Merge branch 'main' into bugfix/event-emitter-fix

- Removed unused import of BaseTool from langchain_core.tools.
- Updated type hints in crew.py to streamline code and improve readability.
- Cleaned up whitespace for better code formatting.

Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

docs/concepts/event-listener.mdx

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

docs/custom_llm.md

@@ -0,0 +1,642 @@
+# 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

@@ -97,14 +97,20 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks",
+              "how-to/conditional-tasks"
+            ]
+          },
+          {
+            "group": "Agent Monitoring & Observability",
+            "pages": [
               "how-to/weave-integration",
               "how-to/agentops-observability",
+              "how-to/langfuse-observability",
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
-              "how-to/portkey-observability",
-              "how-to/langfuse-observability"
+              "how-to/opik-observability",
+              "how-to/portkey-observability"
             ]
           },
           {
@@ -223,4 +229,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/how-to/agentops-observability.mdx

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

docs/how-to/langfuse-observability.mdx

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

docs/how-to/langtrace-observability.mdx

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

docs/how-to/openlit-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Agent Monitoring with OpenLIT
+title: OpenLIT Integration
 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

@@ -0,0 +1,124 @@
+---
+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.
+
+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 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)
+
+      <Frame caption="Opik Agent Dashboard">
+        <img src="/images/opik-crewai-dashboard.png" alt="Opik agent monitoring example with CrewAI" />
+      </Frame>
+    </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: Agent Monitoring with Portkey
+title: Portkey Integration
 description: How to use Portkey with CrewAI
 icon: key
 ---

docs/how-to/weave-integration.mdx

@@ -1,7 +1,7 @@
 ---
 title: Weave Integration
 description: Learn how to use Weights & Biases (W&B) Weave to track, experiment with, evaluate, and improve your CrewAI applications.
-icon: insights
+icon: radar
 ---
 
 # Weave Overview

pyproject.toml

@@ -64,6 +64,9 @@ mem0 = ["mem0ai>=0.1.29"]
 docling = [
     "docling>=2.12.0",
 ]
+aisuite = [
+    "aisuite>=0.1.10",
+]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/__init__.py

@@ -5,6 +5,7 @@ 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
 
@@ -21,6 +22,7 @@ __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 LLM
+from crewai.llm import BaseLLM
 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[LLM], Any] = Field(
+    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -118,7 +118,9 @@ 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, LLM):
+        if self.function_calling_llm and not isinstance(
+            self.function_calling_llm, BaseLLM
+        ):
             self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
@@ -140,15 +142,13 @@ 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=knowledge_agent_name,
+                        collection_name=self.role,
                         storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:

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 LLM
+from crewai.llm import BaseLLM
 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: LLM = llm
+        self.llm: BaseLLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -87,8 +87,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        self.stop = stop_words
-        self.llm.stop = list(set(self.llm.stop + self.stop))
+        existing_stop = self.llm.stop or []
+        self.llm.stop = list(
+            set(
+                existing_stop + self.stop
+                if isinstance(existing_stop, list)
+                else 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
+from crewai.llm import LLM, BaseLLM
 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]:
+def initialize_chat_llm(crew: Crew) -> Optional[LLM | BaseLLM]:
     """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
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union, cast
 
 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
+from crewai.llm import LLM, BaseLLM
 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 Tool
+from crewai.tools.base_tool import BaseTool, 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[Any] = Field(
+    manager_llm: Optional[Union[str, InstanceOf[BaseLLM], 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: str = Field(
+    prompt_file: Optional[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[Any] = Field(
+    planning_llm: Optional[Union[str, InstanceOf[BaseLLM], 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[Any] = Field(
+    chat_llm: Optional[Union[str, InstanceOf[BaseLLM], 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:
         """
@@ -819,7 +819,12 @@ 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 []
-            tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
+            # 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),
+            )
 
             self._log_task_start(task, agent_to_use.role)
 
@@ -838,7 +843,7 @@ class Crew(BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=tools_for_task,
+                    tools=cast(List[BaseTool], tools_for_task),
                 )
                 futures.append((task, future, task_index))
             else:
@@ -850,7 +855,7 @@ class Crew(BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=tools_for_task,
+                    tools=cast(List[BaseTool], tools_for_task),
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -888,10 +893,12 @@ class Crew(BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: List[Tool]
-    ) -> List[Tool]:
+        self, agent: BaseAgent, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         # Add delegation tools if agent allows delegation
-        if agent.allow_delegation:
+        if hasattr(agent, "allow_delegation") and getattr(
+            agent, "allow_delegation", False
+        ):
             if self.process == Process.hierarchical:
                 if self.manager_agent:
                     tools = self._update_manager_tools(task, tools)
@@ -900,17 +907,24 @@ class Crew(BaseModel):
                         "Manager agent is required for hierarchical process."
                     )
 
