Add type ignore comment to fix type checking issue

Co-Authored-By: Joe Moura <joao@crewai.com>

.pre-commit-config.yaml

@@ -4,6 +4,4 @@ repos:
     hooks:
       - id: ruff
         args: ["--fix"]
-        exclude: ^src/crewai/cli/templates/
       - id: ruff-format
-        exclude: ^src/crewai/cli/templates/

docs/concepts/collaboration.mdx

@@ -23,7 +23,8 @@ The `Crew` class has been enriched with several attributes to support advanced f
 | **Process Flow** (`process`)           | Defines execution logic (e.g., sequential, hierarchical) for task distribution.                                                                                                                                                |
 | **Verbose Logging** (`verbose`)        | Provides detailed logging for monitoring and debugging. Accepts integer and boolean values to control verbosity level.                                                                                                       |
 | **Rate Limiting** (`max_rpm`)          | Limits requests per minute to optimize resource usage. Setting guidelines depend on task complexity and load.                                                                                                                 |
-| **Internationalization / Customization** (`prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)                                                                                      |
+| **Internationalization / Customization** (`language`, `prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)                                                                                      |
+| **Execution and Output Handling** (`full_output`) | Controls output granularity, distinguishing between full and final outputs.                                                                                                                                                  |
 | **Callback and Telemetry** (`step_callback`, `task_callback`) | Enables step-wise and task-level execution monitoring and telemetry for performance analytics.                                                                                                                               |
 | **Crew Sharing** (`share_crew`)        | Allows sharing crew data with CrewAI for model improvement. Privacy implications and benefits should be considered.                                                                                                          |
 | **Usage Metrics** (`usage_metrics`)    | Logs all LLM usage metrics during task execution for performance insights.                                                                                                                           |
@@ -48,4 +49,4 @@ Consider a crew with a researcher agent tasked with data gathering and a writer
 
 ## Conclusion
 
-The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
+The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

docs/concepts/crews.mdx

@@ -20,10 +20,13 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
+| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
+| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
 | **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |

docs/concepts/flows.mdx

@@ -545,20 +545,16 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
-## Adding Agents to Flows
+## Adding LiteAgent to Flows
 
-Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
+LiteAgents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use a LiteAgent within a flow to perform market research:
 
 ```python
-import asyncio
-from typing import Any, Dict, List
-
-from crewai_tools import SerperDevTool
+from typing import List, cast
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
 from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
 from crewai.flow.flow import Flow, listen, start
-
+from crewai.lite_agent import LiteAgent
 
 # Define a structured output format
 class MarketAnalysis(BaseModel):
@@ -566,30 +562,28 @@ class MarketAnalysis(BaseModel):
     market_size: str = Field(description="Estimated market size")
     competitors: List[str] = Field(description="Major competitors in the space")
 
-
 # Define flow state
 class MarketResearchState(BaseModel):
     product: str = ""
     analysis: MarketAnalysis | None = None
 
-
-# Create a flow class
 class MarketResearchFlow(Flow[MarketResearchState]):
     @start()
-    def initialize_research(self) -> Dict[str, Any]:
+    def initialize_research(self):
         print(f"Starting market research for {self.state.product}")
-        return {"product": self.state.product}
 
     @listen(initialize_research)
-    async def analyze_market(self) -> Dict[str, Any]:
-        # Create an Agent for market research
-        analyst = Agent(
+    def analyze_market(self):
+        # Create a LiteAgent for market research
+        analyst = LiteAgent(
             role="Market Research Analyst",
             goal=f"Analyze the market for {self.state.product}",
             backstory="You are an experienced market analyst with expertise in "
             "identifying market trends and opportunities.",
-            tools=[SerperDevTool()],
+            llm="gpt-4o",
+            tools=[WebsiteSearchTool()],
             verbose=True,
+            response_format=MarketAnalysis,
         )
 
         # Define the research query
@@ -598,65 +592,49 @@ class MarketResearchFlow(Flow[MarketResearchState]):
         1. Key market trends
         2. Market size
         3. Major competitors
-
+        
         Format your response according to the specified structure.
         """
 
-        # Execute the analysis with structured output format
-        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
-        if result.pydantic:
-            print("result", result.pydantic)
-        else:
-            print("result", result)
-
-        # Return the analysis to update the state
-        return {"analysis": result.pydantic}
+        # Execute the analysis
+        result = analyst.kickoff(query)
+        self.state.analysis = cast(MarketAnalysis, result.pydantic)
+        return result.pydantic
 
     @listen(analyze_market)
-    def present_results(self, analysis) -> None:
+    def present_results(self):
+        analysis = self.state.analysis
+        if analysis is None:
+            print("No analysis results available")
+            return
+
         print("\nMarket Analysis Results")
         print("=====================")
 
-        if isinstance(analysis, dict):
-            # If we got a dict with 'analysis' key, extract the actual analysis object
-            market_analysis = analysis.get("analysis")
-        else:
-            market_analysis = analysis
+        print("\nKey Market Trends:")
+        for trend in analysis.key_trends:
+            print(f"- {trend}")
 
-        if market_analysis and isinstance(market_analysis, MarketAnalysis):
-            print("\nKey Market Trends:")
-            for trend in market_analysis.key_trends:
-                print(f"- {trend}")
-
-            print(f"\nMarket Size: {market_analysis.market_size}")
-
-            print("\nMajor Competitors:")
-            for competitor in market_analysis.competitors:
-                print(f"- {competitor}")
-        else:
-            print("No structured analysis data available.")
-            print("Raw analysis:", analysis)
+        print(f"\nMarket Size: {analysis.market_size}")
 
+        print("\nMajor Competitors:")
+        for competitor in analysis.competitors:
+            print(f"- {competitor}")
 
 # Usage example
-async def run_flow():
-    flow = MarketResearchFlow()
-    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
-    return result
-
-
-# Run the flow
-if __name__ == "__main__":
-    asyncio.run(run_flow())
+flow = MarketResearchFlow()
+result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
 ```
 
-This example demonstrates several key features of using Agents in flows:
+This example demonstrates several key features of using LiteAgents in flows:
 
 1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
 
 2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
 
-3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+3. **Tool Integration**: LiteAgents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+
+If you want to learn more about LiteAgents, check out the [LiteAgent](/concepts/lite-agent) page.
 
 ## Adding Crews to Flows
 

docs/concepts/lite-agent.mdx

@@ -0,0 +1,242 @@
+---
+title: LiteAgent
+description: A lightweight, single-purpose agent for simple autonomous tasks within the CrewAI framework.
+icon: feather
+---
+
+## Overview
+
+A `LiteAgent` is a streamlined version of CrewAI's Agent, designed for simpler, standalone tasks that don't require the full complexity of a crew-based workflow. It's perfect for quick automations, single-purpose tasks, or when you need a lightweight solution.
+
+<Tip>
+  Think of a LiteAgent as a specialized worker that excels at individual tasks.
+  While regular Agents are team players in a crew, LiteAgents are solo
+  performers optimized for specific operations.
+</Tip>
+
+## LiteAgent Attributes
+
+| Attribute                        | Parameter         | Type                   | Description                                                     |
+| :------------------------------- | :---------------- | :--------------------- | :-------------------------------------------------------------- |
+| **Role**                         | `role`            | `str`                  | Defines the agent's function and expertise.                     |
+| **Goal**                         | `goal`            | `str`                  | The specific objective that guides the agent's actions.         |
+| **Backstory**                    | `backstory`       | `str`                  | Provides context and personality to the agent.                  |
+| **LLM** _(optional)_             | `llm`             | `Union[str, LLM, Any]` | Language model powering the agent. Defaults to "gpt-4".         |
+| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`       | Capabilities available to the agent. Defaults to an empty list. |
+| **Verbose** _(optional)_         | `verbose`         | `bool`                 | Enable detailed execution logs. Default is False.               |
+| **Response Format** _(optional)_ | `response_format` | `Type[BaseModel]`      | Pydantic model for structured output. Optional.                 |
+
+## Creating a LiteAgent
+
+Here's a simple example of creating and using a standalone LiteAgent:
+
+```python
+from typing import List, cast
+
+from crewai_tools import SerperDevTool
+from pydantic import BaseModel, Field
+
+from crewai.lite_agent import LiteAgent
+
+
+# Define a structured output format
+class MovieReview(BaseModel):
+    title: str = Field(description="The title of the movie")
+    rating: float = Field(description="Rating out of 10")
+    pros: List[str] = Field(description="List of positive aspects")
+    cons: List[str] = Field(description="List of negative aspects")
+
+
+# Create a LiteAgent
+critic = LiteAgent(
+    role="Movie Critic",
+    goal="Provide insightful movie reviews",
+    backstory="You are an experienced film critic known for balanced, thoughtful reviews.",
+    tools=[SerperDevTool()],
+    verbose=True,
+    response_format=MovieReview,
+)
+
+# Use the agent
+query = """
+Review the movie 'Inception'. Include:
+1. Your rating out of 10
+2. Key positive aspects
+3. Areas that could be improved
+"""
+
+result = critic.kickoff(query)
+
+
+# Access the structured output
+review = cast(MovieReview, result.pydantic)
+print(f"\nMovie Review: {review.title}")
+print(f"Rating: {review.rating}/10")
+print("\nPros:")
+for pro in review.pros:
+    print(f"- {pro}")
+print("\nCons:")
+for con in review.cons:
+    print(f"- {con}")
+
+```
+
+This example demonstrates the core features of a LiteAgent:
+
+- Structured output using Pydantic models
+- Tool integration with WebSearchTool
+- Simple execution with `kickoff()`
+- Easy access to both raw and structured results
+
+## Using LiteAgent in a Flow
+
+For more complex scenarios, you can integrate LiteAgents into a Flow. Here's an example of a market research flow:
+
+````python
+from typing import List
+from pydantic import BaseModel, Field
+from crewai.flow.flow import Flow, start, listen
+from crewai.lite_agent import LiteAgent
+from crewai.tools import WebSearchTool
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str] = Field(description="List of identified market trends")
+    market_size: str = Field(description="Estimated market size")
+    competitors: List[str] = Field(description="Major competitors in the space")
+
+# Define flow state
+class MarketResearchState(BaseModel):
+    product: str = ""
+    analysis: MarketAnalysis = None
+
+# Create a flow class
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self, product: str):
+        print(f"Starting market research for {product}")
+        self.state.product = product
+
+    @listen(initialize_research)
+    async def analyze_market(self):
+        # Create a LiteAgent for market research
+        analyst = LiteAgent(
+            role="Market Research Analyst",
+            goal=f"Analyze the market for {self.state.product}",
+            backstory="You are an experienced market analyst with expertise in "
+                     "identifying market trends and opportunities.",
+            tools=[WebSearchTool()],
+            verbose=True,
+            response_format=MarketAnalysis
+        )
+
+        # Define the research query
+        query = f"""
+        Research the market for {self.state.product}. Include:
+        1. Key market trends
+        2. Market size
+        3. Major competitors
+
+        Format your response according to the specified structure.
+        """
+
+        # Execute the analysis
+        result = await analyst.kickoff_async(query)
+        self.state.analysis = result.pydantic
+        return result.pydantic
+
+    @listen(analyze_market)
+    def present_results(self):
+        analysis = self.state.analysis
+        print("\nMarket Analysis Results")
+        print("=====================")
+
+        print("\nKey Market Trends:")
+        for trend in analysis.key_trends:
+            print(f"- {trend}")
+
+        print(f"\nMarket Size: {analysis.market_size}")
+
+        print("\nMajor Competitors:")
+        for competitor in analysis.competitors:
+            print(f"- {competitor}")
+
+# Usage example
+import asyncio
+
+async def run_flow():
+    flow = MarketResearchFlow()
+    result = await flow.kickoff(inputs={"product": "AI-powered chatbots"})
+    return result
+
+# Run the flow
+if __name__ == "__main__":
+    asyncio.run(run_flow())
+
+## Key Features
+
+### 1. Simplified Setup
+Unlike regular Agents, LiteAgents are designed for quick setup and standalone operation. They don't require crew configuration or task management.
+
+### 2. Structured Output
+LiteAgents support Pydantic models for response formatting, making it easy to get structured, type-safe data from your agent's operations.
+
+### 3. Tool Integration
+Just like regular Agents, LiteAgents can use tools to enhance their capabilities:
+```python
+from crewai.tools import SerperDevTool, CalculatorTool
+
+agent = LiteAgent(
+    role="Research Assistant",
+    goal="Find and analyze information",
+    tools=[SerperDevTool(), CalculatorTool()],
+    verbose=True
+)
+````
+
+### 4. Async Support
+
+LiteAgents support asynchronous execution through the `kickoff_async` method, making them suitable for non-blocking operations in your application.
+
+## Response Formatting
+
+LiteAgents support structured output through Pydantic models using the `response_format` parameter. This feature ensures type safety and consistent output structure, making it easier to work with agent responses in your application.
+
+### Basic Usage
+
+```python
+from pydantic import BaseModel, Field
+
+class SearchResult(BaseModel):
+    title: str = Field(description="The title of the found content")
+    summary: str = Field(description="A brief summary of the content")
+    relevance_score: float = Field(description="Relevance score from 0 to 1")
+
+agent = LiteAgent(
+    role="Search Specialist",
+    goal="Find and summarize relevant information",
+    response_format=SearchResult
+)
+
+result = await agent.kickoff_async("Find information about quantum computing")
+print(f"Title: {result.pydantic.title}")
+print(f"Summary: {result.pydantic.summary}")
+print(f"Relevance: {result.pydantic.relevance_score}")
+```
+
+### Handling Responses
+
+When using `response_format`, the agent's response will be available in two forms:
+
+1. **Raw Response**: Access the unstructured string response
+
+   ```python
+   result = await agent.kickoff_async("Analyze the market")
+   print(result.raw)  # Original LLM response
+   ```
+
+2. **Structured Response**: Access the parsed Pydantic model
+   ```python
+   print(result.pydantic)  # Parsed response as Pydantic model
+   print(result.pydantic.dict())  # Convert to dictionary
+   ```

