role='AWS Service Researcher',
-    goal='Gather information about AWS services',
-    backstory='I am specialized in finding detailed AWS service information.',
-    tools=[initial_tool]
-)
-
-analyst = Agent(
-    role='Service Compatibility Analyst',
-    goal='Analyze service compatibility and requirements',
-    backstory='I analyze AWS services for compatibility and integration possibilities.',
-    tools=[followup_tool]
-)
-
-summarizer = Agent(
-    role='Technical Documentation Writer',
-    goal='Create clear technical summaries',
-    backstory='I specialize in creating clear, concise technical documentation.',
-    tools=[final_tool]
-)
-
-# Create tasks
-research_task = Task(
-    description="Find all available AWS services in us-west-2 region.",
-    agent=researcher
-)
-
-analysis_task = Task(
-    description="Analyze which services support IPv6 and their implementation requirements.",
-    agent=analyst
-)
-
-summary_task = Task(
-    description="Create a summary of IPv6-compatible services and their key features.",
-    agent=summarizer
-)
-
-# Create a crew with the agents and tasks
-crew = Crew(
-    agents=[researcher, analyst, summarizer],
-    tasks=[research_task, analysis_task, summary_task],
-    process=Process.sequential,
-    verbose=2
-)
-
-# Run the crew
-result = crew.kickoff()
-```
-
-## Use Cases
-
-### Hybrid Multi-Agent Collaborations
-- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
-- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
-- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
-
-### Data Sovereignty and Compliance
-- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
-- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
-- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
-
-### Seamless AWS Service Integration
-- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
-- Enable CrewAI agents to interact with AWS services through natural language requests
-- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
-
-### Scalable Hybrid Agent Architectures
-- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
-- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
-
-### Cross-Organizational Agent Collaboration
-- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
-- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
-- Build agent ecosystems that span organizational boundaries while maintaining security and data control

docs/tools/bedrockkbretriever.mdx

@@ -1,165 +0,0 @@
----
-title: 'Bedrock Knowledge Base Retriever'
-description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
-icon: aws
----
-
-# `BedrockKBRetrieverTool`
-
-The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
-
-## Installation
-
-```bash
-uv pip install 'crewai[tools]'
-```
-
-## Requirements
-
-- AWS credentials configured (either through environment variables or AWS CLI)
-- `boto3` and `python-dotenv` packages
-- Access to Amazon Bedrock Knowledge Base
-
-## Usage
-
-Here's how to use the tool with a CrewAI agent:
-
-```python {2, 4-17}
-from crewai import Agent, Task, Crew
-from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
-
-# Initialize the tool
-kb_tool = BedrockKBRetrieverTool(
-    knowledge_base_id="your-kb-id",
-    number_of_results=5
-)
-
-# Create a CrewAI agent that uses the tool
-researcher = Agent(
-    role='Knowledge Base Researcher',
-    goal='Find information about company policies',
-    backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
-    tools=[kb_tool],
-    verbose=True
-)
-
-# Create a task for the agent
-research_task = Task(
-    description="Find our company's remote work policy and summarize the key points.",
-    agent=researcher
-)
-
-# Create a crew with the agent
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    verbose=2
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)   
-```
-
-## Tool Arguments
-
-| Argument | Type | Required | Default | Description |
-|:---------|:-----|:---------|:---------|:-------------|
-| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
-| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
-| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
-| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
-| **next_token** | `str` | No | None | Token for pagination |
-
-## Environment Variables
-
-```bash
-BEDROCK_KB_ID=your-knowledge-base-id  # Alternative to passing knowledge_base_id
-AWS_REGION=your-aws-region            # Defaults to us-east-1
-AWS_ACCESS_KEY_ID=your-access-key     # Required for AWS authentication
-AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
-```
-
-## Response Format
-
-The tool returns results in JSON format:
-
-```json
-{
-  "results": [
-    {
-      "content": "Retrieved text content",
-      "content_type": "text",
-      "source_type": "S3",
-      "source_uri": "s3://bucket/document.pdf",
-      "score": 0.95,
-      "metadata": {
-        "additional": "metadata"
-      }
-    }
-  ],
-  "nextToken": "pagination-token",
-  "guardrailAction": "NONE"
-}
-```
-
-## Advanced Usage
-
-### Custom Retrieval Configuration
-
-```python
-kb_tool = BedrockKBRetrieverTool(
-    knowledge_base_id="your-kb-id",
-    retrieval_configuration={
-        "vectorSearchConfiguration": {
-            "numberOfResults": 10,
-            "overrideSearchType": "HYBRID"
-        }
-    }
-)
-
-policy_expert = Agent(
-    role='Policy Expert',
-    goal='Analyze company policies in detail',
-    backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
-    tools=[kb_tool]
-)
-```
-
-## Supported Data Sources
-
-- Amazon S3
-- Confluence
-- Salesforce
-- SharePoint
-- Web pages
-- Custom document locations
-- Amazon Kendra
-- SQL databases    
-
-## Use Cases
-
-### Enterprise Knowledge Integration
-- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
-- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
-- Create agents that can answer questions based on your internal documentation while maintaining data security
-
-### Specialized Domain Knowledge
-- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
-- Leverage existing knowledge repositories that are already maintained in your AWS environment
-- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
-
-### Data-Driven Decision Making
-- Ground CrewAI agent responses in your actual company data rather than general knowledge
-- Ensure agents provide recommendations based on your specific business context and documentation
-- Reduce hallucinations by retrieving factual information from your knowledge bases
-
-### Scalable Information Access
-- Access terabytes of organizational knowledge without embedding it all into your models
-- Dynamically query only the relevant information needed for specific tasks
-- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
-
-### Compliance and Governance
-- Ensure CrewAI agents provide responses that align with your company's approved documentation
-- Create auditable trails of information sources used by your agents
-- Maintain control over what information sources your agents can access

pyproject.toml

@@ -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/agent_builder/base_agent.py

@@ -25,7 +25,6 @@ from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
-from crewai.utilities.string_utils import interpolate_only
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -334,15 +333,9 @@ class BaseAgent(ABC, BaseModel):
             self._original_backstory = self.backstory
 
         if inputs:
-            self.role = interpolate_only(
-                input_string=self._original_role, inputs=inputs
-            )
-            self.goal = interpolate_only(
-                input_string=self._original_goal, inputs=inputs
-            )
-            self.backstory = interpolate_only(
-                input_string=self._original_backstory, inputs=inputs
-            )
+            self.role = self._original_role.format(**inputs)
+            self.goal = self._original_goal.format(**inputs)
+            self.backstory = self._original_backstory.format(**inputs)
 
     def set_cache_handler(self, cache_handler: CacheHandler) -> None:
         """Set the cache handler for the agent.

