Merge branch 'main' into bugfix-python-3-10

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>

.github/workflows/tests.yml

@@ -12,6 +12,9 @@ jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -21,9 +24,8 @@ jobs:
         with:
           enable-cache: true
 
-
-      - name: Set up Python
-        run: uv python install 3.12.8
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
 
       - name: Install the project
         run: uv sync --dev --all-extras

.gitignore

@@ -25,4 +25,5 @@ agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
-conceptual_plan.md
+conceptual_plan.md
+build_image

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/concepts/agents.mdx

@@ -18,6 +18,18 @@ In the CrewAI framework, an `Agent` is an autonomous unit that can:
 Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
 </Tip>
 
+<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
+CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+
+![Visual Agent Builder Screenshot](../images/enterprise/crew-studio-quickstart)
+
+The Visual Agent Builder enables:
+- Intuitive agent configuration with form-based interfaces
+- Real-time testing and validation
+- Template library with pre-configured agent types
+- Easy customization of agent attributes and behaviors
+</Note>
+
 ## Agent Attributes
 
 | Attribute                               | Parameter                | Type                          | Description                                                                                                          |
@@ -233,7 +245,7 @@ custom_agent = Agent(
 
 #### Code Execution
 - `allow_code_execution`: Must be True to run code
-- `code_execution_mode`: 
+- `code_execution_mode`:
   - `"safe"`: Uses Docker (recommended for production)
   - `"unsafe"`: Direct execution (use only in trusted environments)
 

docs/concepts/collaboration.mdx

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

docs/concepts/crews.mdx

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

docs/concepts/event-listener.mdx

@@ -18,6 +18,20 @@ CrewAI uses an event bus architecture to emit events throughout the execution li
 
 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.
 
+<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
+CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+
+![Prompt Tracing Dashboard](../images/enterprise/prompt-tracing.png)
+
+With Prompt Tracing you can:
+- View the complete history of all prompts sent to your LLM
+- Track token usage and costs
+- Debug agent reasoning failures
+- Share prompt sequences with your team
+- Compare different prompt strategies
+- Export traces for compliance and auditing
+</Note>
+
 ## Creating a Custom Event Listener
 
 To create a custom event listener, you need to:
@@ -40,17 +54,17 @@ from crewai.utilities.events.base_event_listener import BaseEventListener
 class MyCustomListener(BaseEventListener):
     def __init__(self):
         super().__init__()
-    
+
     def setup_listeners(self, crewai_event_bus):
         @crewai_event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source, event):
             print(f"Crew '{event.crew_name}' has started execution!")
-        
+
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             print(f"Crew '{event.crew_name}' has completed execution!")
             print(f"Output: {event.output}")
-        
+
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
         def on_agent_execution_completed(source, event):
             print(f"Agent '{event.agent.role}' completed task")
@@ -83,7 +97,7 @@ my_listener = MyCustomListener()
 
 class MyCustomCrew:
     # Your crew implementation...
-    
+
     def crew(self):
         return Crew(
             agents=[...],
@@ -106,7 +120,7 @@ my_listener = MyCustomListener()
 
 class MyCustomFlow(Flow):
     # Your flow implementation...
-    
+
     @start()
     def first_step(self):
         # ...
@@ -324,9 +338,9 @@ with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)
     def temp_handler(source, event):
         print("This handler only exists within this context")
-    
+
     # Do something that emits events
-    
+
 # Outside the context, the temporary handler is removed
 ```
 

docs/concepts/flows.mdx

@@ -545,6 +545,119 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
+## Adding Agents to Flows
+
+Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
+
+```python
+import asyncio
+from typing import Any, Dict, List
+
+from crewai_tools import SerperDevTool
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent
+from crewai.flow.flow import Flow, listen, start
+
+
+# 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
+
+
+# Create a flow class
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self) -> Dict[str, Any]:
+        print(f"Starting market research for {self.state.product}")
+        return {"product": self.state.product}
+
+    @listen(initialize_research)
+    async def analyze_market(self) -> Dict[str, Any]:
+        # Create an Agent for market research
+        analyst = Agent(
+            role="Market Research Analyst",
+            goal=f"Analyze the market for {self.state.product}",
+            backstory="You are an experienced market analyst with expertise in "
+            "identifying market trends and opportunities.",
+            tools=[SerperDevTool()],
+            verbose=True,
+        )
+
+        # 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 with structured output format
+        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
+        if result.pydantic:
+            print("result", result.pydantic)
+        else:
+            print("result", result)
+
+        # Return the analysis to update the state
+        return {"analysis": result.pydantic}
+
+    @listen(analyze_market)
+    def present_results(self, analysis) -> None:
+        print("\nMarket Analysis Results")
+        print("=====================")
+
+        if isinstance(analysis, dict):
+            # If we got a dict with 'analysis' key, extract the actual analysis object
+            market_analysis = analysis.get("analysis")
+        else:
+            market_analysis = analysis
+
+        if market_analysis and isinstance(market_analysis, MarketAnalysis):
+            print("\nKey Market Trends:")
+            for trend in market_analysis.key_trends:
+                print(f"- {trend}")
+
+            print(f"\nMarket Size: {market_analysis.market_size}")
+
+            print("\nMajor Competitors:")
+            for competitor in market_analysis.competitors:
+                print(f"- {competitor}")
+        else:
+            print("No structured analysis data available.")
+            print("Raw analysis:", analysis)
+
+
+# Usage example
+async def run_flow():
+    flow = MarketResearchFlow()
+    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
+    return result
+
+
+# Run the flow
+if __name__ == "__main__":
+    asyncio.run(run_flow())
+```
+
+This example demonstrates several key features of using Agents in flows:
+
+1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
+
+2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
+
+3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+
 ## Adding Crews to Flows
 
 Creating a flow with multiple crews in CrewAI is straightforward.

docs/concepts/memory.mdx

@@ -18,7 +18,8 @@ reason, and learn from past interactions.
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
-| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
+| **External Memory**  | Enables integration with external memory systems and providers (like Mem0), allowing for specialized memory storage and retrieval across different applications. Supports custom storage implementations for flexible memory management. |
+| **User Memory**      | ⚠️ **DEPRECATED**: This component is deprecated and will be removed in a future version. Please use [External Memory](#using-external-memory) instead. |
 
 ## How Memory Systems Empower Agents
 
@@ -274,6 +275,102 @@ crew = Crew(
 )
 ```
 