docs/docs.json

@@ -66,6 +66,7 @@
               "concepts/tasks",
               "concepts/crews",
               "concepts/flows",
+              "concepts/lite-agent",
               "concepts/knowledge",
               "concepts/llms",
               "concepts/processes",
@@ -76,7 +77,9 @@
               "concepts/testing",
               "concepts/cli",
               "concepts/tools",
-              "concepts/event-listener"
+              "concepts/event-listener",
+              "concepts/langchain-tools",
+              "concepts/llamaindex-tools"
             ]
           },
           {
@@ -95,9 +98,7 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks",
-              "how-to/langchain-tools",
-              "how-to/llamaindex-tools"
+              "how-to/conditional-tasks"
             ]
           },
           {
@@ -196,11 +197,6 @@
           "anchor": "Community",
           "href": "https://community.crewai.com",
           "icon": "discourse"
-        },
-        {
-          "anchor": "Tutorials",
-          "href": "https://www.youtube.com/@crewAIInc",
-          "icon": "youtube"
         }
       ]
     }
@@ -235,4 +231,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/installation.mdx

@@ -4,21 +4,6 @@ description: Get started with CrewAI - Install, configure, and build your first
 icon: wrench
 ---
 
-## Video Tutorial
-Watch this video tutorial for a step-by-step demonstration of the installation process:
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/-kSOTtYzgEw"
-  title="CrewAI Installation Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-## Text Tutorial
 <Note>
   **Python Version Requirements**
 

docs/telemetry.mdx

@@ -22,16 +22,7 @@ usage of tools, API calls, responses, any data processed by the agents, or secre
 When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected 
 to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. 
 Users should carefully consider the content of their crews and tasks before enabling `share_crew`. 
-Users can disable telemetry by setting the environment variable `CREWAI_DISABLE_TELEMETRY` to `true` or by setting `OTEL_SDK_DISABLED` to `true` (note that the latter disables all OpenTelemetry instrumentation globally).
-
-### Examples:
-```python
-# Disable CrewAI telemetry only
-os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
-
-# Disable all OpenTelemetry (including CrewAI)
-os.environ['OTEL_SDK_DISABLED'] = 'true'
-```
+Users can disable telemetry by setting the environment variable `OTEL_SDK_DISABLED` to `true`.
 
 ### Data Explanation:
 | Defaulted | Data                                      | Reason and Specifics                                                                                                       |
@@ -64,4 +55,4 @@ This enables a deeper insight into usage patterns.
 <Warning>
     If you enable `share_crew`, the collected data may include personal information if it has been incorporated into crew configurations, task descriptions, or outputs. 
     Users should carefully review their data and ensure compliance with GDPR and other applicable privacy regulations before enabling this feature.
-</Warning>
+</Warning>

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.114.0"
+version = "0.108.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.40.1"]
+tools = ["crewai-tools~=0.38.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -2,14 +2,12 @@ import warnings
 
 from crewai.agent import Agent
 from crewai.crew import Crew
-from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 from crewai.process import Process
 from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
 
 warnings.filterwarnings(
     "ignore",
@@ -17,16 +15,14 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.114.0"
+__version__ = "0.108.0"
 __all__ = [
     "Agent",
     "Crew",
-    "CrewOutput",
     "Process",
     "Task",
     "LLM",
     "BaseLLM",
     "Flow",
     "Knowledge",
-    "TaskOutput",
 ]

src/crewai/agent.py

@@ -1,6 +1,7 @@
+import re
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Sequence, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -10,7 +11,6 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
-from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
@@ -367,12 +367,8 @@ class Agent(BaseAgent):
                 "info", "Coding tools not available. Install crewai_tools. "
             )
 
-    def get_output_converter(
-        self, agent, llm, text, model, instructions
-    ):  # Add agent parameter
-        return Converter(
-            agent=agent, llm=llm, text=text, model=model, instructions=instructions
-        )
+    def get_output_converter(self, llm, text, model, instructions):
+        return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
     def _training_handler(self, task_prompt: str) -> str:
         """Handle training data for the agent task prompt to improve output on Training."""
@@ -453,74 +449,3 @@ class Agent(BaseAgent):
 
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
-
-    def kickoff(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
-    ) -> LiteAgentOutput:
-        """
-        Execute the agent with the given messages using a LiteAgent instance.
-
-        This method is useful when you want to use the Agent configuration but
-        with the simpler and more direct execution flow of LiteAgent.
-
-        Args:
-            messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output.
-
-        Returns:
-            LiteAgentOutput: The result of the agent execution.
-        """
-        lite_agent = LiteAgent(
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
-            i18n=self.i18n,
-        )
-
-        return lite_agent.kickoff(messages)
-
-    async def kickoff_async(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
-    ) -> LiteAgentOutput:
-        """
-        Execute the agent asynchronously with the given messages using a LiteAgent instance.
-
-        This is the async version of the kickoff method.
-
-        Args:
-            messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output.
-
-        Returns:
-            LiteAgentOutput: The result of the agent execution.
-        """
-        lite_agent = LiteAgent(
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
-            i18n=self.i18n,
-        )
-
-        return await lite_agent.kickoff_async(messages)

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

@@ -60,7 +60,7 @@ def test():
         "current_year": str(datetime.now().year)
     }
     try:
-        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), eval_llm=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while testing the crew: {e}")

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0,<1.0.0"
+    "crewai[tools]>=0.108.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0,<1.0.0",
+    "crewai[tools]>=0.108.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0"
+    "crewai[tools]>=0.108.0"
 ]
 
 [tool.crewai]

src/crewai/cli/tools/main.py

@@ -153,12 +153,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         login_response_json = login_response.json()
 
         settings = Settings()
-        settings.tool_repository_username = login_response_json["credential"][
-            "username"
-        ]
-        settings.tool_repository_password = login_response_json["credential"][
-            "password"
-        ]
+        settings.tool_repository_username = login_response_json["credential"]["username"]
+        settings.tool_repository_password = login_response_json["credential"]["password"]
         settings.dump()
 
         console.print(
@@ -183,7 +179,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             capture_output=False,
             env=self._build_env_with_credentials(repository_handle),
             text=True,
-            check=True,
+            check=True
         )
 
         if add_package_result.stderr:
@@ -208,11 +204,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings = Settings()
 
         env = os.environ.copy()
-        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
-            settings.tool_repository_username or ""
-        )
-        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
-            settings.tool_repository_password or ""
-        )
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
 
         return env

src/crewai/crew.py

@@ -297,7 +297,9 @@ class Crew(BaseModel):
             )
             self._external_memory = (
                 # External memory doesn’t support a default value since it was designed to be managed entirely externally
-                self.external_memory.set_crew(self) if self.external_memory else None
+                self.external_memory.set_crew(self)
+                if self.external_memory
+                else None
             )
             if (
                 self.memory_config

src/crewai/flow/flow_visualizer.py

@@ -34,13 +34,13 @@ class FlowPlot:
         ValueError
             If flow object is invalid or missing required attributes.
         """
-        if not hasattr(flow, "_methods"):
+        if not hasattr(flow, '_methods'):
             raise ValueError("Invalid flow object: missing '_methods' attribute")
-        if not hasattr(flow, "_listeners"):
+        if not hasattr(flow, '_listeners'):
             raise ValueError("Invalid flow object: missing '_listeners' attribute")
-        if not hasattr(flow, "_start_methods"):
+        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
@@ -65,7 +65,7 @@ class FlowPlot:
         """
         if not filename or not isinstance(filename, str):
             raise ValueError("Filename must be a non-empty string")
-
+            
         try:
             # Initialize network
             net = Network(
@@ -121,9 +121,7 @@ class FlowPlot:
                 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)}"
-                )
+                raise RuntimeError(f"Failed to generate network visualization: {str(e)}")
 
             # Save the final HTML content to the file
             try:
@@ -131,9 +129,7 @@ class FlowPlot:
                     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)}"
-                )
+                raise IOError(f"Failed to save flow visualization to {filename}.html: {str(e)}")
 
         except (ValueError, RuntimeError, IOError) as e:
             raise e
@@ -169,9 +165,7 @@ class FlowPlot:
         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
-            )
+            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):
@@ -203,7 +197,6 @@ class FlowPlot:
             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}")

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

@@ -43,18 +43,18 @@ def safe_path_join(*parts: str, root: Union[str, Path, None] = None) -> str:
 
         # 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
@@ -84,17 +84,17 @@ def validate_path_exists(path: Union[str, Path], file_type: str = "file") -> str
     """
     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
@@ -126,9 +126,9 @@ def list_files(directory: Union[str, Path], pattern: str = "*") -> List[str]:
         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

src/crewai/flow/persistence/base.py

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

src/crewai/flow/persistence/decorators.py

@@ -48,7 +48,7 @@ LOG_MESSAGES = {
     "save_state": "Saving flow state to memory for ID: {}",
     "save_error": "Failed to persist state for method {}: {}",
     "state_missing": "Flow instance has no state",
-    "id_missing": "Flow state must have an 'id' field for persistence",
+    "id_missing": "Flow state must have an 'id' field for persistence"
 }
 
 
@@ -58,13 +58,7 @@ class PersistenceDecorator:
     _printer = Printer()  # Class-level printer instance
 
     @classmethod
-    def persist_state(
-        cls,
-        flow_instance: Any,
-        method_name: str,
-        persistence_instance: FlowPersistence,
-        verbose: bool = False,
-    ) -> None:
+    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence, verbose: bool = False) -> None:
         """Persist flow state with proper error handling and logging.
 
         This method handles the persistence of flow state data, including proper
@@ -82,24 +76,22 @@ class PersistenceDecorator:
             AttributeError: If flow instance lacks required state attributes
         """
         try:
-            state = getattr(flow_instance, "state", None)
+            state = getattr(flow_instance, 'state', None)
             if state is None:
                 raise ValueError("Flow instance has no state")
 
             flow_uuid: Optional[str] = None
             if isinstance(state, dict):
-                flow_uuid = state.get("id")
+                flow_uuid = state.get('id')
             elif isinstance(state, BaseModel):
-                flow_uuid = getattr(state, "id", None)
+                flow_uuid = getattr(state, 'id', None)
 
             if not flow_uuid:
                 raise ValueError("Flow state must have an 'id' field for persistence")
 
             # Log state saving only if verbose is True
             if verbose:
-                cls._printer.print(
-                    LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan"
-                )
+                cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
                 logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
 
             try:
@@ -152,10 +144,7 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
             def begin(self):
                 pass
     """
-
-    def decorator(
-        target: Union[Type, Callable[..., T]],
-    ) -> Union[Type, Callable[..., T]]:
+    def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
         """Decorator that handles both class and method decoration."""
         actual_persistence = persistence or SQLiteFlowPersistence()
 
@@ -165,8 +154,8 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
 
             @functools.wraps(original_init)
             def new_init(self: Any, *args: Any, **kwargs: Any) -> None:
-                if "persistence" not in kwargs:
-                    kwargs["persistence"] = actual_persistence
+                if 'persistence' not in kwargs:
+                    kwargs['persistence'] = actual_persistence
                 original_init(self, *args, **kwargs)
 
             setattr(target, "__init__", new_init)
@@ -176,11 +165,11 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
 
             for name, method in target.__dict__.items():
                 if callable(method) and (
-                    hasattr(method, "__is_start_method__")
-                    or hasattr(method, "__trigger_methods__")
-                    or hasattr(method, "__condition_type__")
-                    or hasattr(method, "__is_flow_method__")
-                    or hasattr(method, "__is_router__")
+                    hasattr(method, "__is_start_method__") or
+                    hasattr(method, "__trigger_methods__") or
+                    hasattr(method, "__condition_type__") or
+                    hasattr(method, "__is_flow_method__") or
+                    hasattr(method, "__is_router__")
                 ):
                     original_methods[name] = method
 
