Merge branch 'main' into feat/individual-react-agent

fix expected output.
missing expected_output on task throws errors

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>

README.md

@@ -401,11 +401,16 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Using Crews and Flows Together
 
-CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
+CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
+CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
+- `or_`: Triggers when any of the specified conditions are met. 
+- `and_`Triggers when all of the specified conditions are met.
+
+Here's how you can orchestrate multiple Crews within a Flow:
 
 ```python
-from crewai.flow.flow import Flow, listen, start, router
-from crewai import Crew, Agent, Task
+from crewai.flow.flow import Flow, listen, start, router, or_
+from crewai import Crew, Agent, Task, Process
 from pydantic import BaseModel
 
 # Define structured state for precise control
@@ -479,7 +484,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
         )
         return strategy_crew.kickoff()
 
-    @listen("medium_confidence", "low_confidence")
+    @listen(or_("medium_confidence", "low_confidence"))
     def request_additional_analysis(self):
         self.state.recommendations.append("Gather more data")
         return "Additional analysis required"

docs/changelog.mdx

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

docs/concepts/event-listener.mdx

@@ -1,6 +1,7 @@
 ---
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
+icon: spinner
 ---
 
 # Event Listeners
@@ -12,7 +13,7 @@ CrewAI provides a powerful event system that allows you to listen for and react
 CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
 
 1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **CrewEvent**: Base class for all events in the system
+2. **BaseEvent**: Base class for all events in the system
 3. **BaseEventListener**: Abstract base class for creating custom event listeners
 
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
@@ -233,7 +234,7 @@ Each event handler receives two parameters:
 1. **source**: The object that emitted the event
 2. **event**: The event instance, containing event-specific data
 
-The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
+The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
 
 - **timestamp**: The time when the event was emitted
 - **type**: A string identifier for the event type

docs/concepts/flows.mdx

@@ -545,6 +545,97 @@ 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 LiteAgent to Flows
+
+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
+from typing import List, cast
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
+from pydantic import BaseModel, Field
+from crewai.flow.flow import Flow, listen, start
+from crewai.lite_agent import LiteAgent
+
+# 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 = None
+
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self):
+        print(f"Starting market research for {self.state.product}")
+
+    @listen(initialize_research)
+    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.",
+            llm="gpt-4o",
+            tools=[WebsiteSearchTool()],
+            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 = analyst.kickoff(query)
+        self.state.analysis = cast(MarketAnalysis, result.pydantic)
+        return result.pydantic
+
+    @listen(analyze_market)
+    def present_results(self):
+        analysis = self.state.analysis
+        if analysis is None:
+            print("No analysis results available")
+            return
+
+        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
+flow = MarketResearchFlow()
+result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
+```
+
+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**: 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
 
 Creating a flow with multiple crews in CrewAI is straightforward.

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/concepts/memory.mdx

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

docs/custom_llm.md

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

docs/docs.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "palm",
+  "theme": "mint",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",
@@ -66,6 +66,7 @@
               "concepts/tasks",
               "concepts/crews",
               "concepts/flows",
+              "concepts/lite-agent",
               "concepts/knowledge",
               "concepts/llms",
               "concepts/processes",
@@ -97,13 +98,21 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks",
+              "how-to/conditional-tasks"
+            ]
+          },
+          {
+            "group": "Agent Monitoring & Observability",
+            "pages": [
               "how-to/agentops-observability",
+              "how-to/langfuse-observability",
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
+              "how-to/opik-observability",
+              "how-to/phoenix-observability",
               "how-to/portkey-observability",
-              "how-to/langfuse-observability"
+              "how-to/weave-integration"
             ]
           },
           {
@@ -111,6 +120,8 @@
             "pages": [
               "tools/aimindtool",
               "tools/apifyactorstool",
+              "tools/bedrockinvokeagenttool",
+              "tools/bedrockkbretriever",
               "tools/bravesearchtool",
               "tools/browserbaseloadtool",
               "tools/codedocssearchtool",
@@ -220,4 +231,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/how-to/agentops-observability.mdx

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

docs/how-to/kickoff-async.mdx

@@ -92,12 +92,14 @@ coding_agent = Agent(
 # Create tasks that require code execution
 task_1 = Task(
     description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent
+    agent=coding_agent,
+    expected_output="The average age of the participants."
 )
 
 task_2 = Task(
     description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent
+    agent=coding_agent,
+    expected_output="The average age of the participants."
 )
 
 # Create two crews and add tasks

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

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

docs/how-to/langfuse-observability.mdx

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

docs/how-to/langtrace-observability.mdx

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

docs/how-to/mlflow-observability.mdx

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

docs/how-to/openlit-observability.mdx

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

docs/how-to/opik-observability.mdx

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

docs/how-to/phoenix-observability.mdx

@@ -0,0 +1,145 @@
+---
+title: Agent Monitoring with Arize Phoenix
+description: Learn how to integrate Arize Phoenix with CrewAI via OpenTelemetry using OpenInference
+icon: magnifying-glass-chart
+---
+
+# Integrate Arize Phoenix with CrewAI
+
+This guide demonstrates how to integrate **Arize Phoenix** with **CrewAI** using OpenTelemetry via the [OpenInference](https://github.com/openinference/openinference) SDK. By the end of this guide, you will be able to trace your CrewAI agents and easily debug your agents.
+
+> **What is Arize Phoenix?** [Arize Phoenix](https://phoenix.arize.com) is an LLM observability platform that provides tracing and evaluation for AI applications.
+
+[![Watch a Video Demo of Our Integration with Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
+
+## Get Started
+
+We'll walk through a simple example of using CrewAI and integrating it with Arize Phoenix via OpenTelemetry using OpenInference.
+
+You can also access this guide on [Google Colab](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/crewai_tracing_tutorial.ipynb).
+
+### Step 1: Install Dependencies
+
+```bash
+pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoenix-otel
+```
+
+### Step 2: Set Up Environment Variables
+
+Setup Phoenix Cloud API keys and configure OpenTelemetry to send traces to Phoenix. Phoenix Cloud is a hosted version of Arize Phoenix, but it is not required to use this integration.
+
+You can get your free Serper API key [here](https://serper.dev/).
+
+```python
+import os
+from getpass import getpass
+
+# Get your Phoenix Cloud credentials
+PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix Cloud API Key: ")
+
+# Get API keys for services
+OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
+SERPER_API_KEY = getpass("🔑 Enter your Serper API key: ")
+
+# Set environment variables
+os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
+os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, change this to your own endpoint if you are using a self-hosted instance
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+os.environ["SERPER_API_KEY"] = SERPER_API_KEY
+```
+
+### Step 3: Initialize OpenTelemetry with Phoenix
+
+Initialize the OpenInference OpenTelemetry instrumentation SDK to start capturing traces and send them to Phoenix. 
+
+```python
+from phoenix.otel import register
+
+tracer_provider = register(
+    project_name="crewai-tracing-demo",
+    auto_instrument=True,
+)
+```
+
+### Step 4: Create a CrewAI Application
+
+We'll create a CrewAI application where two agents collaborate to research and write a blog post about AI advancements.
+
+```python
+from crewai import Agent, Crew, Process, Task
+from crewai_tools import SerperDevTool
+
+search_tool = SerperDevTool()
+
+# Define your agents with roles and goals
+researcher = Agent(
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You work at a leading tech think tank.
+    Your expertise lies in identifying emerging trends.
+    You have a knack for dissecting complex data and presenting actionable insights.""",
+    verbose=True,
+    allow_delegation=False,
+    # You can pass an optional llm attribute specifying what model you wanna use.
+    # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
+    tools=[search_tool],
+)
+writer = Agent(
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
+    You transform complex concepts into compelling narratives.""",
+    verbose=True,
+    allow_delegation=True,
+)
+
+# Create tasks for your agents
+task1 = Task(
+    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+    Identify key trends, breakthrough technologies, and potential industry impacts.""",
+    expected_output="Full analysis report in bullet points",
+    agent=researcher,
+)
+
+task2 = Task(
+    description="""Using the insights provided, develop an engaging blog
+    post that highlights the most significant AI advancements.
+    Your post should be informative yet accessible, catering to a tech-savvy audience.
+    Make it sound cool, avoid complex words so it doesn't sound like AI.""",
+    expected_output="Full blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+# Instantiate your crew with a sequential process
+crew = Crew(
+    agents=[researcher, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
+)
+
+# Get your crew to work!
+result = crew.kickoff()
+
+print("######################")
+print(result)
+```
+
+### Step 5: View Traces in Phoenix
+
+After running the agent, you can view the traces generated by your CrewAI application in Phoenix. You should see detailed steps of the agent interactions and LLM calls, which can help you debug and optimize your AI agents.
+
+Log into your Phoenix Cloud account and navigate to the project you specified in the `project_name` parameter. You'll see a timeline view of your trace with all the agent interactions, tool usages, and LLM calls.
+
+![Example trace in Phoenix showing agent interactions](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
+
+
+### Version Compatibility Information
+- Python 3.8+
+- CrewAI >= 0.86.0
+- Arize Phoenix >= 7.0.1
+- OpenTelemetry SDK >= 1.31.0
+
+
+### References
+- [Phoenix Documentation](https://docs.arize.com/phoenix/) - Overview of the Phoenix platform.
+- [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework.
+- [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
+- [OpenInference GitHub](https://github.com/openinference/openinference) - Source code for OpenInference SDK.

docs/how-to/portkey-observability.mdx

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

docs/how-to/weave-integration.mdx

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

docs/tools/bedrockinvokeagenttool.mdx

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

docs/tools/bedrockkbretriever.mdx

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

pyproject.toml

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

src/crewai/__init__.py

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

src/crewai/agent.py

@@ -11,13 +11,18 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
-from crewai.llm import LLM
+from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
 from crewai.task import Task
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
+from crewai.utilities.agent_utils import (
+    get_tool_names,
+    parse_tools,
+    render_text_description_and_args,
+)
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
 from crewai.utilities.events.agent_events import (
@@ -71,10 +76,10 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: Union[str, InstanceOf[LLM], Any] = Field(
+    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -86,9 +91,6 @@ class Agent(BaseAgent):
     response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
-    tools_results: Optional[List[Any]] = Field(
-        default=[], description="Results of the tools used by the agent."
-    )
     allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
@@ -118,7 +120,9 @@ class Agent(BaseAgent):
         self.agent_ops_agent_name = self.role
 
         self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
+        if self.function_calling_llm and not isinstance(
+            self.function_calling_llm, BaseLLM
+        ):
             self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
@@ -140,15 +144,13 @@ class Agent(BaseAgent):
                 self.embedder = crew_embedder
 
             if self.knowledge_sources:
-                full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
-                knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
                     self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
                         embedder=self.embedder,
-                        collection_name=knowledge_agent_name,
+                        collection_name=self.role,
                         storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:
@@ -300,12 +302,12 @@ class Agent(BaseAgent):
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        tools = tools or self.tools or []
-        parsed_tools = self._parse_tools(tools)
+        raw_tools: List[BaseTool] = tools or self.tools or []
+        parsed_tools = parse_tools(raw_tools)
 
         prompt = Prompts(
             agent=self,
-            tools=tools,
+            has_tools=len(raw_tools) > 0,
             i18n=self.i18n,
             use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
@@ -327,12 +329,12 @@ class Agent(BaseAgent):
             crew=self.crew,
             tools=parsed_tools,
             prompt=prompt,
-            original_tools=tools,
+            original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
-            tools_names=self.__tools_names(parsed_tools),
-            tools_description=self._render_text_description_and_args(parsed_tools),
+            tools_names=get_tool_names(parsed_tools),
+            tools_description=render_text_description_and_args(parsed_tools),
             step_callback=self.step_callback,
             function_calling_llm=self.function_calling_llm,
             respect_context_window=self.respect_context_window,
@@ -367,25 +369,6 @@ class Agent(BaseAgent):
     def get_output_converter(self, llm, text, model, instructions):
         return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    def _parse_tools(self, tools: List[Any]) -> List[Any]:  # type: ignore
-        """Parse tools to be used for the task."""
-        tools_list = []
-        try:
-            # tentatively try to import from crewai_tools import BaseTool as CrewAITool
-            from crewai.tools import BaseTool as CrewAITool
-
-            for tool in tools:
-                if isinstance(tool, CrewAITool):
-                    tools_list.append(tool.to_structured_tool())
-                else:
-                    tools_list.append(tool)
-        except ModuleNotFoundError:
-            tools_list = []
-            for tool in tools:
-                tools_list.append(tool)
-
-        return tools_list
-
     def _training_handler(self, task_prompt: str) -> str:
         """Handle training data for the agent task prompt to improve output on Training."""
         if data := CrewTrainingHandler(TRAINING_DATA_FILE).load():
@@ -431,23 +414,6 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
-        """Render the tool name, description, and args in plain text.
-
-            Output will be in the format of:
-
-            .. code-block:: markdown
-
-            search: This tool is used for search, args: {"query": {"type": "string"}}
-            calculator: This tool is used for math, \
-            args: {"expression": {"type": "string"}}
-        """
-        tool_strings = []
-        for tool in tools:
-            tool_strings.append(tool.description)
-
-        return "\n".join(tool_strings)
-
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
         if not shutil.which("docker"):
@@ -467,10 +433,6 @@ class Agent(BaseAgent):
                 f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
             )
 
-    @staticmethod
-    def __tools_names(tools) -> str:
-        return ", ".join([t.name for t in tools])
-
     def __repr__(self):
         return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"
 
@@ -483,3 +445,6 @@ class Agent(BaseAgent):
             Fingerprint: The agent's fingerprint
         """
         return self.security_config.fingerprint
+
+    def set_fingerprint(self, fingerprint: Fingerprint):
+        self.security_config.fingerprint = fingerprint

src/crewai/agents/agent_builder/base_agent.py

@@ -2,7 +2,7 @@ import uuid
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Dict, List, Optional, TypeVar
+from typing import Any, Callable, Dict, List, Optional, TypeVar
 
 from pydantic import (
     UUID4,
@@ -25,6 +25,7 @@ from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
+from crewai.utilities.string_utils import interpolate_only
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -71,8 +72,6 @@ class BaseAgent(ABC, BaseModel):
             Interpolate inputs into the agent description and backstory.
         set_cache_handler(cache_handler: CacheHandler) -> None:
             Set the cache handler for the agent.
-        increment_formatting_errors() -> None:
-            Increment formatting errors.
         copy() -> "BaseAgent":
             Create a copy of the agent.
         set_rpm_controller(rpm_controller: RPMController) -> None:
@@ -90,9 +89,6 @@ class BaseAgent(ABC, BaseModel):
     _original_backstory: Optional[str] = PrivateAttr(default=None)
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
-    formatting_errors: int = Field(
-        default=0, description="Number of formatting errors."
-    )
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
@@ -134,6 +130,9 @@ class BaseAgent(ABC, BaseModel):
         default_factory=ToolsHandler,
         description="An instance of the ToolsHandler class.",
     )
+    tools_results: List[Dict[str, Any]] = Field(
+        default=[], description="Results of the tools used by the agent."
+    )
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
     )
@@ -152,6 +151,9 @@ class BaseAgent(ABC, BaseModel):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
+    callbacks: List[Callable] = Field(
+        default=[], description="Callbacks to be used for the agent"
+    )
 
     @model_validator(mode="before")
     @classmethod
@@ -253,10 +255,6 @@ class BaseAgent(ABC, BaseModel):
     def create_agent_executor(self, tools=None) -> None:
         pass
 
-    @abstractmethod
-    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
-        pass
-
     @abstractmethod
     def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
@@ -333,9 +331,15 @@ class BaseAgent(ABC, BaseModel):
             self._original_backstory = self.backstory
 
         if inputs:
-            self.role = self._original_role.format(**inputs)
-            self.goal = self._original_goal.format(**inputs)
-            self.backstory = self._original_backstory.format(**inputs)
+            self.role = interpolate_only(
+                input_string=self._original_role, inputs=inputs
+            )
+            self.goal = interpolate_only(
+                input_string=self._original_goal, inputs=inputs
+            )
+            self.backstory = interpolate_only(
+                input_string=self._original_backstory, inputs=inputs
+            )
 
     def set_cache_handler(self, cache_handler: CacheHandler) -> None:
         """Set the cache handler for the agent.