+### Using External Memory
+
+External Memory is a powerful feature that allows you to integrate external memory systems with your CrewAI applications. This is particularly useful when you want to use specialized memory providers or maintain memory across different applications.
+
+#### Basic Usage with Mem0
+
+The most common way to use External Memory is with Mem0 as the provider:
+
+```python
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+
+agent = Agent(
+    role="You are a helpful assistant",
+    goal="Plan a vacation for the user",
+    backstory="You are a helpful assistant that can plan a vacation for the user",
+    verbose=True,
+)
+task = Task(
+    description="Give things related to the user's vacation",
+    expected_output="A plan for the vacation",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    external_memory=ExternalMemory(
+        embedder_config={"provider": "mem0", "config": {"user_id": "U-123"}} # you can provide an entire Mem0 configuration
+    ),
+)
+
+crew.kickoff(
+    inputs={"question": "which destination is better for a beach vacation?"}
+)
+```
+
+#### Using External Memory with Custom Storage
+
+You can also create custom storage implementations for External Memory. Here's an example of how to create a custom storage:
+
+```python
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.storage.interface import Storage
+
+
+class CustomStorage(Storage):
+    def __init__(self):
+        self.memories = []
+
+    def save(self, value, metadata=None, agent=None):
+        self.memories.append({"value": value, "metadata": metadata, "agent": agent})
+
+    def search(self, query, limit=10, score_threshold=0.5):
+        # Implement your search logic here
+        return []
+
+    def reset(self):
+        self.memories = []
+
+
+# Create external memory with custom storage
+external_memory = ExternalMemory(
+    storage=CustomStorage(),
+    embedder_config={"provider": "mem0", "config": {"user_id": "U-123"}},
+)
+
+agent = Agent(
+    role="You are a helpful assistant",
+    goal="Plan a vacation for the user",
+    backstory="You are a helpful assistant that can plan a vacation for the user",
+    verbose=True,
+)
+task = Task(
+    description="Give things related to the user's vacation",
+    expected_output="A plan for the vacation",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    external_memory=external_memory,
+)
+
+crew.kickoff(
+    inputs={"question": "which destination is better for a beach vacation?"}
+)
+```
 
 
 ## Additional Embedding Providers

docs/concepts/tasks.mdx

@@ -12,6 +12,18 @@ Tasks provide all necessary details for execution, such as a description, the ag
 
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
+<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
+CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
+
+![Task Builder Screenshot](../images/enterprise/crew-studio-quickstart.png)
+
+The Visual Task Builder enables:
+- Drag-and-drop task creation
+- Visual task dependencies and flow
+- Real-time testing and validation
+- Easy sharing and collaboration
+</Note>
+
 ### Task Execution Flow
 
 Tasks can be executed in two ways:
@@ -414,7 +426,7 @@ It's also important to note that the output of the final task of a crew becomes
 ### Using `output_pydantic`
 The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
 
-Here’s an example demonstrating how to use output_pydantic:
+Here's an example demonstrating how to use output_pydantic:
 
 ```python Code
 import json
@@ -495,7 +507,7 @@ In this example:
 ### Using `output_json`
 The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
 
-Here’s an example demonstrating how to use `output_json`:
+Here's an example demonstrating how to use `output_json`:
 