src/crewai/agents/crew_agent_executor.py

@@ -13,7 +13,7 @@ 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
@@ -61,7 +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
@@ -87,14 +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:

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.",
     )
@@ -489,7 +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:
         """
@@ -819,12 +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)
 
@@ -843,7 +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:
@@ -855,7 +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)
@@ -893,12 +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)
@@ -907,24 +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:
@@ -932,13 +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}
@@ -949,41 +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:
@@ -991,7 +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:
@@ -999,9 +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])
@@ -1009,7 +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 = (
@@ -1250,14 +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(
@@ -1265,12 +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/persistence/base.py

@@ -8,45 +8,45 @@ from pydantic import BaseModel
 
 class FlowPersistence(abc.ABC):
     """Abstract base class for flow state persistence.
-    
+
     This class defines the interface that all persistence implementations must follow.
     It supports both structured (Pydantic BaseModel) and unstructured (dict) states.
     """
-    
+
     @abc.abstractmethod
     def init_db(self) -> None:
         """Initialize the persistence backend.
-        
+
         This method should handle any necessary setup, such as:
         - Creating tables
         - Establishing connections
         - Setting up indexes
         """
         pass
-    
+
     @abc.abstractmethod
     def save_state(
         self,
         flow_uuid: str,
         method_name: str,
-        state_data: Union[Dict[str, Any], BaseModel]
+        state_data: Union[Dict[str, Any], BaseModel],
     ) -> None:
         """Persist the flow state after method completion.
-        
+
         Args:
             flow_uuid: Unique identifier for the flow instance
             method_name: Name of the method that just completed
             state_data: Current state data (either dict or Pydantic model)
         """
         pass
-    
+
     @abc.abstractmethod
     def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.
-        
+
         Args:
             flow_uuid: Unique identifier for the flow instance
-            
+
         Returns:
             The most recent state as a dictionary, or None if no state exists
         """

src/crewai/flow/persistence/sqlite.py

@@ -11,6 +11,7 @@ from typing import Any, Dict, Optional, Union
 from pydantic import BaseModel
 
 from crewai.flow.persistence.base import FlowPersistence
+from crewai.flow.state_utils import to_serializable
 
 
 class SQLiteFlowPersistence(FlowPersistence):
@@ -78,34 +79,53 @@ class SQLiteFlowPersistence(FlowPersistence):
             flow_uuid: Unique identifier for the flow instance
             method_name: Name of the method that just completed
             state_data: Current state data (either dict or Pydantic model)
-        """
-        # Convert state_data to dict, handling both Pydantic and dict cases
-        if isinstance(state_data, BaseModel):
-            state_dict = dict(state_data)  # Use dict() for better type compatibility
-        elif isinstance(state_data, dict):
-            state_dict = state_data
-        else:
-            raise ValueError(
-                f"state_data must be either a Pydantic BaseModel or dict, got {type(state_data)}"
-            )
 
-        with sqlite3.connect(self.db_path) as conn:
-            conn.execute(
-                """
-            INSERT INTO flow_states (
-                flow_uuid,
-                method_name,
-                timestamp,
-                state_json
-            ) VALUES (?, ?, ?, ?)
-            """,
-                (
-                    flow_uuid,
-                    method_name,
-                    datetime.now(timezone.utc).isoformat(),
-                    json.dumps(state_dict),
-                ),
-            )
+        Raises:
+            ValueError: If state_data is neither a dict nor a BaseModel
+            RuntimeError: If database operations fail
+            TypeError: If JSON serialization fails
+        """
+        try:
+            # Convert state_data to a JSON-serializable dict using the helper method
+            state_dict = to_serializable(state_data)
+
+            # Try to serialize to JSON to catch any serialization issues early
+            try:
+                state_json = json.dumps(state_dict)
+            except (TypeError, ValueError, OverflowError) as json_err:
+                raise TypeError(
+                    f"Failed to serialize state to JSON: {json_err}"
+                ) from json_err
+
+            # Perform database operation with error handling
+            try:
+                with sqlite3.connect(self.db_path) as conn:
+                    conn.execute(
+                        """
+                    INSERT INTO flow_states (
+                        flow_uuid,
+                        method_name,
+                        timestamp,
+                        state_json
+                    ) VALUES (?, ?, ?, ?)
+                    """,
+                        (
+                            flow_uuid,
+                            method_name,
+                            datetime.now(timezone.utc).isoformat(),
+                            state_json,
+                        ),
+                    )
+            except sqlite3.Error as db_err:
+                raise RuntimeError(f"Database operation failed: {db_err}") from db_err
+
+        except Exception as e:
+            # Log the error but don't crash the application
+            import logging
+
+            logging.error(f"Failed to save flow state: {e}")
+            # Re-raise to allow caller to handle or ignore
+            raise
 
     def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.

src/crewai/flow/state_utils.py

@@ -1,39 +1,18 @@
 import json
-import uuid
 from datetime import date, datetime
+from enum import Enum
 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.
 
@@ -43,7 +22,6 @@ 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:
@@ -52,39 +30,23 @@ 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, Enum):
+        return obj.value
     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/task.py

@@ -2,7 +2,6 @@ import datetime
 import inspect
 import json
 import logging
-import re
 import threading
 import uuid
 from concurrent.futures import Future
@@ -50,7 +49,6 @@ from crewai.utilities.events import (
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
-from crewai.utilities.string_utils import interpolate_only
 
 
 class Task(BaseModel):
@@ -509,9 +507,7 @@ class Task(BaseModel):
             return
 
         try:
-            self.description = interpolate_only(
-                input_string=self._original_description, inputs=inputs
-            )
+            self.description = self._original_description.format(**inputs)
         except KeyError as e:
             raise ValueError(
                 f"Missing required template variable '{e.args[0]}' in description"
@@ -520,7 +516,7 @@ class Task(BaseModel):
             raise ValueError(f"Error interpolating description: {str(e)}") from e
 
         try:
-            self.expected_output = interpolate_only(
+            self.expected_output = self.interpolate_only(
                 input_string=self._original_expected_output, inputs=inputs
             )
         except (KeyError, ValueError) as e:
@@ -528,7 +524,7 @@ class Task(BaseModel):
 
         if self.output_file is not None:
             try:
-                self.output_file = interpolate_only(
+                self.output_file = self.interpolate_only(
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
@@ -559,6 +555,72 @@ class Task(BaseModel):
                 f"\n\n{conversation_instruction}\n\n{conversation_history}"
             )
 