@@ -188,30 +177,18 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
             for name, method in original_methods.items():
                 if asyncio.iscoroutinefunction(method):
                     # Create a closure to capture the current name and method
-                    def create_async_wrapper(
-                        method_name: str, original_method: Callable
-                    ):
+                    def create_async_wrapper(method_name: str, original_method: Callable):
                         @functools.wraps(original_method)
-                        async def method_wrapper(
-                            self: Any, *args: Any, **kwargs: Any
-                        ) -> Any:
+                        async def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = await original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose
-                            )
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
-
                         return method_wrapper
 
                     wrapped = create_async_wrapper(name, method)
 
                     # Preserve all original decorators and attributes
-                    for attr in [
-                        "__is_start_method__",
-                        "__trigger_methods__",
-                        "__condition_type__",
-                        "__is_router__",
-                    ]:
+                    for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                         if hasattr(method, attr):
                             setattr(wrapped, attr, getattr(method, attr))
                     setattr(wrapped, "__is_flow_method__", True)
@@ -220,28 +197,18 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
                     setattr(target, name, wrapped)
                 else:
                     # Create a closure to capture the current name and method
-                    def create_sync_wrapper(
-                        method_name: str, original_method: Callable
-                    ):
+                    def create_sync_wrapper(method_name: str, original_method: Callable):
                         @functools.wraps(original_method)
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose
-                            )
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
-
                         return method_wrapper
 
                     wrapped = create_sync_wrapper(name, method)
 
                     # Preserve all original decorators and attributes
-                    for attr in [
-                        "__is_start_method__",
-                        "__trigger_methods__",
-                        "__condition_type__",
-                        "__is_router__",
-                    ]:
+                    for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                         if hasattr(method, attr):
                             setattr(wrapped, attr, getattr(method, attr))
                     setattr(wrapped, "__is_flow_method__", True)
@@ -256,49 +223,29 @@ def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False
             setattr(method, "__is_flow_method__", True)
 
             if asyncio.iscoroutinefunction(method):
-
                 @functools.wraps(method)
-                async def method_async_wrapper(
-                    flow_instance: Any, *args: Any, **kwargs: Any
-                ) -> T:
+                async def method_async_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
                     method_coro = method(flow_instance, *args, **kwargs)
                     if asyncio.iscoroutine(method_coro):
                         result = await method_coro
                     else:
                         result = method_coro
-                    PersistenceDecorator.persist_state(
-                        flow_instance, method.__name__, actual_persistence, verbose
-                    )
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
                     return result
 
-                for attr in [
-                    "__is_start_method__",
-                    "__trigger_methods__",
-                    "__condition_type__",
-                    "__is_router__",
-                ]:
+                for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                     if hasattr(method, attr):
                         setattr(method_async_wrapper, attr, getattr(method, attr))
                 setattr(method_async_wrapper, "__is_flow_method__", True)
                 return cast(Callable[..., T], method_async_wrapper)
             else:
-
                 @functools.wraps(method)
-                def method_sync_wrapper(
-                    flow_instance: Any, *args: Any, **kwargs: Any
-                ) -> T:
+                def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
                     result = method(flow_instance, *args, **kwargs)
-                    PersistenceDecorator.persist_state(
-                        flow_instance, method.__name__, actual_persistence, verbose
-                    )
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
                     return result
 
-                for attr in [
-                    "__is_start_method__",
-                    "__trigger_methods__",
-                    "__condition_type__",
-                    "__is_router__",
-                ]:
+                for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                     if hasattr(method, attr):
                         setattr(method_sync_wrapper, attr, getattr(method, attr))
                 setattr(method_sync_wrapper, "__is_flow_method__", True)

src/crewai/flow/visualization_utils.py

@@ -56,7 +56,6 @@ def method_calls_crew(method: Any) -> bool:
 
     class CrewCallVisitor(ast.NodeVisitor):
         """AST visitor to detect .crew() method calls."""
-
         def __init__(self):
             self.found = False
 
@@ -75,7 +74,7 @@ def add_nodes_to_network(
     net: Any,
     flow: Any,
     node_positions: Dict[str, Tuple[float, float]],
-    node_styles: Dict[str, Dict[str, Any]],
+    node_styles: Dict[str, Dict[str, Any]]
 ) -> None:
     """
     Add nodes to the network visualization with appropriate styling.
@@ -99,7 +98,6 @@ def add_nodes_to_network(
     - Crew methods
     - Regular methods
     """
-
     def human_friendly_label(method_name):
         return method_name.replace("_", " ").title()
 
@@ -142,7 +140,7 @@ def compute_positions(
     flow: Any,
     node_levels: Dict[str, int],
     y_spacing: float = 150,
-    x_spacing: float = 150,
+    x_spacing: float = 150
 ) -> Dict[str, Tuple[float, float]]:
     """
     Compute the (x, y) positions for each node in the flow graph.
@@ -183,7 +181,7 @@ def add_edges(
     net: Any,
     flow: Any,
     node_positions: Dict[str, Tuple[float, float]],
-    colors: Dict[str, str],
+    colors: Dict[str, str]
 ) -> None:
     edge_smooth: Dict[str, Union[str, float]] = {"type": "continuous"}  # Default value
     """

src/crewai/lite_agent.py

@@ -1,4 +1,6 @@
 import asyncio
+import json
+import re
 import uuid
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Optional, Type, Union, cast

src/crewai/llm.py

@@ -839,9 +839,13 @@ class LLM(BaseLLM):
 
         # Validate message format first
         for msg in messages:
-            if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
+            if not isinstance(msg, dict) or "role" not in msg:
                 raise TypeError(
-                    "Invalid message format. Each message must be a dict with 'role' and 'content' keys"
+                    "Invalid message format. Each message must be a dict with 'role' key"
+                )
+            if "content" not in msg and msg["role"] != "system":
+                raise TypeError(
+                    "Invalid message format. Each non-system message must have a 'content' key"
                 )
 
         # Handle O1 models specially
@@ -868,6 +872,19 @@ class LLM(BaseLLM):
                 messages.append({"role": "user", "content": "Please continue."})
             return messages
 
+        if "qwen" in self.model.lower():
+            formatted_messages = []
+            for msg in messages:
+                if not isinstance(msg.get("content"), str):
+                    formatted_messages.append(msg)
+                    continue
+                
+                formatted_messages.append({
+                    "role": msg["role"],
+                    "content": [{"type": "text", "text": msg["content"]}]  # type: ignore
+                })
+            return formatted_messages
+
         # Handle Anthropic models
         if not self.is_anthropic:
             return messages

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -18,7 +18,9 @@ class KickoffTaskOutputsSQLiteStorage:
     An updated SQLite storage class for kickoff task outputs storage.
     """
 
-    def __init__(self, db_path: Optional[str] = None) -> None:
+    def __init__(
+        self, db_path: Optional[str] = None
+    ) -> None:
         if db_path is None:
             # Get the parent directory of the default db path and create our db file there
             db_path = str(Path(db_storage_path()) / "latest_kickoff_task_outputs.db")
@@ -144,9 +146,7 @@ class KickoffTaskOutputsSQLiteStorage:
                 conn.commit()
 
                 if cursor.rowcount == 0:
-                    logger.warning(
-                        f"No row found with task_index {task_index}. No update performed."
-                    )
+                    logger.warning(f"No row found with task_index {task_index}. No update performed.")
         except sqlite3.Error as e:
             error_msg = DatabaseError.format_error(DatabaseError.UPDATE_ERROR, e)
             logger.error(error_msg)

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -12,7 +12,9 @@ class LTMSQLiteStorage:
     An updated SQLite storage class for LTM data storage.
     """
 
-    def __init__(self, db_path: Optional[str] = None) -> None:
+    def __init__(
+        self, db_path: Optional[str] = None
+    ) -> None:
         if db_path is None:
             # Get the parent directory of the default db path and create our db file there
             db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db")

src/crewai/project/crew_base.py

@@ -137,11 +137,13 @@ def CrewBase(cls: T) -> T:
                 all_functions, "is_cache_handler"
             )
             callbacks = self._filter_functions(all_functions, "is_callback")
+            agents = self._filter_functions(all_functions, "is_agent")
 
             for agent_name, agent_info in self.agents_config.items():
                 self._map_agent_variables(
                     agent_name,
                     agent_info,
+                    agents,
                     llms,
                     tool_functions,
                     cache_handler_functions,
@@ -152,6 +154,7 @@ def CrewBase(cls: T) -> T:
             self,
             agent_name: str,
             agent_info: Dict[str, Any],
+            agents: Dict[str, Callable],
             llms: Dict[str, Callable],
             tool_functions: Dict[str, Callable],
             cache_handler_functions: Dict[str, Callable],
@@ -169,14 +172,9 @@ def CrewBase(cls: T) -> T:
                 ]
 
             if function_calling_llm := agent_info.get("function_calling_llm"):
-                try:
-                    self.agents_config[agent_name]["function_calling_llm"] = llms[
-                        function_calling_llm
-                    ]()
-                except KeyError:
-                    self.agents_config[agent_name]["function_calling_llm"] = (
-                        function_calling_llm
-                    )
+                self.agents_config[agent_name]["function_calling_llm"] = agents[
+                    function_calling_llm
+                ]()
 
             if step_callback := agent_info.get("step_callback"):
                 self.agents_config[agent_name]["step_callback"] = callbacks[

src/crewai/security/fingerprint.py

@@ -26,55 +26,46 @@ class Fingerprint(BaseModel):
         metadata (Dict[str, Any]): Additional metadata associated with this fingerprint
     """
 
-    uuid_str: str = Field(
-        default_factory=lambda: str(uuid.uuid4()),
-        description="String representation of the UUID",
-    )
-    created_at: datetime = Field(
-        default_factory=datetime.now, description="When this fingerprint was created"
-    )
-    metadata: Dict[str, Any] = Field(
-        default_factory=dict, description="Additional metadata for this fingerprint"
-    )
+    uuid_str: str = Field(default_factory=lambda: str(uuid.uuid4()), description="String representation of the UUID")
+    created_at: datetime = Field(default_factory=datetime.now, description="When this fingerprint was created")
+    metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata for this fingerprint")
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    @field_validator("metadata")
+    
+    @field_validator('metadata')
     @classmethod
     def validate_metadata(cls, v):
         """Validate that metadata is a dictionary with string keys and valid values."""
         if not isinstance(v, dict):
             raise ValueError("Metadata must be a dictionary")
-
+        
         # Validate that all keys are strings
         for key, value in v.items():
             if not isinstance(key, str):
                 raise ValueError(f"Metadata keys must be strings, got {type(key)}")
-
+            
             # Validate nested dictionaries (prevent deeply nested structures)
             if isinstance(value, dict):
                 # Check for nested dictionaries (limit depth to 1)
                 for nested_key, nested_value in value.items():
                     if not isinstance(nested_key, str):
-                        raise ValueError(
-                            f"Nested metadata keys must be strings, got {type(nested_key)}"
-                        )
+                        raise ValueError(f"Nested metadata keys must be strings, got {type(nested_key)}")
                     if isinstance(nested_value, dict):
                         raise ValueError("Metadata can only be nested one level deep")
-
+        
         # Check for maximum metadata size (prevent DoS)
         if len(str(v)) > 10000:  # Limit metadata size to 10KB
             raise ValueError("Metadata size exceeds maximum allowed (10KB)")
-
+            
         return v
 
     def __init__(self, **data):
         """Initialize a Fingerprint with auto-generated uuid_str and created_at."""
         # Remove uuid_str and created_at from data to ensure they're auto-generated
-        if "uuid_str" in data:
-            data.pop("uuid_str")
-        if "created_at" in data:
-            data.pop("created_at")
+        if 'uuid_str' in data:
+            data.pop('uuid_str')
+        if 'created_at' in data:
+            data.pop('created_at')
 
         # Call the parent constructor with the modified data
         super().__init__(**data)
@@ -97,21 +88,19 @@ class Fingerprint(BaseModel):
         """
         if not isinstance(seed, str):
             raise ValueError("Seed must be a string")
-
+        
         if not seed.strip():
             raise ValueError("Seed cannot be empty or whitespace")
-
+            
         # Create a deterministic UUID using v5 (SHA-1)
         # Custom namespace for CrewAI to enhance security
 
         # Using a unique namespace specific to CrewAI to reduce collision risks
-        CREW_AI_NAMESPACE = uuid.UUID("f47ac10b-58cc-4372-a567-0e02b2c3d479")
+        CREW_AI_NAMESPACE = uuid.UUID('f47ac10b-58cc-4372-a567-0e02b2c3d479')
         return str(uuid.uuid5(CREW_AI_NAMESPACE, seed))
 
     @classmethod
-    def generate(
-        cls, seed: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None
-    ) -> "Fingerprint":
+    def generate(cls, seed: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> 'Fingerprint':
         """
         Static factory method to create a new Fingerprint.
 