@@ -349,9 +353,6 @@ class BaseAgent(ABC, BaseModel):
             self.tools_handler.cache = cache_handler
         self.create_agent_executor()
 
-    def increment_formatting_errors(self) -> None:
-        self.formatting_errors += 1
-
     def set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,5 +1,5 @@
 import time
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
@@ -15,9 +15,9 @@ if TYPE_CHECKING:
 
 
 class CrewAgentExecutorMixin:
-    crew: Optional["Crew"]
-    agent: Optional["BaseAgent"]
-    task: Optional["Task"]
+    crew: "Crew"
+    agent: "BaseAgent"
+    task: "Task"
     iterations: int
     max_iter: int
     _i18n: I18N

src/crewai/agents/crew_agent_executor.py

@@ -1,42 +1,40 @@
 import json
 import re
-from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
-    FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
     AgentAction,
     AgentFinish,
-    CrewAgentParser,
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.llm import LLM
+from crewai.llm import BaseLLM
 from crewai.tools.base_tool import BaseTool
-from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
+from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.tools.tool_types import ToolResult
 from crewai.utilities import I18N, Printer
+from crewai.utilities.agent_utils import (
+    enforce_rpm_limit,
+    format_message_for_llm,
+    get_llm_response,
+    handle_agent_action_core,
+    handle_context_length,
+    handle_max_iterations_exceeded,
+    handle_output_parser_exception,
+    handle_unknown_error,
+    has_reached_max_iterations,
+    is_context_length_exceeded,
+    process_llm_response,
+    show_agent_logs,
+)
 from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
-from crewai.utilities.events import (
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-    crewai_event_bus,
-)
-from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
-from crewai.utilities.exceptions.context_window_exceeding_exception import (
-    LLMContextLengthExceededException,
-)
 from crewai.utilities.logger import Logger
+from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
-@dataclass
-class ToolResult:
-    result: Any
-    result_as_answer: bool
-
-
 class CrewAgentExecutor(CrewAgentExecutorMixin):
     _logger: Logger = Logger()
 
@@ -48,7 +46,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         agent: BaseAgent,
         prompt: dict[str, str],
         max_iter: int,
-        tools: List[BaseTool],
+        tools: List[CrewStructuredTool],
         tools_names: str,
         stop_words: List[str],
         tools_description: str,
@@ -61,7 +59,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm: LLM = llm
+        self.llm: BaseLLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -84,21 +82,27 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
-        self.tool_name_to_tool_map: Dict[str, BaseTool] = {
+        self.tool_name_to_tool_map: Dict[str, Union[CrewStructuredTool, BaseTool]] = {
             tool.name: tool for tool in self.tools
         }
-        self.stop = stop_words
-        self.llm.stop = list(set(self.llm.stop + self.stop))
+        existing_stop = self.llm.stop or []
+        self.llm.stop = list(
+            set(
+                existing_stop + self.stop
+                if isinstance(existing_stop, list)
+                else self.stop
+            )
+        )
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
             system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
             user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
-            self.messages.append(self._format_msg(system_prompt, role="system"))
-            self.messages.append(self._format_msg(user_prompt))
+            self.messages.append(format_message_for_llm(system_prompt, role="system"))
+            self.messages.append(format_message_for_llm(user_prompt))
         else:
             user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
-            self.messages.append(self._format_msg(user_prompt))
+            self.messages.append(format_message_for_llm(user_prompt))
 
         self._show_start_logs()
 
@@ -113,7 +117,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
             raise
         except Exception as e:
-            self._handle_unknown_error(e)
+            handle_unknown_error(self._printer, e)
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
                 raise e
@@ -135,20 +139,51 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         formatted_answer = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
-                if self._has_reached_max_iterations():
-                    formatted_answer = self._handle_max_iterations_exceeded(
-                        formatted_answer
+                if has_reached_max_iterations(self.iterations, self.max_iter):
+                    formatted_answer = handle_max_iterations_exceeded(
+                        formatted_answer,
+                        printer=self._printer,
+                        i18n=self._i18n,
+                        messages=self.messages,
+                        llm=self.llm,
+                        callbacks=self.callbacks,
                     )
-                    break
 
-                self._enforce_rpm_limit()
+                enforce_rpm_limit(self.request_within_rpm_limit)
 
-                answer = self._get_llm_response()
-                formatted_answer = self._process_llm_response(answer)
+                answer = get_llm_response(
+                    llm=self.llm,
+                    messages=self.messages,
+                    callbacks=self.callbacks,
+                    printer=self._printer,
+                )
+                formatted_answer = process_llm_response(answer, self.use_stop_words)
 
                 if isinstance(formatted_answer, AgentAction):
-                    tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer
+                    # Extract agent fingerprint if available
+                    fingerprint_context = {}
+                    if (
+                        self.agent
+                        and hasattr(self.agent, "security_config")
+                        and hasattr(self.agent.security_config, "fingerprint")
+                    ):
+                        fingerprint_context = {
+                            "agent_fingerprint": str(
+                                self.agent.security_config.fingerprint
+                            )
+                        }
+
+                    tool_result = execute_tool_and_check_finality(
+                        agent_action=formatted_answer,
+                        fingerprint_context=fingerprint_context,
+                        tools=self.tools,
+                        i18n=self._i18n,
+                        agent_key=self.agent.key if self.agent else None,
+                        agent_role=self.agent.role if self.agent else None,
+                        tools_handler=self.tools_handler,
+                        task=self.task,
+                        agent=self.agent,
+                        function_calling_llm=self.function_calling_llm,
                     )
                     formatted_answer = self._handle_agent_action(
                         formatted_answer, tool_result
@@ -158,17 +193,30 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._append_message(formatted_answer.text, role="assistant")
 
             except OutputParserException as e:
-                formatted_answer = self._handle_output_parser_exception(e)
+                formatted_answer = handle_output_parser_exception(
+                    e=e,
+                    messages=self.messages,
+                    iterations=self.iterations,
+                    log_error_after=self.log_error_after,
+                    printer=self._printer,
+                )
 
             except Exception as e:
                 if e.__class__.__module__.startswith("litellm"):
                     # Do not retry on litellm errors
                     raise e
-                if self._is_context_length_exceeded(e):
-                    self._handle_context_length()
+                if is_context_length_exceeded(e):
+                    handle_context_length(
+                        respect_context_window=self.respect_context_window,
+                        printer=self._printer,
+                        messages=self.messages,
+                        llm=self.llm,
+                        callbacks=self.callbacks,
+                        i18n=self._i18n,
+                    )
                     continue
                 else:
-                    self._handle_unknown_error(e)
+                    handle_unknown_error(self._printer, e)
                     raise e
             finally:
                 self.iterations += 1
@@ -181,89 +229,27 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _handle_unknown_error(self, exception: Exception) -> None:
-        """Handle unknown errors by informing the user."""
-        self._printer.print(
-            content="An unknown error occurred. Please check the details below.",
-            color="red",
-        )
-        self._printer.print(
-            content=f"Error details: {exception}",
-            color="red",
-        )
-
-    def _has_reached_max_iterations(self) -> bool:
-        """Check if the maximum number of iterations has been reached."""
-        return self.iterations >= self.max_iter
-
-    def _enforce_rpm_limit(self) -> None:
-        """Enforce the requests per minute (RPM) limit if applicable."""
-        if self.request_within_rpm_limit:
-            self.request_within_rpm_limit()
-
-    def _get_llm_response(self) -> str:
-        """Call the LLM and return the response, handling any invalid responses."""
-        try:
-            answer = self.llm.call(
-                self.messages,
-                callbacks=self.callbacks,
-            )
-        except Exception as e:
-            self._printer.print(
-                content=f"Error during LLM call: {e}",
-                color="red",
-            )
-            raise e
-
-        if not answer:
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        return answer
-
-    def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
-        """Process the LLM response and format it into an AgentAction or AgentFinish."""
-        if not self.use_stop_words:
-            try:
-                # Preliminary parsing to check for errors.
-                self._format_answer(answer)
-            except OutputParserException as e:
-                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                    answer = answer.split("Observation:")[0].strip()
-
-        return self._format_answer(answer)
-
     def _handle_agent_action(
         self, formatted_answer: AgentAction, tool_result: ToolResult
     ) -> Union[AgentAction, AgentFinish]:
         """Handle the AgentAction, execute tools, and process the results."""
+        # Special case for add_image_tool
         add_image_tool = self._i18n.tools("add_image")
         if (
             isinstance(add_image_tool, dict)
             and formatted_answer.tool.casefold().strip()
             == add_image_tool.get("name", "").casefold().strip()
         ):
-            self.messages.append(tool_result.result)
-            return formatted_answer  # Continue the loop
+            self.messages.append({"role": "assistant", "content": tool_result.result})
+            return formatted_answer
 
-        if self.step_callback:
-            self.step_callback(tool_result)
-
-        formatted_answer.text += f"\nObservation: {tool_result.result}"
-        formatted_answer.result = tool_result.result
-
-        if tool_result.result_as_answer:
-            return AgentFinish(
-                thought="",
-                output=tool_result.result,
-                text=formatted_answer.text,
-            )
-
-        self._show_logs(formatted_answer)
-        return formatted_answer
+        return handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            messages=self.messages,
+            step_callback=self.step_callback,
+            show_logs=self._show_logs,
+        )
 
     def _invoke_step_callback(self, formatted_answer) -> None:
         """Invoke the step callback if it exists."""
@@ -272,151 +258,33 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _append_message(self, text: str, role: str = "assistant") -> None:
         """Append a message to the message list with the given role."""
-        self.messages.append(self._format_msg(text, role=role))
-
-    def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
-        """Handle OutputParserException by updating messages and formatted_answer."""
-        self.messages.append({"role": "user", "content": e.error})
-
-        formatted_answer = AgentAction(
-            text=e.error,
-            tool="",
-            tool_input="",
-            thought="",
-        )
-
-        if self.iterations > self.log_error_after:
-            self._printer.print(
-                content=f"Error parsing LLM output, agent will retry: {e.error}",
-                color="red",
-            )
-
-        return formatted_answer
-
-    def _is_context_length_exceeded(self, exception: Exception) -> bool:
-        """Check if the exception is due to context length exceeding."""
-        return LLMContextLengthExceededException(
-            str(exception)
-        )._is_context_limit_error(str(exception))
+        self.messages.append(format_message_for_llm(text, role=role))
 
     def _show_start_logs(self):
+        """Show logs for the start of agent execution."""
         if self.agent is None:
             raise ValueError("Agent cannot be None")
-        if self.agent.verbose or (
-            hasattr(self, "crew") and getattr(self.crew, "verbose", False)
-        ):
-            agent_role = self.agent.role.split("\n")[0]
-            self._printer.print(
-                content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
-            )
-            description = (
+        show_agent_logs(
+            printer=self._printer,
+            agent_role=self.agent.role,
+            task_description=(
                 getattr(self.task, "description") if self.task else "Not Found"
-            )
-            self._printer.print(
-                content=f"\033[95m## Task:\033[00m \033[92m{description}\033[00m"
-            )
+            ),
+            verbose=self.agent.verbose
+            or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
+        )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
+        """Show logs for the agent's execution."""
         if self.agent is None:
             raise ValueError("Agent cannot be None")
-        if self.agent.verbose or (
-            hasattr(self, "crew") and getattr(self.crew, "verbose", False)
-        ):
-            agent_role = self.agent.role.split("\n")[0]
-            if isinstance(formatted_answer, AgentAction):
-                thought = re.sub(r"\n+", "\n", formatted_answer.thought)
-                formatted_json = json.dumps(
-                    formatted_answer.tool_input,
-                    indent=2,
-                    ensure_ascii=False,
-                )
-                self._printer.print(
-                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
-                )
-                if thought and thought != "":
-                    self._printer.print(
-                        content=f"\033[95m## Thought:\033[00m \033[92m{thought}\033[00m"
-                    )
-                self._printer.print(
-                    content=f"\033[95m## Using tool:\033[00m \033[92m{formatted_answer.tool}\033[00m"
-                )
-                self._printer.print(
-                    content=f"\033[95m## Tool Input:\033[00m \033[92m\n{formatted_json}\033[00m"
-                )
-                self._printer.print(
-                    content=f"\033[95m## Tool Output:\033[00m \033[92m\n{formatted_answer.result}\033[00m"
-                )
-            elif isinstance(formatted_answer, AgentFinish):
-                self._printer.print(
-                    content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
-                )
-                self._printer.print(
-                    content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
-                )
-
-    def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
-        try:
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageStartedEvent(
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                    ),
-                )
-            tool_usage = ToolUsage(
-                tools_handler=self.tools_handler,
-                tools=self.tools,
-                original_tools=self.original_tools,
-                tools_description=self.tools_description,
-                tools_names=self.tools_names,
-                function_calling_llm=self.function_calling_llm,
-                task=self.task,  # type: ignore[arg-type]
-                agent=self.agent,
-                action=agent_action,
-            )
-            tool_calling = tool_usage.parse_tool_calling(agent_action.text)
-
-            if isinstance(tool_calling, ToolUsageErrorException):
-                tool_result = tool_calling.message
-                return ToolResult(result=tool_result, result_as_answer=False)
-            else:
-                if tool_calling.tool_name.casefold().strip() in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ]:
-                    tool_result = tool_usage.use(tool_calling, agent_action.text)
-                    tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
-                    if tool:
-                        return ToolResult(
-                            result=tool_result, result_as_answer=tool.result_as_answer
-                        )
-                else:
-                    tool_result = self._i18n.errors("wrong_tool_name").format(
-                        tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
-                    )
-                return ToolResult(result=tool_result, result_as_answer=False)
-
-        except Exception as e:
-            # TODO: drop
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageErrorEvent(  # validation error
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                        error=str(e),
-                    ),
-                )
-            raise e
+        show_agent_logs(
+            printer=self._printer,
+            agent_role=self.agent.role,
+            formatted_answer=formatted_answer,
+            verbose=self.agent.verbose
+            or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
+        )
 
     def _summarize_messages(self) -> None:
         messages_groups = []
@@ -424,47 +292,33 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             content = message["content"]
             cut_size = self.llm.get_context_window_size()
             for i in range(0, len(content), cut_size):
-                messages_groups.append(content[i : i + cut_size])
+                messages_groups.append({"content": content[i : i + cut_size]})
 
         summarized_contents = []
         for group in messages_groups:
             summary = self.llm.call(
                 [
-                    self._format_msg(
+                    format_message_for_llm(
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
-                    self._format_msg(
-                        self._i18n.slice("summarize_instruction").format(group=group),
+                    format_message_for_llm(
+                        self._i18n.slice("summarize_instruction").format(
+                            group=group["content"]
+                        ),
                     ),
                 ],
                 callbacks=self.callbacks,
             )
-            summarized_contents.append(summary)
+            summarized_contents.append({"content": str(summary)})
 
-        merged_summary = " ".join(str(content) for content in summarized_contents)
+        merged_summary = " ".join(content["content"] for content in summarized_contents)
 
         self.messages = [
-            self._format_msg(
+            format_message_for_llm(
                 self._i18n.slice("summary").format(merged_summary=merged_summary)
             )
         ]
 
-    def _handle_context_length(self) -> None:
-        if self.respect_context_window:
-            self._printer.print(
-                content="Context length exceeded. Summarizing content to fit the model context window.",
-                color="yellow",
-            )
-            self._summarize_messages()
-        else:
-            self._printer.print(
-                content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                color="red",
-            )
-            raise SystemExit(
-                "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
-            )
-
     def _handle_crew_training_output(
         self, result: AgentFinish, human_feedback: Optional[str] = None
     ) -> None:
@@ -517,13 +371,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         prompt = prompt.replace("{tools}", inputs["tools"])
         return prompt
 
-    def _format_answer(self, answer: str) -> Union[AgentAction, AgentFinish]:
-        return CrewAgentParser(agent=self.agent).parse(answer)
-
-    def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
-        prompt = prompt.rstrip()
-        return {"role": role, "content": prompt}
-
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
         """Handle human feedback with different flows for training vs regular use.
 
@@ -550,7 +397,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """Process feedback for training scenarios with single iteration."""
         self._handle_crew_training_output(initial_answer, feedback)
         self.messages.append(
-            self._format_msg(
+            format_message_for_llm(
                 self._i18n.slice("feedback_instructions").format(feedback=feedback)
             )
         )
@@ -579,7 +426,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
         """Process a single feedback iteration."""
         self.messages.append(
-            self._format_msg(
+            format_message_for_llm(
                 self._i18n.slice("feedback_instructions").format(feedback=feedback)
             )
         )
@@ -604,45 +451,3 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             ),
             color="red",
         )
-
-    def _handle_max_iterations_exceeded(self, formatted_answer):
-        """
-        Handles the case when the maximum number of iterations is exceeded.
-        Performs one more LLM call to get the final answer.
-
-        Parameters:
-            formatted_answer: The last formatted answer from the agent.
-
-        Returns:
-            The final formatted answer after exceeding max iterations.
-        """
-        self._printer.print(
-            content="Maximum iterations reached. Requesting final answer.",
-            color="yellow",
-        )
-
-        if formatted_answer and hasattr(formatted_answer, "text"):
-            assistant_message = (
-                formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
-            )
-        else:
-            assistant_message = self._i18n.errors("force_final_answer")
-
-        self.messages.append(self._format_msg(assistant_message, role="assistant"))
-
-        # Perform one more LLM call to get the final answer
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
-
-        if answer is None or answer == "":
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        formatted_answer = self._format_answer(answer)
-        # Return the formatted answer, regardless of its type
-        return formatted_answer

src/crewai/agents/parser.py

@@ -1,5 +1,5 @@
 import re
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 from json_repair import repair_json
 
@@ -67,9 +67,23 @@ class CrewAgentParser:
     _i18n: I18N = I18N()
     agent: Any = None
 
-    def __init__(self, agent: Any):
+    def __init__(self, agent: Optional[Any] = None):
         self.agent = agent
 
+    @staticmethod
+    def parse_text(text: str) -> Union[AgentAction, AgentFinish]:
+        """
+        Static method to parse text into an AgentAction or AgentFinish without needing to instantiate the class.
