Suppressed userWarnings from litellm pydantic issues (#1833)

* Suppressed userWarnings from litellm pydantic issues

* change litellm version

* Fix failling ollama tasks

README.md

@@ -85,7 +85,6 @@ First, install CrewAI:
 ```shell
 pip install crewai
 ```
-
 If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
 
 ```shell
@@ -93,6 +92,22 @@ pip install 'crewai[tools]'
 ```
 The command above installs the basic package and also adds extra components which require more dependencies to function.
 
+### Troubleshooting Dependencies
+
+If you encounter issues during installation or usage, here are some common solutions:
+
+#### Common Issues
+
+1. **ModuleNotFoundError: No module named 'tiktoken'**
+   - Install tiktoken explicitly: `pip install 'crewai[embeddings]'`
+   - If using embedchain or other tools: `pip install 'crewai[tools]'`
+
+2. **Failed building wheel for tiktoken**
+   - Ensure Rust compiler is installed (see installation steps above)
+   - For Windows: Verify Visual C++ Build Tools are installed
+   - Try upgrading pip: `pip install --upgrade pip`
+   - If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary`
+
 ### 2. Setting Up Your Crew with the YAML Configuration
 
 To create a new CrewAI project, run the following CLI (Command Line Interface) command:

pyproject.toml

@@ -8,27 +8,38 @@ authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
 dependencies = [
+    # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
+    "litellm>=1.56.4",
+    "instructor>=1.3.3",
+    
+    # Text Processing
+    "pdfplumber>=0.11.4",
+    "regex>=2024.9.11",
+    
+    # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
-    "instructor>=1.3.3",
-    "regex>=2024.9.11",
-    "click>=8.1.7",
+    
+    # Data Handling
+    "chromadb>=0.5.23",
+    "openpyxl>=3.1.5",
+    "pyvis>=0.3.2",
+    
+    # Authentication and Security
+    "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
+    
+    # Configuration and Utils
+    "click>=8.1.7",
     "appdirs>=1.4.4",
     "jsonref>=1.1.0",
     "json-repair>=0.25.2",
-    "auth0-python>=4.7.1",
-    "litellm>=1.44.22",
-    "pyvis>=0.3.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "chromadb>=0.5.23",
-    "pdfplumber>=0.11.4",
-    "openpyxl>=3.1.5",
     "blinker>=1.9.0",
 ]
 
@@ -39,6 +50,9 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = ["crewai-tools>=0.17.0"]
+embeddings = [
+    "tiktoken~=0.7.0"
+]
 agentops = ["agentops>=0.3.0"]
 fastembed = ["fastembed>=0.4.1"]
 pdfplumber = [

src/crewai/crew.py

@@ -726,11 +726,7 @@ class Crew(BaseModel):
 
             # Determine which tools to use - task tools take precedence over agent tools
             tools_for_task = task.tools or agent_to_use.tools or []
-            tools_for_task = self._prepare_tools(
-                agent_to_use,
-                task,
-                tools_for_task
-            )
+            tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
 
             self._log_task_start(task, agent_to_use.role)
 
@@ -797,14 +793,18 @@ class Crew(BaseModel):
             return skipped_task_output
         return None
 
-    def _prepare_tools(self, agent: BaseAgent, task: Task, tools: List[Tool]) -> List[Tool]:
+    def _prepare_tools(
+        self, agent: BaseAgent, task: Task, tools: List[Tool]
+    ) -> List[Tool]:
         # Add delegation tools if agent allows delegation
         if agent.allow_delegation:
             if self.process == Process.hierarchical:
                 if self.manager_agent:
                     tools = self._update_manager_tools(task, tools)
                 else:
-                    raise ValueError("Manager agent is required for hierarchical process.")
+                    raise ValueError(
+                        "Manager agent is required for hierarchical process."
+                    )
 
             elif agent and agent.allow_delegation:
                 tools = self._add_delegation_tools(task, tools)
@@ -823,7 +823,9 @@ class Crew(BaseModel):
             return self.manager_agent
         return task.agent
 
-    def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> List[Tool]:
+    def _merge_tools(
+        self, existing_tools: List[Tool], new_tools: List[Tool]
+    ) -> List[Tool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
             return existing_tools
@@ -839,7 +841,9 @@ class Crew(BaseModel):
 
         return tools
 
-    def _inject_delegation_tools(self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]):
+    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)
 
@@ -856,7 +860,9 @@ class Crew(BaseModel):
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
                 tools = []
-            tools = self._inject_delegation_tools(tools, task.agent, agents_for_delegation)
+            tools = self._inject_delegation_tools(
+                tools, task.agent, agents_for_delegation
+            )
         return tools
 
     def _log_task_start(self, task: Task, role: str = "None"):
@@ -870,7 +876,9 @@ class Crew(BaseModel):
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
             else:
-                tools = self._inject_delegation_tools(tools, self.manager_agent, self.agents)
+                tools = self._inject_delegation_tools(
+                    tools, self.manager_agent, self.agents
+                )
         return tools
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):

src/crewai/flow/flow.py

@@ -30,7 +30,47 @@ from crewai.telemetry import Telemetry
 T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
 
 
-def start(condition=None):
+def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
+    """
+    Marks a method as a flow's starting point.
+
+    This decorator designates a method as an entry point for the flow execution.
+    It can optionally specify conditions that trigger the start based on other
+    method executions.
+
+    Parameters
+    ----------
+    condition : Optional[Union[str, dict, Callable]], optional
+        Defines when the start method should execute. Can be:
+        - str: Name of a method that triggers this start
+        - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
+        - Callable: A method reference that triggers this start
+        Default is None, meaning unconditional start.
+
+    Returns
+    -------
+    Callable
+        A decorator function that marks the method as a flow start point.
+
+    Raises
+    ------
+    ValueError
+        If the condition format is invalid.
+
+    Examples
+    --------
+    >>> @start()  # Unconditional start
+    >>> def begin_flow(self):
+    ...     pass
+
+    >>> @start("method_name")  # Start after specific method
+    >>> def conditional_start(self):
+    ...     pass
+
+    >>> @start(and_("method1", "method2"))  # Start after multiple methods
+    >>> def complex_start(self):
+    ...     pass
+    """
     def decorator(func):
         func.__is_start_method__ = True
         if condition is not None:
@@ -55,8 +95,42 @@ def start(condition=None):
 
     return decorator
 
+def listen(condition: Union[str, dict, Callable]) -> Callable:
+    """
+    Creates a listener that executes when specified conditions are met.
 
-def listen(condition):
+    This decorator sets up a method to execute in response to other method
+    executions in the flow. It supports both simple and complex triggering
+    conditions.
+
+    Parameters
+    ----------
+    condition : Union[str, dict, Callable]
+        Specifies when the listener should execute. Can be:
+        - str: Name of a method that triggers this listener
+        - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
+        - Callable: A method reference that triggers this listener
+
+    Returns
+    -------
+    Callable
+        A decorator function that sets up the method as a listener.
+
+    Raises
+    ------
+    ValueError
+        If the condition format is invalid.
+
+    Examples
+    --------
+    >>> @listen("process_data")  # Listen to single method
+    >>> def handle_processed_data(self):
+    ...     pass
+
+    >>> @listen(or_("success", "failure"))  # Listen to multiple methods
+    >>> def handle_completion(self):
+    ...     pass
+    """
     def decorator(func):
         if isinstance(condition, str):
             func.__trigger_methods__ = [condition]
@@ -80,10 +154,49 @@ def listen(condition):
     return decorator
 
 
-def router(condition):
+def router(condition: Union[str, dict, Callable]) -> Callable:
+    """
+    Creates a routing method that directs flow execution based on conditions.
+
+    This decorator marks a method as a router, which can dynamically determine
+    the next steps in the flow based on its return value. Routers are triggered
+    by specified conditions and can return constants that determine which path
+    the flow should take.
+
+    Parameters
+    ----------
+    condition : Union[str, dict, Callable]
+        Specifies when the router should execute. Can be:
+        - str: Name of a method that triggers this router
+        - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
+        - Callable: A method reference that triggers this router
+
+    Returns
+    -------
+    Callable
+        A decorator function that sets up the method as a router.
+
+    Raises
+    ------
+    ValueError
+        If the condition format is invalid.
+
+    Examples
+    --------
+    >>> @router("check_status")
+    >>> def route_based_on_status(self):
+    ...     if self.state.status == "success":
+    ...         return SUCCESS
+    ...     return FAILURE
+
+    >>> @router(and_("validate", "process"))
+    >>> def complex_routing(self):
+    ...     if all([self.state.valid, self.state.processed]):
+    ...         return CONTINUE
+    ...     return STOP
+    """
     def decorator(func):
         func.__is_router__ = True
-        # Handle conditions like listen/start
         if isinstance(condition, str):
             func.__trigger_methods__ = [condition]
             func.__condition_type__ = "OR"
@@ -105,8 +218,39 @@ def router(condition):
 
     return decorator
 
+def or_(*conditions: Union[str, dict, Callable]) -> dict:
+    """
+    Combines multiple conditions with OR logic for flow control.
 