+    def interpolate_only(
+        self,
+        input_string: Optional[str],
+        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
+    ) -> str:
+        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
+
+        Args:
+            input_string: The string containing template variables to interpolate.
+                         Can be None or empty, in which case an empty string is returned.
+            inputs: Dictionary mapping template variables to their values.
+                   Supported value types are strings, integers, floats, and dicts/lists
+                   containing only these types and other nested dicts/lists.
+
+        Returns:
+            The interpolated string with all template variables replaced with their values.
+            Empty string if input_string is None or empty.
+
+        Raises:
+            ValueError: If a value contains unsupported types
+        """
+
+        # Validation function for recursive type checking
+        def validate_type(value: Any) -> None:
+            if value is None:
+                return
+            if isinstance(value, (str, int, float, bool)):
+                return
+            if isinstance(value, (dict, list)):
+                for item in value.values() if isinstance(value, dict) else value:
+                    validate_type(item)
+                return
+            raise ValueError(
+                f"Unsupported type {type(value).__name__} in inputs. "
+                "Only str, int, float, bool, dict, and list are allowed."
+            )
+
+        # Validate all input values
+        for key, value in inputs.items():
+            try:
+                validate_type(value)
+            except ValueError as e:
+                raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
+
+        if input_string is None or not input_string:
+            return ""
+        if "{" not in input_string and "}" not in input_string:
+            return input_string
+        if not inputs:
+            raise ValueError(
+                "Inputs dictionary cannot be empty when interpolating variables"
+            )
+        try:
+            escaped_string = input_string.replace("{", "{{").replace("}", "}}")
+
+            for key in inputs.keys():
+                escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}")
+
+            return escaped_string.format(**inputs)
+        except KeyError as e:
+            raise KeyError(
+                f"Template variable '{e.args[0]}' not found in inputs dictionary"
+            ) from e
+        except ValueError as e:
+            raise ValueError(f"Error during string interpolation: {str(e)}") from e
+
     def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
         self.tools_errors += 1
@@ -572,15 +634,7 @@ 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 +657,7 @@ 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/tools/tool_usage.py

@@ -117,10 +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
@@ -184,9 +181,7 @@ 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()
@@ -207,7 +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:
@@ -249,7 +244,6 @@ class ToolUsage:
             tool_calling=calling,
             from_cache=from_cache,
             started_at=started_at,
-            result=result,
         )
 
         if (
@@ -386,7 +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):
@@ -394,7 +388,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f"{self._i18n.errors('tool_arguments_error')}"
+                    f'{self._i18n.errors("tool_arguments_error")}'
                 )
 
         return ToolCalling(
@@ -422,7 +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)
 
@@ -498,12 +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)
@@ -512,7 +501,6 @@ 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))

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/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/events/llm_events.py

@@ -12,15 +12,10 @@ class LLMCallType(Enum):
 
 
 class LLMCallStartedEvent(CrewEvent):
-    """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.)
-    """
+    """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

src/crewai/utilities/events/tool_usage_events.py

@@ -30,7 +30,6 @@ class ToolUsageFinishedEvent(ToolUsageEvent):
     started_at: datetime
     finished_at: datetime
     from_cache: bool = False
-    output: Any
     type: str = "tool_usage_finished"
 
 

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/formatter.py

@@ -1,12 +1,10 @@
-import re
-from typing import TYPE_CHECKING, List
+from typing import List
 
-if TYPE_CHECKING:
-    from crewai.task import Task
-    from crewai.tasks.task_output import TaskOutput
+from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
 
 
-def aggregate_raw_outputs_from_task_outputs(task_outputs: List["TaskOutput"]) -> str:
+def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> str:
     """Generate string context from the task outputs."""
     dividers = "\n\n----------\n\n"
 
@@ -15,7 +13,7 @@ def aggregate_raw_outputs_from_task_outputs(task_outputs: List["TaskOutput"]) ->
     return context
 
 
-def aggregate_raw_outputs_from_tasks(tasks: List["Task"]) -> str:
+def aggregate_raw_outputs_from_tasks(tasks: List[Task]) -> str:
     """Generate string context from the tasks."""
     task_outputs = [task.output for task in tasks if task.output is not None]
 

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)

src/crewai/utilities/string_utils.py

@@ -1,82 +0,0 @@
-import re
-from typing import Any, Dict, List, Optional, Union
-
-
-def interpolate_only(
-    input_string: Optional[str],
-    inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
-) -> str:
-    """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
-    Only interpolates placeholders that follow the pattern {variable_name} where
-    variable_name starts with a letter/underscore and contains only letters, numbers, and underscores.
-
-    Args:
-        input_string: The string containing template variables to interpolate.
-                     Can be None or empty, in which case an empty string is returned.
-        inputs: Dictionary mapping template variables to their values.
-               Supported value types are strings, integers, floats, and dicts/lists
-               containing only these types and other nested dicts/lists.
-
-    Returns:
-        The interpolated string with all template variables replaced with their values.
-        Empty string if input_string is None or empty.
-
-    Raises:
-        ValueError: If a value contains unsupported types or a template variable is missing
-    """
-
-    # Validation function for recursive type checking
-    def validate_type(value: Any) -> None:
-        if value is None:
-            return
-        if isinstance(value, (str, int, float, bool)):
-            return
-        if isinstance(value, (dict, list)):
-            for item in value.values() if isinstance(value, dict) else value:
-                validate_type(item)
-            return
-        raise ValueError(
-            f"Unsupported type {type(value).__name__} in inputs. "
-            "Only str, int, float, bool, dict, and list are allowed."
-        )
-
-    # Validate all input values
-    for key, value in inputs.items():
-        try:
-            validate_type(value)
-        except ValueError as e:
-            raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
-
-    if input_string is None or not input_string:
-        return ""
-    if "{" not in input_string and "}" not in input_string:
-        return input_string
-    if not inputs:
-        raise ValueError(
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-
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-    # Matches {variable_name} where variable_name starts with a letter/underscore
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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."