+
+        Args:
+            text: The text to parse.
+
+        Returns:
+            Either an AgentAction or AgentFinish based on the parsed content.
+        """
+        parser = CrewAgentParser()
+        return parser.parse(text)
+
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         thought = self._extract_thought(text)
         includes_answer = FINAL_ANSWER_ACTION in text
@@ -77,22 +91,7 @@ class CrewAgentParser:
             r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
         )
         action_match = re.search(regex, text, re.DOTALL)
-        if action_match:
-            if includes_answer:
-                raise OutputParserException(
-                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}"
-                )
-            action = action_match.group(1)
-            clean_action = self._clean_action(action)
-
-            action_input = action_match.group(2).strip()
-
-            tool_input = action_input.strip(" ").strip('"')
-            safe_tool_input = self._safe_repair_json(tool_input)
-
-            return AgentAction(thought, clean_action, safe_tool_input, text)
-
-        elif includes_answer:
+        if includes_answer:
             final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
             # Check whether the final answer ends with triple backticks.
             if final_answer.endswith("```"):
@@ -103,22 +102,30 @@ class CrewAgentParser:
                     final_answer = final_answer[:-3].rstrip()
             return AgentFinish(thought, final_answer, text)
 
+        elif action_match:
+            action = action_match.group(1)
+            clean_action = self._clean_action(action)
+
+            action_input = action_match.group(2).strip()
+
+            tool_input = action_input.strip(" ").strip('"')
+            safe_tool_input = self._safe_repair_json(tool_input)
+
+            return AgentAction(thought, clean_action, safe_tool_input, text)
+
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
-            self.agent.increment_formatting_errors()
             raise OutputParserException(
                 f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
             )
         elif not re.search(
             r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
         ):
-            self.agent.increment_formatting_errors()
             raise OutputParserException(
                 MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
             )
         else:
             format = self._i18n.slice("format_without_tools")
             error = f"{format}"
-            self.agent.increment_formatting_errors()
             raise OutputParserException(
                 error,
             )

src/crewai/cli/crew_chat.py

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

src/crewai/crew.py

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

src/crewai/flow/persistence/base.py

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

src/crewai/flow/persistence/sqlite.py

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

src/crewai/knowledge/storage/knowledge_storage.py

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

src/crewai/lite_agent.py

@@ -0,0 +1,518 @@
+import asyncio
+import json
+import re
+import uuid
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Optional, Type, Union, cast
+
+from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
+
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
+from crewai.agents.cache import CacheHandler
+from crewai.agents.parser import (
+    AgentAction,
+    AgentFinish,
+    OutputParserException,
+)
+from crewai.llm import LLM
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.utilities import I18N
+from crewai.utilities.agent_utils import (
+    enforce_rpm_limit,
+    format_message_for_llm,
+    get_llm_response,
+    get_tool_names,
+    handle_agent_action_core,
+    handle_context_length,
+    handle_max_iterations_exceeded,
+    handle_output_parser_exception,
+    handle_unknown_error,
+    has_reached_max_iterations,
+    is_context_length_exceeded,
+    parse_tools,
+    process_llm_response,
+    render_text_description_and_args,
+    show_agent_logs,
+)
+from crewai.utilities.converter import convert_to_model, generate_model_description
+from crewai.utilities.events.agent_events import (
+    LiteAgentExecutionCompletedEvent,
+    LiteAgentExecutionErrorEvent,
+    LiteAgentExecutionStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.llm_events import (
+    LLMCallCompletedEvent,
+    LLMCallFailedEvent,
+    LLMCallStartedEvent,
+    LLMCallType,
+)
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.llm_utils import create_llm
+from crewai.utilities.printer import Printer
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+from crewai.utilities.tool_utils import execute_tool_and_check_finality
+
+
+class LiteAgentOutput(BaseModel):
+    """Class that represents the result of a LiteAgent execution."""
+
+    model_config = {"arbitrary_types_allowed": True}
+
+    raw: str = Field(description="Raw output of the agent", default="")
+    pydantic: Optional[BaseModel] = Field(
+        description="Pydantic output of the agent", default=None
+    )
+    agent_role: str = Field(description="Role of the agent that produced this output")
+    usage_metrics: Optional[Dict[str, Any]] = Field(
+        description="Token usage metrics for this execution", default=None
+    )
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert pydantic_output to a dictionary."""
+        if self.pydantic:
+            return self.pydantic.model_dump()
+        return {}
+
+    def __str__(self) -> str:
+        """String representation of the output."""
+        if self.pydantic:
+            return str(self.pydantic)
+        return self.raw
+
+
+class LiteAgent(BaseModel):
+    """
+    A lightweight agent that can process messages and use tools.
+
+    This agent is simpler than the full Agent class, focusing on direct execution
+    rather than task delegation. It's designed to be used for simple interactions
+    where a full crew is not needed.
+
+    Attributes:
+        role: The role of the agent.
+        goal: The objective of the agent.
+        backstory: The backstory of the agent.
+        llm: The language model that will run the agent.
+        tools: Tools at the agent's disposal.
+        verbose: Whether the agent execution should be in verbose mode.
+        max_iterations: Maximum number of iterations for tool usage.
+        max_execution_time: Maximum execution time in seconds.
+        response_format: Optional Pydantic model for structured output.
+    """
+
+    model_config = {"arbitrary_types_allowed": True}
+
+    # Core Agent Properties
+    role: str = Field(description="Role of the agent")
+    goal: str = Field(description="Goal of the agent")
+    backstory: str = Field(description="Backstory of the agent")
+    llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+        default=None, description="Language model that will run the agent"
+    )
+    tools: List[BaseTool] = Field(
+        default_factory=list, description="Tools at agent's disposal"
+    )
+
+    # Execution Control Properties
+    max_iterations: int = Field(
+        default=15, description="Maximum number of iterations for tool usage"
+    )
+    max_execution_time: Optional[int] = Field(
+        default=None, description="Maximum execution time in seconds"
+    )
+    respect_context_window: bool = Field(
+        default=True,
+        description="Whether to respect the context window of the LLM",
+    )
+    use_stop_words: bool = Field(
+        default=True,
+        description="Whether to use stop words to prevent the LLM from using tools",
+    )
+    request_within_rpm_limit: Optional[Callable[[], bool]] = Field(
+        default=None,
+        description="Callback to check if the request is within the RPM limit",
+    )
+    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
+
+    # Output and Formatting Properties
+    response_format: Optional[Type[BaseModel]] = Field(
+        default=None, description="Pydantic model for structured output"
+    )
+    verbose: bool = Field(
+        default=False, description="Whether to print execution details"
+    )
+    callbacks: List[Callable] = Field(
+        default=[], description="Callbacks to be used for the agent"
+    )
+
+    # State and Results
+    tools_results: List[Dict[str, Any]] = Field(
+        default=[], description="Results of the tools used by the agent."
+    )
+
+    # Private Attributes
+    _parsed_tools: List[CrewStructuredTool] = PrivateAttr(default_factory=list)
+    _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
+    _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
+    _key: str = PrivateAttr(default_factory=lambda: str(uuid.uuid4()))
+    _messages: List[Dict[str, str]] = PrivateAttr(default_factory=list)
+    _iterations: int = PrivateAttr(default=0)
+    _printer: Printer = PrivateAttr(default_factory=Printer)
+
+    @model_validator(mode="after")
+    def setup_llm(self):
+        """Set up the LLM and other components after initialization."""
+        self.llm = create_llm(self.llm)
+        if not isinstance(self.llm, LLM):
+            raise ValueError("Unable to create LLM instance")
+
+        # Initialize callbacks
+        token_callback = TokenCalcHandler(token_cost_process=self._token_process)
+        self._callbacks = [token_callback]
+
+        return self
+
+    @model_validator(mode="after")
+    def parse_tools(self):
+        """Parse the tools and convert them to CrewStructuredTool instances."""
+        self._parsed_tools = parse_tools(self.tools)
+
+        return self
+
+    @property
+    def key(self) -> str:
+        """Get the unique key for this agent instance."""
+        return self._key
+
+    @property
+    def _original_role(self) -> str:
+        """Return the original role for compatibility with tool interfaces."""
+        return self.role
+
+    def kickoff(self, messages: Union[str, List[Dict[str, str]]]) -> LiteAgentOutput:
+        """
+        Execute the agent with the given messages.
+
+        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.
+
+        Returns:
+            LiteAgentOutput: The result of the agent execution.
+        """
+        # Create agent info for event emission
+        agent_info = {
+            "role": self.role,
+            "goal": self.goal,
+            "backstory": self.backstory,
+            "tools": self._parsed_tools,
+            "verbose": self.verbose,
+        }
+
+        try:
+            # Reset state for this run
+            self._iterations = 0
+            self.tools_results = []
+
+            # Format messages for the LLM
+            self._messages = self._format_messages(messages)
+
+            # Emit event for agent execution start
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionStartedEvent(
+                    agent_info=agent_info,
+                    tools=self._parsed_tools,
+                    messages=messages,
+                ),
+            )
+
+            # Execute the agent using invoke loop
+            agent_finish = self._invoke_loop()
+            formatted_result: Optional[BaseModel] = None
+            if self.response_format:
+                try:
+                    # Cast to BaseModel to ensure type safety
+                    result = self.response_format.model_validate_json(
+                        agent_finish.output
+                    )
+                    if isinstance(result, BaseModel):
+                        formatted_result = result
+                except Exception as e:
+                    self._printer.print(
+                        content=f"Failed to parse output into response format: {str(e)}",
+                        color="yellow",
+                    )
+
+            # Calculate token usage metrics
+            usage_metrics = self._token_process.get_summary()
+
+            # Create output
+            output = LiteAgentOutput(
+                raw=agent_finish.output,
+                pydantic=formatted_result,
+                agent_role=self.role,
+                usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            )
+
+            # Emit completion event
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionCompletedEvent(
+                    agent_info=agent_info,
+                    output=agent_finish.output,
+                ),
+            )
+
+            return output
+
+        except Exception as e:
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
+            handle_unknown_error(self._printer, e)
+            # Emit error event
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionErrorEvent(
+                    agent_info=agent_info,
+                    error=str(e),
+                ),
+            )
+            raise e
+
+    async def kickoff_async(
+        self, messages: Union[str, List[Dict[str, str]]]
+    ) -> LiteAgentOutput:
+        """
+        Execute the agent asynchronously with the given messages.
+
+        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.
+
+        Returns:
+            LiteAgentOutput: The result of the agent execution.
+        """
+        return await asyncio.to_thread(self.kickoff, messages)
+
+    def _get_default_system_prompt(self) -> str:
+        """Get the default system prompt for the agent."""
+        base_prompt = ""
+        if self._parsed_tools:
+            # Use the prompt template for agents with tools
+            base_prompt = self.i18n.slice("lite_agent_system_prompt_with_tools").format(
+                role=self.role,
+                backstory=self.backstory,
+                goal=self.goal,
+                tools=render_text_description_and_args(self._parsed_tools),
+                tool_names=get_tool_names(self._parsed_tools),
+            )
+        else:
+            # Use the prompt template for agents without tools
+            base_prompt = self.i18n.slice(
+                "lite_agent_system_prompt_without_tools"
+            ).format(
+                role=self.role,
+                backstory=self.backstory,
+                goal=self.goal,
+            )
+
+        # Add response format instructions if specified
+        if self.response_format:
+            schema = generate_model_description(self.response_format)
+            base_prompt += self.i18n.slice("lite_agent_response_format").format(
+                response_format=schema
+            )
+
+        return base_prompt
+
+    def _format_messages(
+        self, messages: Union[str, List[Dict[str, str]]]
+    ) -> List[Dict[str, str]]:
+        """Format messages for the LLM."""
+        if isinstance(messages, str):
+            messages = [{"role": "user", "content": messages}]
+
+        system_prompt = self._get_default_system_prompt()
+
+        # Add system message at the beginning
+        formatted_messages = [{"role": "system", "content": system_prompt}]
+
+        # Add the rest of the messages
+        formatted_messages.extend(messages)
+
+        return formatted_messages
+
+    def _invoke_loop(self) -> AgentFinish:
+        """
+        Run the agent's thought process until it reaches a conclusion or max iterations.
+
+        Returns:
+            AgentFinish: The final result of the agent execution.
+        """
+        # Execute the agent loop
+        formatted_answer = None
+        while not isinstance(formatted_answer, AgentFinish):
+            try:
+                if has_reached_max_iterations(self._iterations, self.max_iterations):
+                    formatted_answer = handle_max_iterations_exceeded(
+                        formatted_answer,
+                        printer=self._printer,
+                        i18n=self.i18n,
+                        messages=self._messages,
+                        llm=cast(LLM, self.llm),
+                        callbacks=self._callbacks,
+                    )
+
+                enforce_rpm_limit(self.request_within_rpm_limit)
+
+                # Emit LLM call started event
+                crewai_event_bus.emit(
+                    self,
+                    event=LLMCallStartedEvent(
+                        messages=self._messages,
+                        tools=None,
+                        callbacks=self._callbacks,
+                    ),
+                )
+
+                try:
+                    answer = get_llm_response(
+                        llm=cast(LLM, self.llm),
+                        messages=self._messages,
+                        callbacks=self._callbacks,
+                        printer=self._printer,
+                    )
+
+                    # Emit LLM call completed event
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallCompletedEvent(
+                            response=answer,
+                            call_type=LLMCallType.LLM_CALL,
+                        ),
+                    )
+                except Exception as e:
+                    # Emit LLM call failed event
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallFailedEvent(error=str(e)),
+                    )
+                    raise e
+
+                formatted_answer = process_llm_response(answer, self.use_stop_words)
+
+                if isinstance(formatted_answer, AgentAction):
+                    # Emit tool usage started event
+                    crewai_event_bus.emit(
+                        self,
+                        event=ToolUsageStartedEvent(
+                            agent_key=self.key,
+                            agent_role=self.role,
+                            tool_name=formatted_answer.tool,
+                            tool_args=formatted_answer.tool_input,
+                            tool_class=formatted_answer.tool,
+                        ),
+                    )
+
+                    try:
+                        tool_result = execute_tool_and_check_finality(
+                            agent_action=formatted_answer,
+                            tools=self._parsed_tools,
+                            i18n=self.i18n,
+                            agent_key=self.key,
+                            agent_role=self.role,
+                        )
+                        # Emit tool usage finished event
+                        crewai_event_bus.emit(
+                            self,
+                            event=ToolUsageFinishedEvent(
+                                agent_key=self.key,
+                                agent_role=self.role,
+                                tool_name=formatted_answer.tool,
+                                tool_args=formatted_answer.tool_input,
+                                tool_class=formatted_answer.tool,
+                                started_at=datetime.now(),
+                                finished_at=datetime.now(),
+                                output=tool_result.result,
+                            ),
+                        )
+                    except Exception as e:
+                        # Emit tool usage error event
+                        crewai_event_bus.emit(
+                            self,
+                            event=ToolUsageErrorEvent(
+                                agent_key=self.key,
+                                agent_role=self.role,
+                                tool_name=formatted_answer.tool,
+                                tool_args=formatted_answer.tool_input,
+                                tool_class=formatted_answer.tool,
+                                error=str(e),
+                            ),
+                        )
+                        raise e
+
+                    formatted_answer = handle_agent_action_core(
+                        formatted_answer=formatted_answer,
+                        tool_result=tool_result,
+                        show_logs=self._show_logs,
+                    )
+
+                self._append_message(formatted_answer.text, role="assistant")
+            except OutputParserException as e:
+                formatted_answer = handle_output_parser_exception(
+                    e=e,
+                    messages=self._messages,
+                    iterations=self._iterations,
+                    log_error_after=3,
+                    printer=self._printer,
+                )
+
+            except Exception as e:
+                if e.__class__.__module__.startswith("litellm"):
+                    # Do not retry on litellm errors
+                    raise e
+                if is_context_length_exceeded(e):
+                    handle_context_length(
+                        respect_context_window=self.respect_context_window,
+                        printer=self._printer,
+                        messages=self._messages,
+                        llm=cast(LLM, self.llm),
+                        callbacks=self._callbacks,
+                        i18n=self.i18n,
+                    )
+                    continue
+                else:
+                    handle_unknown_error(self._printer, e)
+                    raise e
+
+            finally:
+                self._iterations += 1
+
+        assert isinstance(formatted_answer, AgentFinish)
+        self._show_logs(formatted_answer)
+        return formatted_answer
+
+    def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
+        """Show logs for the agent's execution."""
+        show_agent_logs(
+            printer=self._printer,
+            agent_role=self.role,
+            formatted_answer=formatted_answer,
+            verbose=self.verbose,
+        )
+
+    def _append_message(self, text: str, role: str = "assistant") -> None:
+        """Append a message to the message list with the given role."""