-def or_(*conditions):
+    Creates a condition that is satisfied when any of the specified conditions
+    are met. This is used with @start, @listen, or @router decorators to create
+    complex triggering conditions.
+
+    Parameters
+    ----------
+    *conditions : Union[str, dict, Callable]
+        Variable number of conditions that can be:
+        - str: Method names
+        - dict: Existing condition dictionaries
+        - Callable: Method references
+
+    Returns
+    -------
+    dict
+        A condition dictionary with format:
+        {"type": "OR", "methods": list_of_method_names}
+
+    Raises
+    ------
+    ValueError
+        If any condition is invalid.
+
+    Examples
+    --------
+    >>> @listen(or_("success", "timeout"))
+    >>> def handle_completion(self):
+    ...     pass
+    """
     methods = []
     for condition in conditions:
         if isinstance(condition, dict) and "methods" in condition:
@@ -120,7 +264,39 @@ def or_(*conditions):
     return {"type": "OR", "methods": methods}
 
 
-def and_(*conditions):
+def and_(*conditions: Union[str, dict, Callable]) -> dict:
+    """
+    Combines multiple conditions with AND logic for flow control.
+
+    Creates a condition that is satisfied only when all specified conditions
+    are met. This is used with @start, @listen, or @router decorators to create
+    complex triggering conditions.
+
+    Parameters
+    ----------
+    *conditions : Union[str, dict, Callable]
+        Variable number of conditions that can be:
+        - str: Method names
+        - dict: Existing condition dictionaries
+        - Callable: Method references
+
+    Returns
+    -------
+    dict
+        A condition dictionary with format:
+        {"type": "AND", "methods": list_of_method_names}
+
+    Raises
+    ------
+    ValueError
+        If any condition is invalid.
+
+    Examples
+    --------
+    >>> @listen(and_("validated", "processed"))
+    >>> def handle_complete_data(self):
+    ...     pass
+    """
     methods = []
     for condition in conditions:
         if isinstance(condition, dict) and "methods" in condition:
@@ -286,6 +462,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return final_output
 
     async def _execute_start_method(self, start_method_name: str) -> None:
+        """
+        Executes a flow's start method and its triggered listeners.
+
+        This internal method handles the execution of methods marked with @start
+        decorator and manages the subsequent chain of listener executions.
+
+        Parameters
+        ----------
+        start_method_name : str
+            The name of the start method to execute.
+
+        Notes
+        -----
+        - Executes the start method and captures its result
+        - Triggers execution of any listeners waiting on this start method
+        - Part of the flow's initialization sequence
+        """
         result = await self._execute_method(
             start_method_name, self._methods[start_method_name]
         )
@@ -306,6 +499,28 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
+        """
+        Executes all listeners and routers triggered by a method completion.
+
+        This internal method manages the execution flow by:
+        1. First executing all triggered routers sequentially
+        2. Then executing all triggered listeners in parallel
+
+        Parameters
+        ----------
+        trigger_method : str
+            The name of the method that triggered these listeners.
+        result : Any
+            The result from the triggering method, passed to listeners
+            that accept parameters.
+
+        Notes
+        -----
+        - Routers are executed sequentially to maintain flow control
+        - Each router's result becomes the new trigger_method
+        - Normal listeners are executed in parallel for efficiency
+        - Listeners can receive the trigger method's result as a parameter
+        """
         # First, handle routers repeatedly until no router triggers anymore
         while True:
             routers_triggered = self._find_triggered_methods(
@@ -335,6 +550,33 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
     ) -> List[str]:
+        """
+        Finds all methods that should be triggered based on conditions.
+
+        This internal method evaluates both OR and AND conditions to determine
+        which methods should be executed next in the flow.
+
+        Parameters
+        ----------
+        trigger_method : str
+            The name of the method that just completed execution.
+        router_only : bool
+            If True, only consider router methods.
+            If False, only consider non-router methods.
+
+        Returns
+        -------
+        List[str]
+            Names of methods that should be triggered.
+
+        Notes
+        -----
+        - Handles both OR and AND conditions:
+          * OR: Triggers if any condition is met
+          * AND: Triggers only when all conditions are met
+        - Maintains state for AND conditions using _pending_and_listeners
+        - Separates router and normal listener evaluation
+        """
         triggered = []
         for listener_name, (condition_type, methods) in self._listeners.items():
             is_router = listener_name in self._routers
@@ -363,6 +605,33 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return triggered
 
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
+        """
+        Executes a single listener method with proper event handling.
+
+        This internal method manages the execution of an individual listener,
+        including parameter inspection, event emission, and error handling.
+
+        Parameters
+        ----------
+        listener_name : str
+            The name of the listener method to execute.
+        result : Any
+            The result from the triggering method, which may be passed
+            to the listener if it accepts parameters.
+
+        Notes
+        -----
+        - Inspects method signature to determine if it accepts the trigger result
+        - Emits events for method execution start and finish
+        - Handles errors gracefully with detailed logging
+        - Recursively triggers listeners of this listener
+        - Supports both parameterized and parameter-less listeners
+
+        Error Handling
+        -------------
+        Catches and logs any exceptions during execution, preventing
+        individual listener failures from breaking the entire flow.
+        """
         try:
             method = self._methods[listener_name]
 

src/crewai/flow/flow_visualizer.py

@@ -1,12 +1,14 @@
 # flow_visualizer.py
 
 import os
+from pathlib import Path
 
 from pyvis.network import Network
 
 from crewai.flow.config import COLORS, NODE_STYLES
 from crewai.flow.html_template_handler import HTMLTemplateHandler
 from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items
+from crewai.flow.path_utils import safe_path_join, validate_path_exists
 from crewai.flow.utils import calculate_node_levels
 from crewai.flow.visualization_utils import (
     add_edges,
@@ -16,89 +18,209 @@ from crewai.flow.visualization_utils import (
 
 
 class FlowPlot:
+    """Handles the creation and rendering of flow visualization diagrams."""
+
     def __init__(self, flow):
+        """
+        Initialize FlowPlot with a flow object.
+
+        Parameters
+        ----------
+        flow : Flow
+            A Flow instance to visualize.
+
+        Raises
+        ------
+        ValueError
+            If flow object is invalid or missing required attributes.
+        """
+        if not hasattr(flow, '_methods'):
+            raise ValueError("Invalid flow object: missing '_methods' attribute")
+        if not hasattr(flow, '_listeners'):
+            raise ValueError("Invalid flow object: missing '_listeners' attribute")
+        if not hasattr(flow, '_start_methods'):
+            raise ValueError("Invalid flow object: missing '_start_methods' attribute")
+            
         self.flow = flow
         self.colors = COLORS
         self.node_styles = NODE_STYLES
 
     def plot(self, filename):
-        net = Network(
-            directed=True,
-            height="750px",
-            width="100%",
-            bgcolor=self.colors["bg"],
-            layout=None,
-        )
-
-        # Set options to disable physics
-        net.set_options(
-            """
-            var options = {
-                "nodes": {
-                    "font": {
-                        "multi": "html"
-                    }
-                },
-                "physics": {
-                    "enabled": false
-                }
-            }
         """
-        )
+        Generate and save an HTML visualization of the flow.
 
-        # Calculate levels for nodes
-        node_levels = calculate_node_levels(self.flow)
+        Parameters
+        ----------
+        filename : str
+            Name of the output file (without extension).
 
-        # Compute positions
-        node_positions = compute_positions(self.flow, node_levels)
+        Raises
+        ------
+        ValueError
+            If filename is invalid or network generation fails.
+        IOError
+            If file operations fail or visualization cannot be generated.
+        RuntimeError
+            If network visualization generation fails.
+        """
+        if not filename or not isinstance(filename, str):
+            raise ValueError("Filename must be a non-empty string")
+            
+        try:
+            # Initialize network
+            net = Network(
+                directed=True,
+                height="750px",
+                width="100%",
+                bgcolor=self.colors["bg"],
+                layout=None,
+            )
 
-        # Add nodes to the network
-        add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
+            # Set options to disable physics
+            net.set_options(
+                """
+                var options = {
+                    "nodes": {
+                        "font": {
+                            "multi": "html"
+                        }
+                    },
+                    "physics": {
+                        "enabled": false
+                    }
+                }
+            """
+            )
 
-        # Add edges to the network
-        add_edges(net, self.flow, node_positions, self.colors)
+            # Calculate levels for nodes
+            try:
+                node_levels = calculate_node_levels(self.flow)
+            except Exception as e:
+                raise ValueError(f"Failed to calculate node levels: {str(e)}")
 
-        network_html = net.generate_html()
-        final_html_content = self._generate_final_html(network_html)
+            # Compute positions
+            try:
+                node_positions = compute_positions(self.flow, node_levels)
+            except Exception as e:
+                raise ValueError(f"Failed to compute node positions: {str(e)}")
 
-        # Save the final HTML content to the file
-        with open(f"{filename}.html", "w", encoding="utf-8") as f:
-            f.write(final_html_content)
-        print(f"Plot saved as {filename}.html")
+            # Add nodes to the network
+            try:
+                add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
+            except Exception as e:
+                raise RuntimeError(f"Failed to add nodes to network: {str(e)}")
 
-        self._cleanup_pyvis_lib()
+            # Add edges to the network
+            try:
+                add_edges(net, self.flow, node_positions, self.colors)
+            except Exception as e:
+                raise RuntimeError(f"Failed to add edges to network: {str(e)}")
+
+            # Generate HTML
+            try:
+                network_html = net.generate_html()
+                final_html_content = self._generate_final_html(network_html)
+            except Exception as e:
+                raise RuntimeError(f"Failed to generate network visualization: {str(e)}")
+
+            # Save the final HTML content to the file
+            try:
+                with open(f"{filename}.html", "w", encoding="utf-8") as f:
+                    f.write(final_html_content)
+                print(f"Plot saved as {filename}.html")
+            except IOError as e:
+                raise IOError(f"Failed to save flow visualization to {filename}.html: {str(e)}")
+
+        except (ValueError, RuntimeError, IOError) as e:
+            raise e
+        except Exception as e:
+            raise RuntimeError(f"Unexpected error during flow visualization: {str(e)}")
+        finally:
+            self._cleanup_pyvis_lib()
 
     def _generate_final_html(self, network_html):
-        # Extract just the body content from the generated HTML
-        current_dir = os.path.dirname(__file__)
-        template_path = os.path.join(
-            current_dir, "assets", "crewai_flow_visual_template.html"
-        )
-        logo_path = os.path.join(current_dir, "assets", "crewai_logo.svg")
+        """
+        Generate the final HTML content with network visualization and legend.
 