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-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():
     """

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

@@ -6,7 +6,7 @@ import pytest
 from pydantic import BaseModel
 
 from crewai.flow import Flow
-from crewai.flow.state_utils import export_state, to_serializable, to_string
+from crewai.flow.state_utils import export_state, to_string
 
 
 class Address(BaseModel):
@@ -148,23 +148,3 @@ def test_depth_limit(mock_flow):
             }
         }
     }
-
-
-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/task_test.py

@@ -15,7 +15,6 @@ from crewai import Agent, Crew, Process, Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.converter import Converter
-from crewai.utilities.string_utils import interpolate_only
 
 
 def test_task_tool_reflect_agent_tools():
@@ -787,25 +786,6 @@ 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.",
@@ -842,7 +822,7 @@ def test_interpolate_only():
 
     # Test JSON structure preservation
     json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
-    result = interpolate_only(
+    result = task.interpolate_only(
         input_string=json_string,
         inputs={"placeholder": "the data", "nestedVal": "something else"},
     )
@@ -853,18 +833,20 @@ def test_interpolate_only():
 
     # Test normal string interpolation
     normal_string = "Hello {name}, welcome to {place}!"
-    result = interpolate_only(
+    result = task.interpolate_only(
         input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
     )
     assert result == "Hello John, welcome to CrewAI!"
 
     # Test empty string
-    result = interpolate_only(input_string="", inputs={"unused": "value"})
+    result = task.interpolate_only(input_string="", inputs={"unused": "value"})
     assert result == ""
 
     # Test string with no placeholders
     no_placeholders = "Hello, this is a test"
-    result = interpolate_only(input_string=no_placeholders, inputs={"unused": "value"})
+    result = task.interpolate_only(
+        input_string=no_placeholders, inputs={"unused": "value"}
+    )
     assert result == no_placeholders
 
 
@@ -876,7 +858,7 @@ def test_interpolate_only_with_dict_inside_expected_output():
     )
 
     json_string = '{"questions": {"main_question": "What is the user\'s name?", "secondary_question": "What is the user\'s age?"}}'
-    result = interpolate_only(
+    result = task.interpolate_only(
         input_string=json_string,
         inputs={
             "questions": {
@@ -890,16 +872,18 @@ def test_interpolate_only_with_dict_inside_expected_output():
     assert result == json_string
 
     normal_string = "Hello {name}, welcome to {place}!"
-    result = interpolate_only(
+    result = task.interpolate_only(
         input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
     )
     assert result == "Hello John, welcome to CrewAI!"
 
-    result = interpolate_only(input_string="", inputs={"unused": "value"})
+    result = task.interpolate_only(input_string="", inputs={"unused": "value"})
     assert result == ""
 
     no_placeholders = "Hello, this is a test"
-    result = interpolate_only(input_string=no_placeholders, inputs={"unused": "value"})
+    result = task.interpolate_only(
+        input_string=no_placeholders, inputs={"unused": "value"}
+    )
     assert result == no_placeholders
 
 
@@ -1101,12 +1085,12 @@ def test_interpolate_with_list_of_strings():
     # Test simple list of strings
     input_str = "Available items: {items}"
     inputs = {"items": ["apple", "banana", "cherry"]}
-    result = interpolate_only(input_str, inputs)
+    result = task.interpolate_only(input_str, inputs)
     assert result == f"Available items: {inputs['items']}"
 
     # Test empty list
     empty_list_input = {"items": []}
-    result = interpolate_only(input_str, empty_list_input)
+    result = task.interpolate_only(input_str, empty_list_input)
     assert result == "Available items: []"
 
 
@@ -1122,7 +1106,7 @@ def test_interpolate_with_list_of_dicts():
             {"name": "Bob", "age": 25, "skills": ["Java", "Cloud"]},
         ]
     }
-    result = interpolate_only("{people}", input_data)
+    result = task.interpolate_only("{people}", input_data)
 
     parsed_result = eval(result)
     assert isinstance(parsed_result, list)
@@ -1154,7 +1138,7 @@ def test_interpolate_with_nested_structures():
             ],
         }
     }
-    result = interpolate_only("{company}", input_data)
+    result = task.interpolate_only("{company}", input_data)
     parsed = eval(result)
 
     assert parsed["name"] == "TechCorp"
@@ -1177,7 +1161,7 @@ def test_interpolate_with_special_characters():
             "empty": "",
         }
     }
-    result = interpolate_only("{special_data}", input_data)
+    result = task.interpolate_only("{special_data}", input_data)
     parsed = eval(result)
 
     assert parsed["quotes"] == """This has "double" and 'single' quotes"""
@@ -1204,7 +1188,7 @@ def test_interpolate_mixed_types():
             },
         }
     }
-    result = interpolate_only("{data}", input_data)
+    result = task.interpolate_only("{data}", input_data)
     parsed = eval(result)
 
     assert parsed["name"] == "Test Dataset"
@@ -1232,7 +1216,7 @@ def test_interpolate_complex_combination():
             },
         ]
     }
-    result = interpolate_only("{report}", input_data)
+    result = task.interpolate_only("{report}", input_data)
     parsed = eval(result)
 
     assert len(parsed) == 2
@@ -1249,7 +1233,7 @@ def test_interpolate_invalid_type_validation():
 
     # Test with invalid top-level type
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
+        task.interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
 
     assert "Unsupported type set" in str(excinfo.value)
 
@@ -1262,7 +1246,7 @@ def test_interpolate_invalid_type_validation():
         }
     }
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only("{data}", {"data": invalid_nested})
+        task.interpolate_only("{data}", {"data": invalid_nested})
     assert "Unsupported type set" in str(excinfo.value)
 
 
@@ -1281,22 +1265,24 @@ def test_interpolate_custom_object_validation():
 