+        self._messages.append(format_message_for_llm(text, role=role))

src/crewai/llm.py

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

src/crewai/llms/base_llm.py

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

src/crewai/llms/third_party/ai_suite.py

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

src/crewai/memory/contextual/contextual_memory.py

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

src/crewai/memory/storage/mem0_storage.py

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

src/crewai/memory/user/user_memory.py

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

src/crewai/task.py

@@ -2,6 +2,7 @@ import datetime
 import inspect
 import json
 import logging
+import re
 import threading
 import uuid
 from concurrent.futures import Future
@@ -49,6 +50,7 @@ from crewai.utilities.events import (
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
+from crewai.utilities.string_utils import interpolate_only
 
 
 class Task(BaseModel):
@@ -386,7 +388,7 @@ class Task(BaseModel):
             tools = tools or self.tools or []
 
             self.processed_by_agents.add(agent.role)
-            crewai_event_bus.emit(self, TaskStartedEvent(context=context))
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))
             result = agent.execute_task(
                 task=self,
                 context=context,
@@ -462,11 +464,11 @@ class Task(BaseModel):
                     )
                 )
                 self._save_file(content)
-            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output))
+            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output, task=self))
             return task_output
         except Exception as e:
             self.end_time = datetime.datetime.now()
-            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e)))
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))
             raise e  # Re-raise the exception after emitting the event
 
     def prompt(self) -> str:
@@ -507,7 +509,9 @@ class Task(BaseModel):
             return
 
         try:
-            self.description = self._original_description.format(**inputs)
+            self.description = interpolate_only(
+                input_string=self._original_description, inputs=inputs
+            )
         except KeyError as e:
             raise ValueError(
                 f"Missing required template variable '{e.args[0]}' in description"
@@ -516,7 +520,7 @@ class Task(BaseModel):
             raise ValueError(f"Error interpolating description: {str(e)}") from e
 
         try:
-            self.expected_output = self.interpolate_only(
+            self.expected_output = interpolate_only(
                 input_string=self._original_expected_output, inputs=inputs
             )
         except (KeyError, ValueError) as e:
@@ -524,7 +528,7 @@ class Task(BaseModel):
 
         if self.output_file is not None:
             try:
-                self.output_file = self.interpolate_only(
+                self.output_file = interpolate_only(
                     input_string=self._original_output_file, inputs=inputs
                 )
             except (KeyError, ValueError) as e:
@@ -555,72 +559,6 @@ class Task(BaseModel):
                 f"\n\n{conversation_instruction}\n\n{conversation_history}"
             )
 
-    def interpolate_only(
-        self,
-        input_string: Optional[str],
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
-    ) -> str:
-        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
-
-        Args:
-            input_string: The string containing template variables to interpolate.
-                         Can be None or empty, in which case an empty string is returned.
-            inputs: Dictionary mapping template variables to their values.
-                   Supported value types are strings, integers, floats, and dicts/lists
-                   containing only these types and other nested dicts/lists.
-
-        Returns:
-            The interpolated string with all template variables replaced with their values.
-            Empty string if input_string is None or empty.
-
-        Raises:
-            ValueError: If a value contains unsupported types
-        """
-
-        # Validation function for recursive type checking
-        def validate_type(value: Any) -> None:
-            if value is None:
-                return
-            if isinstance(value, (str, int, float, bool)):
-                return
-            if isinstance(value, (dict, list)):
-                for item in value.values() if isinstance(value, dict) else value:
-                    validate_type(item)
-                return
-            raise ValueError(
-                f"Unsupported type {type(value).__name__} in inputs. "
-                "Only str, int, float, bool, dict, and list are allowed."
-            )
-
-        # Validate all input values
-        for key, value in inputs.items():
-            try:
-                validate_type(value)
-            except ValueError as e:
-                raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
-
-        if input_string is None or not input_string:
-            return ""
-        if "{" not in input_string and "}" not in input_string:
-            return input_string
-        if not inputs:
-            raise ValueError(
-                "Inputs dictionary cannot be empty when interpolating variables"
-            )
-        try:
-            escaped_string = input_string.replace("{", "{{").replace("}", "}}")
-
-            for key in inputs.keys():
-                escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}")
-
-            return escaped_string.format(**inputs)
-        except KeyError as e:
-            raise KeyError(
-                f"Template variable '{e.args[0]}' not found in inputs dictionary"
-            ) from e
-        except ValueError as e:
-            raise ValueError(f"Error during string interpolation: {str(e)}") from e
-
     def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
         self.tools_errors += 1
@@ -634,7 +572,15 @@ class Task(BaseModel):
     def copy(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Create a deep copy of the Task."""
+        """Creates a deep copy of the Task while preserving its original class type.
+
+        Args:
+            agents: List of agents available for the task.
+            task_mapping: Dictionary mapping task IDs to Task instances.
+
+        Returns:
+            A copy of the task with the same class type as the original.
+        """
         exclude = {
             "id",
             "agent",
@@ -657,7 +603,7 @@ class Task(BaseModel):
         cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
         cloned_tools = copy(self.tools) if self.tools else []
 
-        copied_task = Task(
+        copied_task = self.__class__(
             **copied_data,
             context=cloned_context,
             agent=cloned_agent,

src/crewai/telemetry/telemetry.py

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

src/crewai/tools/base_tool.py

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

src/crewai/tools/tool_types.py

@@ -0,0 +1,9 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class ToolResult:
+    """Result of tool execution."""
+
+    result: str
+    result_as_answer: bool = False

src/crewai/tools/tool_usage.py

@@ -2,10 +2,11 @@ import ast
 import datetime
 import json
 import time
+from dataclasses import dataclass
 from difflib import SequenceMatcher
 from json import JSONDecodeError
 from textwrap import dedent
-from typing import Any, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
 import json5
 from json_repair import repair_json
@@ -13,10 +14,13 @@ from json_repair import repair_json
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.task import Task
 from crewai.telemetry import Telemetry
-from crewai.tools import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
-from crewai.utilities import I18N, Converter, ConverterError, Printer
+from crewai.utilities import I18N, Converter, Printer
+from crewai.utilities.agent_utils import (
+    get_tool_names,
+    render_text_description_and_args,
+)
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import (
     ToolSelectionErrorEvent,
@@ -25,6 +29,10 @@ from crewai.utilities.events.tool_usage_events import (
     ToolValidateInputErrorEvent,
 )
 
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.lite_agent import LiteAgent
+
 OPENAI_BIGGER_MODELS = [
     "gpt-4",
     "gpt-4o",
@@ -60,31 +68,29 @@ class ToolUsage:
 
     def __init__(
         self,
-        tools_handler: ToolsHandler,
-        tools: List[BaseTool],
-        original_tools: List[Any],
-        tools_description: str,
-        tools_names: str,
-        task: Task,
+        tools_handler: Optional[ToolsHandler],
+        tools: List[CrewStructuredTool],
+        task: Optional[Task],
         function_calling_llm: Any,
-        agent: Any,
-        action: Any,
+        agent: Optional[Union["BaseAgent", "LiteAgent"]] = None,
+        action: Any = None,
+        fingerprint_context: Optional[Dict[str, str]] = None,
     ) -> None:
-        self._i18n: I18N = agent.i18n
+        self._i18n: I18N = agent.i18n if agent else I18N()
         self._printer: Printer = Printer()
         self._telemetry: Telemetry = Telemetry()
         self._run_attempts: int = 1
         self._max_parsing_attempts: int = 3
         self._remember_format_after_usages: int = 3
         self.agent = agent
-        self.tools_description = tools_description
-        self.tools_names = tools_names
+        self.tools_description = render_text_description_and_args(tools)
+        self.tools_names = get_tool_names(tools)
         self.tools_handler = tools_handler
-        self.original_tools = original_tools
         self.tools = tools
         self.task = task
         self.action = action
         self.function_calling_llm = function_calling_llm
+        self.fingerprint_context = fingerprint_context or {}
 
         # Set the maximum parsing attempts for bigger models
         if (
@@ -103,29 +109,35 @@ class ToolUsage:
     ) -> str:
         if isinstance(calling, ToolUsageErrorException):
             error = calling.message
-            if self.agent.verbose:
+            if self.agent and self.agent.verbose:
                 self._printer.print(content=f"\n\n{error}\n", color="red")
-            self.task.increment_tools_errors()
+            if self.task:
+                self.task.increment_tools_errors()
             return error
 
         try:
             tool = self._select_tool(calling.tool_name)
         except Exception as e:
             error = getattr(e, "message", str(e))
-            self.task.increment_tools_errors()
-            if self.agent.verbose:
+            if self.task:
+                self.task.increment_tools_errors()
+            if self.agent and self.agent.verbose:
                 self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
 
-        if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]:  # type: ignore
+        if (
+            isinstance(tool, CrewStructuredTool)
+            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
+        ):
             try:
                 result = self._use(tool_string=tool_string, tool=tool, calling=calling)
                 return result
 
             except Exception as e:
                 error = getattr(e, "message", str(e))
-                self.task.increment_tools_errors()
-                if self.agent.verbose:
+                if self.task:
+                    self.task.increment_tools_errors()
+                if self.agent and self.agent.verbose:
                     self._printer.print(content=f"\n\n{error}\n", color="red")
                 return error
 
@@ -134,9 +146,9 @@ class ToolUsage:
     def _use(
         self,
         tool_string: str,
-        tool: Any,
+        tool: CrewStructuredTool,
         calling: Union[ToolCalling, InstructorToolCalling],
-    ) -> str:  # TODO: Fix this return type
+    ) -> str:
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
@@ -151,24 +163,29 @@ class ToolUsage:
                 return result  # type: ignore # Fix the return type of this function
 
             except Exception:
-                self.task.increment_tools_errors()
+                if self.task:
+                    self.task.increment_tools_errors()
 
         started_at = time.time()
         from_cache = False
+        result = None  # type: ignore
 
-        result = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
-        # check if cache is available
-        if self.tools_handler.cache:
-            result = self.tools_handler.cache.read(  # type: ignore # Incompatible types in assignment (expression has type "str | None", variable has type "str")
+        if self.tools_handler and self.tools_handler.cache:
+            result = self.tools_handler.cache.read(
                 tool=calling.tool_name, input=calling.arguments
-            )
+            )  # type: ignore
             from_cache = result is not None
 
-        original_tool = next(
-            (ot for ot in self.original_tools if ot.name == tool.name), None
+        available_tool = next(
+            (
+                available_tool
+                for available_tool in self.tools
+                if available_tool.name == tool.name
+            ),
+            None,
         )
 
-        if result is None:  #! finecwg: if not result --> if result is None
+        if result is None:
             try:
                 if calling.tool_name in [
                     "Delegate work to coworker",
@@ -177,22 +194,31 @@ class ToolUsage:
                     coworker = (
                         calling.arguments.get("coworker") if calling.arguments else None
                     )
-                    self.task.increment_delegations(coworker)
+                    if self.task:
+                        self.task.increment_delegations(coworker)
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.model_json_schema()[
+                            "properties"
+                        ].keys()  # type: ignore
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
                             if k in acceptable_args
                         }
+                        # Add fingerprint metadata if available
+                        arguments = self._add_fingerprint_metadata(arguments)
                         result = tool.invoke(input=arguments)
                     except Exception:
                         arguments = calling.arguments
+                        # Add fingerprint metadata if available
+                        arguments = self._add_fingerprint_metadata(arguments)
                         result = tool.invoke(input=arguments)
                 else:
-                    result = tool.invoke(input={})
+                    # Add fingerprint metadata even to empty arguments
+                    arguments = self._add_fingerprint_metadata({})
+                    result = tool.invoke(input=arguments)
             except Exception as e:
                 self.on_tool_error(tool=tool, tool_calling=calling, e=e)
                 self._run_attempts += 1
@@ -202,25 +228,27 @@ class ToolUsage:
                         error=e, tool=tool.name, tool_inputs=tool.description
                     )
                     error = ToolUsageErrorException(
-                        f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
+                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                     ).message
-                    self.task.increment_tools_errors()
-                    if self.agent.verbose:
+                    if self.task:
+                        self.task.increment_tools_errors()
+                    if self.agent and self.agent.verbose:
                         self._printer.print(
                             content=f"\n\n{error_message}\n", color="red"
                         )
                     return error  # type: ignore # No return value expected
 
-                self.task.increment_tools_errors()
+                if self.task:
+                    self.task.increment_tools_errors()
                 return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
 
             if self.tools_handler:
                 should_cache = True
                 if (
-                    hasattr(original_tool, "cache_function")
-                    and original_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    hasattr(available_tool, "cache_function")
+                    and available_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
                 ):
-                    should_cache = original_tool.cache_function(  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    should_cache = available_tool.cache_function(  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
                         calling.arguments, result
                     )
 
@@ -244,44 +272,50 @@ class ToolUsage:
             tool_calling=calling,
             from_cache=from_cache,
             started_at=started_at,
+            result=result,
         )
 
         if (
-            hasattr(original_tool, "result_as_answer")
-            and original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+            hasattr(available_tool, "result_as_answer")
+            and available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
         ):
-            result_as_answer = original_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
-            data["result_as_answer"] = result_as_answer
+            result_as_answer = available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
+            data["result_as_answer"] = result_as_answer  # type: ignore
 
-        self.agent.tools_results.append(data)
+        if self.agent and hasattr(self.agent, "tools_results"):
+            self.agent.tools_results.append(data)
 
-        return result  # type: ignore # No return value expected
-
-    def _format_result(self, result: Any) -> None:
-        self.task.used_tools += 1
-        if self._should_remember_format():  # type: ignore # "_should_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
-            result = self._remember_format(result=result)  # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
         return result
 
-    def _should_remember_format(self) -> bool:
-        return self.task.used_tools % self._remember_format_after_usages == 0
+    def _format_result(self, result: Any) -> str:
+        if self.task:
+            self.task.used_tools += 1
+        if self._should_remember_format():
+            result = self._remember_format(result=result)
+        return str(result)
 
-    def _remember_format(self, result: str) -> None:
+    def _should_remember_format(self) -> bool:
+        if self.task:
+            return self.task.used_tools % self._remember_format_after_usages == 0
+        return False
+
+    def _remember_format(self, result: str) -> str:
         result = str(result)
         result += "\n\n" + self._i18n.slice("tools").format(
             tools=self.tools_description, tool_names=self.tools_names
         )
-        return result  # type: ignore # No return value expected
+        return result
 
     def _check_tool_repeated_usage(
         self, calling: Union[ToolCalling, InstructorToolCalling]
-    ) -> None:
+    ) -> bool:
         if not self.tools_handler:
-            return False  # type: ignore # No return value expected
+            return False
         if last_tool_usage := self.tools_handler.last_used_tool:
-            return (calling.tool_name == last_tool_usage.tool_name) and (  # type: ignore # No return value expected
+            return (calling.tool_name == last_tool_usage.tool_name) and (
                 calling.arguments == last_tool_usage.arguments
             )
+        return False
 
     def _select_tool(self, tool_name: str) -> Any:
         order_tools = sorted(
@@ -300,10 +334,11 @@ class ToolUsage:
                 > 0.85
             ):
                 return tool
-        self.task.increment_tools_errors()
-        tool_selection_data = {
-            "agent_key": self.agent.key,
-            "agent_role": self.agent.role,
+        if self.task:
+            self.task.increment_tools_errors()
+        tool_selection_data: Dict[str, Any] = {
+            "agent_key": getattr(self.agent, "key", None) if self.agent else None,
+            "agent_role": getattr(self.agent, "role", None) if self.agent else None,
             "tool_name": tool_name,
             "tool_args": {},
             "tool_class": self.tools_description,
@@ -336,7 +371,9 @@ class ToolUsage:
             descriptions.append(tool.description)
         return "\n--\n".join(descriptions)
 
-    def _function_calling(self, tool_string: str):
+    def _function_calling(
+        self, tool_string: str
+    ) -> Union[ToolCalling, InstructorToolCalling]:
         model = (
             InstructorToolCalling
             if self.function_calling_llm.supports_function_calling()
@@ -358,18 +395,14 @@ class ToolUsage:
             max_attempts=1,
         )
         tool_object = converter.to_pydantic()
-        calling = ToolCalling(
-            tool_name=tool_object["tool_name"],
-            arguments=tool_object["arguments"],
-            log=tool_string,  # type: ignore
-        )
+        if not isinstance(tool_object, (ToolCalling, InstructorToolCalling)):
+            raise ToolUsageErrorException("Failed to parse tool calling")
 
-        if isinstance(calling, ConverterError):
-            raise calling
+        return tool_object
 
-        return calling
-
-    def _original_tool_calling(self, tool_string: str, raise_error: bool = False):
+    def _original_tool_calling(
+        self, tool_string: str, raise_error: bool = False
+    ) -> Union[ToolCalling, InstructorToolCalling, ToolUsageErrorException]:
         tool_name = self.action.tool
         tool = self._select_tool(tool_name)
         try:
@@ -380,7 +413,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f'{self._i18n.errors("tool_arguments_error")}'
+                    f"{self._i18n.errors('tool_arguments_error')}"
                 )
 
         if not isinstance(arguments, dict):
@@ -388,18 +421,17 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f'{self._i18n.errors("tool_arguments_error")}'
+                    f"{self._i18n.errors('tool_arguments_error')}"
                 )
 
         return ToolCalling(
             tool_name=tool.name,
             arguments=arguments,
-            log=tool_string,
         )
 
     def _tool_calling(
         self, tool_string: str
-    ) -> Union[ToolCalling, InstructorToolCalling]:
+    ) -> Union[ToolCalling, InstructorToolCalling, ToolUsageErrorException]:
         try:
             try:
                 return self._original_tool_calling(tool_string, raise_error=True)
@@ -412,11 +444,12 @@ class ToolUsage:
             self._run_attempts += 1
             if self._run_attempts > self._max_parsing_attempts:
                 self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                self.task.increment_tools_errors()
-                if self.agent.verbose:
+                if self.task:
+                    self.task.increment_tools_errors()
+                if self.agent and self.agent.verbose:
                     self._printer.print(content=f"\n\n{e}\n", color="red")
                 return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                    f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
+                    f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                 )
             return self._tool_calling(tool_string)
 
@@ -443,6 +476,7 @@ class ToolUsage:
             if isinstance(arguments, dict):
                 return arguments
         except (ValueError, SyntaxError):
+            repaired_input = repair_json(tool_input)
             pass  # Continue to the next parsing attempt
 
         # Attempt 3: Parse as JSON5
@@ -455,7 +489,7 @@ class ToolUsage:
 
         # Attempt 4: Repair JSON
         try:
-            repaired_input = repair_json(tool_input, skip_json_loads=True)
+            repaired_input = str(repair_json(tool_input, skip_json_loads=True))
             self._printer.print(
                 content=f"Repaired JSON: {repaired_input}", color="blue"
             )
@@ -475,24 +509,39 @@ class ToolUsage:
 
     def _emit_validate_input_error(self, final_error: str):
         tool_selection_data = {
-            "agent_key": self.agent.key,
-            "agent_role": self.agent.role,
+            "agent_key": getattr(self.agent, "key", None) if self.agent else None,
+            "agent_role": getattr(self.agent, "role", None) if self.agent else None,
             "tool_name": self.action.tool,
             "tool_args": str(self.action.tool_input),
             "tool_class": self.__class__.__name__,
+            "agent": self.agent,  # Adding agent for fingerprint extraction
         }
 
+        # Include fingerprint context if available
+        if self.fingerprint_context:
+            tool_selection_data.update(self.fingerprint_context)
+
         crewai_event_bus.emit(
             self,
             ToolValidateInputErrorEvent(**tool_selection_data, error=final_error),
         )
 
-    def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
+    def on_tool_error(
+        self,
+        tool: Any,
+        tool_calling: Union[ToolCalling, InstructorToolCalling],
+        e: Exception,
+    ) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)
         crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
 
     def on_tool_use_finished(
-        self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
+        self,
+        tool: Any,
+        tool_calling: Union[ToolCalling, InstructorToolCalling],
+        from_cache: bool,
+        started_at: float,
+        result: Any,
     ) -> None:
         finished_at = time.time()
         event_data = self._prepare_event_data(tool, tool_calling)
@@ -501,17 +550,75 @@ class ToolUsage:
                 "started_at": datetime.datetime.fromtimestamp(started_at),
                 "finished_at": datetime.datetime.fromtimestamp(finished_at),
                 "from_cache": from_cache,
+                "output": result,
             }
         )
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
-    def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
-        return {
-            "agent_key": self.agent.key,
-            "agent_role": (self.agent._original_role or self.agent.role),
+    def _prepare_event_data(
+        self, tool: Any, tool_calling: Union[ToolCalling, InstructorToolCalling]
+    ) -> dict:
+        event_data = {
             "run_attempts": self._run_attempts,
-            "delegations": self.task.delegations,
+            "delegations": self.task.delegations if self.task else 0,
             "tool_name": tool.name,
             "tool_args": tool_calling.arguments,
             "tool_class": tool.__class__.__name__,
+            "agent_key": (
+                getattr(self.agent, "key", "unknown") if self.agent else "unknown"
+            ),
+            "agent_role": (
+                getattr(self.agent, "_original_role", None)
+                or getattr(self.agent, "role", "unknown")
+                if self.agent
+                else "unknown"
+            ),
         }
+
+        # Include fingerprint context if available
+        if self.fingerprint_context:
+            event_data.update(self.fingerprint_context)
+
+        return event_data
+
+    def _add_fingerprint_metadata(self, arguments: dict) -> dict:
+        """Add fingerprint metadata to tool arguments if available.