-        html_handler = HTMLTemplateHandler(template_path, logo_path)
-        network_body = html_handler.extract_body_content(network_html)
+        Parameters
+        ----------
+        network_html : str
+            HTML content generated by pyvis Network.
 
-        # Generate the legend items HTML
-        legend_items = get_legend_items(self.colors)
-        legend_items_html = generate_legend_items_html(legend_items)
-        final_html_content = html_handler.generate_final_html(
-            network_body, legend_items_html
-        )
-        return final_html_content
+        Returns
+        -------
+        str
+            Complete HTML content with styling and legend.
+
+        Raises
+        ------
+        IOError
+            If template or logo files cannot be accessed.
+        ValueError
+            If network_html is invalid.
+        """
+        if not network_html:
+            raise ValueError("Invalid network HTML content")
+
+        try:
+            # Extract just the body content from the generated HTML
+            current_dir = os.path.dirname(__file__)
+            template_path = safe_path_join("assets", "crewai_flow_visual_template.html", root=current_dir)
+            logo_path = safe_path_join("assets", "crewai_logo.svg", root=current_dir)
+
+            if not os.path.exists(template_path):
+                raise IOError(f"Template file not found: {template_path}")
+            if not os.path.exists(logo_path):
+                raise IOError(f"Logo file not found: {logo_path}")
+
+            html_handler = HTMLTemplateHandler(template_path, logo_path)
+            network_body = html_handler.extract_body_content(network_html)
+
+            # Generate the legend items HTML
+            legend_items = get_legend_items(self.colors)
+            legend_items_html = generate_legend_items_html(legend_items)
+            final_html_content = html_handler.generate_final_html(
+                network_body, legend_items_html
+            )
+            return final_html_content
+        except Exception as e:
+            raise IOError(f"Failed to generate visualization HTML: {str(e)}")
 
     def _cleanup_pyvis_lib(self):
-        # Clean up the generated lib folder
-        lib_folder = os.path.join(os.getcwd(), "lib")
+        """
+        Clean up the generated lib folder from pyvis.
+
+        This method safely removes the temporary lib directory created by pyvis
+        during network visualization generation.
+        """
         try:
+            lib_folder = safe_path_join("lib", root=os.getcwd())
             if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
                 import shutil
-
                 shutil.rmtree(lib_folder)
+        except ValueError as e:
+            print(f"Error validating lib folder path: {e}")
         except Exception as e:
-            print(f"Error cleaning up {lib_folder}: {e}")
+            print(f"Error cleaning up lib folder: {e}")
 
 
 def plot_flow(flow, filename="flow_plot"):
+    """
+    Convenience function to create and save a flow visualization.
+
+    Parameters
+    ----------
+    flow : Flow
+        Flow instance to visualize.
+    filename : str, optional
+        Output filename without extension, by default "flow_plot".
+
+    Raises
+    ------
+    ValueError
+        If flow object or filename is invalid.
+    IOError
+        If file operations fail.
+    """
     visualizer = FlowPlot(flow)
     visualizer.plot(filename)

src/crewai/flow/html_template_handler.py

@@ -1,26 +1,53 @@
 import base64
 import re
+from pathlib import Path
+
+from crewai.flow.path_utils import safe_path_join, validate_path_exists
 
 
 class HTMLTemplateHandler:
+    """Handles HTML template processing and generation for flow visualization diagrams."""
+
     def __init__(self, template_path, logo_path):
-        self.template_path = template_path
-        self.logo_path = logo_path
+        """
+        Initialize HTMLTemplateHandler with validated template and logo paths.
+
+        Parameters
+        ----------
+        template_path : str
+            Path to the HTML template file.
+        logo_path : str
+            Path to the logo image file.
+
+        Raises
+        ------
+        ValueError
+            If template or logo paths are invalid or files don't exist.
+        """
+        try:
+            self.template_path = validate_path_exists(template_path, "file")
+            self.logo_path = validate_path_exists(logo_path, "file")
+        except ValueError as e:
+            raise ValueError(f"Invalid template or logo path: {e}")
 
     def read_template(self):
+        """Read and return the HTML template file contents."""
         with open(self.template_path, "r", encoding="utf-8") as f:
             return f.read()
 
     def encode_logo(self):
+        """Convert the logo SVG file to base64 encoded string."""
         with open(self.logo_path, "rb") as logo_file:
             logo_svg_data = logo_file.read()
             return base64.b64encode(logo_svg_data).decode("utf-8")
 
     def extract_body_content(self, html):
+        """Extract and return content between body tags from HTML string."""
         match = re.search("<body.*?>(.*?)</body>", html, re.DOTALL)
         return match.group(1) if match else ""
 
     def generate_legend_items_html(self, legend_items):
+        """Generate HTML markup for the legend items."""
         legend_items_html = ""
         for item in legend_items:
             if "border" in item:
@@ -48,6 +75,7 @@ class HTMLTemplateHandler:
         return legend_items_html
 
     def generate_final_html(self, network_body, legend_items_html, title="Flow Plot"):
+        """Combine all components into final HTML document with network visualization."""
         html_template = self.read_template()
         logo_svg_base64 = self.encode_logo()
 

src/crewai/flow/legend_generator.py

@@ -1,3 +1,4 @@
+
 def get_legend_items(colors):
     return [
         {"label": "Start Method", "color": colors["start"]},

src/crewai/flow/path_utils.py

@@ -0,0 +1,135 @@
+"""
+Path utilities for secure file operations in CrewAI flow module.
+
+This module provides utilities for secure path handling to prevent directory
+traversal attacks and ensure paths remain within allowed boundaries.
+"""
+
+import os
+from pathlib import Path
+from typing import List, Union
+
+
+def safe_path_join(*parts: str, root: Union[str, Path, None] = None) -> str:
+    """
+    Safely join path components and ensure the result is within allowed boundaries.
+
+    Parameters
+    ----------
+    *parts : str
+        Variable number of path components to join.
+    root : Union[str, Path, None], optional
+        Root directory to use as base. If None, uses current working directory.
+
+    Returns
+    -------
+    str
+        String representation of the resolved path.
+
+    Raises
+    ------
+    ValueError
+        If the resulting path would be outside the root directory
+        or if any path component is invalid.
+    """
+    if not parts:
+        raise ValueError("No path components provided")
+
+    try:
+        # Convert all parts to strings and clean them
+        clean_parts = [str(part).strip() for part in parts if part]
+        if not clean_parts:
+            raise ValueError("No valid path components provided")
+
+        # Establish root directory
+        root_path = Path(root).resolve() if root else Path.cwd()
+        
+        # Join and resolve the full path
+        full_path = Path(root_path, *clean_parts).resolve()
+        
+        # Check if the resolved path is within root
+        if not str(full_path).startswith(str(root_path)):
+            raise ValueError(
+                f"Invalid path: Potential directory traversal. Path must be within {root_path}"
+            )
+            
+        return str(full_path)
+        
+    except Exception as e:
+        if isinstance(e, ValueError):
+            raise
+        raise ValueError(f"Invalid path components: {str(e)}")
+
+
+def validate_path_exists(path: Union[str, Path], file_type: str = "file") -> str:
+    """
+    Validate that a path exists and is of the expected type.
+
+    Parameters
+    ----------
+    path : Union[str, Path]
+        Path to validate.
+    file_type : str, optional
+        Expected type ('file' or 'directory'), by default 'file'.
+
+    Returns
+    -------
+    str
+        Validated path as string.
+
+    Raises
+    ------
+    ValueError
+        If path doesn't exist or is not of expected type.
+    """
+    try:
+        path_obj = Path(path).resolve()
+        
+        if not path_obj.exists():
+            raise ValueError(f"Path does not exist: {path}")
+            
+        if file_type == "file" and not path_obj.is_file():
+            raise ValueError(f"Path is not a file: {path}")
+        elif file_type == "directory" and not path_obj.is_dir():
+            raise ValueError(f"Path is not a directory: {path}")
+            
+        return str(path_obj)
+        
+    except Exception as e:
+        if isinstance(e, ValueError):
+            raise
+        raise ValueError(f"Invalid path: {str(e)}")
+
+
+def list_files(directory: Union[str, Path], pattern: str = "*") -> List[str]:
+    """
+    Safely list files in a directory matching a pattern.
+
+    Parameters
+    ----------
+    directory : Union[str, Path]
+        Directory to search in.
+    pattern : str, optional
+        Glob pattern to match files against, by default "*".
+
+    Returns
+    -------
+    List[str]
+        List of matching file paths.
+
+    Raises
+    ------
+    ValueError
+        If directory is invalid or inaccessible.
+    """
+    try:
+        dir_path = Path(directory).resolve()
+        if not dir_path.is_dir():
+            raise ValueError(f"Not a directory: {directory}")
+            
+        return [str(p) for p in dir_path.glob(pattern) if p.is_file()]
+        
+    except Exception as e:
+        if isinstance(e, ValueError):
+            raise
+        raise ValueError(f"Error listing files: {str(e)}")

src/crewai/flow/utils.py

@@ -1,9 +1,25 @@
+"""
+Utility functions for flow visualization and dependency analysis.
+
+This module provides core functionality for analyzing and manipulating flow structures,
+including node level calculation, ancestor tracking, and return value analysis.
+Functions in this module are primarily used by the visualization system to create
+accurate and informative flow diagrams.
+
+Example
+-------
+>>> flow = Flow()
+>>> node_levels = calculate_node_levels(flow)
+>>> ancestors = build_ancestor_dict(flow)
+"""
+
 import ast
 import inspect
 import textwrap
+from typing import Any, Dict, List, Optional, Set, Union
 