     # Test with custom object at top level
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only("{obj}", {"obj": CustomObject(5)})  # type: ignore we are purposely testing this failure
+        task.interpolate_only("{obj}", {"obj": CustomObject(5)})  # type: ignore we are purposely testing this failure
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with nested custom object in dictionary
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only("{data}", {"data": {"valid": 1, "invalid": CustomObject(5)}})
+        task.interpolate_only(
+            "{data}", {"data": {"valid": 1, "invalid": CustomObject(5)}}
+        )
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with nested custom object in list
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only("{data}", {"data": [1, "valid", CustomObject(5)]})
+        task.interpolate_only("{data}", {"data": [1, "valid", CustomObject(5)]})
     assert "Unsupported type CustomObject" in str(excinfo.value)
 
     # Test with deeply nested custom object
     with pytest.raises(ValueError) as excinfo:
-        interpolate_only(
+        task.interpolate_only(
             "{data}", {"data": {"level1": {"level2": [{"level3": CustomObject(5)}]}}}
         )
     assert "Unsupported type CustomObject" in str(excinfo.value)
@@ -1320,7 +1306,7 @@ def test_interpolate_valid_complex_types():
     }
 
     # Should not raise any errors
-    result = interpolate_only("{data}", {"data": valid_data})
+    result = task.interpolate_only("{data}", {"data": valid_data})
     parsed = eval(result)
     assert parsed["name"] == "Valid Dataset"
     assert parsed["stats"]["nested"]["deeper"]["b"] == 2.5
@@ -1333,16 +1319,16 @@ def test_interpolate_edge_cases():
     )
 
     # Test empty dict and list
-    assert interpolate_only("{}", {"data": {}}) == "{}"
-    assert interpolate_only("[]", {"data": []}) == "[]"
+    assert task.interpolate_only("{}", {"data": {}}) == "{}"
+    assert task.interpolate_only("[]", {"data": []}) == "[]"
 
     # Test numeric types
-    assert interpolate_only("{num}", {"num": 42}) == "42"
-    assert interpolate_only("{num}", {"num": 3.14}) == "3.14"
+    assert task.interpolate_only("{num}", {"num": 42}) == "42"
+    assert task.interpolate_only("{num}", {"num": 3.14}) == "3.14"
 
     # Test boolean values (valid JSON types)
-    assert interpolate_only("{flag}", {"flag": True}) == "True"
-    assert interpolate_only("{flag}", {"flag": False}) == "False"
+    assert task.interpolate_only("{flag}", {"flag": True}) == "True"
+    assert task.interpolate_only("{flag}", {"flag": False}) == "False"
 
 
 def test_interpolate_valid_types():
@@ -1360,7 +1346,7 @@ def test_interpolate_valid_types():
         "nested": {"flag": True, "empty": None},
     }
 
-    result = interpolate_only("{data}", {"data": valid_data})
+    result = task.interpolate_only("{data}", {"data": valid_data})
     parsed = eval(result)
 
     assert parsed["active"] is True

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/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())

tests/utilities/test_converter.py

@@ -1,35 +1,17 @@
 import json
 import os
-from typing import Dict, List, Optional
+from datetime import date, datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional, Union, cast
 from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 from pydantic import BaseModel
 
-from crewai.llm import LLM
-from crewai.utilities.converter import (
-    Converter,
-    ConverterError,
-    convert_to_model,
-    convert_with_instructions,
-    create_converter,
-    generate_model_description,
-    get_conversion_instructions,
-    handle_partial_json,
-    validate_model,
-)
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+from crewai.flow.state_utils import _to_serializable_key, to_serializable, to_string
 
 
 # Sample Pydantic models for testing
-class EmailResponse(BaseModel):
-    previous_message_content: str
-
-
-class EmailResponses(BaseModel):
-    responses: list[EmailResponse]
-
-
 class SimpleModel(BaseModel):
     name: str
     age: int
@@ -52,560 +34,190 @@ class Person(BaseModel):
     address: Address
 
 
-class CustomConverter(Converter):
-    pass
+class Color(Enum):
+    RED = "red"
+    GREEN = "green"
+    BLUE = "blue"
 
 
-# Fixtures
-@pytest.fixture
-def mock_agent():
-    agent = Mock()
-    agent.function_calling_llm = None
-    agent.llm = Mock()
-    return agent
+class EnumModel(BaseModel):
+    name: str
+    color: Color
 
 
-# Tests for convert_to_model
-def test_convert_to_model_with_valid_json():
-    result = '{"name": "John", "age": 30}'
-    output = convert_to_model(result, SimpleModel, None, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "John"
-    assert output.age == 30
+class OptionalModel(BaseModel):
+    name: str
+    age: Optional[int]
 
 
-def test_convert_to_model_with_invalid_json():
-    result = '{"name": "John", "age": "thirty"}'
-    with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
-        mock_handle.return_value = "Fallback result"
-        output = convert_to_model(result, SimpleModel, None, None)
-        assert output == "Fallback result"
+class ListModel(BaseModel):
+    items: List[int]
 
 
-def test_convert_to_model_with_no_model():
-    result = "Plain text"
-    output = convert_to_model(result, None, None, None)
-    assert output == "Plain text"
+class UnionModel(BaseModel):
+    field: Union[int, str, None]
 
 
-def test_convert_to_model_with_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-            }
-        ]
+# Tests for to_serializable function
+def test_to_serializable_primitives():
+    """Test serialization of primitive types."""
+    assert to_serializable("test string") == "test string"
+    assert to_serializable(42) == 42
+    assert to_serializable(3.14) == 3.14
+    assert to_serializable(True) == True
+    assert to_serializable(None) is None
+
+
+def test_to_serializable_dates():
+    """Test serialization of date and datetime objects."""
+    test_date = date(2023, 1, 15)
+    test_datetime = datetime(2023, 1, 15, 10, 30, 45)
+
+    assert to_serializable(test_date) == "2023-01-15"
+    assert to_serializable(test_datetime) == "2023-01-15T10:30:45"
+
+
+def test_to_serializable_collections():
+    """Test serialization of lists, tuples, and sets."""
+    test_list = [1, "two", 3.0]
+    test_tuple = (4, "five", 6.0)
+    test_set = {7, "eight", 9.0}
+
+    assert to_serializable(test_list) == [1, "two", 3.0]
+    assert to_serializable(test_tuple) == [4, "five", 6.0]
+
+    # For sets, we can't rely on order, so we'll verify differently
+    serialized_set = to_serializable(test_set)
+    assert isinstance(serialized_set, list)
+    assert len(serialized_set) == 3
+    assert 7 in serialized_set
+    assert "eight" in serialized_set
+    assert 9.0 in serialized_set
+
+
+def test_to_serializable_dict():
+    """Test serialization of dictionaries."""
+    test_dict = {"a": 1, "b": "two", "c": [3, 4, 5]}
+
+    assert to_serializable(test_dict) == {"a": 1, "b": "two", "c": [3, 4, 5]}
+
+
+def test_to_serializable_pydantic_models():
+    """Test serialization of Pydantic models."""
+    simple = SimpleModel(name="John", age=30)
+
+    assert to_serializable(simple) == {"name": "John", "age": 30}
+
+
+def test_to_serializable_nested_models():
+    """Test serialization of nested Pydantic models."""
+    simple = SimpleModel(name="John", age=30)
+    nested = NestedModel(id=1, data=simple)
+
+    assert to_serializable(nested) == {"id": 1, "data": {"name": "John", "age": 30}}
+
+
+def test_to_serializable_complex_model():
+    """Test serialization of a complex model with nested structures."""
+    person = Person(
+        name="Jane",
+        age=28,
+        address=Address(street="123 Main St", city="Anytown", zip_code="12345"),
+    )
+
+    assert to_serializable(person) == {
+        "name": "Jane",
+        "age": 28,
+        "address": {"street": "123 Main St", "city": "Anytown", "zip_code": "12345"},
     }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )
 