-            elif agent and agent.allow_delegation:
+            elif agent:
                 tools = self._add_delegation_tools(task, tools)
 
         # Add code execution tools if agent allows code execution
-        if agent.allow_code_execution:
+        if hasattr(agent, "allow_code_execution") and getattr(
+            agent, "allow_code_execution", False
+        ):
             tools = self._add_code_execution_tools(agent, tools)
 
-        if agent and agent.multimodal:
+        if (
+            agent
+            and hasattr(agent, "multimodal")
+            and getattr(agent, "multimodal", False)
+        ):
             tools = self._add_multimodal_tools(agent, tools)
 
-        return tools
+        # Return a List[BaseTool] which is compatible with both Task.execute_sync and Task.execute_async
+        return cast(List[BaseTool], tools)
 
     def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
@@ -918,11 +932,13 @@ class Crew(BaseModel):
         return task.agent
 
     def _merge_tools(
-        self, existing_tools: List[Tool], new_tools: List[Tool]
-    ) -> List[Tool]:
+        self,
+        existing_tools: Union[List[Tool], List[BaseTool]],
+        new_tools: Union[List[Tool], List[BaseTool]],
+    ) -> List[BaseTool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
-            return existing_tools
+            return cast(List[BaseTool], existing_tools)
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -933,23 +949,41 @@ class Crew(BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return tools
+        return cast(List[BaseTool], tools)
 
     def _inject_delegation_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)
+        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)
 
-    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_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_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_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_delegation_tools(self, task: Task, tools: List[Tool]):
+    def _add_delegation_tools(
+        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         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:
@@ -957,7 +991,7 @@ class Crew(BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return tools
+        return cast(List[BaseTool], tools)
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
@@ -965,7 +999,9 @@ class Crew(BaseModel):
                 task_name=task.name, task=task.description, agent=role, status="started"
             )
 
-    def _update_manager_tools(self, task: Task, tools: List[Tool]):
+    def _update_manager_tools(
+        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         if self.manager_agent:
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
@@ -973,7 +1009,7 @@ class Crew(BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return tools
+        return cast(List[BaseTool], tools)
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
         context = (
@@ -1214,13 +1250,14 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: Union[str, InstanceOf[LLM]],
+        eval_llm: Union[str, InstanceOf[BaseLLM]],
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
-            eval_llm = create_llm(eval_llm)
-            if not eval_llm:
+            # Create LLM instance and ensure it's of type LLM for CrewEvaluator
+            llm_instance = create_llm(eval_llm)
+            if not llm_instance:
                 raise ValueError("Failed to create LLM instance.")
 
             crewai_event_bus.emit(
@@ -1228,12 +1265,12 @@ class Crew(BaseModel):
                 CrewTestStartedEvent(
                     crew_name=self.name or "crew",
                     n_iterations=n_iterations,
-                    eval_llm=eval_llm,
+                    eval_llm=llm_instance,
                     inputs=inputs,
                 ),
             )
             test_crew = self.copy()
-            evaluator = CrewEvaluator(test_crew, eval_llm)  # type: ignore[arg-type]
+            evaluator = CrewEvaluator(test_crew, llm_instance)
 
             for i in range(1, n_iterations + 1):
                 evaluator.set_iteration(i)

src/crewai/flow/state_utils.py

@@ -1,4 +1,5 @@
 import json
+import uuid
 from datetime import date, datetime
 from typing import Any, Dict, List, Union
 
@@ -32,7 +33,7 @@ def export_state(flow: Flow) -> dict[str, Serializable]:
 
 
 def to_serializable(
-    obj: Any, max_depth: int = 5, _current_depth: int = 0
+    obj: Any, exclude: set[str] | None = None, max_depth: int = 5, _current_depth: int = 0
 ) -> Serializable:
     """Converts a Python object into a JSON-compatible representation.
 