+
+        Args:
+            arguments: The original tool arguments
+
+        Returns:
+            Updated arguments dictionary with fingerprint metadata
+        """
+        # Create a shallow copy to avoid modifying the original
+        arguments = arguments.copy()
+
+        # Add security metadata under a designated key
+        if "security_context" not in arguments:
+            arguments["security_context"] = {}
+
+        security_context = arguments["security_context"]
+
+        # Add agent fingerprint if available
+        if self.agent and hasattr(self.agent, "security_config"):
+            security_config = getattr(self.agent, "security_config", None)
+            if security_config and hasattr(security_config, "fingerprint"):
+                try:
+                    security_context["agent_fingerprint"] = (
+                        security_config.fingerprint.to_dict()
+                    )
+                except AttributeError:
+                    pass
+
+        # Add task fingerprint if available
+        if self.task and hasattr(self.task, "security_config"):
+            security_config = getattr(self.task, "security_config", None)
+            if security_config and hasattr(security_config, "fingerprint"):
+                try:
+                    security_context["task_fingerprint"] = (
+                        security_config.fingerprint.to_dict()
+                    )
+                except AttributeError:
+                    pass
+
+        return arguments

src/crewai/translations/en.json

@@ -24,7 +24,10 @@
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
-    "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary."
+    "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
+    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
+    "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
+    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",

src/crewai/utilities/agent_utils.py

@@ -0,0 +1,431 @@
+import json
+import re
+from typing import Any, Callable, Dict, List, Optional, Sequence, Union
+
+from crewai.agents.parser import (
+    FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
+    AgentAction,
+    AgentFinish,
+    CrewAgentParser,
+    OutputParserException,
+)
+from crewai.llm import LLM
+from crewai.llms.base_llm import BaseLLM
+from crewai.tools import BaseTool as CrewAITool
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.tools.tool_types import ToolResult
+from crewai.utilities import I18N, Printer
+from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededException,
+)
+
+
+def parse_tools(tools: List[BaseTool]) -> List[CrewStructuredTool]:
+    """Parse tools to be used for the task."""
+    tools_list = []
+
+    for tool in tools:
+        if isinstance(tool, CrewAITool):
+            tools_list.append(tool.to_structured_tool())
+        else:
+            raise ValueError("Tool is not a CrewStructuredTool or BaseTool")
+
+    return tools_list
+
+
+def get_tool_names(tools: Sequence[Union[CrewStructuredTool, BaseTool]]) -> str:
+    """Get the names of the tools."""
+    return ", ".join([t.name for t in tools])
+
+
+def render_text_description_and_args(
+    tools: Sequence[Union[CrewStructuredTool, BaseTool]],
+) -> str:
+    """Render the tool name, description, and args in plain text.
+    
+        search: This tool is used for search, args: {"query": {"type": "string"}}
+        calculator: This tool is used for math, \
+        args: {"expression": {"type": "string"}}
+    """
+    tool_strings = []
+    for tool in tools:
+        tool_strings.append(tool.description)
+
+    return "\n".join(tool_strings)
+
+
+def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool:
+    """Check if the maximum number of iterations has been reached."""
+    return iterations >= max_iterations
+
+
+def handle_max_iterations_exceeded(
+    formatted_answer: Union[AgentAction, AgentFinish, None],
+    printer: Printer,
+    i18n: I18N,
+    messages: List[Dict[str, str]],
+    llm: Union[LLM, BaseLLM],
+    callbacks: List[Any],
+) -> Union[AgentAction, AgentFinish]:
+    """
+    Handles the case when the maximum number of iterations is exceeded.
+    Performs one more LLM call to get the final answer.
+
+    Parameters:
+        formatted_answer: The last formatted answer from the agent.
+
+    Returns:
+        The final formatted answer after exceeding max iterations.
+    """
+    printer.print(
+        content="Maximum iterations reached. Requesting final answer.",
+        color="yellow",
+    )
+
+    if formatted_answer and hasattr(formatted_answer, "text"):
+        assistant_message = (
+            formatted_answer.text + f'\n{i18n.errors("force_final_answer")}'
+        )
+    else:
+        assistant_message = i18n.errors("force_final_answer")
+
+    messages.append(format_message_for_llm(assistant_message, role="assistant"))
+
+    # Perform one more LLM call to get the final answer
+    answer = llm.call(
+        messages,
+        callbacks=callbacks,
+    )
+
+    if answer is None or answer == "":
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
+        raise ValueError("Invalid response from LLM call - None or empty.")
+
+    formatted_answer = format_answer(answer)
+    # Return the formatted answer, regardless of its type
+    return formatted_answer
+
+
+def format_message_for_llm(prompt: str, role: str = "user") -> Dict[str, str]:
+    prompt = prompt.rstrip()
+    return {"role": role, "content": prompt}
+
+
+def format_answer(answer: str) -> Union[AgentAction, AgentFinish]:
+    """Format a response from the LLM into an AgentAction or AgentFinish."""
+    try:
+        return CrewAgentParser.parse_text(answer)
+    except Exception:
+        # If parsing fails, return a default AgentFinish
+        return AgentFinish(
+            thought="Failed to parse LLM response",
+            output=answer,
+            text=answer,
+        )
+
+
+def enforce_rpm_limit(
+    request_within_rpm_limit: Optional[Callable[[], bool]] = None,
+) -> None:
+    """Enforce the requests per minute (RPM) limit if applicable."""
+    if request_within_rpm_limit:
+        request_within_rpm_limit()
+
+
+def get_llm_response(
+    llm: Union[LLM, BaseLLM],
+    messages: List[Dict[str, str]],
+    callbacks: List[Any],
+    printer: Printer,
+) -> str:
+    """Call the LLM and return the response, handling any invalid responses."""
+    try:
+        answer = llm.call(
+            messages,
+            callbacks=callbacks,
+        )
+    except Exception as e:
+        printer.print(
+            content=f"Error during LLM call: {e}",
+            color="red",
+        )
+        raise e
+    if not answer:
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
+        raise ValueError("Invalid response from LLM call - None or empty.")
+
+    return answer
+
+
+def process_llm_response(
+    answer: str, use_stop_words: bool
+) -> Union[AgentAction, AgentFinish]:
+    """Process the LLM response and format it into an AgentAction or AgentFinish."""
+    if not use_stop_words:
+        try:
+            # Preliminary parsing to check for errors.
+            format_answer(answer)
+        except OutputParserException as e:
+            if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
+                answer = answer.split("Observation:")[0].strip()
+
+    return format_answer(answer)
+
+
+def handle_agent_action_core(
+    formatted_answer: AgentAction,
+    tool_result: ToolResult,
+    messages: Optional[List[Dict[str, str]]] = None,
+    step_callback: Optional[Callable] = None,
+    show_logs: Optional[Callable] = None,
+) -> Union[AgentAction, AgentFinish]:
+    """Core logic for handling agent actions and tool results.
+
+    Args:
+        formatted_answer: The agent's action
+        tool_result: The result of executing the tool
+        messages: Optional list of messages to append results to
+        step_callback: Optional callback to execute after processing
+        show_logs: Optional function to show logs
+
+    Returns:
+        Either an AgentAction or AgentFinish
+    """
+    if step_callback:
+        step_callback(tool_result)
+
+    formatted_answer.text += f"\nObservation: {tool_result.result}"
+    formatted_answer.result = tool_result.result
+
+    if tool_result.result_as_answer:
+        return AgentFinish(
+            thought="",
+            output=tool_result.result,
+            text=formatted_answer.text,
+        )
+
+    if show_logs:
+        show_logs(formatted_answer)
+
+    if messages is not None:
+        messages.append({"role": "assistant", "content": tool_result.result})
+
+    return formatted_answer
+
+
+def handle_unknown_error(printer: Any, exception: Exception) -> None:
+    """Handle unknown errors by informing the user.
+
+    Args:
+        printer: Printer instance for output
+        exception: The exception that occurred
+    """
+    printer.print(
+        content="An unknown error occurred. Please check the details below.",
+        color="red",
+    )
+    printer.print(
+        content=f"Error details: {exception}",
+        color="red",
+    )
+
+
+def handle_output_parser_exception(
+    e: OutputParserException,
+    messages: List[Dict[str, str]],
+    iterations: int,
+    log_error_after: int = 3,
+    printer: Optional[Any] = None,
+) -> AgentAction:
+    """Handle OutputParserException by updating messages and formatted_answer.
+
+    Args:
+        e: The OutputParserException that occurred
+        messages: List of messages to append to
+        iterations: Current iteration count
+        log_error_after: Number of iterations after which to log errors
+        printer: Optional printer instance for logging
+
+    Returns:
+        AgentAction: A formatted answer with the error
+    """
+    messages.append({"role": "user", "content": e.error})
+
+    formatted_answer = AgentAction(
+        text=e.error,
+        tool="",
+        tool_input="",
+        thought="",
+    )
+
+    if iterations > log_error_after and printer:
+        printer.print(
+            content=f"Error parsing LLM output, agent will retry: {e.error}",
+            color="red",
+        )
+
+    return formatted_answer
+
+
+def is_context_length_exceeded(exception: Exception) -> bool:
+    """Check if the exception is due to context length exceeding.
+
+    Args:
+        exception: The exception to check
+
+    Returns:
+        bool: True if the exception is due to context length exceeding
+    """
+    return LLMContextLengthExceededException(str(exception))._is_context_limit_error(
+        str(exception)
+    )
+
+
+def handle_context_length(
+    respect_context_window: bool,
+    printer: Any,
+    messages: List[Dict[str, str]],
+    llm: Any,
+    callbacks: List[Any],
+    i18n: Any,
+) -> None:
+    """Handle context length exceeded by either summarizing or raising an error.
+
+    Args:
+        respect_context_window: Whether to respect context window
+        printer: Printer instance for output
+        messages: List of messages to summarize
+        llm: LLM instance for summarization
+        callbacks: List of callbacks for LLM
+        i18n: I18N instance for messages
+    """
+    if respect_context_window:
+        printer.print(
+            content="Context length exceeded. Summarizing content to fit the model context window.",
+            color="yellow",
+        )
+        summarize_messages(messages, llm, callbacks, i18n)
+    else:
+        printer.print(
+            content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
+            color="red",
+        )
+        raise SystemExit(
+            "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
+        )
+
+
+def summarize_messages(
+    messages: List[Dict[str, str]],
+    llm: Any,
+    callbacks: List[Any],
+    i18n: Any,
+) -> None:
+    """Summarize messages to fit within context window.
+
+    Args:
+        messages: List of messages to summarize
+        llm: LLM instance for summarization
+        callbacks: List of callbacks for LLM
+        i18n: I18N instance for messages
+    """
+    messages_groups = []
+    for message in messages:
+        content = message["content"]
+        cut_size = llm.get_context_window_size()
+        for i in range(0, len(content), cut_size):
+            messages_groups.append({"content": content[i : i + cut_size]})
+
+    summarized_contents = []
+    for group in messages_groups:
+        summary = llm.call(
+            [
+                format_message_for_llm(
+                    i18n.slice("summarizer_system_message"), role="system"
+                ),
+                format_message_for_llm(
+                    i18n.slice("summarize_instruction").format(group=group["content"]),
+                ),
+            ],
+            callbacks=callbacks,
+        )
+        summarized_contents.append({"content": str(summary)})
+
+    merged_summary = " ".join(content["content"] for content in summarized_contents)
+
+    messages.clear()
+    messages.append(
+        format_message_for_llm(
+            i18n.slice("summary").format(merged_summary=merged_summary)
+        )
+    )
+
+
+def show_agent_logs(
+    printer: Printer,
+    agent_role: str,
+    formatted_answer: Optional[Union[AgentAction, AgentFinish]] = None,
+    task_description: Optional[str] = None,
+    verbose: bool = False,
+) -> None:
+    """Show agent logs for both start and execution states.