 
-def test_convert_to_model_with_escaped_special_characters():
-    json_string_test = json.dumps(
-        {
-            "responses": [
-                {
-                    "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-                }
-            ]
-        }
-    )
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
-    )
+def test_to_serializable_enum():
+    """Test serialization of Enum values."""
+    model = EnumModel(name="ColorTest", color=Color.RED)
 
+    assert to_serializable(model) == {"name": "ColorTest", "color": "red"}
 
-def test_convert_to_model_with_multiple_special_characters():
-    json_string_test = """
-    {
-        "responses": [
-            {
-                "previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-            }
-        ]
-    }
-    """
-    output = convert_to_model(json_string_test, EmailResponses, None, None)
-    assert isinstance(output, EmailResponses)
-    assert len(output.responses) == 1
-    assert (
-        output.responses[0].previous_message_content
-        == "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
-    )
 
+def test_to_serializable_optional_fields():
+    """Test serialization of models with optional fields."""
+    model_with_age = OptionalModel(name="WithAge", age=25)
+    model_without_age = OptionalModel(name="WithoutAge", age=None)
 
-# Tests for validate_model
-def test_validate_model_pydantic_output():
-    result = '{"name": "Alice", "age": 25}'
-    output = validate_model(result, SimpleModel, False)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 25
+    assert to_serializable(model_with_age) == {"name": "WithAge", "age": 25}
+    assert to_serializable(model_without_age) == {"name": "WithoutAge", "age": None}
 
 
-def test_validate_model_json_output():
-    result = '{"name": "Bob", "age": 40}'
-    output = validate_model(result, SimpleModel, True)
-    assert isinstance(output, dict)
-    assert output == {"name": "Bob", "age": 40}
+def test_to_serializable_list_field():
+    """Test serialization of models with list fields."""
+    model = ListModel(items=[1, 2, 3, 4, 5])
 
+    assert to_serializable(model) == {"items": [1, 2, 3, 4, 5]}
 
-# Tests for handle_partial_json
-def test_handle_partial_json_with_valid_partial():
-    result = 'Some text {"name": "Charlie", "age": 35} more text'
-    output = handle_partial_json(result, SimpleModel, False, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Charlie"
-    assert output.age == 35
 
+def test_to_serializable_union_field():
+    """Test serialization of models with union fields."""
+    model_int = UnionModel(field=42)
+    model_str = UnionModel(field="test")
+    model_none = UnionModel(field=None)
 
-def test_handle_partial_json_with_invalid_partial(mock_agent):
-    result = "No valid JSON here"
-    with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
-        mock_convert.return_value = "Converted result"
-        output = handle_partial_json(result, SimpleModel, False, mock_agent)
-        assert output == "Converted result"
+    assert to_serializable(model_int) == {"field": 42}
+    assert to_serializable(model_str) == {"field": "test"}
+    assert to_serializable(model_none) == {"field": None}
 
 
-# Tests for convert_with_instructions
-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_success(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
-    mock_create_converter.return_value = mock_converter
+def test_to_serializable_max_depth():
+    """Test max depth parameter to prevent infinite recursion."""
+    # Create recursive structure
+    a: Dict[str, Any] = {"name": "a"}
+    b: Dict[str, Any] = {"name": "b", "ref": a}
+    a["ref"] = b  # Create circular reference
 
-    result = "Some text to convert"
-    output = convert_with_instructions(result, SimpleModel, False, mock_agent)
+    result = to_serializable(a, max_depth=3)
 
-    assert isinstance(output, SimpleModel)
-    assert output.name == "David"
-    assert output.age == 50
+    assert isinstance(result, dict)
+    assert "name" in result
+    assert "ref" in result
+    assert isinstance(result["ref"], dict)
+    assert "ref" in result["ref"]
+    assert isinstance(result["ref"]["ref"], dict)
+    # At depth 3, it should convert to string
+    assert isinstance(result["ref"]["ref"]["ref"], str)
 
 
-@patch("crewai.utilities.converter.create_converter")
-@patch("crewai.utilities.converter.get_conversion_instructions")
-def test_convert_with_instructions_failure(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
-    mock_get_instructions.return_value = "Instructions"
-    mock_converter = Mock()
-    mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
-    mock_create_converter.return_value = mock_converter
+def test_to_serializable_non_serializable():
+    """Test serialization of objects that aren't directly JSON serializable."""
 