 
-def get_possible_return_constants(function):
+def get_possible_return_constants(function: Any) -> Optional[List[str]]:
     try:
         source = inspect.getsource(function)
     except OSError:
@@ -77,11 +93,34 @@ def get_possible_return_constants(function):
     return list(return_values) if return_values else None
 
 
-def calculate_node_levels(flow):
-    levels = {}
-    queue = []
-    visited = set()
-    pending_and_listeners = {}
+def calculate_node_levels(flow: Any) -> Dict[str, int]:
+    """
+    Calculate the hierarchical level of each node in the flow.
+
+    Performs a breadth-first traversal of the flow graph to assign levels
+    to nodes, starting with start methods at level 0.
+
+    Parameters
+    ----------
+    flow : Any
+        The flow instance containing methods, listeners, and router configurations.
+
+    Returns
+    -------
+    Dict[str, int]
+        Dictionary mapping method names to their hierarchical levels.
+
+    Notes
+    -----
+    - Start methods are assigned level 0
+    - Each subsequent connected node is assigned level = parent_level + 1
+    - Handles both OR and AND conditions for listeners
+    - Processes router paths separately
+    """
+    levels: Dict[str, int] = {}
+    queue: List[str] = []
+    visited: Set[str] = set()
+    pending_and_listeners: Dict[str, Set[str]] = {}
 
     # Make all start methods at level 0
     for method_name, method in flow._methods.items():
@@ -140,7 +179,20 @@ def calculate_node_levels(flow):
     return levels
 
 
-def count_outgoing_edges(flow):
+def count_outgoing_edges(flow: Any) -> Dict[str, int]:
+    """
+    Count the number of outgoing edges for each method in the flow.
+
+    Parameters
+    ----------
+    flow : Any
+        The flow instance to analyze.
+
+    Returns
+    -------
+    Dict[str, int]
+        Dictionary mapping method names to their outgoing edge count.
+    """
     counts = {}
     for method_name in flow._methods:
         counts[method_name] = 0
@@ -152,16 +204,53 @@ def count_outgoing_edges(flow):
     return counts
 
 
-def build_ancestor_dict(flow):
-    ancestors = {node: set() for node in flow._methods}
-    visited = set()
+def build_ancestor_dict(flow: Any) -> Dict[str, Set[str]]:
+    """
+    Build a dictionary mapping each node to its ancestor nodes.
+
+    Parameters
+    ----------
+    flow : Any
+        The flow instance to analyze.
+
+    Returns
+    -------
+    Dict[str, Set[str]]
+        Dictionary mapping each node to a set of its ancestor nodes.
+    """
+    ancestors: Dict[str, Set[str]] = {node: set() for node in flow._methods}
+    visited: Set[str] = set()
     for node in flow._methods:
         if node not in visited:
             dfs_ancestors(node, ancestors, visited, flow)
     return ancestors
 
 
-def dfs_ancestors(node, ancestors, visited, flow):
+def dfs_ancestors(
+    node: str,
+    ancestors: Dict[str, Set[str]],
+    visited: Set[str],
+    flow: Any
+) -> None:
+    """
+    Perform depth-first search to build ancestor relationships.
+
+    Parameters
+    ----------
+    node : str
+        Current node being processed.
+    ancestors : Dict[str, Set[str]]
+        Dictionary tracking ancestor relationships.
+    visited : Set[str]
+        Set of already visited nodes.
+    flow : Any
+        The flow instance being analyzed.
+
+    Notes
+    -----
+    This function modifies the ancestors dictionary in-place to build
+    the complete ancestor graph.
+    """
     if node in visited:
         return
     visited.add(node)
@@ -185,12 +274,48 @@ def dfs_ancestors(node, ancestors, visited, flow):
                     dfs_ancestors(listener_name, ancestors, visited, flow)
 
 
-def is_ancestor(node, ancestor_candidate, ancestors):
+def is_ancestor(node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]) -> bool:
+    """
+    Check if one node is an ancestor of another.
+
+    Parameters
+    ----------
+    node : str
+        The node to check ancestors for.
+    ancestor_candidate : str
+        The potential ancestor node.
+    ancestors : Dict[str, Set[str]]
+        Dictionary containing ancestor relationships.
+
+    Returns
+    -------
+    bool
+        True if ancestor_candidate is an ancestor of node, False otherwise.
+    """
     return ancestor_candidate in ancestors.get(node, set())
 
 
-def build_parent_children_dict(flow):
-    parent_children = {}
+def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]:
+    """
+    Build a dictionary mapping parent nodes to their children.
+
+    Parameters
+    ----------
+    flow : Any
+        The flow instance to analyze.
+
+    Returns
+    -------
+    Dict[str, List[str]]
+        Dictionary mapping parent method names to lists of their child method names.
+
+    Notes
+    -----
+    - Maps listeners to their trigger methods
+    - Maps router methods to their paths and listeners
+    - Children lists are sorted for consistent ordering
+    """
+    parent_children: Dict[str, List[str]] = {}
 
     # Map listeners to their trigger methods
     for listener_name, (_, trigger_methods) in flow._listeners.items():
@@ -214,7 +339,24 @@ def build_parent_children_dict(flow):
     return parent_children
 
 
-def get_child_index(parent, child, parent_children):
+def get_child_index(parent: str, child: str, parent_children: Dict[str, List[str]]) -> int:
+    """
+    Get the index of a child node in its parent's sorted children list.
+
+    Parameters
+    ----------
+    parent : str
+        The parent node name.
+    child : str
+        The child node name to find the index for.
+    parent_children : Dict[str, List[str]]
+        Dictionary mapping parents to their children lists.
+
+    Returns
+    -------
+    int
+        Zero-based index of the child in its parent's sorted children list.
+    """
     children = parent_children.get(parent, [])
     children.sort()
     return children.index(child)

src/crewai/flow/visualization_utils.py

@@ -1,5 +1,23 @@
+"""
+Utilities for creating visual representations of flow structures.
+
+This module provides functions for generating network visualizations of flows,
+including node placement, edge creation, and visual styling. It handles the
+conversion of flow structures into visual network graphs with appropriate
+styling and layout.
+
+Example
+-------
+>>> flow = Flow()
+>>> net = Network(directed=True)
+>>> node_positions = compute_positions(flow, node_levels)
+>>> add_nodes_to_network(net, flow, node_positions, node_styles)
+>>> add_edges(net, flow, node_positions, colors)
+"""
+
 import ast
 import inspect
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from .utils import (
     build_ancestor_dict,
@@ -9,8 +27,25 @@ from .utils import (
 )
 
 
-def method_calls_crew(method):
-    """Check if the method calls `.crew()`."""
+def method_calls_crew(method: Any) -> bool:
+    """
+    Check if the method contains a call to `.crew()`.
+
+    Parameters
+    ----------
+    method : Any
+        The method to analyze for crew() calls.
+
+    Returns
+    -------
+    bool
+        True if the method calls .crew(), False otherwise.
+
+    Notes
+    -----
+    Uses AST analysis to detect method calls, specifically looking for
+    attribute access of 'crew'.
+    """
     try:
         source = inspect.getsource(method)
         source = inspect.cleandoc(source)
@@ -20,6 +55,7 @@ def method_calls_crew(method):
         return False
 
     class CrewCallVisitor(ast.NodeVisitor):
+        """AST visitor to detect .crew() method calls."""
         def __init__(self):
             self.found = False
 
@@ -34,7 +70,34 @@ def method_calls_crew(method):
     return visitor.found
 
 
-def add_nodes_to_network(net, flow, node_positions, node_styles):
+def add_nodes_to_network(
+    net: Any,
+    flow: Any,
+    node_positions: Dict[str, Tuple[float, float]],
+    node_styles: Dict[str, Dict[str, Any]]
+) -> None:
+    """
+    Add nodes to the network visualization with appropriate styling.
+
+    Parameters
+    ----------
+    net : Any
+        The pyvis Network instance to add nodes to.
+    flow : Any
+        The flow instance containing method information.
+    node_positions : Dict[str, Tuple[float, float]]
+        Dictionary mapping node names to their (x, y) positions.
+    node_styles : Dict[str, Dict[str, Any]]
+        Dictionary containing style configurations for different node types.
+
+    Notes
+    -----
+    Node types include:
+    - Start methods
+    - Router methods
+    - Crew methods
+    - Regular methods
+    """
     def human_friendly_label(method_name):
         return method_name.replace("_", " ").title()
 
@@ -73,9 +136,33 @@ def add_nodes_to_network(net, flow, node_positions, node_styles):
         )
 
 
-def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150):
-    level_nodes = {}
-    node_positions = {}
+def compute_positions(
+    flow: Any,
+    node_levels: Dict[str, int],
+    y_spacing: float = 150,
+    x_spacing: float = 150
+) -> Dict[str, Tuple[float, float]]:
+    """
+    Compute the (x, y) positions for each node in the flow graph.
+
+    Parameters
+    ----------
+    flow : Any
+        The flow instance to compute positions for.
+    node_levels : Dict[str, int]
+        Dictionary mapping node names to their hierarchical levels.
+    y_spacing : float, optional
+        Vertical spacing between levels, by default 150.
+    x_spacing : float, optional
+        Horizontal spacing between nodes, by default 150.
+
+    Returns
+    -------
+    Dict[str, Tuple[float, float]]
+        Dictionary mapping node names to their (x, y) coordinates.
+    """
+    level_nodes: Dict[int, List[str]] = {}
+    node_positions: Dict[str, Tuple[float, float]] = {}
 
     for method_name, level in node_levels.items():
         level_nodes.setdefault(level, []).append(method_name)
@@ -90,7 +177,33 @@ def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150):
     return node_positions
 