+
+    Args:
+        printer: Printer instance for output
+        agent_role: Role of the agent
+        formatted_answer: Optional AgentAction or AgentFinish for execution logs
+        task_description: Optional task description for start logs
+        verbose: Whether to show verbose output
+    """
+    if not verbose:
+        return
+
+    agent_role = agent_role.split("\n")[0]
+
+    if formatted_answer is None:
+        # Start logs
+        printer.print(
+            content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
+        )
+        if task_description:
+            printer.print(
+                content=f"\033[95m## Task:\033[00m \033[92m{task_description}\033[00m"
+            )
+    else:
+        # Execution logs
+        printer.print(
+            content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
+        )
+
+        if isinstance(formatted_answer, AgentAction):
+            thought = re.sub(r"\n+", "\n", formatted_answer.thought)
+            formatted_json = json.dumps(
+                formatted_answer.tool_input,
+                indent=2,
+                ensure_ascii=False,
+            )
+            if thought and thought != "":
+                printer.print(
+                    content=f"\033[95m## Thought:\033[00m \033[92m{thought}\033[00m"
+                )
+            printer.print(
+                content=f"\033[95m## Using tool:\033[00m \033[92m{formatted_answer.tool}\033[00m"
+            )
+            printer.print(
+                content=f"\033[95m## Tool Input:\033[00m \033[92m\n{formatted_json}\033[00m"
+            )
+            printer.print(
+                content=f"\033[95m## Tool Output:\033[00m \033[92m\n{formatted_answer.result}\033[00m"
+            )
+        elif isinstance(formatted_answer, AgentFinish):
+            printer.print(
+                content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
+            )

src/crewai/utilities/chromadb.py

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

src/crewai/utilities/converter.py

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

src/crewai/utilities/evaluators/crew_evaluator_handler.py

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

src/crewai/utilities/evaluators/task_evaluator.py

@@ -45,7 +45,7 @@ class TaskEvaluator:
 
     def evaluate(self, task, output) -> TaskEvaluation:
         crewai_event_bus.emit(
-            self, TaskEvaluationEvent(evaluation_type="task_evaluation")
+            self, TaskEvaluationEvent(evaluation_type="task_evaluation", task=task)
         )
         evaluation_query = (
             f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"

src/crewai/utilities/events/agent_events.py

@@ -1,16 +1,16 @@
-from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 
-from .base_events import CrewEvent
+from .base_events import BaseEvent
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
 
 
-class AgentExecutionStartedEvent(CrewEvent):
+class AgentExecutionStartedEvent(BaseEvent):
     """Event emitted when an agent starts executing a task"""
 
     agent: BaseAgent
@@ -21,8 +21,20 @@ class AgentExecutionStartedEvent(CrewEvent):
 
     model_config = {"arbitrary_types_allowed": True}
 
+    def __init__(self, **data):
+        super().__init__(**data)
+        # Set fingerprint data from the agent
+        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
+            self.source_fingerprint = self.agent.fingerprint.uuid_str
+            self.source_type = "agent"
+            if (
+                hasattr(self.agent.fingerprint, "metadata")
+                and self.agent.fingerprint.metadata
+            ):
+                self.fingerprint_metadata = self.agent.fingerprint.metadata
 
-class AgentExecutionCompletedEvent(CrewEvent):
+
+class AgentExecutionCompletedEvent(BaseEvent):
     """Event emitted when an agent completes executing a task"""
 
     agent: BaseAgent
@@ -30,11 +42,63 @@ class AgentExecutionCompletedEvent(CrewEvent):
     output: str
     type: str = "agent_execution_completed"
 
+    def __init__(self, **data):
+        super().__init__(**data)
+        # Set fingerprint data from the agent
+        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
+            self.source_fingerprint = self.agent.fingerprint.uuid_str
+            self.source_type = "agent"
+            if (
+                hasattr(self.agent.fingerprint, "metadata")
+                and self.agent.fingerprint.metadata
+            ):
+                self.fingerprint_metadata = self.agent.fingerprint.metadata
 
-class AgentExecutionErrorEvent(CrewEvent):
+
+class AgentExecutionErrorEvent(BaseEvent):
     """Event emitted when an agent encounters an error during execution"""
 
     agent: BaseAgent
     task: Any
     error: str
     type: str = "agent_execution_error"
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        # Set fingerprint data from the agent
+        if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
+            self.source_fingerprint = self.agent.fingerprint.uuid_str
+            self.source_type = "agent"
+            if (
+                hasattr(self.agent.fingerprint, "metadata")
+                and self.agent.fingerprint.metadata
+            ):
+                self.fingerprint_metadata = self.agent.fingerprint.metadata
+
+
+# New event classes for LiteAgent
+class LiteAgentExecutionStartedEvent(BaseEvent):
+    """Event emitted when a LiteAgent starts executing"""
+
+    agent_info: Dict[str, Any]
+    tools: Optional[Sequence[Union[BaseTool, CrewStructuredTool]]]
+    messages: Union[str, List[Dict[str, str]]]
+    type: str = "lite_agent_execution_started"
+
+    model_config = {"arbitrary_types_allowed": True}
+
+
+class LiteAgentExecutionCompletedEvent(BaseEvent):
+    """Event emitted when a LiteAgent completes execution"""
+
+    agent_info: Dict[str, Any]
+    output: str
+    type: str = "lite_agent_execution_completed"
+
+
+class LiteAgentExecutionErrorEvent(BaseEvent):
+    """Event emitted when a LiteAgent encounters an error during execution"""
+
+    agent_info: Dict[str, Any]
+    error: str
+    type: str = "lite_agent_execution_error"

src/crewai/utilities/events/base_events.py

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

src/crewai/utilities/events/crew_events.py

@@ -1,81 +1,102 @@
-from typing import Any, Dict, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
 
-from pydantic import InstanceOf
+from crewai.utilities.events.base_events import BaseEvent
 
-from crewai.utilities.events.base_events import CrewEvent
+if TYPE_CHECKING:
+    from crewai.crew import Crew
+else:
+    Crew = Any
 
 
-class CrewKickoffStartedEvent(CrewEvent):
-    """Event emitted when a crew starts execution"""
+class CrewBaseEvent(BaseEvent):
+    """Base class for crew events with fingerprint handling"""
 
     crew_name: Optional[str]
+    crew: Optional[Crew] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.set_crew_fingerprint()
+
+    def set_crew_fingerprint(self) -> None:
+        if self.crew and hasattr(self.crew, "fingerprint") and self.crew.fingerprint:
+            self.source_fingerprint = self.crew.fingerprint.uuid_str
+            self.source_type = "crew"
+            if (
+                hasattr(self.crew.fingerprint, "metadata")
+                and self.crew.fingerprint.metadata
+            ):
+                self.fingerprint_metadata = self.crew.fingerprint.metadata
+
+    def to_json(self, exclude: set[str] | None = None):
+        if exclude is None:
+            exclude = set()
+        exclude.add("crew")
+        return super().to_json(exclude=exclude)
+
+
+class CrewKickoffStartedEvent(CrewBaseEvent):
+    """Event emitted when a crew starts execution"""
+
     inputs: Optional[Dict[str, Any]]
     type: str = "crew_kickoff_started"
 
 
-class CrewKickoffCompletedEvent(CrewEvent):
+class CrewKickoffCompletedEvent(CrewBaseEvent):
     """Event emitted when a crew completes execution"""
 
-    crew_name: Optional[str]
     output: Any
     type: str = "crew_kickoff_completed"
 
 
-class CrewKickoffFailedEvent(CrewEvent):
+class CrewKickoffFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete execution"""
 
     error: str
-    crew_name: Optional[str]
     type: str = "crew_kickoff_failed"
 
 
-class CrewTrainStartedEvent(CrewEvent):
+class CrewTrainStartedEvent(CrewBaseEvent):
     """Event emitted when a crew starts training"""
 
-    crew_name: Optional[str]
     n_iterations: int
     filename: str
     inputs: Optional[Dict[str, Any]]
     type: str = "crew_train_started"
 
 
-class CrewTrainCompletedEvent(CrewEvent):
+class CrewTrainCompletedEvent(CrewBaseEvent):
     """Event emitted when a crew completes training"""
 
-    crew_name: Optional[str]
     n_iterations: int
     filename: str
     type: str = "crew_train_completed"
 
 
-class CrewTrainFailedEvent(CrewEvent):
+class CrewTrainFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete training"""
 
     error: str
-    crew_name: Optional[str]
     type: str = "crew_train_failed"
 
 
-class CrewTestStartedEvent(CrewEvent):
+class CrewTestStartedEvent(CrewBaseEvent):
     """Event emitted when a crew starts testing"""
 
-    crew_name: Optional[str]
     n_iterations: int
     eval_llm: Optional[Union[str, Any]]
     inputs: Optional[Dict[str, Any]]
     type: str = "crew_test_started"
 
 
-class CrewTestCompletedEvent(CrewEvent):
+class CrewTestCompletedEvent(CrewBaseEvent):
     """Event emitted when a crew completes testing"""
 
-    crew_name: Optional[str]
     type: str = "crew_test_completed"
 
 
-class CrewTestFailedEvent(CrewEvent):
+class CrewTestFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete testing"""
 
     error: str
-    crew_name: Optional[str]
     type: str = "crew_test_failed"

src/crewai/utilities/events/crewai_event_bus.py

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

src/crewai/utilities/events/event_listener.py

@@ -16,7 +16,13 @@ from crewai.utilities.events.llm_events import (
 )
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
 
-from .agent_events import AgentExecutionCompletedEvent, AgentExecutionStartedEvent
+from .agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionStartedEvent,
+    LiteAgentExecutionCompletedEvent,
+    LiteAgentExecutionErrorEvent,
+    LiteAgentExecutionStartedEvent,
+)
 from .crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -65,7 +71,7 @@ class EventListener(BaseEventListener):
             self._telemetry.set_tracer()
             self.execution_spans = {}
             self._initialized = True
-            self.formatter = ConsoleFormatter()
+            self.formatter = ConsoleFormatter(verbose=True)
 
     # ----------- CREW EVENTS -----------
 
@@ -171,6 +177,36 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
+        # ----------- LITE AGENT EVENTS -----------
+
+        @crewai_event_bus.on(LiteAgentExecutionStartedEvent)
+        def on_lite_agent_execution_started(
+            source, event: LiteAgentExecutionStartedEvent
+        ):
+            """Handle LiteAgent execution started event."""
+            self.formatter.handle_lite_agent_execution(
+                event.agent_info["role"], status="started", **event.agent_info
+            )
+
+        @crewai_event_bus.on(LiteAgentExecutionCompletedEvent)
+        def on_lite_agent_execution_completed(
+            source, event: LiteAgentExecutionCompletedEvent
+        ):
+            """Handle LiteAgent execution completed event."""
+            self.formatter.handle_lite_agent_execution(
+                event.agent_info["role"], status="completed", **event.agent_info
+            )
+
+        @crewai_event_bus.on(LiteAgentExecutionErrorEvent)
+        def on_lite_agent_execution_error(source, event: LiteAgentExecutionErrorEvent):
+            """Handle LiteAgent execution error event."""
+            self.formatter.handle_lite_agent_execution(
+                event.agent_info["role"],
+                status="failed",
+                error=event.error,
+                **event.agent_info,
+            )
+
         # ----------- FLOW EVENTS -----------
 
         @crewai_event_bus.on(FlowCreatedEvent)

src/crewai/utilities/events/flow_events.py

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

src/crewai/utilities/events/llm_events.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any, Dict, List, Optional, Union
 
-from crewai.utilities.events.base_events import CrewEvent
+from crewai.utilities.events.base_events import BaseEvent
 
 
 class LLMCallType(Enum):
@@ -11,17 +11,22 @@ class LLMCallType(Enum):
     LLM_CALL = "llm_call"
 
 
-class LLMCallStartedEvent(CrewEvent):
-    """Event emitted when a LLM call starts"""
+class LLMCallStartedEvent(BaseEvent):
+    """Event emitted when a LLM call starts
+
+    Attributes:
+        messages: Content can be either a string or a list of dictionaries that support
+            multimodal content (text, images, etc.)
+    """
 
     type: str = "llm_call_started"
-    messages: Union[str, List[Dict[str, str]]]
+    messages: Union[str, List[Dict[str, Any]]]
     tools: Optional[List[dict]] = None
     callbacks: Optional[List[Any]] = None
     available_functions: Optional[Dict[str, Any]] = None
 
 
-class LLMCallCompletedEvent(CrewEvent):
+class LLMCallCompletedEvent(BaseEvent):
     """Event emitted when a LLM call completes"""
 
     type: str = "llm_call_completed"
@@ -29,14 +34,14 @@ class LLMCallCompletedEvent(CrewEvent):
     call_type: LLMCallType
 
 
-class LLMCallFailedEvent(CrewEvent):
+class LLMCallFailedEvent(BaseEvent):
     """Event emitted when a LLM call fails"""
 
     error: str
     type: str = "llm_call_failed"
 
 
-class LLMStreamChunkEvent(CrewEvent):
+class LLMStreamChunkEvent(BaseEvent):
     """Event emitted when a streaming chunk is received"""
 
     type: str = "llm_stream_chunk"

src/crewai/utilities/events/task_events.py

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

src/crewai/utilities/events/tool_usage_events.py

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

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

@@ -1,4 +1,4 @@
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 
 from rich.console import Console
 from rich.panel import Panel
@@ -13,6 +13,7 @@ class ConsoleFormatter:
     current_tool_branch: Optional[Tree] = None
     current_flow_tree: Optional[Tree] = None
     current_method_branch: Optional[Tree] = None
+    current_lite_agent_branch: Optional[Tree] = None
     tool_usage_counts: Dict[str, int] = {}
 
     def __init__(self, verbose: bool = False):
@@ -390,21 +391,24 @@ class ConsoleFormatter:
         crew_tree: Optional[Tree],
     ) -> Optional[Tree]:
         """Handle tool usage started event."""
-        if not self.verbose or agent_branch is None or crew_tree is None:
+        if not self.verbose:
+            return None
+
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None or tree_to_use is None:
             return None
 
         # Update tool usage count
         self.tool_usage_counts[tool_name] = self.tool_usage_counts.get(tool_name, 0) + 1
 
-        # Find existing tool node or create new one
-        tool_branch = None
-        for child in agent_branch.children:
-            if tool_name in str(child.label):
-                tool_branch = child
-                break
-
-        if not tool_branch:
-            tool_branch = agent_branch.add("")
+        # Find or create tool node
+        tool_branch = self.current_tool_branch
+        if tool_branch is None:
+            tool_branch = branch_to_use.add("")
+            self.current_tool_branch = tool_branch
 
         # Update label with current count
         self.update_tree_label(
@@ -414,11 +418,10 @@ class ConsoleFormatter:
             "yellow",
         )
 
-        self.print(crew_tree)
-        self.print()
-
-        # Set the current_tool_branch attribute directly
-        self.current_tool_branch = tool_branch
+        # Only print if this is a new tool usage
+        if tool_branch not in branch_to_use.children:
+            self.print(tree_to_use)
+            self.print()
 
         return tool_branch
 
@@ -429,17 +432,29 @@ class ConsoleFormatter:
         crew_tree: Optional[Tree],
     ) -> None:
         """Handle tool usage finished event."""
-        if not self.verbose or tool_branch is None or crew_tree is None:
+        if not self.verbose or tool_branch is None:
             return
 
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
+        if tree_to_use is None:
+            return
+
+        # Update the existing tool node's label
         self.update_tree_label(
             tool_branch,
             "🔧",
             f"Used {tool_name} ({self.tool_usage_counts[tool_name]})",
             "green",
         )
-        self.print(crew_tree)
-        self.print()
+
+        # Clear the current tool branch as we're done with it
+        self.current_tool_branch = None
+
+        # Only print if we have a valid tree and the tool node is still in it
+        if isinstance(tree_to_use, Tree) and tool_branch in tree_to_use.children:
+            self.print(tree_to_use)
+            self.print()
 
     def handle_tool_usage_error(
         self,
@@ -452,6 +467,9 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
+
         if tool_branch:
             self.update_tree_label(
                 tool_branch,
@@ -459,8 +477,9 @@ class ConsoleFormatter:
                 f"{tool_name} ({self.tool_usage_counts[tool_name]})",
                 "red",
             )
-            self.print(crew_tree)
-            self.print()
+            if tree_to_use:
+                self.print(tree_to_use)
+                self.print()
 
         # Show error panel
         error_content = self.create_status_content(
@@ -474,19 +493,23 @@ class ConsoleFormatter:
         crew_tree: Optional[Tree],
     ) -> Optional[Tree]:
         """Handle LLM call started event."""
-        if not self.verbose or agent_branch is None or crew_tree is None:
+        if not self.verbose:
             return None
 
-        # Only add thinking status if it doesn't exist
-        if not any("Thinking" in str(child.label) for child in agent_branch.children):
-            tool_branch = agent_branch.add("")
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None or tree_to_use is None:
+            return None
+
+        # Only add thinking status if we don't have a current tool branch
+        if self.current_tool_branch is None:
+            tool_branch = branch_to_use.add("")
             self.update_tree_label(tool_branch, "🧠", "Thinking...", "blue")
-            self.print(crew_tree)
-            self.print()
-
-            # Set the current_tool_branch attribute directly
             self.current_tool_branch = tool_branch
-
+            self.print(tree_to_use)
+            self.print()
             return tool_branch
         return None
 
@@ -497,19 +520,27 @@ class ConsoleFormatter:
         crew_tree: Optional[Tree],
     ) -> None:
         """Handle LLM call completed event."""
-        if (
-            not self.verbose
-            or tool_branch is None
-            or agent_branch is None
-            or crew_tree is None
-        ):
+        if not self.verbose or tool_branch is None:
             return
 
-        # Remove the thinking status node when complete
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        branch_to_use = self.current_lite_agent_branch or agent_branch
+        tree_to_use = branch_to_use or crew_tree
+
+        if branch_to_use is None or tree_to_use is None:
+            return
+
+        # Remove the thinking status node when complete, but only if it exists
         if "Thinking" in str(tool_branch.label):
-            agent_branch.children.remove(tool_branch)
-            self.print(crew_tree)
-            self.print()
+            try:
+                # Check if the node is actually in the children list
+                if tool_branch in branch_to_use.children:
+                    branch_to_use.children.remove(tool_branch)
+                    self.print(tree_to_use)
+                    self.print()
+            except Exception:
+                # If any error occurs during removal, just continue without removing
+                pass
 
     def handle_llm_call_failed(
         self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -518,11 +549,15 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
+
         # Update tool branch if it exists
         if tool_branch:
             tool_branch.label = Text("❌ LLM Failed", style="red bold")
-            self.print(crew_tree)
-            self.print()
+            if tree_to_use:
+                self.print(tree_to_use)
+                self.print()
 
         # Show error panel
         error_content = Text()
@@ -587,6 +622,7 @@ class ConsoleFormatter:
             for child in flow_tree.children:
                 if "Running tests" in str(child.label):
                     child.label = Text("✅ Tests completed successfully", style="green")
+                    break
 
             self.print(flow_tree)
             self.print()
@@ -656,3 +692,94 @@ class ConsoleFormatter:
 
         self.print_panel(failure_content, "Test Failure", "red")
         self.print()
+
+    def create_lite_agent_branch(self, lite_agent_role: str) -> Optional[Tree]:
+        """Create and initialize a lite agent branch."""