@@ -42,6 +43,7 @@ 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:
@@ -50,21 +52,39 @@ def to_serializable(
     if _current_depth >= max_depth:
         return repr(obj)
 
+    if exclude is None:
+        exclude = set()
+
     if isinstance(obj, (str, int, float, bool, type(None))):
         return obj
+    elif isinstance(obj, uuid.UUID):
+        return str(obj)
     elif isinstance(obj, (date, datetime)):
         return obj.isoformat()
     elif isinstance(obj, (list, tuple, set)):
-        return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
+        return [
+            to_serializable(
+                item, max_depth=max_depth, _current_depth=_current_depth + 1
+            )
+            for item in obj
+        ]
     elif isinstance(obj, dict):
         return {
             _to_serializable_key(key): to_serializable(
-                value, max_depth, _current_depth + 1
+                obj=value,
+                exclude=exclude,
+                max_depth=max_depth,
+                _current_depth=_current_depth + 1,
             )
             for key, value in obj.items()
+            if key not in exclude
         }
     elif isinstance(obj, BaseModel):
-        return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
+        return to_serializable(
+            obj=obj.model_dump(exclude=exclude),
+            max_depth=max_depth,
+            _current_depth=_current_depth + 1,
+        )
     else:
         return repr(obj)
 

src/crewai/knowledge/storage/knowledge_storage.py

@@ -14,6 +14,7 @@ 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
@@ -99,7 +100,8 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             )
             if self.app:
                 self.collection = self.app.get_or_create_collection(
-                    name=collection_name, embedding_function=self.embedder
+                    name=sanitize_collection_name(collection_name),
+                    embedding_function=self.embedder,
                 )
             else:
                 raise Exception("Vector Database Client not initialized")

src/crewai/llm.py

@@ -40,6 +40,7 @@ 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,
@@ -218,7 +219,7 @@ class StreamingChoices(TypedDict):
     finish_reason: Optional[str]
 
 
-class LLM:
+class LLM(BaseLLM):
     def __init__(
         self,
         model: str,

src/crewai/llms/base_llm.py

@@ -0,0 +1,91 @@
+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

@@ -0,0 +1,38 @@
+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

@@ -572,7 +572,15 @@ class Task(BaseModel):
     def copy(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Create a deep copy of the 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.
+        """
         exclude = {
             "id",
             "agent",
@@ -595,7 +603,7 @@ 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 = Task(
+        copied_task = self.__class__(
             **copied_data,
             context=cloned_context,
             agent=cloned_agent,

src/crewai/utilities/chromadb.py

@@ -0,0 +1,62 @@
+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 LLM
+from crewai.llm import BaseLLM
 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 (LLM): Language model instance to use for evaluations
+        eval_llm (BaseLLM): 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[LLM]):
+    def __init__(self, crew, eval_llm: InstanceOf[BaseLLM]):
         self.crew = crew
         self.llm = eval_llm
         self._telemetry = Telemetry()

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

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

src/crewai/utilities/llm_utils.py

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

tests/agent_test.py

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

tests/cassettes/test_before_kickoff_callback.yaml

@@ -710,4 +710,117 @@ interactions:
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     http_version: HTTP/1.1
     status_code: 200
+- request:
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+      "model": "gpt-4o", "stop": ["\nObservation:"]}'
+    headers:
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tests/cassettes/test_custom_llm_implementation.yaml

@@ -0,0 +1,107 @@
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+- request:
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tests/cassettes/test_custom_llm_within_crew.yaml

@@ -0,0 +1,305 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are a helpful assistant."},
+      {"role": "user", "content": [{"role": "system", "content": "You are Say Hi.
+      You just say hi to the user\nYour personal goal is: Say hi to the user\nTo give
+      my best complete final answer to the task respond using the exact following
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+      "content": "\nCurrent Task: Say hi to the user\n\nThis is the expected criteria
+      for your final answer: A greeting to the user\nyou MUST return the actual complete
+      content as the final answer, not a summary.\n\nBegin! This is VERY important
+      to you, use the tools available and give your best Final Answer, your job depends
+      on it!\n\nThought:"}]}], "model": "gpt-4o-mini", "tools": null}'
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tests/custom_llm_test.py

@@ -0,0 +1,359 @@
+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_string
+from crewai.flow.state_utils import export_state, to_serializable, to_string
 
 
 class Address(BaseModel):
@@ -148,3 +148,23 @@ 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

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

tests/utilities/test_chromadb_utils.py

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

uv.lock

@@ -1,42 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "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'",
-    "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 >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(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 platform_system != 'Linux' and sys_platform == 'darwin')",
-    "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')",
-    "python_full_version >= '3.12' and 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' 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 platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "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'",
-    "(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')",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(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 sys_platform != 'darwin' and sys_platform != 'linux')",
-    "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')",
+    "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')",
 ]
 
 [[package]]
@@ -66,7 +42,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.11"
+version = "3.10.10"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -75,56 +51,55 @@ dependencies = [
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
-    { name = "propcache" },
     { name = "yarl" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/17/7e/16e57e6cf20eb62481a2f9ce8674328407187950ccc602ad07c685279141/aiohttp-3.10.10.tar.gz", hash = "sha256:0631dd7c9f0822cc61c88586ca76d5b5ada26538097d0f1df510b082bad3411a", size = 7542993 }
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