@@ -126,7 +115,7 @@ class Fingerprint(BaseModel):
         fingerprint = cls(metadata=metadata or {})
         if seed:
             # For seed-based generation, we need to manually set the uuid_str after creation
-            object.__setattr__(fingerprint, "uuid_str", cls._generate_uuid(seed))
+            object.__setattr__(fingerprint, 'uuid_str', cls._generate_uuid(seed))
         return fingerprint
 
     def __str__(self) -> str:
@@ -153,11 +142,11 @@ class Fingerprint(BaseModel):
         return {
             "uuid_str": self.uuid_str,
             "created_at": self.created_at.isoformat(),
-            "metadata": self.metadata,
+            "metadata": self.metadata
         }
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "Fingerprint":
+    def from_dict(cls, data: Dict[str, Any]) -> 'Fingerprint':
         """
         Create a Fingerprint from a dictionary representation.
 
@@ -174,10 +163,8 @@ class Fingerprint(BaseModel):
 
         # For consistency with existing stored fingerprints, we need to manually set these
         if "uuid_str" in data:
-            object.__setattr__(fingerprint, "uuid_str", data["uuid_str"])
+            object.__setattr__(fingerprint, 'uuid_str', data["uuid_str"])
         if "created_at" in data and isinstance(data["created_at"], str):
-            object.__setattr__(
-                fingerprint, "created_at", datetime.fromisoformat(data["created_at"])
-            )
+            object.__setattr__(fingerprint, 'created_at', datetime.fromisoformat(data["created_at"]))
 
         return fingerprint

src/crewai/security/security_config.py

@@ -38,27 +38,29 @@ class SecurityConfig(BaseModel):
     )
 
     version: str = Field(
-        default="1.0.0", description="Version of the security configuration"
+        default="1.0.0", 
+        description="Version of the security configuration"
     )
 
     fingerprint: Fingerprint = Field(
-        default_factory=Fingerprint, description="Unique identifier for the component"
+        default_factory=Fingerprint, 
+        description="Unique identifier for the component"
     )
-
+    
     def is_compatible(self, min_version: str) -> bool:
         """
         Check if this security configuration is compatible with the minimum required version.
-
+        
         Args:
             min_version (str): Minimum required version in semver format (e.g., "1.0.0")
-
+            
         Returns:
             bool: True if this configuration is compatible, False otherwise
         """
         # Simple version comparison (can be enhanced with packaging.version if needed)
         current = [int(x) for x in self.version.split(".")]
         minimum = [int(x) for x in min_version.split(".")]
-
+        
         # Compare major, minor, patch versions
         for c, m in zip(current, minimum):
             if c > m:
@@ -67,19 +69,19 @@ class SecurityConfig(BaseModel):
                 return False
         return True
 
-    @model_validator(mode="before")
+    @model_validator(mode='before')
     @classmethod
     def validate_fingerprint(cls, values):
         """Ensure fingerprint is properly initialized."""
         if isinstance(values, dict):
             # Handle case where fingerprint is not provided or is None
-            if "fingerprint" not in values or values["fingerprint"] is None:
-                values["fingerprint"] = Fingerprint()
+            if 'fingerprint' not in values or values['fingerprint'] is None:
+                values['fingerprint'] = Fingerprint()
             # Handle case where fingerprint is a string (seed)
-            elif isinstance(values["fingerprint"], str):
-                if not values["fingerprint"].strip():
+            elif isinstance(values['fingerprint'], str):
+                if not values['fingerprint'].strip():
                     raise ValueError("Fingerprint seed cannot be empty")
-                values["fingerprint"] = Fingerprint.generate(seed=values["fingerprint"])
+                values['fingerprint'] = Fingerprint.generate(seed=values['fingerprint'])
         return values
 
     def to_dict(self) -> Dict[str, Any]:
@@ -89,11 +91,13 @@ class SecurityConfig(BaseModel):
         Returns:
             Dict[str, Any]: Dictionary representation of the security config
         """
-        result = {"fingerprint": self.fingerprint.to_dict()}
+        result = {
+            "fingerprint": self.fingerprint.to_dict()
+        }
         return result
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "SecurityConfig":
+    def from_dict(cls, data: Dict[str, Any]) -> 'SecurityConfig':
         """
         Create a SecurityConfig from a dictionary.
 
@@ -107,10 +111,6 @@ class SecurityConfig(BaseModel):
         data_copy = data.copy()
 
         fingerprint_data = data_copy.pop("fingerprint", None)
-        fingerprint = (
-            Fingerprint.from_dict(fingerprint_data)
-            if fingerprint_data
-            else Fingerprint()
-        )
+        fingerprint = Fingerprint.from_dict(fingerprint_data) if fingerprint_data else Fingerprint()
 
         return cls(fingerprint=fingerprint)

src/crewai/task.py

@@ -193,6 +193,7 @@ class Task(BaseModel):
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
+
                 return_annotation_args = get_args(return_annotation)
                 if not (
                     get_origin(return_annotation) is tuple
@@ -463,9 +464,7 @@ class Task(BaseModel):
                     )
                 )
                 self._save_file(content)
-            crewai_event_bus.emit(
-                self, TaskCompletedEvent(output=task_output, task=self)
-            )
+            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output, task=self))
             return task_output
         except Exception as e:
             self.end_time = datetime.datetime.now()

src/crewai/tasks/guardrail_result.py

@@ -22,7 +22,6 @@ class GuardrailResult(BaseModel):
         result (Any, optional): The validated/transformed result if successful
         error (str, optional): Error message if validation failed
     """
-
     success: bool
     result: Optional[Any] = None
     error: Optional[str] = None
@@ -33,13 +32,9 @@ class GuardrailResult(BaseModel):
         values = info.data
         if "success" in values:
             if values["success"] and v and "error" in values and values["error"]:
-                raise ValueError(
-                    "Cannot have both result and error when success is True"
-                )
+                raise ValueError("Cannot have both result and error when success is True")
             if not values["success"] and v and "result" in values and values["result"]:
-                raise ValueError(
-                    "Cannot have both result and error when success is False"
-                )
+                raise ValueError("Cannot have both result and error when success is False")
         return v
 
     @classmethod
@@ -57,5 +52,5 @@ class GuardrailResult(BaseModel):
         return cls(
             success=success,
             result=data if success else None,
-            error=data if not success else None,
+            error=data if not success else None
         )

src/crewai/telemetry/telemetry.py

@@ -45,10 +45,10 @@ class Telemetry:
     """
 
     def __init__(self):
-        self.ready: bool = False
-        self.trace_set: bool = False
+        self.ready = False
+        self.trace_set = False
 
-        if self._is_telemetry_disabled():
+        if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true":
             return
 
         try:
@@ -76,13 +76,6 @@ class Telemetry:
                 raise  # Re-raise the exception to not interfere with system signals
             self.ready = False
 
-    def _is_telemetry_disabled(self) -> bool:
-        """Check if telemetry should be disabled based on environment variables."""
-        return (
-            os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true"
-            or os.getenv("CREWAI_DISABLE_TELEMETRY", "false").lower() == "true"
-        )
-
     def set_tracer(self):
         if self.ready and not self.trace_set:
             try:

src/crewai/tools/agent_tools/add_image_tool.py

@@ -7,19 +7,6 @@ from crewai.utilities import I18N
 
 i18n = I18N()
 
-def _get_add_image_tool_name() -> str:
-    """Safely get the tool name from i18n."""
-    tool_info = i18n.tools("add_image")
-    if isinstance(tool_info, dict):
-        return tool_info.get("name", "Add Image")
-    return "Add Image" # Default name if not a dict
-
-def _get_add_image_tool_description() -> str:
-    """Safely get the tool description from i18n."""
-    tool_info = i18n.tools("add_image")
-    if isinstance(tool_info, dict):
-        return tool_info.get("description", "Tool for adding images to the content")
-    return "Tool for adding images to the content" # Default description if not a dict
 
 class AddImageToolSchema(BaseModel):
     image_url: str = Field(..., description="The URL or path of the image to add")
@@ -31,8 +18,8 @@ class AddImageToolSchema(BaseModel):
 class AddImageTool(BaseTool):
     """Tool for adding images to the content"""
 
-    name: str = Field(default_factory=_get_add_image_tool_name)
-    description: str = Field(default_factory=_get_add_image_tool_description)
+    name: str = Field(default_factory=lambda: i18n.tools("add_image")["name"])  # type: ignore
+    description: str = Field(default_factory=lambda: i18n.tools("add_image")["description"])  # type: ignore
     args_schema: type[BaseModel] = AddImageToolSchema
 
     def _run(

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -47,7 +47,10 @@ class BaseAgentTool(BaseTool):
         return coworker
 
     def _execute(
-        self, agent_name: Optional[str], task: str, context: Optional[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.
@@ -74,46 +77,36 @@ class BaseAgentTool(BaseTool):
             # when it should look like this:
             # {"task": "....", "coworker": "...."}
             sanitized_name = self.sanitize_agent_name(agent_name)
-            logger.debug(
-                f"Sanitized agent name from '{agent_name}' to '{sanitized_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}")
 
-            matching_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 self.sanitize_agent_name(available_agent.role) == sanitized_name
             ]
-            logger.debug(
-                f"Found {len(matching_agents)} matching agents for role '{sanitized_name}'"
-            )
+            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"- {self.sanitize_agent_name(agent.role)}"
-                        for agent in self.agents
-                    ]
+                    [f"- {self.sanitize_agent_name(agent.role)}" for agent in self.agents]
                 ),
-                error=str(e),
+                error=str(e)
             )
 
-        if not matching_agents:
+        if not agent:
             # No matching agent found after sanitization
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
-                    [
-                        f"- {self.sanitize_agent_name(agent.role)}"
-                        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}'",
+                error=f"No agent found with role '{sanitized_name}'"
             )
 
-        agent: BaseAgent = matching_agents[0]
+        agent = agent[0]
         try:
             task_with_assigned_agent = Task(
                 description=task,
@@ -121,12 +114,11 @@ class BaseAgentTool(BaseTool):
                 expected_output=agent.i18n.slice("manager_request"),
                 i18n=agent.i18n,
             )
-            logger.debug(
-                f"Created task for agent '{self.sanitize_agent_name(agent.role)}': {task}"
-            )
+            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)
+                agent_role=self.sanitize_agent_name(agent.role),
+                error=str(e)
             )

src/crewai/tools/base_tool.py

@@ -244,13 +244,9 @@ def to_langchain(
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
-def tool(*args, result_as_answer=False):
+def tool(*args):
     """
     Decorator to create a tool from a function.
-
-    Args:
-        *args: Positional arguments, either the function to decorate or the tool name.
-        result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.
     """
 
     def _make_with_name(tool_name: str) -> Callable:
@@ -276,7 +272,6 @@ def tool(*args, result_as_answer=False):
                 description=f.__doc__,
                 func=f,
                 args_schema=args_schema,
-                result_as_answer=result_as_answer,
             )
 
         return _make_tool

src/crewai/tools/tool_usage.py

@@ -380,7 +380,6 @@ class ToolUsage:
             else ToolCalling
         )
         converter = Converter(
-            agent=None, # Agent not needed here as function calling is supported/used
             text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
             llm=self.function_calling_llm,
             model=model,

src/crewai/utilities/converter.py

@@ -2,7 +2,7 @@ import json
 import re
 from typing import Any, Optional, Type, Union, get_args, get_origin
 
-from pydantic import BaseModel, Field, ValidationError
+from pydantic import BaseModel, ValidationError
 
 from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
 from crewai.utilities.printer import Printer
@@ -20,27 +20,18 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
-    agent: Any = Field(description="The agent instance associated with this converter.")
-
     def to_pydantic(self, current_attempt=1) -> BaseModel:
         """Convert text to pydantic."""
         try:
             if self.llm.supports_function_calling():
                 result = self._create_instructor().to_pydantic()
             else:
-                messages = []
-                if self.agent and getattr(self.agent, "use_system_prompt", True):
-                    messages.append({"role": "system", "content": self.instructions})
-                    messages.append({"role": "user", "content": self.text})
-                else:
-                    messages.append(
-                        {
-                            "role": "user",
-                            "content": f"{self.instructions}\n\n{self.text}",
-                        }
-                    )
-
-                response = self.llm.call(messages)  # Assign the result to 'response'
+                response = self.llm.call(
+                    [
+                        {"role": "system", "content": self.instructions},
+                        {"role": "user", "content": self.text},
+                    ]
+                )
                 try:
                     # Try to directly validate the response JSON
                     result = self.model.model_validate_json(response)
@@ -83,20 +74,14 @@ class Converter(OutputConverter):
             if self.llm.supports_function_calling():
                 return self._create_instructor().to_json()
             else:
-                messages = []
-                if self.agent and getattr(self.agent, "use_system_prompt", True):
-                    messages.append({"role": "system", "content": self.instructions})
-                    messages.append({"role": "user", "content": self.text})
-                else:
-                    messages.append(
-                        {
-                            "role": "user",
-                            "content": f"{self.instructions}\n\n{self.text}",
-                        }
+                return json.dumps(
+                    self.llm.call(
+                        [
+                            {"role": "system", "content": self.instructions},
+                            {"role": "user", "content": self.text},
+                        ]
                     )
-
-                llm_result = self.llm.call(messages)
-                return json.dumps(llm_result)
+                )
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_json(current_attempt + 1)
@@ -254,11 +239,11 @@ def create_converter(
 ) -> Converter:
     if agent and not converter_cls:
         if hasattr(agent, "get_output_converter"):
-            converter = agent.get_output_converter(agent=agent, *args, **kwargs)
+            converter = agent.get_output_converter(*args, **kwargs)
         else:
             raise AttributeError("Agent does not have a 'get_output_converter' method")
     elif converter_cls:
-        converter = converter_cls(agent=agent, *args, **kwargs)
+        converter = converter_cls(*args, **kwargs)
     else:
         raise ValueError("Either agent or converter_cls must be provided")
 

src/crewai/utilities/crew_json_encoder.py

@@ -11,7 +11,6 @@ from pydantic import BaseModel
 
 class CrewJSONEncoder(json.JSONEncoder):
     """Custom JSON encoder for CrewAI objects and special types."""
-
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -8,7 +8,6 @@ from crewai.agents.parser import OutputParserException
 
 """Parser for converting text outputs into Pydantic models."""
 