 
-def add_edges(net, flow, node_positions, colors):
+def add_edges(
+    net: Any,
+    flow: Any,
+    node_positions: Dict[str, Tuple[float, float]],
+    colors: Dict[str, str]
+) -> None:
+    edge_smooth: Dict[str, Union[str, float]] = {"type": "continuous"}  # Default value
+    """
+    Add edges to the network visualization with appropriate styling.
+
+    Parameters
+    ----------
+    net : Any
+        The pyvis Network instance to add edges to.
+    flow : Any
+        The flow instance containing edge information.
+    node_positions : Dict[str, Tuple[float, float]]
+        Dictionary mapping node names to their positions.
+    colors : Dict[str, str]
+        Dictionary mapping edge types to their colors.
+
+    Notes
+    -----
+    - Handles both normal listener edges and router edges
+    - Applies appropriate styling (color, dashes) based on edge type
+    - Adds curvature to edges when needed (cycles or multiple children)
+    """
     ancestors = build_ancestor_dict(flow)
     parent_children = build_parent_children_dict(flow)
 
@@ -126,7 +239,7 @@ def add_edges(net, flow, node_positions, colors):
                     else:
                         edge_smooth = {"type": "cubicBezier"}
                 else:
-                    edge_smooth = False
+                    edge_smooth.update({"type": "continuous"})
 
                 edge_style = {
                     "color": edge_color,
@@ -189,7 +302,7 @@ def add_edges(net, flow, node_positions, colors):
                             else:
                                 edge_smooth = {"type": "cubicBezier"}
                         else:
-                            edge_smooth = False
+                            edge_smooth.update({"type": "continuous"})
 
                         edge_style = {
                             "color": colors["router_edge"],

src/crewai/llm.py

@@ -6,8 +6,10 @@ import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
 
-import litellm
-from litellm import get_supported_openai_params
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", UserWarning)
+    import litellm
+    from litellm import get_supported_openai_params
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
@@ -138,7 +140,7 @@ class LLM:
         self.kwargs = kwargs
 
         litellm.drop_params = True
-        litellm.set_verbose = False
+
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Optional, Union
 
 from pydantic import Field
@@ -7,6 +8,8 @@ from crewai.task import Task
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
+logger = logging.getLogger(__name__)
+
 
 class BaseAgentTool(BaseTool):
     """Base class for agent-related tools"""
@@ -16,6 +19,25 @@ class BaseAgentTool(BaseTool):
         default_factory=I18N, description="Internationalization settings"
     )
 
+    def sanitize_agent_name(self, name: str) -> str:
+        """
+        Sanitize agent role name by normalizing whitespace and setting to lowercase.
+        Converts all whitespace (including newlines) to single spaces and removes quotes.
+
+        Args:
+            name (str): The agent role name to sanitize
+
+        Returns:
+            str: The sanitized agent role name, with whitespace normalized,
+                 converted to lowercase, and quotes removed
+        """
+        if not name:
+            return ""
+        # Normalize all whitespace (including newlines) to single spaces
+        normalized = " ".join(name.split())
+        # Remove quotes and convert to lowercase
+        return normalized.replace('"', "").casefold()
+
     def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]:
         coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
         if coworker:
@@ -25,11 +47,27 @@ class BaseAgentTool(BaseTool):
         return coworker
 
     def _execute(
-        self, agent_name: Union[str, None], task: str, context: Union[str, None]
+        self,
+        agent_name: Optional[str],
+        task: str,
+        context: Optional[str] = None
     ) -> str:
+        """
+        Execute delegation to an agent with case-insensitive and whitespace-tolerant matching.
+        
+        Args:
+            agent_name: Name/role of the agent to delegate to (case-insensitive)
+            task: The specific question or task to delegate
+            context: Optional additional context for the task execution
+            
+        Returns:
+            str: The execution result from the delegated agent or an error message
+                 if the agent cannot be found
+        """
         try:
             if agent_name is None:
                 agent_name = ""
+                logger.debug("No agent name provided, using empty string")
 
             # It is important to remove the quotes from the agent name.
             # The reason we have to do this is because less-powerful LLM's
@@ -38,31 +76,49 @@ class BaseAgentTool(BaseTool):
             # {"task": "....", "coworker": "....
             # when it should look like this:
             # {"task": "....", "coworker": "...."}
-            agent_name = agent_name.casefold().replace('"', "").replace("\n", "")
+            sanitized_name = self.sanitize_agent_name(agent_name)
+            logger.debug(f"Sanitized agent name from '{agent_name}' to '{sanitized_name}'")
+
+            available_agents = [agent.role for agent in self.agents]
+            logger.debug(f"Available agents: {available_agents}")
+
             agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
                 available_agent
                 for available_agent in self.agents
-                if available_agent.role.casefold().replace("\n", "") == agent_name
+                if self.sanitize_agent_name(available_agent.role) == sanitized_name
             ]
-        except Exception as _:
+            logger.debug(f"Found {len(agent)} matching agents for role '{sanitized_name}'")
+        except (AttributeError, ValueError) as e:
+            # Handle specific exceptions that might occur during role name processing
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
-                    [f"- {agent.role.casefold()}" for agent in self.agents]
-                )
+                    [f"- {self.sanitize_agent_name(agent.role)}" for agent in self.agents]
+                ),
+                error=str(e)
             )
 
         if not agent:
+            # No matching agent found after sanitization
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
-                    [f"- {agent.role.casefold()}" for agent in self.agents]
-                )
+                    [f"- {self.sanitize_agent_name(agent.role)}" for agent in self.agents]
+                ),
+                error=f"No agent found with role '{sanitized_name}'"
             )
 
         agent = agent[0]
-        task_with_assigned_agent = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
-            description=task,
-            agent=agent,
-            expected_output=agent.i18n.slice("manager_request"),
-            i18n=agent.i18n,
-        )
-        return agent.execute_task(task_with_assigned_agent, context)
+        try:
+            task_with_assigned_agent = Task(
+                description=task,
+                agent=agent,
+                expected_output=agent.i18n.slice("manager_request"),
+                i18n=agent.i18n,
+            )
+            logger.debug(f"Created task for agent '{self.sanitize_agent_name(agent.role)}': {task}")
+            return agent.execute_task(task_with_assigned_agent, context)
+        except Exception as e:
+            # Handle task creation or execution errors
+            return self.i18n.errors("agent_tool_execution_error").format(
+                agent_role=self.sanitize_agent_name(agent.role),
+                error=str(e)
+            )

src/crewai/translations/en.json

@@ -33,7 +33,8 @@
     "tool_usage_error": "I encountered an error: {error}",
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
     "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
-    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}"
+    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
+    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}"
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",

src/crewai/utilities/internal_instructor.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import Any, Optional, Type
 
 
@@ -25,9 +26,10 @@ class InternalInstructor:
         if self.agent and not self.llm:
             self.llm = self.agent.function_calling_llm or self.agent.llm
 
-        # Lazy import
-        import instructor
-        from litellm import completion
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            import instructor
+            from litellm import completion
 
         self._client = instructor.from_litellm(
             completion,

src/crewai/utilities/planning_handler.py

@@ -1,3 +1,5 @@
+import json
+import logging
 from typing import Any, List, Optional
 
 from pydantic import BaseModel, Field
@@ -5,6 +7,8 @@ from pydantic import BaseModel, Field
 from crewai.agent import Agent
 from crewai.task import Task
 
+logger = logging.getLogger(__name__)
+
 
 class PlanPerTask(BaseModel):
     task: str = Field(..., description="The task for which the plan is created")
@@ -68,19 +72,39 @@ class CrewPlanner:
             output_pydantic=PlannerTaskPydanticOutput,
         )
 
+    def _get_agent_knowledge(self, task: Task) -> List[str]:
+        """
+        Safely retrieve knowledge source content from the task's agent.
+        
+        Args:
+            task: The task containing an agent with potential knowledge sources
+            
+        Returns:
+            List[str]: A list of knowledge source strings
+        """
+        try:
+            if task.agent and task.agent.knowledge_sources:
+                return [source.content for source in task.agent.knowledge_sources]
+        except AttributeError:
+            logger.warning("Error accessing agent knowledge sources")
+        return []
+
     def _create_tasks_summary(self) -> str:
         """Creates a summary of all tasks."""
         tasks_summary = []
         for idx, task in enumerate(self.tasks):
-            tasks_summary.append(
-                f"""
+            knowledge_list = self._get_agent_knowledge(task)
+            task_summary = f"""
                 Task Number {idx + 1} - {task.description}
                 "task_description": {task.description}
                 "task_expected_output": {task.expected_output}
                 "agent": {task.agent.role if task.agent else "None"}
                 "agent_goal": {task.agent.goal if task.agent else "None"}
                 "task_tools": {task.tools}
-                "agent_tools": {task.agent.tools if task.agent else "None"}
-                """
-            )
+                "agent_tools": %s%s""" % (
+                    f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
+                    f',\n                "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
+                )
+            
+            tasks_summary.append(task_summary)
         return " ".join(tasks_summary)

src/crewai/utilities/token_counter_callback.py

@@ -1,3 +1,5 @@
+import warnings
+
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
 