+        if not self.verbose:
+            return None
+
+        # Create initial tree for LiteAgent if it doesn't exist
+        if not self.current_lite_agent_branch:
+            lite_agent_label = Text()
+            lite_agent_label.append("🤖 LiteAgent: ", style="cyan bold")
+            lite_agent_label.append(lite_agent_role, style="cyan")
+            lite_agent_label.append("\n    Status: ", style="white")
+            lite_agent_label.append("In Progress", style="yellow")
+
+            lite_agent_tree = Tree(lite_agent_label)
+            self.current_lite_agent_branch = lite_agent_tree
+            self.print(lite_agent_tree)
+            self.print()
+
+        return self.current_lite_agent_branch
+
+    def update_lite_agent_status(
+        self,
+        lite_agent_branch: Optional[Tree],
+        lite_agent_role: str,
+        status: str = "completed",
+        **fields: Dict[str, Any],
+    ) -> None:
+        """Update lite agent status in the tree."""
+        if not self.verbose or lite_agent_branch is None:
+            return
+
+        # Determine style based on status
+        if status == "completed":
+            prefix, style = "✅ LiteAgent:", "green"
+            status_text = "Completed"
+            title = "LiteAgent Completion"
+        elif status == "failed":
+            prefix, style = "❌ LiteAgent:", "red"
+            status_text = "Failed"
+            title = "LiteAgent Error"
+        else:
+            prefix, style = "🤖 LiteAgent:", "yellow"
+            status_text = "In Progress"
+            title = "LiteAgent Status"
+
+        # Update the tree label
+        lite_agent_label = Text()
+        lite_agent_label.append(f"{prefix} ", style=f"{style} bold")
+        lite_agent_label.append(lite_agent_role, style=style)
+        lite_agent_label.append("\n    Status: ", style="white")
+        lite_agent_label.append(status_text, style=f"{style} bold")
+        lite_agent_branch.label = lite_agent_label
+
+        self.print(lite_agent_branch)
+        self.print()
+
+        # Show status panel if additional fields are provided
+        if fields:
+            content = self.create_status_content(
+                f"LiteAgent {status.title()}", lite_agent_role, style, **fields
+            )
+            self.print_panel(content, title, style)
+
+    def handle_lite_agent_execution(
+        self,
+        lite_agent_role: str,
+        status: str = "started",
+        error: Any = None,
+        **fields: Dict[str, Any],
+    ) -> None:
+        """Handle lite agent execution events with consistent formatting."""
+        if not self.verbose:
+            return
+
+        if status == "started":
+            # Create or get the LiteAgent branch
+            lite_agent_branch = self.create_lite_agent_branch(lite_agent_role)
+            if lite_agent_branch and fields:
+                # Show initial status panel
+                content = self.create_status_content(
+                    "LiteAgent Session Started", lite_agent_role, "cyan", **fields
+                )
+                self.print_panel(content, "LiteAgent Started", "cyan")
+        else:
+            # Update existing LiteAgent branch
+            if error:
+                fields["Error"] = error
+            self.update_lite_agent_status(
+                self.current_lite_agent_branch, lite_agent_role, status, **fields
+            )

src/crewai/utilities/formatter.py

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

src/crewai/utilities/llm_utils.py

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

src/crewai/utilities/prompts.py

@@ -9,7 +9,7 @@ class Prompts(BaseModel):
     """Manages and generates prompts for a generic agent."""
 
     i18n: I18N = Field(default=I18N())
-    tools: list[Any] = Field(default=[])
+    has_tools: bool = False
     system_template: Optional[str] = None
     prompt_template: Optional[str] = None
     response_template: Optional[str] = None
@@ -19,7 +19,7 @@ class Prompts(BaseModel):
     def task_execution(self) -> dict[str, str]:
         """Generate a standard prompt for task execution."""
         slices = ["role_playing"]
-        if len(self.tools) > 0:
+        if self.has_tools:
             slices.append("tools")
         else:
             slices.append("no_tools")

src/crewai/utilities/serialization.py

@@ -1,6 +1,6 @@
 import json
+import uuid
 from datetime import date, datetime
-from enum import Enum
 from typing import Any, Dict, List, Union
 
 from pydantic import BaseModel
@@ -12,7 +12,10 @@ Serializable = Union[
 
 
 def to_serializable(
-    obj: Any, max_depth: int = 5, _current_depth: int = 0
+    obj: Any,
+    exclude: set[str] | None = None,
+    max_depth: int = 5,
+    _current_depth: int = 0,
 ) -> Serializable:
     """Converts a Python object into a JSON-compatible representation.
 
@@ -22,6 +25,7 @@ def to_serializable(
 
     Args:
         obj (Any): Object to transform.
+        exclude (set[str], optional): Set of keys to exclude from the result.
         max_depth (int, optional): Maximum recursion depth. Defaults to 5.
 
     Returns:
@@ -30,23 +34,39 @@ def to_serializable(
     if _current_depth >= max_depth:
         return repr(obj)
 
+    if exclude is None:
+        exclude = set()
+
     if isinstance(obj, (str, int, float, bool, type(None))):
         return obj
-    elif isinstance(obj, Enum):
-        return obj.value
+    elif isinstance(obj, uuid.UUID):
+        return str(obj)
     elif isinstance(obj, (date, datetime)):
         return obj.isoformat()
     elif isinstance(obj, (list, tuple, set)):
-        return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
+        return [
+            to_serializable(
+                item, max_depth=max_depth, _current_depth=_current_depth + 1
+            )
+            for item in obj
+        ]
     elif isinstance(obj, dict):
         return {
             _to_serializable_key(key): to_serializable(
-                value, max_depth, _current_depth + 1
+                obj=value,
+                exclude=exclude,
+                max_depth=max_depth,
+                _current_depth=_current_depth + 1,
             )
             for key, value in obj.items()
+            if key not in exclude
         }
     elif isinstance(obj, BaseModel):
-        return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
+        return to_serializable(
+            obj=obj.model_dump(exclude=exclude),
+            max_depth=max_depth,
+            _current_depth=_current_depth + 1,
+        )
     else:
         return repr(obj)
 

src/crewai/utilities/string_utils.py

@@ -0,0 +1,82 @@
+import re
+from typing import Any, Dict, List, Optional, Union
+
+
+def interpolate_only(
+    input_string: Optional[str],
+    inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
+) -> str:
+    """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
+    Only interpolates placeholders that follow the pattern {variable_name} where
+    variable_name starts with a letter/underscore and contains only letters, numbers, and underscores.
+
+    Args:
+        input_string: The string containing template variables to interpolate.
+                     Can be None or empty, in which case an empty string is returned.
+        inputs: Dictionary mapping template variables to their values.
+               Supported value types are strings, integers, floats, and dicts/lists
+               containing only these types and other nested dicts/lists.
+
+    Returns:
+        The interpolated string with all template variables replaced with their values.
+        Empty string if input_string is None or empty.
+
+    Raises:
+        ValueError: If a value contains unsupported types or a template variable is missing
+    """
+
+    # Validation function for recursive type checking
+    def validate_type(value: Any) -> None:
+        if value is None:
+            return
+        if isinstance(value, (str, int, float, bool)):
+            return
+        if isinstance(value, (dict, list)):
+            for item in value.values() if isinstance(value, dict) else value:
+                validate_type(item)
+            return
+        raise ValueError(
+            f"Unsupported type {type(value).__name__} in inputs. "
+            "Only str, int, float, bool, dict, and list are allowed."
+        )
+
+    # Validate all input values
+    for key, value in inputs.items():
+        try:
+            validate_type(value)
+        except ValueError as e:
+            raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
+
+    if input_string is None or not input_string:
+        return ""
+    if "{" not in input_string and "}" not in input_string:
+        return input_string
+    if not inputs:
+        raise ValueError(
+            "Inputs dictionary cannot be empty when interpolating variables"
+        )
+
+    # The regex pattern to find valid variable placeholders
+    # Matches {variable_name} where variable_name starts with a letter/underscore
+    # and contains only letters, numbers, and underscores
+    pattern = r"\{([A-Za-z_][A-Za-z0-9_]*)\}"
+
+    # Find all matching variables in the input string
+    variables = re.findall(pattern, input_string)
+    result = input_string
+
+    # Check if all variables exist in inputs
+    missing_vars = [var for var in variables if var not in inputs]
+    if missing_vars:
+        raise KeyError(
+            f"Template variable '{missing_vars[0]}' not found in inputs dictionary"
+        )
+
+    # Replace each variable with its value
+    for var in variables:
+        if var in inputs:
+            placeholder = "{" + var + "}"
+            value = str(inputs[var])
+            result = result.replace(placeholder, value)
+
+    return result

src/crewai/utilities/tool_utils.py

@@ -0,0 +1,126 @@
+from typing import Any, Dict, List, Optional
+
+from crewai.agents.parser import AgentAction
+from crewai.security import Fingerprint
+from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.tools.tool_types import ToolResult
+from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.i18n import I18N
+
+
+def execute_tool_and_check_finality(
+    agent_action: AgentAction,
+    tools: List[CrewStructuredTool],
+    i18n: I18N,
+    agent_key: Optional[str] = None,
+    agent_role: Optional[str] = None,
+    tools_handler: Optional[Any] = None,
+    task: Optional[Any] = None,
+    agent: Optional[Any] = None,
+    function_calling_llm: Optional[Any] = None,
+    fingerprint_context: Optional[Dict[str, str]] = None,
+) -> ToolResult:
+    """Execute a tool and check if the result should be treated as a final answer.
+
+    Args:
+        agent_action: The action containing the tool to execute
+        tools: List of available tools
+        i18n: Internationalization settings
+        agent_key: Optional key for event emission
+        agent_role: Optional role for event emission
+        tools_handler: Optional tools handler for tool execution
+        task: Optional task for tool execution
+        agent: Optional agent instance for tool execution
+        function_calling_llm: Optional LLM for function calling
+
+    Returns:
+        ToolResult containing the execution result and whether it should be treated as a final answer
+    """
+    try:
+        # Create tool name to tool map
+        tool_name_to_tool_map = {tool.name: tool for tool in tools}
+
+        # Emit tool usage event if agent info is available
+        if agent_key and agent_role and agent:
+            fingerprint_context = fingerprint_context or {}
+            if agent:
+                if hasattr(agent, "set_fingerprint") and callable(
+                    agent.set_fingerprint
+                ):
+                    if isinstance(fingerprint_context, dict):
+                        try:
+                            fingerprint_obj = Fingerprint.from_dict(fingerprint_context)
+                            agent.set_fingerprint(fingerprint_obj)
+                        except Exception as e:
+                            raise ValueError(f"Failed to set fingerprint: {e}")
+
+            event_data = {
+                "agent_key": agent_key,
+                "agent_role": agent_role,
+                "tool_name": agent_action.tool,
+                "tool_args": agent_action.tool_input,
+                "tool_class": agent_action.tool,
+                "agent": agent,
+            }
+            event_data.update(fingerprint_context)
+            crewai_event_bus.emit(
+                agent,
+                event=ToolUsageStartedEvent(
+                    **event_data,
+                ),
+            )
+
+        # Create tool usage instance
+        tool_usage = ToolUsage(
+            tools_handler=tools_handler,
+            tools=tools,
+            function_calling_llm=function_calling_llm,
+            task=task,
+            agent=agent,
+            action=agent_action,
+        )
+
+        # Parse tool calling
+        tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+
+        if isinstance(tool_calling, ToolUsageErrorException):
+            return ToolResult(tool_calling.message, False)
+
+        # Check if tool name matches
+        if tool_calling.tool_name.casefold().strip() in [
+            name.casefold().strip() for name in tool_name_to_tool_map
+        ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+            name.casefold().strip() for name in tool_name_to_tool_map
+        ]:
+            tool_result = tool_usage.use(tool_calling, agent_action.text)
+            tool = tool_name_to_tool_map.get(tool_calling.tool_name)
+            if tool:
+                return ToolResult(tool_result, tool.result_as_answer)
+
+        # Handle invalid tool name
+        tool_result = i18n.errors("wrong_tool_name").format(
+            tool=tool_calling.tool_name,
+            tools=", ".join([tool.name.casefold() for tool in tools]),
+        )
+        return ToolResult(tool_result, False)
+
+    except Exception as e:
+        # Emit error event if agent info is available
+        if agent_key and agent_role and agent:
+            crewai_event_bus.emit(
+                agent,
+                event=ToolUsageErrorEvent(
+                    agent_key=agent_key,
+                    agent_role=agent_role,
+                    tool_name=agent_action.tool,
+                    tool_args=agent_action.tool_input,
+                    tool_class=agent_action.tool,
+                    error=str(e),
+                ),
+            )
+        raise e

tests/agent_test.py

@@ -9,7 +9,7 @@ import pytest
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
-from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
+from crewai.agents.parser import CrewAgentParser, OutputParserException
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
@@ -18,7 +18,6 @@ from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
 from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.llm_events import LLMStreamChunkEvent
 from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
 
 
@@ -375,7 +374,7 @@ def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm="o1-preview",
+        llm=LLM(model="o3-mini"),
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
@@ -401,7 +400,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm="o1-preview",
+        llm="o3-mini",
         max_iter=3,
         use_system_prompt=False,
         allow_delegation=False,
@@ -443,7 +442,7 @@ def test_agent_custom_max_iterations():
             task=task,
             tools=[get_final_answer],
         )
-        assert private_mock.call_count == 2
+        assert private_mock.call_count == 3
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -531,7 +530,7 @@ def test_agent_moved_on_after_max_iterations():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        max_iter=3,
+        max_iter=5,
         allow_delegation=False,
     )
 
@@ -552,6 +551,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
     def get_final_answer() -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
+        return 42
 
     agent = Agent(
         role="test role",
@@ -573,7 +573,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
             task=task,
             tools=[get_final_answer],
         )
-        assert output == "The final answer is 42."
+        assert output == "42"
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -863,25 +863,6 @@ def test_agent_function_calling_llm():
         mock_original_tool_calling.assert_called()
 
 
-def test_agent_count_formatting_error():
-    from unittest.mock import patch
-
-    agent1 = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-    )
-
-    parser = CrewAgentParser(agent=agent1)
-
-    with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
-        test_text = "This text does not match expected formats."
-        with pytest.raises(OutputParserException):
-            parser.parse(test_text)
-        mock_count_errors.assert_called_once()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
     from crewai.tools import BaseTool
@@ -1305,46 +1286,55 @@ def test_llm_call_with_error():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_context_length_exceeds_limit():
+    # Import necessary modules
+    from crewai.utilities.agent_utils import handle_context_length
+    from crewai.utilities.i18n import I18N
+    from crewai.utilities.printer import Printer
+
+    # Create mocks for dependencies
+    printer = Printer()
+    i18n = I18N()
+
+    # Create an agent just for its LLM
     agent = Agent(
         role="test role",
         goal="test goal",
         backstory="test backstory",
-    )
-    original_action = AgentAction(
-        tool="test_tool",
-        tool_input="test_input",
-        text="test_log",
-        thought="test_thought",
+        respect_context_window=True,
     )
 
-    with patch.object(
-        CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
-    ) as private_mock:
-        task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            expected_output="The final answer",
-        )
-        agent.execute_task(
-            task=task,
-        )
-        private_mock.assert_called_once()
-        with patch.object(
-            CrewAgentExecutor, "_handle_context_length"
-        ) as mock_handle_context:
-            mock_handle_context.side_effect = ValueError(
-                "Context length limit exceeded"
+    llm = agent.llm
+
+    # Create test messages
+    messages = [
+        {
+            "role": "user",
+            "content": "This is a test message that would exceed context length",
+        }
+    ]
+
+    # Set up test parameters
+    respect_context_window = True
+    callbacks = []
+
+    # Apply our patch to summarize_messages to force an error
+    with patch("crewai.utilities.agent_utils.summarize_messages") as mock_summarize:
+        mock_summarize.side_effect = ValueError("Context length limit exceeded")
+
+        # Directly call handle_context_length with our parameters
+        with pytest.raises(ValueError) as excinfo:
+            handle_context_length(
+                respect_context_window=respect_context_window,
+                printer=printer,
+                messages=messages,
+                llm=llm,
+                callbacks=callbacks,
+                i18n=i18n,
             )
 
-            long_input = "This is a very long input. " * 10000
-
-            # Attempt to handle context length, expecting the mocked error
-            with pytest.raises(ValueError) as excinfo:
-                agent.agent_executor._handle_context_length(
-                    [(original_action, long_input)]
-                )
-
-            assert "Context length limit exceeded" in str(excinfo.value)
-            mock_handle_context.assert_called_once()
+        # Verify our patch was called and raised the correct error
+        assert "Context length limit exceeded" in str(excinfo.value)
+        mock_summarize.assert_called_once()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1353,7 +1343,7 @@ def test_handle_context_length_exceeds_limit_cli_no():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        sliding_context_window=False,
+        respect_context_window=False,
     )
     task = Task(description="test task", agent=agent, expected_output="test output")
 
@@ -1369,8 +1359,8 @@ def test_handle_context_length_exceeds_limit_cli_no():
         )
         private_mock.assert_called_once()
         pytest.raises(SystemExit)
-        with patch.object(
-            CrewAgentExecutor, "_handle_context_length"
+        with patch(
+            "crewai.utilities.agent_utils.handle_context_length"
         ) as mock_handle_context:
             mock_handle_context.assert_not_called()
 
@@ -1621,6 +1611,38 @@ def test_agent_with_knowledge_sources():
         assert "red" in result.raw.lower()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_knowledge_sources_extensive_role():
+    content = "Brandon's favorite color is red and he likes Mexican food."