-
 class CrewPydanticOutputParser:
     """Parses text outputs into specified Pydantic models."""
 

src/crewai/utilities/errors.py

@@ -1,5 +1,4 @@
 """Error message definitions for CrewAI database operations."""
-
 from typing import Optional
 
 

src/crewai/utilities/evaluators/task_evaluator.py

@@ -65,18 +65,13 @@ class TaskEvaluator:
             instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
 
         converter = Converter(
-            agent=self.original_agent, # Pass agent
             llm=self.llm,
             text=evaluation_query,
             model=TaskEvaluation,
             instructions=instructions,
         )
 
-        result = converter.to_pydantic()
-        if isinstance(result, TaskEvaluation):
-            return result
-        else:
-            raise TypeError(f"Expected TaskEvaluation, got {type(result)}")
+        return converter.to_pydantic()
 
     def evaluate_training_data(
         self, training_data: dict, agent_id: str
@@ -139,7 +134,6 @@ class TaskEvaluator:
             instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
         converter = Converter(
-            agent=self.original_agent, # Pass agent
             llm=self.llm,
             text=evaluation_query,
             model=TrainingTaskEvaluation,
@@ -147,7 +141,4 @@ class TaskEvaluator:
         )
 
         pydantic_result = converter.to_pydantic()
-        if isinstance(pydantic_result, TrainingTaskEvaluation):
-            return pydantic_result
-        else:
-            raise TypeError(f"Expected TrainingTaskEvaluation, got {type(pydantic_result)}")
+        return pydantic_result

src/crewai/utilities/file_handler.py

@@ -7,33 +7,27 @@ from typing import Union
 
 class FileHandler:
     """Handler for file operations supporting both JSON and text-based logging.
-
+    
     Args:
         file_path (Union[bool, str]): Path to the log file or boolean flag
     """
 
     def __init__(self, file_path: Union[bool, str]):
         self._initialize_path(file_path)
-
+        
     def _initialize_path(self, file_path: Union[bool, str]):
         if file_path is True:  # File path is boolean True
             self._path = os.path.join(os.curdir, "logs.txt")
-
+        
         elif isinstance(file_path, str):  # File path is a string
             if file_path.endswith((".json", ".txt")):
-                self._path = (
-                    file_path  # No modification if the file ends with .json or .txt
-                )
+                self._path = file_path  # No modification if the file ends with .json or .txt
             else:
-                self._path = (
-                    file_path + ".txt"
-                )  # Append .txt if the file doesn't end with .json or .txt
-
+                self._path = file_path + ".txt"  # Append .txt if the file doesn't end with .json or .txt
+        
         else:
-            raise ValueError(
-                "file_path must be a string or boolean."
-            )  # Handle the case where file_path isn't valid
-
+            raise ValueError("file_path must be a string or boolean.")  # Handle the case where file_path isn't valid
+        
     def log(self, **kwargs):
         try:
             now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -51,25 +45,20 @@ class FileHandler:
                     except (json.JSONDecodeError, FileNotFoundError):
                         # If no valid JSON or file doesn't exist, start with an empty list
                         existing_data = [log_entry]
-
+                    
                     with open(self._path, "w", encoding="utf-8") as write_file:
                         json.dump(existing_data, write_file, indent=4)
                         write_file.write("\n")
-
+            
             else:
                 # Append log in plain text format
-                message = (
-                    f"{now}: "
-                    + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
-                    + "\n"
-                )
+                message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
                 with open(self._path, "a", encoding="utf-8") as file:
                     file.write(message)
 
         except Exception as e:
             raise ValueError(f"Failed to log message: {str(e)}")
-
-
+        
 class PickleHandler:
     def __init__(self, file_name: str) -> None:
         """

src/crewai/utilities/i18n.py

@@ -6,10 +6,8 @@ from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
 """Internationalization support for CrewAI prompts and messages."""
 
-
 class I18N(BaseModel):
     """Handles loading and retrieving internationalized prompts."""
-
     _prompts: Dict[str, Dict[str, str]] = PrivateAttr()
     prompt_file: Optional[str] = Field(
         default=None,

src/crewai/utilities/paths.py

@@ -5,7 +5,6 @@ import appdirs
 
 """Path management utilities for CrewAI storage and configuration."""
 
-
 def db_storage_path() -> str:
     """Returns the path for SQLite database storage.
 
@@ -29,4 +28,4 @@ def get_project_directory_name():
     else:
         cwd = Path.cwd()
         project_directory_name = cwd.name
-        return project_directory_name
+        return project_directory_name

src/crewai/utilities/planning_handler.py

@@ -9,10 +9,8 @@ from crewai.task import Task
 """Handles planning and coordination of crew tasks."""
 logger = logging.getLogger(__name__)
 
-
 class PlanPerTask(BaseModel):
     """Represents a plan for a specific task."""
-
     task: str = Field(..., description="The task for which the plan is created")
     plan: str = Field(
         ...,
@@ -22,7 +20,6 @@ class PlanPerTask(BaseModel):
 
 class PlannerTaskPydanticOutput(BaseModel):
     """Output format for task planning results."""
-
     list_of_plans_per_task: List[PlanPerTask] = Field(
         ...,
         description="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
@@ -31,7 +28,6 @@ class PlannerTaskPydanticOutput(BaseModel):
 
 class CrewPlanner:
     """Plans and coordinates the execution of crew tasks."""
-
     def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
         self.tasks = tasks
 
@@ -101,12 +97,8 @@ class CrewPlanner:
         for idx, task in enumerate(self.tasks):
             knowledge_list = self._get_agent_knowledge(task)
             agent_tools = (
-                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 "",
+                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 ""
             )
             task_summary = f"""
                 Task Number {idx + 1} - {task.description}

src/crewai/utilities/task_output_storage_handler.py

@@ -10,10 +10,8 @@ from crewai.task import Task
 
 """Handles storage and retrieval of task execution outputs."""
 
-
 class ExecutionLog(BaseModel):
     """Represents a log entry for task execution."""
-
     task_id: str
     expected_output: Optional[str] = None
     output: Dict[str, Any]
@@ -28,7 +26,6 @@ class ExecutionLog(BaseModel):
 
 """Manages storage and retrieval of task outputs."""
 
-
 class TaskOutputStorageHandler:
     def __init__(self) -> None:
         self.storage = KickoffTaskOutputsSQLiteStorage()

tests/agent_test.py

@@ -259,9 +259,7 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (
-        patch.object(CacheHandler, "read") as read,
-    ):
+    with (patch.object(CacheHandler, "read") as read,):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",

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tests/config/agents.yaml

@@ -8,7 +8,6 @@ researcher:
     developments in {topic}. Known for your ability to find the most relevant
     information and present it in a clear and concise manner.
   verbose: true
-  function_calling_llm: "local_llm"
 
 reporting_analyst:
   role: >
@@ -19,5 +18,4 @@ reporting_analyst:
     You're a meticulous analyst with a keen eye for detail. You're known for
     your ability to turn complex data into clear and concise reports, making
     it easy for others to understand and act on the information you provide.
-  verbose: true
-  function_calling_llm: "online_llm"
+  verbose: true

tests/conftest.py

@@ -8,7 +8,6 @@ from dotenv import load_dotenv
 
 load_result = load_dotenv(override=True)
 
-
 @pytest.fixture(autouse=True)
 def setup_test_environment():
     """Set up test environment with a temporary directory for SQLite storage."""
@@ -16,13 +15,11 @@ def setup_test_environment():
         # Create the directory with proper permissions
         storage_dir = Path(temp_dir) / "crewai_test_storage"
         storage_dir.mkdir(parents=True, exist_ok=True)
-
+        
         # Validate that the directory was created successfully
         if not storage_dir.exists() or not storage_dir.is_dir():
-            raise RuntimeError(
-                f"Failed to create test storage directory: {storage_dir}"
-            )
-
+            raise RuntimeError(f"Failed to create test storage directory: {storage_dir}")
+        
         # Verify directory permissions
         try:
             # Try to create a test file to verify write permissions
@@ -30,13 +27,11 @@ def setup_test_environment():
             test_file.touch()
             test_file.unlink()
         except (OSError, IOError) as e:
-            raise RuntimeError(
-                f"Test storage directory {storage_dir} is not writable: {e}"
-            )
-
+            raise RuntimeError(f"Test storage directory {storage_dir} is not writable: {e}")
+        
         # Set environment variable to point to the test storage directory
         os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
-
+        
         yield
-
+        
         # Cleanup is handled automatically when tempfile context exits

tests/crew_test.py

@@ -2157,6 +2157,7 @@ def test_tools_with_custom_caching():
     with patch.object(
         CacheHandler, "add", wraps=crew._cache_handler.add
     ) as add_to_cache:
+
         result = crew.kickoff()
 
         # Check that add_to_cache was called exactly twice

tests/imports_test.py

@@ -1,15 +0,0 @@
-"""Test that all public API classes are properly importable."""
-
-
-def test_task_output_import():
-    """Test that TaskOutput can be imported from crewai."""
-    from crewai import TaskOutput
-
-    assert TaskOutput is not None
-
-
-def test_crew_output_import():
-    """Test that CrewOutput can be imported from crewai."""
-    from crewai import CrewOutput
-
-    assert CrewOutput is not None

tests/memory/user_memory_test.py

@@ -1,3 +1,4 @@
+
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -11,7 +12,6 @@ class MockCrew:
     def __init__(self, memory_config):
         self.memory_config = memory_config
 
-
 @pytest.fixture
 def user_memory():
     """Fixture to create a UserMemory instance"""
@@ -19,18 +19,17 @@ def user_memory():
         memory_config={
             "provider": "mem0",
             "config": {"user_id": "john"},
-            "user_memory": {},
+            "user_memory" : {}
         }
     )
 
     user_memory = MagicMock(spec=UserMemory)
 
-    with patch.object(Memory, "__new__", return_value=user_memory):
+    with patch.object(Memory,'__new__',return_value=user_memory):
         user_memory_instance = UserMemory(crew=crew)
-
+    
     return user_memory_instance
 
-
 def test_save_and_search(user_memory):
     memory = UserMemoryItem(
         data="""test value test value test value test value test value test value
@@ -41,10 +40,16 @@ def test_save_and_search(user_memory):
     )
 
     with patch.object(UserMemory, "save") as mock_save:
-        user_memory.save(value=memory.data, metadata=memory.metadata, user=memory.user)
+        user_memory.save(
+            value=memory.data,
+            metadata=memory.metadata,
+            user=memory.user
+        )
 
         mock_save.assert_called_once_with(
-            value=memory.data, metadata=memory.metadata, user=memory.user
+            value=memory.data,
+            metadata=memory.metadata,
+            user=memory.user
         )
 
     expected_result = [
@@ -57,9 +62,7 @@ def test_save_and_search(user_memory):
     expected_result = ["mocked_result"]
 