@@ -12,11 +14,13 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
-        usage: Usage = response_obj["usage"]
-        self.token_cost_process.sum_successful_requests(1)
-        self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
-        if usage.prompt_tokens_details:
-            self.token_cost_process.sum_cached_prompt_tokens(
-                usage.prompt_tokens_details.cached_tokens
-            )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            usage: Usage = response_obj["usage"]
+            self.token_cost_process.sum_successful_requests(1)
+            self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
+            self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+            if usage.prompt_tokens_details:
+                self.token_cost_process.sum_cached_prompt_tokens(
+                    usage.prompt_tokens_details.cached_tokens
+                )

tests/agent_test.py

@@ -1445,34 +1445,31 @@ def test_llm_call_with_all_attributes():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_ollama_gemma():
+def test_agent_with_ollama_llama3():
     agent = Agent(
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(
-            model="ollama/gemma2:latest",
-            base_url="http://localhost:8080",
-        ),
+        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
     )
 
     assert isinstance(agent.llm, LLM)
-    assert agent.llm.model == "ollama/gemma2:latest"
-    assert agent.llm.base_url == "http://localhost:8080"
+    assert agent.llm.model == "ollama/llama3.2:3b"
+    assert agent.llm.base_url == "http://localhost:11434"
 
     task = "Respond in 20 words. Who are you?"
     response = agent.llm.call([{"role": "user", "content": task}])
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Gemma" in response or "AI" in response or "language model" in response
+    assert "Llama3" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_ollama_gemma():
+def test_llm_call_with_ollama_llama3():
     llm = LLM(
-        model="ollama/gemma2:latest",
-        base_url="http://localhost:8080",
+        model="ollama/llama3.2:3b",
+        base_url="http://localhost:11434",
         temperature=0.7,
         max_tokens=30,
     )
@@ -1482,7 +1479,7 @@ def test_llm_call_with_ollama_gemma():
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Gemma" in response or "AI" in response or "language model" in response
+    assert "Llama3" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1578,7 +1575,7 @@ def test_agent_execute_task_with_ollama():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
+        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
     )
 
     task = Task(

tests/cassettes/test_agent_execute_task_with_ollama.yaml

@@ -1,42 +1,6 @@
 interactions:
 - request:
-    body: !!binary |
-      CrcCCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSjgIKEgoQY3Jld2FpLnRl
-      bGVtZXRyeRJoChA/Q8UW5bidCRtKvri5fOaNEgh5qLzvLvZJkioQVG9vbCBVc2FnZSBFcnJvcjAB
-      OYjFVQr1TPgXQXCXhwr1TPgXShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuNjEuMHoCGAGFAQABAAAS
-      jQEKEChQTWQ07t26ELkZmP5RresSCHEivRGBpsP7KgpUb29sIFVzYWdlMAE5sKkbC/VM+BdB8MIc
-      C/VM+BdKGgoOY3Jld2FpX3ZlcnNpb24SCAoGMC42MS4wShkKCXRvb2xfbmFtZRIMCgpkdW1teV90
-      b29sSg4KCGF0dGVtcHRzEgIYAXoCGAGFAQABAAA=
-    headers:
-      Accept:
-      - '*/*'
-      Accept-Encoding:
-      - gzip, deflate
-      Connection:
-      - keep-alive
-      Content-Length:
-      - '314'
-      Content-Type:
-      - application/x-protobuf
-      User-Agent:
-      - OTel-OTLP-Exporter-Python/1.27.0
-    method: POST
-    uri: https://telemetry.crewai.com:4319/v1/traces
-  response:
-    body:
-      string: "\n\0"
-    headers:
-      Content-Length:
-      - '2'
-      Content-Type:
-      - application/x-protobuf
-      Date:
-      - Tue, 24 Sep 2024 21:57:54 GMT
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"model": "gemma2:latest", "prompt": "### System:\nYou are test role. test
+    body: '{"model": "llama3.2:3b", "prompt": "### System:\nYou are test role. test
       backstory\nYour personal goal is: test goal\nTo give my best complete final
       answer to the task use the exact following format:\n\nThought: I now can give
       a great answer\nFinal Answer: Your final answer must be the great and the most
@@ -46,36 +10,864 @@ interactions:
       explanation of AI\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:\n\n",
-      "options": {}, "stream": false}'
+      "options": {"stop": ["\nObservation:"]}, "stream": false}'
     headers:
-      Accept:
+      accept:
       - '*/*'
-      Accept-Encoding:
+      accept-encoding:
       - gzip, deflate
-      Connection:
+      connection:
       - keep-alive
-      Content-Length:
-      - '815'
-      Content-Type:
-      - application/json
-      User-Agent:
-      - python-requests/2.31.0
+      content-length:
+      - '839'
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
     method: POST
-    uri: http://localhost:8080/api/generate
+    uri: http://localhost:11434/api/generate
   response:
-    body:
-      string: '{"model":"gemma2:latest","created_at":"2024-09-24T21:57:55.835715Z","response":"Thought:
-        I can explain AI in one sentence. \n\nFinal Answer: Artificial intelligence
-        (AI) is the ability of computer systems to perform tasks that typically require
-        human intelligence, such as learning, problem-solving, and decision-making.  \n","done":true,"done_reason":"stop","context":[106,1645,108,6176,1479,235292,108,2045,708,2121,4731,235265,2121,135147,108,6922,3749,6789,603,235292,2121,6789,108,1469,2734,970,1963,3407,2048,3448,577,573,6911,1281,573,5463,2412,5920,235292,109,65366,235292,590,1490,798,2734,476,1775,3448,108,11263,10358,235292,3883,2048,3448,2004,614,573,1775,578,573,1546,3407,685,3077,235269,665,2004,614,17526,6547,235265,109,235285,44472,1281,1450,32808,235269,970,3356,12014,611,665,235341,109,6176,4926,235292,109,6846,12297,235292,36576,1212,16481,603,575,974,13060,109,1596,603,573,5246,12830,604,861,2048,3448,235292,586,974,235290,47366,15844,576,16481,108,4747,44472,2203,573,5579,3407,3381,685,573,2048,3448,235269,780,476,13367,235265,109,12694,235341,1417,603,50471,2845,577,692,235269,1281,573,8112,2506,578,2734,861,1963,14124,10358,235269,861,3356,12014,611,665,235341,109,65366,235292,109,107,108,106,2516,108,65366,235292,590,798,10200,16481,575,974,13060,235265,235248,109,11263,10358,235292,42456,17273,591,11716,235275,603,573,7374,576,6875,5188,577,3114,13333,674,15976,2817,3515,17273,235269,1582,685,6044,235269,3210,235290,60495,235269,578,4530,235290,14577,235265,139,108],"total_duration":3370959792,"load_duration":20611750,"prompt_eval_count":173,"prompt_eval_duration":688036000,"eval_count":51,"eval_duration":2660291000}'
+    content: '{"model":"llama3.2:3b","created_at":"2024-12-31T16:56:15.759718Z","response":"Final
+      Answer: Artificial Intelligence (AI) refers to the development of computer systems
+      able to perform tasks that typically require human intelligence, including learning,
+      problem-solving, decision-making, and perception.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,2675,527,1296,3560,13,1296,93371,198,7927,4443,5915,374,25,1296,5915,198,1271,3041,856,1888,4686,1620,4320,311,279,3465,1005,279,4839,2768,3645,1473,85269,25,358,1457,649,3041,264,2294,4320,198,19918,22559,25,4718,1620,4320,2011,387,279,2294,323,279,1455,4686,439,3284,11,433,2011,387,15632,7633,382,40,28832,1005,1521,20447,11,856,2683,14117,389,433,2268,14711,2724,1473,5520,5546,25,83017,1148,15592,374,304,832,11914,271,2028,374,279,1755,13186,369,701,1620,4320,25,362,832,1355,18886,16540,315,15592,198,9514,28832,471,279,5150,4686,2262,439,279,1620,4320,11,539,264,12399,382,11382,0,1115,374,48174,3062,311,499,11,1005,279,7526,2561,323,3041,701,1888,13321,22559,11,701,2683,14117,389,433,2268,85269,1473,128009,128006,78191,128007,271,19918,22559,25,59294,22107,320,15836,8,19813,311,279,4500,315,6500,6067,3025,311,2804,9256,430,11383,1397,3823,11478,11,2737,6975,11,3575,99246,11,5597,28846,11,323,21063,13],"total_duration":1156303250,"load_duration":35999125,"prompt_eval_count":181,"prompt_eval_duration":408000000,"eval_count":38,"eval_duration":711000000}'
     headers:
       Content-Length:
-      - '1662'
+      - '1528'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Tue, 24 Sep 2024 21:57:55 GMT
-    status:
-      code: 200
-      message: OK
+      - Tue, 31 Dec 2024 16:56:15 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '23'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
+      Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms
+      and conditions for use, reproduction, distribution \\nand modification of the
+      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
+      manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D
+      or \u201Cyou\u201D means you, or your employer or any other person or entity
+      (if you are \\nentering into this Agreement on such person or entity\u2019s
+      behalf), of the age required under\\napplicable laws, rules or regulations to
+      provide legal consent and that has legal authority\\nto bind your employer or
+      such other person or entity if you are entering in this Agreement\\non their
+      behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models
+      and software and algorithms, including\\nmachine-learning model code, trained
+      model weights, inference-enabling code, training-enabling code,\\nfine-tuning
+      enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama
+      Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation
+      (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
+      or, \\nif you are an entity, your principal place of business is in the EEA
+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
+      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
+      a related website, user interface, blogpost, about page, or product documentation.
+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
+      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
+      monthly active users\\nof the products or services made available by or for
+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
+      you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+      \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
+      brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+      of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
+      to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
+      The term of this Agreement will commence upon your acceptance of this Agreement
+      or access\\nto the Llama Materials and will continue in full force and effect
+      until terminated in accordance with the terms\\nand conditions herein. Meta
+      may terminate this Agreement if you are in breach of any term or condition of
+      this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
+      use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination
+      of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will
+      be governed and construed under the laws of the State of \\nCalifornia without
+      regard to choice of law principles, and the UN Convention on Contracts for the
+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed
+      to promoting safe and fair use of its tools and features, including Llama 3.2.
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-    headers:
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-      - '579'
-      Content-Type:
-      - application/json; charset=utf-8
-      Date:
-      - Tue, 24 Sep 2024 21:57:51 GMT
-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_agent_with_ollama_llama3.yaml