+    string_source = StringKnowledgeSource(content=content)
+
+    with patch(
+        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    ) as MockKnowledge:
+        mock_knowledge_instance = MockKnowledge.return_value
+        mock_knowledge_instance.sources = [string_source]
+        mock_knowledge_instance.query.return_value = [{"content": content}]
+
+        agent = Agent(
+            role="Information Agent with extensive role description that is longer than 80 characters",
+            goal="Provide information based on knowledge sources",
+            backstory="You have access to specific knowledge sources.",
+            llm=LLM(model="gpt-4o-mini"),
+            knowledge_sources=[string_source],
+        )
+
+        task = Task(
+            description="What is Brandon's favorite color?",
+            expected_output="Brandon's favorite color.",
+            agent=agent,
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        result = crew.kickoff()
+
+        assert "red" in result.raw.lower()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources_works_with_copy():
     content = "Brandon's favorite color is red and he likes Mexican food."

tests/agents/test_crew_agent_parser.py

@@ -227,13 +227,6 @@ def test_missing_action_input_error(parser):
     assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
 
 
-def test_action_and_final_answer_error(parser):
-    text = "Thought: I found the information\nAction: search\nAction Input: what is the temperature in SF?\nFinal Answer: The temperature is 100 degrees"
-    with pytest.raises(OutputParserException) as exc_info:
-        parser.parse(text)
-    assert "both perform Action and give a Final Answer" in str(exc_info.value)
-
-
 def test_safe_repair_json(parser):
     invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": Senior Researcher'
     expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'

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tests/crew_test.py

@@ -11,7 +11,9 @@ import pydantic_core
 import pytest
 
 from crewai.agent import Agent
+from crewai.agents import CacheHandler
 from crewai.agents.cache import CacheHandler
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -348,7 +350,7 @@ def test_hierarchical_process():
 
     assert (
         result.raw
-        == "Here are the 5 interesting ideas along with a compelling paragraph for each that showcases how good an article on the topic could be:\n\n1. **The Evolution and Future of AI Agents in Everyday Life**:\nThe rapid development of AI agents from rudimentary virtual assistants like Siri and Alexa to today's sophisticated systems marks a significant technological leap. This article will explore the evolving landscape of AI agents, detailing their seamless integration into daily activities ranging from managing smart home devices to streamlining workflows. We will examine the multifaceted benefits these agents bring, such as increased efficiency and personalized user experiences, while also addressing ethical concerns like data privacy and algorithmic bias. Looking ahead, we will forecast the advancements slated for the next decade, including AI agents in personalized health coaching and automated legal consultancy. With more advanced machine learning algorithms, the potential for these AI systems to revolutionize our daily lives is immense.\n\n2. **AI in Healthcare: Revolutionizing Diagnostics and Treatment**:\nArtificial Intelligence is poised to revolutionize the healthcare sector by offering unprecedented improvements in diagnostic accuracy and personalized treatments. This article will delve into the transformative power of AI in healthcare, highlighting real-world applications like AI-driven imaging technologies that aid in early disease detection and predictive analytics that enable personalized patient care plans. We will discuss the ethical challenges, such as data privacy and the implications of AI-driven decision-making in medicine. Through compelling case studies, we will showcase successful AI implementations that have made significant impacts, ultimately painting a picture of a future where AI plays a central role in proactive and precise healthcare delivery.\n\n3. **The Role of AI in Enhancing Cybersecurity**:\nAs cyber threats become increasingly sophisticated, AI stands at the forefront of the battle against cybercrime. This article will discuss the crucial role AI plays in detecting and responding to threats in real-time, its capacity to predict and prevent potential attacks, and the inherent challenges of an AI-dependent cybersecurity framework. We will highlight recent advancements in AI-based security tools and provide case studies where AI has been instrumental in mitigating cyber threats effectively. By examining these elements, we'll underline the potential and limitations of AI in creating a more secure digital environment, showcasing how it can adapt to evolving threats faster than traditional methods.\n\n4. **The Intersection of AI and Autonomous Vehicles: Driving Towards a Safer Future**:\nThe prospect of AI-driven autonomous vehicles promises to redefine transportation. This article will explore the technological underpinnings of self-driving cars, their developmental milestones, and the hurdles they face, including regulatory and ethical challenges. We will discuss the profound implications for various industries and employment sectors, coupled with the benefits such as reduced traffic accidents, improved fuel efficiency, and enhanced mobility for people with disabilities. By detailing these aspects, the article will offer a comprehensive overview of how AI-powered autonomous vehicles are steering us towards a safer, more efficient future.\n\n5. **AI and the Future of Work: Embracing Change in the Workplace**:\nAI is transforming the workplace by automating mundane tasks, enabling advanced data analysis, and fostering creativity and strategic decision-making. This article will explore the profound impact of AI on the job market, addressing concerns about job displacement and the evolution of new roles that demand reskilling. We will provide insights into the necessity for upskilling to keep pace with an AI-driven economy. Through interviews with industry experts and narratives from workers who have experienced AI's impact firsthand, we will present a balanced perspective. The aim is to paint a future where humans and AI work in synergy, driving innovation and productivity in a continuously evolving workplace landscape."
+        == "**1. The Rise of Autonomous AI Agents in Daily Life**  \nAs artificial intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n**2. Ethical Implications of Generative AI in Creative Industries**  \nThe surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators, examining both the potential for innovation and the risk of devaluing traditional art forms. Highlighting perspectives from creators, legal experts, and ethicists could provide a comprehensive overview of the challenges faced, including copyright concerns and the emotional impact on human artists. This discussion is vital as the creative landscape evolves alongside technological advancements, making it ripe for exploration.\n\n**3. AI in Climate Change Mitigation: Current Solutions and Future Potential**  \nAs the world grapples with climate change, AI technology is increasingly being harnessed to develop innovative solutions for sustainability. From predictive analytics that optimize energy consumption to machine learning algorithms that improve carbon capture methods, AI's potential in environmental science is vast. This topic invites an exploration of existing AI applications in climate initiatives, with a focus on groundbreaking research and initiatives aimed at reducing humanity's carbon footprint. Highlighting successful projects and technology partnerships can illustrate the positive impact AI can have on global climate efforts, inspiring further exploration and investment in this area.\n\n**4. The Future of Work: How AI is Reshaping Employment Landscapes**  \nThe discussions around AI's impact on the workforce are both urgent and complex, as advances in automation and machine learning continue to transform the job market. This article could delve into the current trends of AI-driven job displacement alongside opportunities for upskilling and the creation of new job roles. By examining case studies of companies that integrate AI effectively and the resulting workforce adaptations, readers can gain valuable insights into preparing for a future where humans and AI collaborate. This exploration highlights the importance of policies that promote workforce resilience in the face of change.\n\n**5. Decentralized AI: Exploring the Role of Blockchain in AI Development**  \nAs blockchain technology sweeps through various sectors, its application in AI development presents a fascinating topic worth examining. Decentralized AI could address issues of data privacy, security, and democratization in AI models by allowing users to retain ownership of data while benefiting from AI's capabilities. This article could analyze how decentralized networks are disrupting traditional AI development models, featuring innovative projects that harness the synergy between blockchain and AI. Highlighting potential pitfalls and the future landscape of decentralized AI could stimulate discussion among technologists, entrepreneurs, and policymakers alike.\n\nThese topics not only reflect current trends but also probe deeper into ethical and practical considerations, making them timely and relevant for contemporary audiences."
     )
 
 
@@ -2155,14 +2157,20 @@ def test_tools_with_custom_caching():
     with patch.object(
         CacheHandler, "add", wraps=crew._cache_handler.add
     ) as add_to_cache:
-        with patch.object(CacheHandler, "read", wraps=crew._cache_handler.read) as _:
-            result = crew.kickoff()
-            add_to_cache.assert_called_once_with(
-                tool="multiplcation_tool",
-                input={"first_number": 2, "second_number": 6},
-                output=12,
-            )
-            assert result.raw == "3"
+
+        result = crew.kickoff()
+
+        # Check that add_to_cache was called exactly twice
+        assert add_to_cache.call_count == 2
+
+        # Verify that one of those calls was with the even number that should be cached
+        add_to_cache.assert_any_call(
+            tool="multiplcation_tool",
+            input={"first_number": 2, "second_number": 6},
+            output=12,
+        )
+
+        assert result.raw == "3"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3731,6 +3739,44 @@ def test_multimodal_agent_image_tool_handling():
         assert result["content"][1]["type"] == "image_url"
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_multimodal_agent_describing_image_successfully():
+    """
+    Test that a multimodal agent can process images without validation errors.
+    This test reproduces the scenario from issue #2475.
+    """
+    llm = LLM(model="openai/gpt-4o", temperature=0.7)  # model with vision capabilities
+
+    expert_analyst = Agent(
+        role="Visual Quality Inspector",
+        goal="Perform detailed quality analysis of product images",
+        backstory="Senior quality control expert with expertise in visual inspection",
+        llm=llm,
+        verbose=True,
+        allow_delegation=False,
+        multimodal=True,
+    )
+
+    inspection_task = Task(
+        description="""
+        Analyze the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244 with focus on:
+        1. Quality of materials
+        2. Manufacturing defects
+        3. Compliance with standards
+        Provide a detailed report highlighting any issues found.
+        """,
+        expected_output="A detailed report highlighting any issues found",
+        agent=expert_analyst,
+    )
+
+    crew = Crew(agents=[expert_analyst], tasks=[inspection_task])
+    result = crew.kickoff()
+
+    task_output = result.tasks_output[0]
+    assert isinstance(task_output, TaskOutput)
+    assert task_output.raw == result.raw
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_live_image_analysis():
     """
@@ -4025,3 +4071,51 @@ def test_crew_with_knowledge_sources_works_with_copy():
     assert len(crew_copy.tasks) == len(crew.tasks)
 
     assert len(crew_copy.tasks) == len(crew.tasks)
+
+
+def test_crew_kickoff_for_each_works_with_manager_agent_copy():
+    researcher = Agent(
+        role="Researcher",
+        goal="Conduct thorough research and analysis on AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
+        allow_delegation=False,
+    )
+
+    writer = Agent(
+        role="Senior Writer",
+        goal="Create compelling content about AI and AI agents",
+        backstory="You're a senior writer, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently writing content for a new client.",
+        allow_delegation=False,
+    )
+
+    # Define task
+    task = Task(
+        description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
+        expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+    )
+
+    # Define manager agent
+    manager = Agent(
+        role="Project Manager",
+        goal="Efficiently manage the crew and ensure high-quality task completion",
+        backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
+        allow_delegation=True,
+    )
+
+    # Instantiate crew with a custom manager
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task],
+        manager_agent=manager,
+        process=Process.hierarchical,
+        verbose=True,
+    )
+
+    crew_copy = crew.copy()
+    assert crew_copy.manager_agent is not None
+    assert crew_copy.manager_agent.id != crew.manager_agent.id
+    assert crew_copy.manager_agent.role == crew.manager_agent.role
+    assert crew_copy.manager_agent.goal == crew.manager_agent.goal
+    assert crew_copy.manager_agent.backstory == crew.manager_agent.backstory
+    assert isinstance(crew_copy.manager_agent.agent_executor, CrewAgentExecutor)
+    assert isinstance(crew_copy.manager_agent.cache_handler, CacheHandler)

tests/custom_llm_test.py

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

tests/memory/user_memory_test.py

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

tests/storage/test_mem0_storage.py

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

tests/task_test.py

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

tests/test_lite_agent.py

@@ -0,0 +1,172 @@
+import asyncio
+from typing import cast
+
+import pytest
+from pydantic import BaseModel, Field
+
+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
+
+
+# A simple test tool
+class SecretLookupTool(BaseTool):
+    name: str = "secret_lookup"
+    description: str = "A tool to lookup secrets"
+
+    def _run(self) -> str:
+        return "SUPERSECRETPASSWORD123"
+
+
+# Define Mock Search Tool
+class WebSearchTool(BaseTool):
+    """Tool for searching the web for information."""
+
+    name: str = "search_web"
+    description: str = "Search the web for information about a topic."
+
+    def _run(self, query: str) -> str:
+        """Search the web for information about a topic."""
+        # This is a mock implementation
+        if "tokyo" in query.lower():
+            return "Tokyo's population in 2023 was approximately 21 million people in the city proper, and 37 million in the greater metropolitan area."
+        elif "climate change" in query.lower() and "coral" in query.lower():
+            return "Climate change severely impacts coral reefs through: 1) Ocean warming causing coral bleaching, 2) Ocean acidification reducing calcification, 3) Sea level rise affecting light availability, 4) Increased storm frequency damaging reef structures. Sources: NOAA Coral Reef Conservation Program, Global Coral Reef Alliance."
+        else:
+            return f"Found information about {query}: This is a simulated search result for demonstration purposes."
+
+
+# Define Mock Calculator Tool
+class CalculatorTool(BaseTool):
+    """Tool for performing calculations."""
+
+    name: str = "calculate"
+    description: str = "Calculate the result of a mathematical expression."
+
+    def _run(self, expression: str) -> str:
+        """Calculate the result of a mathematical expression."""
+        try:
+            result = eval(expression, {"__builtins__": {}})
+            return f"The result of {expression} is {result}"
+        except Exception as e:
+            return f"Error calculating {expression}: {str(e)}"
+
+
+# Define a custom response format using Pydantic
+class ResearchResult(BaseModel):
+    """Structure for research results."""
+
+    main_findings: str = Field(description="The main findings from the research")
+    key_points: list[str] = Field(description="List of key points")
+    sources: list[str] = Field(description="List of sources used")
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_with_tools():
+    """Test that LiteAgent can use tools."""
+    # Create a LiteAgent with tools
+    llm = LLM(model="gpt-4o-mini")
+    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.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo and how many people would that be per square kilometer if Tokyo's area is 2,194 square kilometers?"
+    )
+
+    assert (
+        "21 million" in result.raw or "37 million" in result.raw
+    ), "Agent should find Tokyo's population"
+    assert (
+        "per square kilometer" in result.raw
+    ), "Agent should calculate population density"
+
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageStartedEvent)
+    def event_handler(source, event):
+        received_events.append(event)
+
+    agent.kickoff("What are the effects of climate change on coral reefs?")
+
+    # Verify tool usage events were emitted
+    assert len(received_events) > 0, "Tool usage events should be emitted"
+    event = received_events[0]
+    assert isinstance(event, ToolUsageStartedEvent)
+    assert event.agent_role == "Research Assistant"
+    assert event.tool_name == "search_web"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_structured_output():
+    """Test that LiteAgent can return a simple structured output."""
+
+    class SimpleOutput(BaseModel):
+        """Simple structure for agent outputs."""
+
+        summary: str = Field(description="A brief summary of findings")
+        confidence: int = Field(description="Confidence level from 1-100")
+
+    web_search_tool = WebSearchTool()
+
+    llm = LLM(model="gpt-4o-mini")
+    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"
+    )
+
+    print(f"\n=== Agent Result Type: {type(result)}")
+    print(f"=== Agent Result: {result}")
+    print(f"=== Pydantic: {result.pydantic}")
+
+    assert result.pydantic is not None, "Should return a Pydantic model"
+
+    output = cast(SimpleOutput, result.pydantic)
+
+    assert isinstance(output.summary, str), "Summary should be a string"
+    assert len(output.summary) > 0, "Summary should not be empty"
+    assert isinstance(output.confidence, int), "Confidence should be an integer"
+    assert 1 <= output.confidence <= 100, "Confidence should be between 1 and 100"
+
+    assert "tokyo" in output.summary.lower() or "population" in output.summary.lower()
+
+    assert result.usage_metrics is not None
+
+    return result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_returns_usage_metrics():
+    """Test that LiteAgent returns usage metrics."""
+    llm = LLM(model="gpt-4o-mini")
+    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.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
+    )
+
+    assert result.usage_metrics is not None
+    assert result.usage_metrics["total_tokens"] > 0

tests/test_multimodal_validation.py

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