-    result = "Some text to convert"
-    with patch("crewai.utilities.converter.Printer") as mock_printer:
-        output = convert_with_instructions(result, SimpleModel, False, mock_agent)
-        assert output == result
-        mock_printer.return_value.print.assert_called_once()
+    class CustomObject:
+        def __repr__(self):
+            return "CustomObject()"
 
+    obj = CustomObject()
 
-# Tests for get_conversion_instructions
-def test_get_conversion_instructions_gpt():
-    llm = LLM(model="gpt-4o-mini")
-    with patch.object(LLM, "supports_function_calling") as supports_function_calling:
-        supports_function_calling.return_value = True
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        expected_instructions = (
-            "Please convert the following text into valid JSON.\n\n"
-            "Output ONLY the valid JSON and nothing else.\n\n"
-            "The JSON must follow this schema exactly:\n```json\n"
-            f"{model_schema}\n```"
-        )
-        assert instructions == expected_instructions
+    # Should convert to string representation
+    assert to_serializable(obj) == "CustomObject()"
 
 
-def test_get_conversion_instructions_non_gpt():
-    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
-    with patch.object(LLM, "supports_function_calling", return_value=False):
-        instructions = get_conversion_instructions(SimpleModel, llm)
-        assert '"name": str' in instructions
-        assert '"age": int' in instructions
+def test_to_string_conversion():
+    """Test the to_string function."""
+    test_dict = {"name": "Test", "values": [1, 2, 3]}
 
+    # Should convert to a JSON string
+    assert to_string(test_dict) == '{"name": "Test", "values": [1, 2, 3]}'
 
-# Tests for is_gpt
-def test_supports_function_calling_true():
-    llm = LLM(model="gpt-4o")
-    assert llm.supports_function_calling() is True
+    # None should return None
+    assert to_string(None) is None
 
 
-def test_supports_function_calling_false():
-    llm = LLM(model="non-existent-model")
-    assert llm.supports_function_calling() is False
+def test_to_serializable_key():
+    """Test serialization of dictionary keys."""
+    # String and int keys are converted to strings
+    assert _to_serializable_key("test") == "test"
+    assert _to_serializable_key(42) == "42"
 
-
-def test_create_converter_with_mock_agent():
-    mock_agent = MagicMock()
-    mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
-
-    converter = create_converter(
-        agent=mock_agent,
-        llm=Mock(),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, Converter)
-    mock_agent.get_output_converter.assert_called_once()
-
-
-def test_create_converter_with_custom_converter():
-    converter = create_converter(
-        converter_cls=CustomConverter,
-        llm=LLM(model="gpt-4o-mini"),
-        text="Sample",
-        model=SimpleModel,
-        instructions="Convert",
-    )
-
-    assert isinstance(converter, CustomConverter)
-
-
-def test_create_converter_fails_without_agent_or_converter_cls():
-    with pytest.raises(
-        ValueError, match="Either agent or converter_cls must be provided"
-    ):
-        create_converter(
-            llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
-        )
-
-
-def test_generate_model_description_simple_model():
-    description = generate_model_description(SimpleModel)
-    expected_description = '{\n  "name": str,\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_nested_model():
-    description = generate_model_description(NestedModel)
-    expected_description = (
-        '{\n  "id": int,\n  "data": {\n  "name": str,\n  "age": int\n}\n}'
-    )
-    assert description == expected_description
-
-
-def test_generate_model_description_optional_field():
-    class ModelWithOptionalField(BaseModel):
-        name: Optional[str]
-        age: int
-
-    description = generate_model_description(ModelWithOptionalField)
-    expected_description = '{\n  "name": Optional[str],\n  "age": int\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_list_field():
-    class ModelWithListField(BaseModel):
-        items: List[int]
-
-    description = generate_model_description(ModelWithListField)
-    expected_description = '{\n  "items": List[int]\n}'
-    assert description == expected_description
-
-
-def test_generate_model_description_dict_field():
-    class ModelWithDictField(BaseModel):
-        attributes: Dict[str, int]
-
-    description = generate_model_description(ModelWithDictField)
-    expected_description = '{\n  "attributes": Dict[str, int]\n}'
-    assert description == expected_description
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_convert_with_instructions():
-    llm = LLM(model="gpt-4o-mini")
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    # Act
-    output = converter.to_pydantic()
-
-    # Assert
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 30
-
-
-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
-def test_converter_with_llama3_2_model():
-    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
-    sample_text = "Name: Alice Llama, Age: 30"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    assert output.age == 30
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
-def test_converter_with_llama3_1_model():
-    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
-    sample_text = "Name: Alice Llama, Age: 30"
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-    output = converter.to_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice Llama"
-    assert output.age == 30
-
-
-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_nested_model():
-    llm = LLM(model="gpt-4o-mini")
-    sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
-
-    instructions = get_conversion_instructions(Person, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=Person,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, Person)
-    assert output.name == "John Doe"
-    assert output.age == 30
-    assert isinstance(output.address, Address)
-    assert output.address.street == "123 Main St"
-    assert output.address.city == "Anytown"
-    assert output.address.zip_code == "12345"
-
-
-# Tests for error handling
-def test_converter_error_handling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = "Invalid JSON"
-    sample_text = "Name: Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
-
-    assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
-
-
-# Tests for retry logic
-def test_converter_retry_logic():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.side_effect = [
-        "Invalid JSON",
-        "Still invalid",
-        '{"name": "Retry Alice", "age": 30}',
-    ]
-    sample_text = "Name: Retry Alice, Age: 30"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-        max_attempts=3,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Retry Alice"
-    assert output.age == 30
-    assert llm.call.call_count == 3
-
-
-# Tests for optional fields
-def test_converter_with_optional_fields():
-    class OptionalModel(BaseModel):
-        name: str
-        age: Optional[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    # Simulate the LLM's response with 'age' explicitly set to null
-    llm.call.return_value = '{"name": "Bob", "age": null}'
-    sample_text = "Name: Bob, age: None"
-
-    instructions = get_conversion_instructions(OptionalModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=OptionalModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, OptionalModel)
-    assert output.name == "Bob"
-    assert output.age is None
-
-
-# Tests for list fields
-def test_converter_with_list_field():
-    class ListModel(BaseModel):
-        items: List[int]
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"items": [1, 2, 3]}'
-    sample_text = "Items: 1, 2, 3"
-
-    instructions = get_conversion_instructions(ListModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=ListModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, ListModel)
-    assert output.items == [1, 2, 3]
-
-
-# Tests for enums
-from enum import Enum
-
-
-def test_converter_with_enum():
-    class Color(Enum):
-        RED = "red"
-        GREEN = "green"
-        BLUE = "blue"
-
-    class EnumModel(BaseModel):
-        name: str
-        color: Color
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Alice", "color": "red"}'
-    sample_text = "Name: Alice, Color: Red"
-
-    instructions = get_conversion_instructions(EnumModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=EnumModel,
-        instructions=instructions,
-    )
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, EnumModel)
-    assert output.name == "Alice"
-    assert output.color == Color.RED
-
-
-# Tests for ambiguous input
-def test_converter_with_ambiguous_input():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
-    sample_text = "Charlie is thirty years old"
-
-    instructions = get_conversion_instructions(SimpleModel, llm)
-    converter = Converter(
-        llm=llm,
-        text=sample_text,
-        model=SimpleModel,
-        instructions=instructions,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
-
-    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
-
-
-# Tests for function calling support
-def test_converter_with_function_calling():
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = True
-
-    instructor = Mock()
-    instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Eve, Age: 35",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-    converter._create_instructor = Mock(return_value=instructor)
-
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Eve"
-    assert output.age == 35
-    instructor.to_pydantic.assert_called_once()
-
-
-def test_generate_model_description_union_field():
-    class UnionModel(BaseModel):
-        field: int | str | None
-
-    description = generate_model_description(UnionModel)
-    expected_description = '{\n  "field": int | str | None\n}'
-    assert description == expected_description
+    # Complex objects are converted to a unique string
+    obj = object()
+    key_str = _to_serializable_key(obj)
+    assert isinstance(key_str, str)
+    assert "key_" in key_str
+    assert "object" in key_str