@@ -0,0 +1,863 @@
+interactions:
+- request:
+    body: '{"model": "llama3.2:3b", "prompt": "### User:\nRespond in 20 words. Who
+      are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream": false}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '144'
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/generate
+  response:
+    content: '{"model":"llama3.2:3b","created_at":"2024-12-31T16:57:54.063894Z","response":"I''m
+      an AI designed to provide information and assist with inquiries, while maintaining
+      a neutral and respectful tone always.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,2724,512,66454,304,220,508,4339,13,10699,527,499,1980,128009,128006,78191,128007,271,40,2846,459,15592,6319,311,3493,2038,323,7945,449,44983,11,1418,20958,264,21277,323,49150,16630,2744,13],"total_duration":651386042,"load_duration":41061917,"prompt_eval_count":38,"prompt_eval_duration":204000000,"eval_count":23,"eval_duration":405000000}'
+    headers:
+      Content-Length:
+      - '692'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Tue, 31 Dec 2024 16:57:54 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '23'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
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+      and conditions for use, reproduction, distribution \\nand modification of the
+      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
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+      or \u201Cyou\u201D means you, or your employer or any other person or entity
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+      (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
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+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
+      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
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+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
+      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
+      monthly active users\\nof the products or services made available by or for
+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
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+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
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+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
+      you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+      \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
+      brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+      of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
+      to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
+      The term of this Agreement will commence upon your acceptance of this Agreement
+      or access\\nto the Llama Materials and will continue in full force and effect
+      until terminated in accordance with the terms\\nand conditions herein. Meta
+      may terminate this Agreement if you are in breach of any term or condition of
+      this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
+      use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination
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+      regard to choice of law principles, and the UN Convention on Contracts for the
+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed
+      to promoting safe and fair use of its tools and features, including Llama 3.2.
+      If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D).
+      The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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+      you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate
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+      contribute to, encourage, plan, incite, or further illegal or unlawful activity
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+      \       3. Human trafficking, exploitation, and sexual violence\\n        4.
+      The illegal distribution of information or materials to minors, including obscene
+      materials, or failure to employ legally required age-gating in connection with
+      such information or materials.\\n        5. Sexual solicitation\\n        6.
+      Any other criminal activity\\n    1. Engage in, promote, incite, or facilitate
+      the harassment, abuse, threatening, or bullying of individuals or groups of
+      individuals\\n    2. Engage in, promote, incite, or facilitate discrimination
+      or other unlawful or harmful conduct in the provision of employment, employment
+      benefits, credit, housing, other economic benefits, or other essential goods
+      and services\\n    3. Engage in the unauthorized or unlicensed practice of any
+      profession including, but not limited to, financial, legal, medical/health,
+      or related professional practices\\n    4. Collect, process, disclose, generate,
+      or infer private or sensitive information about individuals, including information
+      about individuals\u2019 identity, health, or demographic information, unless
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+      Engage in or facilitate any action or generate any content that infringes, misappropriates,
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+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
+      or remove usage restrictions or other safety measures, or to enable functionality
+      disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the
+      planning or development of activities that present a risk of death or bodily
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+      end users any known dangers of your AI system\\n5. Interact with third party
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+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+      Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
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+      Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
+      3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama
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+      FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
+      \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
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+      if .Tools }}When you receive a tool call response, use the output to format
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+      end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{-
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+      \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date:
+      September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
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+      or \u201Cyou\u201D means you, or your employer or any other person or entity
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+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
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+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
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+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
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+      \       iii. You must retain in all copies of the Llama Materials that you distribute
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+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
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+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
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+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
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+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
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+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
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+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
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+      September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
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+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
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+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
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+      \       iv. Your use of the Llama Materials must comply with applicable laws
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+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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+    headers:
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tests/cassettes/test_llm_call_with_ollama_gemma.yaml

@@ -1,35 +0,0 @@
-interactions:
-- request:
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-    uri: http://localhost:8080/api/generate
-  response:
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tests/cassettes/test_llm_call_with_ollama_llama3.yaml

@@ -0,0 +1,449 @@
+interactions:
+- request:
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+    headers:
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+    method: POST
+    uri: http://localhost:11434/api/generate
+  response:
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+    http_version: HTTP/1.1
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+      \   a. No trademark licenses are granted under this Agreement, and in connection
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+      The illegal distribution of information or materials to minors, including obscene
+      materials, or failure to employ legally required age-gating in connection with
+      such information or materials.\\n        5. Sexual solicitation\\n        6.
+      Any other criminal activity\\n    1. Engage in, promote, incite, or facilitate
+      the harassment, abuse, threatening, or bullying of individuals or groups of
+      individuals\\n    2. Engage in, promote, incite, or facilitate discrimination
+      or other unlawful or harmful conduct in the provision of employment, employment
+      benefits, credit, housing, other economic benefits, or other essential goods
+      and services\\n    3. Engage in the unauthorized or unlicensed practice of any
+      profession including, but not limited to, financial, legal, medical/health,
+      or related professional practices\\n    4. Collect, process, disclose, generate,
+      or infer private or sensitive information about individuals, including information
+      about individuals\u2019 identity, health, or demographic information, unless
+      you have obtained the right to do so in accordance with applicable law\\n    5.
+      Engage in or facilitate any action or generate any content that infringes, misappropriates,
+      or otherwise violates any third-party rights, including the outputs or results
+      of any products or services using the Llama Materials\\n    6. Create, generate,
+      or facilitate the creation of malicious code, malware, computer viruses or do
+      anything else that could disable, overburden, interfere with or impair the proper
+      working, integrity, operation or appearance of a website or computer system\\n
+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
+      or remove usage restrictions or other safety measures, or to enable functionality
+      disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the
+      planning or development of activities that present a risk of death or bodily
+      harm to individuals, including use of Llama 3.2 related to the following:\\n
+      \   8. Military, warfare, nuclear industries or applications, espionage, use
+      for materials or activities that are subject to the International Traffic Arms
+      Regulations (ITAR) maintained by the United States Department of State or to
+      the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons
+      Convention Implementation Act of 1997\\n    9. Guns and illegal weapons (including
+      weapon development)\\n    10. Illegal drugs and regulated/controlled substances\\n
+      \   11. Operation of critical infrastructure, transportation technologies, or
+      heavy machinery\\n    12. Self-harm or harm to others, including suicide, cutting,
+      and eating disorders\\n    13. Any content intended to incite or promote violence,
+      abuse, or any infliction of bodily harm to an individual\\n3. Intentionally
+      deceive or mislead others, including use of Llama 3.2 related to the following:\\n
+      \   14. Generating, promoting, or furthering fraud or the creation or promotion
+      of disinformation\\n    15. Generating, promoting, or furthering defamatory
+      content, including the creation of defamatory statements, images, or other content\\n
+      \   16. Generating, promoting, or further distributing spam\\n    17. Impersonating
+      another individual without consent, authorization, or legal right\\n    18.
+      Representing that the use of Llama 3.2 or outputs are human-generated\\n    19.
+      Generating or facilitating false online engagement, including fake reviews and
+      other means of fake online engagement\\n4. Fail to appropriately disclose to
+      end users any known dangers of your AI system\\n5. Interact with third party
+      tools, models, or software designed to generate unlawful content or engage in
+      unlawful or harmful conduct and/or represent that the outputs of such tools,
+      models, or software are associated with Meta or Llama 3.2\\n\\nWith respect
+      to any multimodal models included in Llama 3.2, the rights granted under Section
+      1(a) of the Llama 3.2 Community License Agreement are not being granted to you
+      if you are an individual domiciled in, or a company with a principal place of
+      business in, the European Union. This restriction does not apply to end users
+      of a product or service that incorporates any such multimodal models.\\n\\nPlease
+      report any violation of this Policy, software \u201Cbug,\u201D or other problems
+      that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+      Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
+      Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
+      Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
+      3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop                           \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop
+      \                          \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop                           \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
+      Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{-
+      if .Tools }}When you receive a tool call response, use the output to format
+      an answer to the orginal user question.\\n\\nYou are a helpful assistant with
+      tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i,
+      $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{-
+      if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{-
+      if and $.Tools $last }}\\n\\nGiven the following functions, please respond with
+      a JSON for a function call with its proper arguments that best answers the given
+      prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\":
+      dictionary of argument name and its value}. Do not use variables.\\n\\n{{ range
+      $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{-
+      else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
+      end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{-
+      if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name
+      }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{
+      .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{-
+      else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{
+      .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
+      end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2024-12-31T11:53:14.529771974-05:00\"}"
+    headers:
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Tue, 31 Dec 2024 17:00:06 GMT
+      Transfer-Encoding:
+      - chunked
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/crew_test.py