     # Use patch.object to mock UserMemory's search method
-    with patch.object(
-        UserMemory, "search", return_value=expected_result
-    ) as mock_search:
+    with patch.object(UserMemory, 'search', return_value=expected_result) as mock_search:
         find = UserMemory.search("test value", score_threshold=0.01)[0]
         mock_search.assert_called_once_with("test value", score_threshold=0.01)
-        assert find == expected_result[0]
+        assert find == expected_result[0]

tests/project_test.py

@@ -2,16 +2,7 @@ import pytest
 
 from crewai.agent import Agent
 from crewai.crew import Crew
-from crewai.llm import LLM
-from crewai.project import (
-    CrewBase,
-    after_kickoff,
-    agent,
-    before_kickoff,
-    crew,
-    llm,
-    task,
-)
+from crewai.project import CrewBase, after_kickoff, agent, before_kickoff, crew, task
 from crewai.task import Task
 
 
@@ -40,14 +31,6 @@ class InternalCrew:
     agents_config = "config/agents.yaml"
     tasks_config = "config/tasks.yaml"
 
-    @llm
-    def local_llm(self):
-        return LLM(
-            model="openai/model_name",
-            api_key="None",
-            base_url="http://xxx.xxx.xxx.xxx:8000/v1",
-        )
-
     @agent
     def researcher(self):
         return Agent(config=self.agents_config["researcher"])
@@ -122,20 +105,6 @@ def test_task_name():
     ), "Custom task name is not being set as expected"
 
 
-def test_agent_function_calling_llm():
-    crew = InternalCrew()
-    llm = crew.local_llm()
-    obj_llm_agent = crew.researcher()
-    assert (
-        obj_llm_agent.function_calling_llm is llm
-    ), "agent's function_calling_llm is incorrect"
-
-    str_llm_agent = crew.reporting_analyst()
-    assert (
-        str_llm_agent.function_calling_llm.model == "online_llm"
-    ), "agent's function_calling_llm is incorrect"
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_modification():
     crew = InternalCrew()

tests/security/test_deterministic_fingerprints.py

@@ -54,7 +54,7 @@ def test_agent_with_deterministic_fingerprint():
         role="Researcher",
         goal="Research quantum computing",
         backstory="Expert in quantum physics",
-        security_config=security_config,
+        security_config=security_config
     )
 
     # Create another agent with the same security config
@@ -62,7 +62,7 @@ def test_agent_with_deterministic_fingerprint():
         role="Completely different role",
         goal="Different goal",
         backstory="Different backstory",
-        security_config=security_config,
+        security_config=security_config
     )
 
     # Both agents should have the same fingerprint UUID
@@ -84,7 +84,9 @@ def test_task_with_deterministic_fingerprint():
 
     # Create an agent first (required for tasks)
     agent = Agent(
-        role="Assistant", goal="Help with tasks", backstory="Helpful AI assistant"
+        role="Assistant",
+        goal="Help with tasks",
+        backstory="Helpful AI assistant"
     )
 
     # Create a task with the deterministic fingerprint
@@ -92,7 +94,7 @@ def test_task_with_deterministic_fingerprint():
         description="Analyze data",
         expected_output="Data analysis report",
         agent=agent,
-        security_config=security_config,
+        security_config=security_config
     )
 
     # Create another task with the same security config
@@ -100,7 +102,7 @@ def test_task_with_deterministic_fingerprint():
         description="Different task description",
         expected_output="Different expected output",
         agent=agent,
-        security_config=security_config,
+        security_config=security_config
     )
 
     # Both tasks should have the same fingerprint UUID
@@ -117,18 +119,36 @@ def test_crew_with_deterministic_fingerprint():
 
     # Create agents for the crew
     agent1 = Agent(
-        role="Researcher", goal="Research information", backstory="Expert researcher"
+        role="Researcher",
+        goal="Research information",
+        backstory="Expert researcher"
     )
 
-    agent2 = Agent(role="Writer", goal="Write reports", backstory="Expert writer")
+    agent2 = Agent(
+        role="Writer",
+        goal="Write reports",
+        backstory="Expert writer"
+    )
 
     # Create a crew with the deterministic fingerprint
-    crew1 = Crew(agents=[agent1, agent2], tasks=[], security_config=security_config)
+    crew1 = Crew(
+        agents=[agent1, agent2],
+        tasks=[],
+        security_config=security_config
+    )
 
     # Create another crew with the same security config but different agents
-    agent3 = Agent(role="Analyst", goal="Analyze data", backstory="Expert analyst")
+    agent3 = Agent(
+        role="Analyst",
+        goal="Analyze data",
+        backstory="Expert analyst"
+    )
 
-    crew2 = Crew(agents=[agent3], tasks=[], security_config=security_config)
+    crew2 = Crew(
+        agents=[agent3],
+        tasks=[],
+        security_config=security_config
+    )
 
     # Both crews should have the same fingerprint UUID
     assert crew1.fingerprint.uuid_str == crew2.fingerprint.uuid_str
@@ -148,7 +168,7 @@ def test_recreating_components_with_same_seed():
         role="Researcher",
         goal="Research topic",
         backstory="Expert researcher",
-        security_config=security_config1,
+        security_config=security_config1
     )
 
     uuid_from_first_session = agent1.fingerprint.uuid_str
@@ -161,7 +181,7 @@ def test_recreating_components_with_same_seed():
         role="Researcher",
         goal="Research topic",
         backstory="Expert researcher",
-        security_config=security_config2,
+        security_config=security_config2
     )
 
     # Should have same UUID across sessions
@@ -189,7 +209,7 @@ def test_security_config_with_seed_string():
         role="Tester",
         goal="Test fingerprints",
         backstory="Expert tester",
-        security_config=security_config,
+        security_config=security_config
     )
 
     # Agent should have the same fingerprint UUID
@@ -216,7 +236,7 @@ def test_complex_component_hierarchy_with_deterministic_fingerprints():
         role="Complex Test Agent",
         goal="Test complex fingerprint scenarios",
         backstory="Expert in testing",
-        security_config=agent_config,
+        security_config=agent_config
     )
 
     # Create a task
@@ -224,11 +244,15 @@ def test_complex_component_hierarchy_with_deterministic_fingerprints():
         description="Test complex fingerprinting",
         expected_output="Verification of fingerprint stability",
         agent=agent,
-        security_config=task_config,
+        security_config=task_config
     )
 
     # Create a crew
-    crew = Crew(agents=[agent], tasks=[task], security_config=crew_config)
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        security_config=crew_config
+    )
 
     # Each component should have its own deterministic fingerprint
     assert agent.fingerprint.uuid_str == agent_fingerprint.uuid_str
@@ -247,4 +271,4 @@ def test_complex_component_hierarchy_with_deterministic_fingerprints():
 
     assert agent_fingerprint.uuid_str == agent_fingerprint2.uuid_str
     assert task_fingerprint.uuid_str == task_fingerprint2.uuid_str
-    assert crew_fingerprint.uuid_str == crew_fingerprint2.uuid_str
+    assert crew_fingerprint.uuid_str == crew_fingerprint2.uuid_str

tests/security/test_fingerprint.py

@@ -170,7 +170,7 @@ def test_fingerprint_from_dict():
     fingerprint_dict = {
         "uuid_str": uuid_str,
         "created_at": created_at_iso,
-        "metadata": metadata,
+        "metadata": metadata
     }
 
     fingerprint = Fingerprint.from_dict(fingerprint_dict)
@@ -207,7 +207,11 @@ def test_invalid_uuid_str():
     uuid_str = "not-a-valid-uuid"
     created_at = datetime.now().isoformat()
 
-    fingerprint_dict = {"uuid_str": uuid_str, "created_at": created_at, "metadata": {}}
+    fingerprint_dict = {
+        "uuid_str": uuid_str,
+        "created_at": created_at,
+        "metadata": {}
+    }
 
     # The Fingerprint.from_dict method accepts even invalid UUIDs
     # This seems to be the current behavior
@@ -239,7 +243,7 @@ def test_fingerprint_metadata_mutation():
     expected_metadata = {
         "version": "1.0",
         "status": "published",
-        "author": "Test Author",
+        "author": "Test Author"
     }
     assert fingerprint.metadata == expected_metadata
 
@@ -256,4 +260,4 @@ def test_fingerprint_metadata_mutation():
 
     # Ensure immutable fields remain unchanged
     assert fingerprint.uuid_str == uuid_str
-    assert fingerprint.created_at == created_at
+    assert fingerprint.created_at == created_at

tests/security/test_integration.py

@@ -15,7 +15,7 @@ def test_agent_with_security_config():
         role="Tester",
         goal="Test fingerprinting",
         backstory="Testing fingerprinting",
-        security_config=security_config,
+        security_config=security_config
     )
 
     assert agent.security_config is not None
@@ -28,7 +28,9 @@ def test_agent_fingerprint_property():
     """Test the fingerprint property on Agent."""
     # Create agent without security_config
     agent = Agent(
-        role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     # Fingerprint should be automatically generated
@@ -43,14 +45,21 @@ def test_crew_with_security_config():
     security_config = SecurityConfig()
 
     agent1 = Agent(
-        role="Tester1", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester1",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     agent2 = Agent(
-        role="Tester2", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester2",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
-    crew = Crew(agents=[agent1, agent2], security_config=security_config)
+    crew = Crew(
+        agents=[agent1, agent2],
+        security_config=security_config
+    )
 
     assert crew.security_config is not None
     assert crew.security_config == security_config
@@ -62,11 +71,15 @@ def test_crew_fingerprint_property():
     """Test the fingerprint property on Crew."""
     # Create crew without security_config
     agent1 = Agent(
-        role="Tester1", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester1",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     agent2 = Agent(
-        role="Tester2", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester2",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     crew = Crew(agents=[agent1, agent2])
@@ -83,14 +96,16 @@ def test_task_with_security_config():
     security_config = SecurityConfig()
 
     agent = Agent(
-        role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     task = Task(
         description="Test task",
         expected_output="Testing output",
         agent=agent,
-        security_config=security_config,
+        security_config=security_config
     )
 
     assert task.security_config is not None
@@ -103,10 +118,16 @@ def test_task_fingerprint_property():
     """Test the fingerprint property on Task."""
     # Create task without security_config
     agent = Agent(
-        role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
-    task = Task(description="Test task", expected_output="Testing output", agent=agent)
+    task = Task(
+        description="Test task",
+        expected_output="Testing output",
+        agent=agent
+    )
 
     # Fingerprint should be automatically generated
     assert task.fingerprint is not None
@@ -118,20 +139,33 @@ def test_end_to_end_fingerprinting():
     """Test end-to-end fingerprinting across Agent, Crew, and Task."""
     # Create components with auto-generated fingerprints
     agent1 = Agent(
-        role="Researcher", goal="Research information", backstory="Expert researcher"
+        role="Researcher",
+        goal="Research information",
+        backstory="Expert researcher"
     )
 
-    agent2 = Agent(role="Writer", goal="Write content", backstory="Expert writer")
+    agent2 = Agent(
+        role="Writer",
+        goal="Write content",
+        backstory="Expert writer"
+    )
 
     task1 = Task(
-        description="Research topic", expected_output="Research findings", agent=agent1
+        description="Research topic",
+        expected_output="Research findings",
+        agent=agent1
     )
 
     task2 = Task(
-        description="Write article", expected_output="Written article", agent=agent2
+        description="Write article",
+        expected_output="Written article",
+        agent=agent2
     )
 
-    crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[task1, task2]
+    )
 
     # Verify all fingerprints were automatically generated
     assert agent1.fingerprint is not None
@@ -146,18 +180,18 @@ def test_end_to_end_fingerprinting():
         agent2.fingerprint.uuid_str,
         task1.fingerprint.uuid_str,
         task2.fingerprint.uuid_str,
-        crew.fingerprint.uuid_str,
+        crew.fingerprint.uuid_str
     ]
-    assert len(fingerprints) == len(
-        set(fingerprints)
-    ), "All fingerprints should be unique"
+    assert len(fingerprints) == len(set(fingerprints)), "All fingerprints should be unique"
 
 
 def test_fingerprint_persistence():
     """Test that fingerprints persist and don't change."""
     # Create an agent and check its fingerprint
     agent = Agent(
-        role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting"
+        role="Tester",
+        goal="Test fingerprinting",
+        backstory="Testing fingerprinting"
     )
 
     # Get initial fingerprint
@@ -167,7 +201,11 @@ def test_fingerprint_persistence():
     assert agent.fingerprint.uuid_str == initial_fingerprint
 
     # Create a task with the agent
-    task = Task(description="Test task", expected_output="Testing output", agent=agent)
+    task = Task(
+        description="Test task",
+        expected_output="Testing output",
+        agent=agent
+    )
 