tests/utilities/test_string_utils.py

@@ -1,187 +0,0 @@
-from typing import Any, Dict, List, Union
-
-import pytest
-
-from crewai.utilities.string_utils import interpolate_only
-
-
-class TestInterpolateOnly:
-    """Tests for the interpolate_only function in string_utils.py."""
-
-    def test_basic_variable_interpolation(self):
-        """Test basic variable interpolation works correctly."""
-        template = "Hello, {name}! Welcome to {company}."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice",
-            "company": "CrewAI",
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Hello, Alice! Welcome to CrewAI."
-
-    def test_multiple_occurrences_of_same_variable(self):
-        """Test that multiple occurrences of the same variable are replaced."""
-        template = "{name} is using {name}'s account."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Bob"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Bob is using Bob's account."
-
-    def test_json_structure_preservation(self):
-        """Test that JSON structures are preserved and not interpolated incorrectly."""
-        template = """
-        Instructions for {agent}:
-
-        Please return the following object:
-
-        {"name": "person's name", "age": 25, "skills": ["coding", "testing"]}
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent": "DevAgent"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "Instructions for DevAgent:" in result
-        assert (
-            '{"name": "person\'s name", "age": 25, "skills": ["coding", "testing"]}'
-            in result
-        )
-
-    def test_complex_nested_json(self):
-        """Test with complex JSON structures containing curly braces."""
-        template = """
-        {agent} needs to process:
-        {
-          "config": {
-            "nested": {
-              "value": 42
-            },
-            "arrays": [1, 2, {"inner": "value"}]
-          }
-        }
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent": "DataProcessor"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "DataProcessor needs to process:" in result
-        assert '"nested": {' in result
-        assert '"value": 42' in result
-        assert '[1, 2, {"inner": "value"}]' in result
-
-    def test_missing_variable(self):
-        """Test that an error is raised when a required variable is missing."""
-        template = "Hello, {name}!"
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "not_name": "Alice"
-        }
-
-        with pytest.raises(KeyError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "template variable" in str(excinfo.value).lower()
-        assert "name" in str(excinfo.value)
-
-    def test_invalid_input_types(self):
-        """Test that an error is raised with invalid input types."""
-        template = "Hello, {name}!"
-        # Using Any for this test since we're intentionally testing an invalid type
-        inputs: Dict[str, Any] = {"name": object()}  # Object is not a valid input type
-
-        with pytest.raises(ValueError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "unsupported type" in str(excinfo.value).lower()
-
-    def test_empty_input_string(self):
-        """Test handling of empty or None input string."""
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice"
-        }
-
-        assert interpolate_only("", inputs) == ""
-        assert interpolate_only(None, inputs) == ""
-
-    def test_no_variables_in_template(self):
-        """Test a template with no variables to replace."""
-        template = "This is a static string with no variables."
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "name": "Alice"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == template
-
-    def test_variable_name_starting_with_underscore(self):
-        """Test variables starting with underscore are replaced correctly."""
-        template = "Variable: {_special_var}"
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "_special_var": "Special Value"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert result == "Variable: Special Value"
-
-    def test_preserves_non_matching_braces(self):
-        """Test that non-matching braces patterns are preserved."""
-        template = (
-            "This {123} and {!var} should not be replaced but {valid_var} should."
-        )
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "valid_var": "works"
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert (
-            result == "This {123} and {!var} should not be replaced but works should."
-        )
-
-    def test_complex_mixed_scenario(self):
-        """Test a complex scenario with both valid variables and JSON structures."""
-        template = """
-        {agent_name} is working on task {task_id}.
-        
-        Instructions:
-        1. Process the data
-        2. Return results as:
-        
-        {
-          "taskId": "{task_id}",
-          "results": {
-            "processed_by": "agent_name",
-            "status": "complete",
-            "values": [1, 2, 3]
-          }
-        }
-        """
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]] = {
-            "agent_name": "AnalyticsAgent",
-            "task_id": "T-12345",
-        }
-
-        result = interpolate_only(template, inputs)
-
-        assert "AnalyticsAgent is working on task T-12345" in result
-        assert '"taskId": "T-12345"' in result
-        assert '"processed_by": "agent_name"' in result  # This shouldn't be replaced
-        assert '"values": [1, 2, 3]' in result
-
-    def test_empty_inputs_dictionary(self):
-        """Test that an error is raised with empty inputs dictionary."""
-        template = "Hello, {name}!"
-        inputs: Dict[str, Any] = {}
-
-        with pytest.raises(ValueError) as excinfo:
-            interpolate_only(template, inputs)
-
-        assert "inputs dictionary cannot be empty" in str(excinfo.value).lower()

uv.lock

@@ -1,18 +1,42 @@
 version = 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')",
+    "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'",
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+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "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'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
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+    "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]]
@@ -42,7 +66,7 @@ wheels = [
 
 [[package]]
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 dependencies = [
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@@ -51,55 +75,56 @@ dependencies = [
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     { name = "frozenlist" },
     { name = "multidict" },
+    { name = "propcache" },
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