@@ -391,6 +391,71 @@ def test_manager_agent_delegating_to_all_agents():
     )
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_manager_agent_delegates_with_varied_role_cases():
+    """
+    Test that the manager agent can delegate to agents regardless of case or whitespace variations in role names.
+    This test verifies the fix for issue #1503 where role matching was too strict.
+    """
+    # Create agents with varied case and whitespace in roles
+    researcher_spaced = Agent(
+        role=" Researcher ",  # Extra spaces
+        goal="Research with spaces in role",
+        backstory="A researcher with spaces in role name",
+        allow_delegation=False,
+    )
+    
+    writer_caps = Agent(
+        role="SENIOR WRITER",  # All caps
+        goal="Write with caps in role",
+        backstory="A writer with caps in role name",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Research and write about AI. The researcher should do the research, and the writer should write it up.",
+        expected_output="A well-researched article about AI.",
+        agent=researcher_spaced,  # Assign to researcher with spaces
+    )
+
+    crew = Crew(
+        agents=[researcher_spaced, writer_caps],
+        process=Process.hierarchical,
+        manager_llm="gpt-4o",
+        tasks=[task],
+    )
+
+    mock_task_output = TaskOutput(
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
+    )
+    task.output = mock_task_output
+
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
+        crew.kickoff()
+
+        # Verify execute_sync was called once
+        mock_execute_sync.assert_called_once()
+
+        # Get the tools argument from the call
+        _, kwargs = mock_execute_sync.call_args
+        tools = kwargs['tools']
+
+        # Verify the delegation tools were passed correctly and can handle case/whitespace variations
+        assert len(tools) == 2
+        
+        # Check delegation tool descriptions (should work despite case/whitespace differences)
+        delegation_tool = tools[0]
+        question_tool = tools[1]
+        
+        assert "Delegate a specific task to one of the following coworkers:" in delegation_tool.description
+        assert " Researcher " in delegation_tool.description or "SENIOR WRITER" in delegation_tool.description
+        
+        assert "Ask a specific question to one of the following coworkers:" in question_tool.description
+        assert " Researcher " in question_tool.description or "SENIOR WRITER" in question_tool.description
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents():
     tasks = [

tests/test_manager_llm_delegation.py

@@ -0,0 +1,55 @@
+from unittest.mock import MagicMock
+
+import pytest
+
+from crewai import Agent, Task
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
+
+class TestAgentTool(BaseAgentTool):
+    """Concrete implementation of BaseAgentTool for testing."""
+    def _run(self, *args, **kwargs):
+        """Implement required _run method."""
+        return "Test response"
+
+@pytest.mark.parametrize("role_name,should_match", [
+    ('Futel Official Infopoint', True),                    # exact match
+    ('  "Futel Official Infopoint"  ', True),             # extra quotes and spaces
+    ('Futel Official Infopoint\n', True),                 # trailing newline
+    ('"Futel Official Infopoint"', True),                 # embedded quotes
+    (' FUTEL\nOFFICIAL   INFOPOINT ', True),             # multiple whitespace and newline
+    ('futel official infopoint', True),                   # lowercase
+    ('FUTEL OFFICIAL INFOPOINT', True),                   # uppercase
+    ('Non Existent Agent', False),                        # non-existent agent
+    (None, False),                                        # None agent name
+])
+def test_agent_tool_role_matching(role_name, should_match):
+    """Test that agent tools can match roles regardless of case, whitespace, and special characters."""
+    # Create test agent
+    test_agent = Agent(
+        role='Futel Official Infopoint',
+        goal='Answer questions about Futel',
+        backstory='Futel Football Club info',
+        allow_delegation=False
+    )
+
+    # Create test agent tool
+    agent_tool = TestAgentTool(
+        name="test_tool",
+        description="Test tool",
+        agents=[test_agent]
+    )
+
+    # Test role matching
+    result = agent_tool._execute(
+        agent_name=role_name,
+        task='Test task',
+        context=None
+    )
+
+    if should_match:
+        assert "coworker mentioned not found" not in result.lower(), \
+            f"Should find agent with role name: {role_name}"
+    else:
+        assert "coworker mentioned not found" in result.lower(), \
+            f"Should not find agent with role name: {role_name}"

tests/utilities/test_knowledge_planning.py

@@ -0,0 +1,84 @@
+"""
+Tests for verifying the integration of knowledge sources in the planning process.
+This module ensures that agent knowledge is properly included during task planning.
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from crewai.agent import Agent
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.task import Task
+from crewai.utilities.planning_handler import CrewPlanner
+
+
+@pytest.fixture
+def mock_knowledge_source():
+    """
+    Create a mock knowledge source with test content.
+    Returns:
+        StringKnowledgeSource:
+            A knowledge source containing AI-related test content
+    """
+    content = """
+    Important context about AI:
+    1. AI systems use machine learning algorithms
+    2. Neural networks are a key component
+    3. Training data is essential for good performance
+    """
+    return StringKnowledgeSource(content=content)
+
+@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
+def test_knowledge_included_in_planning(mock_chroma):
+    """Test that verifies knowledge sources are properly included in planning."""
+    # Mock ChromaDB collection
+    mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
+    mock_collection.add.return_value = None
+    
+    # Create an agent with knowledge
+    agent = Agent(
+        role="AI Researcher",
+        goal="Research and explain AI concepts",
+        backstory="Expert in artificial intelligence",
+        knowledge_sources=[
+            StringKnowledgeSource(
+                content="AI systems require careful training and validation."
+            )
+        ]
+    )
+
+    # Create a task for the agent
+    task = Task(
+        description="Explain the basics of AI systems",
+        expected_output="A clear explanation of AI fundamentals",
+        agent=agent
+    )
+
+    # Create a crew planner
+    planner = CrewPlanner([task], None)
+
+    # Get the task summary
+    task_summary = planner._create_tasks_summary()
+
+    # Verify that knowledge is included in planning when present
+    assert "AI systems require careful training" in task_summary, \
+        "Knowledge content should be present in task summary when knowledge exists"
+    assert '"agent_knowledge"' in task_summary, \
+        "agent_knowledge field should be present in task summary when knowledge exists"
+
+    # Verify that knowledge is properly formatted
+    assert isinstance(task.agent.knowledge_sources, list), \
+        "Knowledge sources should be stored in a list"
+    assert len(task.agent.knowledge_sources) > 0, \
+        "At least one knowledge source should be present"
+    assert task.agent.knowledge_sources[0].content in task_summary, \
+        "Knowledge source content should be included in task summary"
+
+    # Verify that other expected components are still present
+    assert task.description in task_summary, \
+        "Task description should be present in task summary"
+    assert task.expected_output in task_summary, \
+        "Expected output should be present in task summary"
+    assert agent.role in task_summary, \
+        "Agent role should be present in task summary"

tests/utilities/test_planning_handler.py

@@ -1,10 +1,14 @@
-from unittest.mock import patch
+from typing import Optional
+from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel
 
 from crewai.agent import Agent
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
+from crewai.tools.base_tool import BaseTool
 from crewai.utilities.planning_handler import (
     CrewPlanner,
     PlannerTaskPydanticOutput,
@@ -92,7 +96,72 @@ class TestCrewPlanner:
         tasks_summary = crew_planner._create_tasks_summary()
         assert isinstance(tasks_summary, str)
         assert tasks_summary.startswith("\n                Task Number 1 - Task 1")
-        assert tasks_summary.endswith('"agent_tools": []\n                ')
+        assert '"agent_tools": "agent has no tools"' in tasks_summary
+        # Knowledge field should not be present when empty
+        assert '"agent_knowledge"' not in tasks_summary
+
+    @patch('crewai.knowledge.storage.knowledge_storage.chromadb')
+    def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
+        """Test task summary generation with both knowledge and tools present."""
+        # Mock ChromaDB collection
+        mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
+        mock_collection.add.return_value = None
+
+        # Create mock tools with proper string descriptions and structured tool support
+        class MockTool(BaseTool):
+            name: str
+            description: str
+
+            def __init__(self, name: str, description: str):
+                tool_data = {"name": name, "description": description}
+                super().__init__(**tool_data)
+            
+            def __str__(self):
+                return self.name
+            
+            def __repr__(self):
+                return self.name
+            
+            def to_structured_tool(self):
+                return self
+
+            def _run(self, *args, **kwargs):
+                pass
+
+            def _generate_description(self) -> str:
+                """Override _generate_description to avoid args_schema handling."""
+                return self.description
+
+        tool1 = MockTool("tool1", "Tool 1 description")
+        tool2 = MockTool("tool2", "Tool 2 description")
+
+        # Create a task with knowledge and tools
+        task = Task(
+            description="Task with knowledge and tools",
+            expected_output="Expected output",
+            agent=Agent(
+                role="Test Agent",
+                goal="Test Goal",
+                backstory="Test Backstory",
+                tools=[tool1, tool2],
+                knowledge_sources=[
+                    StringKnowledgeSource(content="Test knowledge content")
+                ]
+            )
+        )
+        
+        # Create planner with the new task
+        planner = CrewPlanner([task], None)
+        tasks_summary = planner._create_tasks_summary()
+        
+        # Verify task summary content
+        assert isinstance(tasks_summary, str)
+        assert task.description in tasks_summary
+        assert task.expected_output in tasks_summary
+        assert '"agent_tools": [tool1, tool2]' in tasks_summary
+        assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary
+        assert task.agent.role in tasks_summary
+        assert task.agent.goal in tasks_summary
 
     def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
         with patch.object(Task, "execute_sync") as execute:

uv.lock

@@ -1,10 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
-    "python_full_version >= '3.12.4'",
+    "python_full_version < '3.11' and platform_system == 'Darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version == '3.11.*' and platform_system == 'Darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version >= '3.12.4' and platform_system == 'Darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')",
 ]
 
 [[package]]
@@ -620,6 +628,9 @@ agentops = [
 docling = [
     { name = "docling" },
 ]
+embeddings = [
+    { name = "tiktoken" },
+]
 fastembed = [
     { name = "fastembed" },
 ]
@@ -642,7 +653,6 @@ tools = [
 [package.dev-dependencies]
 dev = [
     { name = "cairosvg" },
-    { name = "crewai-tools" },
     { name = "mkdocs" },
     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
@@ -673,7 +683,7 @@ requires-dist = [
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = ">=1.44.22" },
+    { name = "litellm", specifier = ">=1.56.4" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -688,6 +698,7 @@ requires-dist = [
     { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "pyvis", specifier = ">=0.3.2" },
     { name = "regex", specifier = ">=2024.9.11" },
+    { name = "tiktoken", marker = "extra == 'embeddings'", specifier = "~=0.7.0" },
     { name = "tomli", specifier = ">=2.0.2" },
     { name = "tomli-w", specifier = ">=1.1.0" },
     { name = "uv", specifier = ">=0.4.25" },
@@ -696,7 +707,6 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },
@@ -2219,24 +2229,24 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.50.2"
+version = "1.56.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
     { name = "click" },
+    { name = "httpx" },
     { name = "importlib-metadata" },
     { name = "jinja2" },
     { name = "jsonschema" },
     { name = "openai" },
     { name = "pydantic" },
     { name = "python-dotenv" },
-    { name = "requests" },
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
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