     # Check that task has its own unique fingerprint
     assert task.fingerprint is not None
@@ -185,25 +223,27 @@ def test_shared_security_config_fingerprints():
         role="Researcher",
         goal="Research information",
         backstory="Expert researcher",
-        security_config=shared_security_config,
+        security_config=shared_security_config
     )
 
     agent2 = Agent(
         role="Writer",
         goal="Write content",
         backstory="Expert writer",
-        security_config=shared_security_config,
+        security_config=shared_security_config
     )
 
     task = Task(
         description="Write article",
         expected_output="Written article",
         agent=agent1,
-        security_config=shared_security_config,
+        security_config=shared_security_config
     )
 
     crew = Crew(
-        agents=[agent1, agent2], tasks=[task], security_config=shared_security_config
+        agents=[agent1, agent2],
+        tasks=[task],
+        security_config=shared_security_config
     )
 
     # Verify all components have the same fingerprint UUID
@@ -216,4 +256,4 @@ def test_shared_security_config_fingerprints():
     assert agent1.fingerprint is shared_security_config.fingerprint
     assert agent2.fingerprint is shared_security_config.fingerprint
     assert task.fingerprint is shared_security_config.fingerprint
-    assert crew.fingerprint is shared_security_config.fingerprint
+    assert crew.fingerprint is shared_security_config.fingerprint

tests/security/test_security_config.py

@@ -63,11 +63,13 @@ def test_security_config_from_dict():
     fingerprint_dict = {
         "uuid_str": "b723c6ff-95de-5e87-860b-467b72282bd8",
         "created_at": datetime.now().isoformat(),
-        "metadata": {"version": "1.0"},
+        "metadata": {"version": "1.0"}
     }
 
     # Create a config dict with just the fingerprint
-    config_dict = {"fingerprint": fingerprint_dict}
+    config_dict = {
+        "fingerprint": fingerprint_dict
+    }
 
     # Create config manually since from_dict has a specific implementation
     config = SecurityConfig()
@@ -113,4 +115,4 @@ def test_security_config_json_serialization():
     new_config.fingerprint = new_fingerprint
 
     # Check the new config has the same fingerprint metadata
-    assert new_config.fingerprint.metadata == {"version": "1.0"}
+    assert new_config.fingerprint.metadata == {"version": "1.0"}

tests/telemetry/test_telemetry_disable.py

@@ -1,33 +0,0 @@
-import os
-from unittest.mock import patch
-
-import pytest
-
-from crewai.telemetry import Telemetry
-
-
-@pytest.mark.parametrize(
-    "env_var,value,expected_ready",
-    [
-        ("OTEL_SDK_DISABLED", "true", False),
-        ("OTEL_SDK_DISABLED", "TRUE", False),
-        ("CREWAI_DISABLE_TELEMETRY", "true", False),
-        ("CREWAI_DISABLE_TELEMETRY", "TRUE", False),
-        ("OTEL_SDK_DISABLED", "false", True),
-        ("CREWAI_DISABLE_TELEMETRY", "false", True),
-    ],
-)
-def test_telemetry_environment_variables(env_var, value, expected_ready):
-    """Test telemetry state with different environment variable configurations."""
-    with patch.dict(os.environ, {env_var: value}):
-        with patch("crewai.telemetry.telemetry.TracerProvider"):
-            telemetry = Telemetry()
-            assert telemetry.ready is expected_ready
-
-
-def test_telemetry_enabled_by_default():
-    """Test that telemetry is enabled by default."""
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.telemetry.telemetry.TracerProvider"):
-            telemetry = Telemetry()
-            assert telemetry.ready is True

tests/test_flow_default_override.py

@@ -6,7 +6,6 @@ from crewai.flow.persistence import persist
 
 class PoemState(FlowState):
     """Test state model with default values that should be overridden."""
-
     sentence_count: int = 1000  # Default that should be overridden
     has_set_count: bool = False  # Track whether we've set the count
     poem_type: str = ""
@@ -47,13 +46,11 @@ def test_default_value_override():
 
     # Fourth run - explicit override should work
     flow3 = PoemFlow()
-    flow3.kickoff(
-        inputs={
-            "id": original_uuid,
-            "has_set_count": True,
-            "sentence_count": 5,  # Override persisted value
-        }
-    )
+    flow3.kickoff(inputs={
+        "id": original_uuid,
+        "has_set_count": True,
+        "sentence_count": 5,  # Override persisted value
+    })
     assert flow3.state.sentence_count == 5  # Should use override value
 
     # Third run - should not load sentence_count=2 instead of default 1000
@@ -99,12 +96,17 @@ def test_multi_step_default_override():
 
     # Second run - should load persisted state and update poem type
     flow2 = MultiStepPoemFlow()
-    flow2.kickoff(inputs={"id": original_uuid, "sentence_count": 5})
+    flow2.kickoff(inputs={
+        "id": original_uuid,
+        "sentence_count": 5
+    })
     assert flow2.state.sentence_count == 5
     assert flow2.state.poem_type == "limerick"
 
     # Third run - new flow without persisted state should use defaults
     flow3 = MultiStepPoemFlow()
-    flow3.kickoff(inputs={"id": original_uuid})
+    flow3.kickoff(inputs={
+        "id": original_uuid
+    })
     assert flow3.state.sentence_count == 5
-    assert flow3.state.poem_type == "limerick"
+    assert flow3.state.poem_type == "limerick"

tests/test_lite_agent.py

@@ -4,8 +4,8 @@ from typing import cast
 import pytest
 from pydantic import BaseModel, Field
 
-from crewai import LLM, Agent
-from crewai.lite_agent import LiteAgent, LiteAgentOutput
+from crewai import LLM
+from crewai.lite_agent import LiteAgent
 from crewai.tools import BaseTool
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
@@ -63,74 +63,12 @@ class ResearchResult(BaseModel):
     sources: list[str] = Field(description="List of sources used")
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.parametrize("verbose", [True, False])
-def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
-    """Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
-    # Create a test agent with specific parameters
-    llm = LLM(model="gpt-4o-mini")
-    custom_tools = [WebSearchTool(), CalculatorTool()]
-    max_iter = 10
-    max_execution_time = 300
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test Goal",
-        backstory="Test Backstory",
-        llm=llm,
-        tools=custom_tools,
-        max_iter=max_iter,
-        max_execution_time=max_execution_time,
-        verbose=verbose,
-    )
-
-    # Create a mock to capture the created LiteAgent
-    created_lite_agent = None
-    original_lite_agent = LiteAgent
-
-    # Define a mock LiteAgent class that captures its arguments
-    class MockLiteAgent(original_lite_agent):
-        def __init__(self, **kwargs):
-            nonlocal created_lite_agent
-            created_lite_agent = kwargs
-            super().__init__(**kwargs)
-
-    # Patch the LiteAgent class
-    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
-
-    # Call kickoff to create the LiteAgent
-    agent.kickoff("Test query")
-
-    # Verify all parameters were passed correctly
-    assert created_lite_agent is not None
-    assert created_lite_agent["role"] == "Test Agent"
-    assert created_lite_agent["goal"] == "Test Goal"
-    assert created_lite_agent["backstory"] == "Test Backstory"
-    assert created_lite_agent["llm"] == llm
-    assert len(created_lite_agent["tools"]) == 2
-    assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
-    assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
-    assert created_lite_agent["max_iterations"] == max_iter
-    assert created_lite_agent["max_execution_time"] == max_execution_time
-    assert created_lite_agent["verbose"] == verbose
-    assert created_lite_agent["response_format"] is None
-
-    # Test with a response_format
-    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
-
-    class TestResponse(BaseModel):
-        test_field: str
-
-    agent.kickoff("Test query", response_format=TestResponse)
-    assert created_lite_agent["response_format"] == TestResponse
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_with_tools():
-    """Test that Agent can use tools."""
+    """Test that LiteAgent can use tools."""
     # Create a LiteAgent with tools
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
@@ -168,7 +106,7 @@ def test_lite_agent_with_tools():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_structured_output():
-    """Test that Agent can return a simple structured output."""
+    """Test that LiteAgent can return a simple structured output."""
 
     class SimpleOutput(BaseModel):
         """Simple structure for agent outputs."""
@@ -179,18 +117,18 @@ def test_lite_agent_structured_output():
     web_search_tool = WebSearchTool()
 
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Info Gatherer",
         goal="Provide brief information",
         backstory="You gather and summarize information quickly.",
         llm=llm,
         tools=[web_search_tool],
         verbose=True,
+        response_format=SimpleOutput,
     )
 
     result = agent.kickoff(
-        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence",
-        response_format=SimpleOutput,
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
     )
 
     print(f"\n=== Agent Result Type: {type(result)}")
@@ -217,7 +155,7 @@ def test_lite_agent_structured_output():
 def test_lite_agent_returns_usage_metrics():
     """Test that LiteAgent returns usage metrics."""
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
@@ -232,26 +170,3 @@ def test_lite_agent_returns_usage_metrics():
 
     assert result.usage_metrics is not None
     assert result.usage_metrics["total_tokens"] > 0
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.asyncio
-async def test_lite_agent_returns_usage_metrics_async():
-    """Test that LiteAgent returns usage metrics when run asynchronously."""
-    llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
-        role="Research Assistant",
-        goal="Find information about the population of Tokyo",
-        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
-        llm=llm,
-        tools=[WebSearchTool()],
-        verbose=True,
-    )
-
-    result = await agent.kickoff_async(
-        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
-    )
-    assert isinstance(result, LiteAgentOutput)
-    assert "21 million" in result.raw or "37 million" in result.raw
-    assert result.usage_metrics is not None
-    assert result.usage_metrics["total_tokens"] > 0

tests/test_multimodal_validation.py

@@ -18,7 +18,7 @@ def test_multimodal_agent_with_image_url():
     llm = LLM(
         model="openai/gpt-4o",  # model with vision capabilities
         api_key=OPENAI_API_KEY,
-        temperature=0.7,
+        temperature=0.7
     )
 
     expert_analyst = Agent(
@@ -28,7 +28,7 @@ def test_multimodal_agent_with_image_url():
         llm=llm,
         verbose=True,
         allow_delegation=False,
-        multimodal=True,
+        multimodal=True
     )
 
     inspection_task = Task(
@@ -40,7 +40,7 @@ def test_multimodal_agent_with_image_url():
         Provide a detailed report highlighting any issues found.
         """,
         expected_output="A detailed report highlighting any issues found",
-        agent=expert_analyst,
+        agent=expert_analyst
     )
 
     crew = Crew(agents=[expert_analyst], tasks=[inspection_task])

tests/test_qwen_multimodal.py

@@ -0,0 +1,32 @@
+import pytest
+
+from crewai.llm import LLM
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_qwen_multimodal_content_formatting():
+    """Test that multimodal content is properly formatted for Qwen models."""
+    
+    llm = LLM(model="sambanova/Qwen2.5-72B-Instruct", temperature=0.7)
+    
+    message = {"role": "user", "content": "Describe this image"}
+    formatted = llm._format_messages_for_provider([message])
+    assert isinstance(formatted[0]["content"], list)
+    assert formatted[0]["content"][0]["type"] == "text"
+    assert formatted[0]["content"][0]["text"] == "Describe this image"
+    
+    multimodal_content = [
+        {"type": "text", "text": "What's in this image?"}, 
+        {"type": "image_url", "image_url": "https://example.com/image.jpg"}
+    ]
+    message = {"role": "user", "content": multimodal_content}
+    formatted = llm._format_messages_for_provider([message])
+    assert formatted[0]["content"] == multimodal_content
+    
+    messages = [
+        {"role": "system", "content": "You are a visual analysis assistant."},
+        {"role": "user", "content": multimodal_content}
+    ]
+    formatted = llm._format_messages_for_provider(messages)
+    assert isinstance(formatted[0]["content"], list)
+    assert formatted[1]["content"] == multimodal_content

tests/tools/test_base_tool.py

@@ -100,25 +100,3 @@ def test_default_cache_function_is_true():
     my_tool = MyCustomTool()
     # Assert all the right attributes were defined
     assert my_tool.cache_function()
-
-
-def test_result_as_answer_in_tool_decorator():
-    @tool("Tool with result as answer", result_as_answer=True)
-    def my_tool_with_result_as_answer(question: str) -> str:
-        """This tool will return its result as the final answer."""
-        return question
-
-    assert my_tool_with_result_as_answer.result_as_answer is True
-
-    converted_tool = my_tool_with_result_as_answer.to_structured_tool()
-    assert converted_tool.result_as_answer is True
-
-    @tool("Tool with default result_as_answer")
-    def my_tool_with_default(question: str) -> str:
-        """This tool uses the default result_as_answer value."""
-        return question
-
-    assert my_tool_with_default.result_as_answer is False
-
-    converted_tool = my_tool_with_default.to_structured_tool()
-    assert converted_tool.result_as_answer is False

tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml

@@ -0,0 +1,864 @@
+interactions:
+- request:
+    body: '{"model": "llama3.2:3b", "prompt": "### System:\nPlease convert the following
+      text into valid JSON.\n\nOutput ONLY the valid JSON and nothing else.\n\nThe
+      JSON must follow this format exactly:\n{\n  \"name\": str,\n  \"age\": int\n}\n\n###
+      User:\nName: Alice Llama, Age: 30\n\n", "options": {"stop": []}, "stream": false}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '321'
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.60.2
+    method: POST
+    uri: http://localhost:11434/api/generate
+  response:
+    content: '{"model":"llama3.2:3b","created_at":"2025-02-21T02:57:55.059392Z","response":"{\"name\":
+      \"Alice Llama\", \"age\": 30}","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,5618,5625,279,2768,1495,1139,2764,4823,382,5207,27785,279,2764,4823,323,4400,775,382,791,4823,2011,1833,420,3645,7041,512,517,220,330,609,794,610,345,220,330,425,794,528,198,633,14711,2724,512,678,25,30505,445,81101,11,13381,25,220,966,271,128009,128006,78191,128007,271,5018,609,794,330,62786,445,81101,498,330,425,794,220,966,92],"total_duration":4675906000,"load_duration":836091458,"prompt_eval_count":82,"prompt_eval_duration":3561000000,"eval_count":15,"eval_duration":275000000}'
+    headers:
+      Content-Length:
+      - '761'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Fri, 21 Feb 2025 02:57:55 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/1.60.2
+    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.
+      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
+      Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree
+      you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate
+      the law or others\u2019 rights, including to:\\n    1. Engage in, promote, generate,
+      contribute to, encourage, plan, incite, or further illegal or unlawful activity
+      or content, such as:\\n        1. Violence or terrorism\\n        2. Exploitation
+      or harm to children, including the solicitation, creation, acquisition, or dissemination
+      of child exploitative content or failure to report Child Sexual Abuse Material\\n
+      \       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
+      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\",\"modelfile\":\"# Modelfile generated by \\\"ollama
+      show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n#
+      FROM llama3.2:3b\\n\\nFROM /Users/joaomoura/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
+      \\\"\\\"\\\"\\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 }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER
+      stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE
+      \\\"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
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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*
+      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
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+      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
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+      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
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+      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
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+      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,
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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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+      from and against any claim by any third\\n    party arising out of or related
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+      may terminate this Agreement if you are in breach of any term or condition of
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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
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+      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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+      \       3. Human trafficking, exploitation, and sexual violence\\n        4.
+      The illegal distribution of information or materials to minors, including obscene
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+      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,
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+      Representing that the use of Llama 3.2 or outputs are human-generated\\n    19.
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+      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
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+      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
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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*
+      Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
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+    headers:
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+      Date:
+      - Fri, 21 Feb 2025 02:57:55 GMT
+      Transfer-Encoding:
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+    status_code: 200
+version: 1

tests/utilities/evaluators/test_task_evaluator.py

@@ -46,7 +46,6 @@ def test_evaluate_training_data(converter_mock):
     converter_mock.assert_has_calls(
         [
             mock.call(
-                agent=original_agent, # Add agent argument
                 llm=original_agent.llm,
                 text="Assess the quality of the training data based on the llm output, human feedback , and llm "
                 "output improved result.\n\nIteration: data1\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "

tests/utilities/test_converter.py

@@ -197,101 +197,6 @@ def test_convert_with_instructions_success(
     assert output.age == 50
 
 
-@patch("crewai.utilities.converter.get_conversion_instructions")
-@patch("crewai.utilities.converter.create_converter")
-def test_convert_with_instructions_respects_use_system_prompt_false(
-    mock_create_converter, mock_get_instructions, mock_agent
-):
-    """
-    Test that convert_with_instructions does not use a system prompt
-    when agent.use_system_prompt is False and the LLM doesn't support function calling.
-    """
-    mock_agent.use_system_prompt = False
-    mock_llm = MagicMock()
-    mock_llm.supports_function_calling.return_value = False
-    mock_agent.llm = mock_llm
-    mock_agent.function_calling_llm = None  # Ensure fallback to agent.llm
-
-    mock_get_instructions.return_value = "Test Instructions"
-
-    mock_converter_instance = MagicMock(spec=Converter)
-    mock_converter_instance.agent = mock_agent  # Set the agent on the mock converter
-    mock_converter_instance.llm = mock_llm
-    mock_converter_instance.instructions = "Test Instructions"
-    mock_converter_instance.text = "Some text"
-    mock_converter_instance.model = SimpleModel
-
-    converter = Converter(
-        agent=mock_agent,
-        llm=mock_llm,
-        text="Some text",
-        model=SimpleModel,
-        instructions="Test Instructions",
-    )
-    mock_create_converter.return_value = (
-        converter  # This instance will be used by convert_with_instructions
-    )
-
-    converter.llm.call = MagicMock(return_value='{"name": "Mock Name", "age": 99}')
-
-    convert_with_instructions("Some text", SimpleModel, False, mock_agent)
-
-    converter.llm.call.assert_called_once()
-    call_args = converter.llm.call.call_args[0][0]  # Get the 'messages' list argument
-
-    assert not any(msg.get("role") == "system" for msg in call_args)
-    user_message = next((msg for msg in call_args if msg.get("role") == "user"), None)
-    assert user_message is not None
-    assert "Test Instructions" in user_message["content"]
-    assert "Some text" in user_message["content"]
-    assert user_message["content"].startswith("Test Instructions\n\n")
-
-
-@patch("crewai.utilities.converter.get_conversion_instructions")
-@patch("crewai.utilities.converter.create_converter")
-def test_convert_with_instructions_respects_use_system_prompt_true(
-    mock_create_converter, mock_get_instructions, mock_agent
-):
-    """
-    Test that convert_with_instructions uses a system prompt
-    when agent.use_system_prompt is True and the LLM doesn't support function calling.
-    """
-    mock_agent.use_system_prompt = True  # Explicitly True
-    mock_llm = MagicMock()
-    mock_llm.supports_function_calling.return_value = False
-    mock_agent.llm = mock_llm
-    mock_agent.function_calling_llm = None
-
-    mock_get_instructions.return_value = "Test Instructions"
-
-    converter = Converter(
-        agent=mock_agent,
-        llm=mock_llm,
-        text="Some text",
-        model=SimpleModel,
-        instructions="Test Instructions",
-    )
-    mock_create_converter.return_value = (
-        converter  # This instance will be used by convert_with_instructions
-    )
-
-    converter.llm.call = MagicMock(return_value='{"name": "Mock Name", "age": 99}')
-
-    convert_with_instructions("Some text", SimpleModel, False, mock_agent)
-
-    converter.llm.call.assert_called_once()
-    call_args = converter.llm.call.call_args[0][0]
-
-    system_message = next(
-        (msg for msg in call_args if msg.get("role") == "system"), None
-    )
-    assert system_message is not None
-    assert system_message["content"] == "Test Instructions"
-    user_message = next((msg for msg in call_args if msg.get("role") == "user"), None)
-    assert user_message is not None
-    assert user_message["content"] == "Some text"
-
-
 @patch("crewai.utilities.converter.create_converter")
 @patch("crewai.utilities.converter.get_conversion_instructions")
 def test_convert_with_instructions_failure(
@@ -429,10 +334,7 @@ def test_convert_with_instructions():
     sample_text = "Name: Alice, Age: 30"
 
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()  # Add mock agent if not available
-    mock_agent.use_system_prompt = True  # Default or set as needed
     converter = Converter(
-        agent=mock_agent,  # Add agent argument
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -460,10 +362,7 @@ def test_converter_with_llama3_2_model():
     llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()  # Add mock agent if not available
-    mock_agent.use_system_prompt = True  # Default or set as needed
     converter = Converter(
-        agent=mock_agent,  # Add agent argument
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -481,10 +380,7 @@ def test_converter_with_llama3_1_model():
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()  # Add mock agent if not available
-    mock_agent.use_system_prompt = True  # Default or set as needed
     converter = Converter(
-        agent=mock_agent,  # Add agent argument
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -509,11 +405,7 @@ def test_converter_with_nested_model():
     sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
 
     instructions = get_conversion_instructions(Person, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=Person,
@@ -539,10 +431,7 @@ def test_converter_error_handling():
     sample_text = "Name: Alice, Age: 30"
 
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()  # Add mock agent if not available
-    mock_agent.use_system_prompt = True  # Default or set as needed
     converter = Converter(
-        agent=mock_agent,  # Add agent argument
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -567,11 +456,7 @@ def test_converter_retry_logic():
     sample_text = "Name: Retry Alice, Age: 30"
 
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -600,11 +485,7 @@ def test_converter_with_optional_fields():
     sample_text = "Name: Bob, age: None"
 
     instructions = get_conversion_instructions(OptionalModel, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=OptionalModel,
@@ -629,11 +510,7 @@ def test_converter_with_list_field():
     sample_text = "Items: 1, 2, 3"
 
     instructions = get_conversion_instructions(ListModel, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=ListModel,
@@ -666,11 +543,7 @@ def test_converter_with_enum():
     sample_text = "Name: Alice, Color: Red"
 
     instructions = get_conversion_instructions(EnumModel, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=EnumModel,
@@ -692,11 +565,7 @@ def test_converter_with_ambiguous_input():
     sample_text = "Charlie is thirty years old"
 
     instructions = get_conversion_instructions(SimpleModel, llm)
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text=sample_text,
         model=SimpleModel,
@@ -717,11 +586,7 @@ def test_converter_with_function_calling():
     instructor = Mock()
     instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
 
-    mock_agent = Mock()
-    mock_agent.use_system_prompt = True
-
     converter = Converter(
-        agent=mock_agent,
         llm=llm,
         text="Name: Eve, Age: 35",
         model=SimpleModel,

tests/utilities/test_knowledge_planning.py

@@ -29,14 +29,13 @@ def mock_knowledge_source():
     """
     return StringKnowledgeSource(content=content)
 
-
-@patch("crewai.knowledge.storage.knowledge_storage.chromadb")
+@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",
@@ -46,14 +45,14 @@ def test_knowledge_included_in_planning(mock_chroma):
             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,
+        agent=agent
     )
 
     # Create a crew planner
@@ -63,29 +62,23 @@ def test_knowledge_included_in_planning(mock_chroma):
     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"
+    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"
+    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"
+    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

@@ -100,7 +100,7 @@ class InternalCrewPlanner:
         # Knowledge field should not be present when empty
         assert '"agent_knowledge"' not in tasks_summary
 
-    @patch("crewai.knowledge.storage.knowledge_storage.chromadb")
+    @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
@@ -146,8 +146,8 @@ class InternalCrewPlanner:
                 tools=[tool1, tool2],
                 knowledge_sources=[
                     StringKnowledgeSource(content="Test knowledge content")
-                ],
-            ),
+                ]
+            )
         )
 
         # Create planner with the new task

uv.lock

@@ -1,42 +1,19 @@
 version = 1
+revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -344,7 +321,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "os_name == 'nt'" },
+    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -579,7 +556,7 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
 wheels = [
@@ -630,7 +607,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.114.0"
+version = "0.108.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -718,7 +695,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.40.1" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.38.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -745,6 +722,7 @@ requires-dist = [
     { name = "tomli-w", specifier = ">=1.1.0" },
     { name = "uv", specifier = ">=0.4.25" },
 ]
+provides-extras = ["tools", "embeddings", "agentops", "fastembed", "pdfplumber", "pandas", "openpyxl", "mem0", "docling", "aisuite"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -767,7 +745,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.40.1"
+version = "0.38.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -782,9 +760,9 @@ dependencies = [
     { name = "pytube" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/16/ff/0c16c9943ec1501b12fc72aca7815f191ffe94d5f1fe4e9c353ee8c4ad1d/crewai_tools-0.40.1.tar.gz", hash = "sha256:6af5040b2277df8fd592238a17bf584f95dcc9ef7766236534999c8a9e9d0b52", size = 744094 }
+sdist = { url = "https://files.pythonhosted.org/packages/85/3f/d3b5697b4c6756cec65316c9ea9ccd9054f7b73670d1580befd3632ba031/crewai_tools-0.38.1.tar.gz", hash = "sha256:6abe75b3b339d53a9cf4e2d80124d863ff62a82b36753c30bec64318881876b2", size = 737620 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/35/05/619c00bae2dda038f0d218dd5197120c938e9c9ccef1b9e50cfb037486f6/crewai_tools-0.40.1-py3-none-any.whl", hash = "sha256:8f459f74dee64364bfdbc524c815c4afcfb9ed532b51e6b8b4f616398d46cf1e", size = 573286 },
+    { url = "https://files.pythonhosted.org/packages/2b/2b/a6c9007647ffbb6a3c204b3ef26806030d6b041e3e012d4cec43c21335d6/crewai_tools-0.38.1-py3-none-any.whl", hash = "sha256:d9d3a88060f1f30c8f4ea044f6dd564a50d0a22b8a018a6fcec202b36246b9d8", size = 561414 },
 ]
 
 [[package]]
@@ -2518,7 +2496,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
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+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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 [[package]]
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 version = "2.10.1"
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-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
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