 ```python Code
 import json

docs/concepts/tools.mdx

@@ -15,6 +15,18 @@ A tool in CrewAI is a skill or function that agents can utilize to perform vario
 This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
+<Note type="info" title="Enterprise Enhancement: Tools Repository">
+CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
+
+![Tools Repository Screenshot](../images/enterprise/tools-repository.png)
+
+The Enterprise Tools Repository includes:
+- Pre-built connectors for popular enterprise systems
+- Custom tool creation interface
+- Version control and sharing capabilities
+- Security and compliance features
+</Note>
+
 ## Key Characteristics of Tools
 
 - **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
@@ -79,7 +91,7 @@ research = Task(
 )
 
 write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
+    description='Write an engaging blog post about the AI industry, based on the research analyst's summary. Draw inspiration from the latest blog posts in the directory.',
     expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
     agent=writer,
     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
@@ -141,7 +153,7 @@ Here is a list of the available tools and their descriptions:
 ## Creating your own Tools
 
 <Tip>
-  Developers can craft `custom tools` tailored for their agent’s needs or
+  Developers can craft `custom tools` tailored for their agent's needs or
   utilize pre-built options.
 </Tip>
 

docs/docs.json

@@ -76,9 +76,7 @@
               "concepts/testing",
               "concepts/cli",
               "concepts/tools",
-              "concepts/event-listener",
-              "concepts/langchain-tools",
-              "concepts/llamaindex-tools"
+              "concepts/event-listener"
             ]
           },
           {
@@ -97,20 +95,23 @@
               "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",
+              "how-to/langchain-tools",
+              "how-to/llamaindex-tools"
             ]
           },
           {
             "group": "Agent Monitoring & Observability",
             "pages": [
-              "how-to/weave-integration",
               "how-to/agentops-observability",
+              "how-to/arize-phoenix-observability",
               "how-to/langfuse-observability",
               "how-to/langtrace-observability",
               "how-to/mlflow-observability",
               "how-to/openlit-observability",
               "how-to/opik-observability",
-              "how-to/portkey-observability"
+              "how-to/portkey-observability",
+              "how-to/weave-integration"
             ]
           },
           {
@@ -195,6 +196,11 @@
           "anchor": "Community",
           "href": "https://community.crewai.com",
           "icon": "discourse"
+        },
+        {
+          "anchor": "Tutorials",
+          "href": "https://www.youtube.com/@crewAIInc",
+          "icon": "youtube"
         }
       ]
     }
@@ -229,4 +235,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/how-to/arize-phoenix-observability.mdx

@@ -0,0 +1,145 @@
+---
+title: Arize Phoenix
+description: Arize Phoenix integration for CrewAI with OpenTelemetry and OpenInference
+icon: magnifying-glass-chart
+---
+
+# Arize Phoenix Integration
+
+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/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/installation.mdx

@@ -4,14 +4,29 @@ description: Get started with CrewAI - Install, configure, and build your first
 icon: wrench
 ---
 
+## Video Tutorial
+Watch this video tutorial for a step-by-step demonstration of the installation process:
+
+<iframe
+  width="100%"
+  height="400"
+  src="https://www.youtube.com/embed/-kSOTtYzgEw"
+  title="CrewAI Installation Guide"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+></iframe>
+
+## Text Tutorial
 <Note>
   **Python Version Requirements**
-  
+
   CrewAI requires `Python >=3.10 and <3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```
-  
+
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
@@ -140,6 +155,27 @@ We recommend using the `YAML` template scaffolding for a structured approach to
   </Step>
 </Steps>
 
+## Enterprise Installation Options
+
+<Note type="info">
+For teams and organizations, CrewAI offers enterprise deployment options that eliminate setup complexity:
+
+### CrewAI Enterprise (SaaS)
+- Zero installation required - just sign up for free at [app.crewai.com](https://app.crewai.com)
+- Automatic updates and maintenance
+- Managed infrastructure and scaling
+- Build Crews with no Code
+
+### CrewAI Factory (Self-hosted)
+- Containerized deployment for your infrastructure
+- Supports any hyperscaler including on prem depployments
+- Integration with your existing security systems
+
+<Card title="Explore Enterprise Options" icon="building" href="https://crewai.com/enterprise">
+  Learn about CrewAI's enterprise offerings and schedule a demo
+</Card>
+</Note>
+
 ## Next Steps
 
 <CardGroup cols={2}>

docs/introduction.mdx

@@ -15,6 +15,7 @@ CrewAI empowers developers with both high-level simplicity and precise low-level
 
 With over 100,000 developers certified through our community courses, CrewAI is rapidly becoming the standard for enterprise-ready AI automation.
 
+
 ## How Crews Work
 
 <Note>

docs/quickstart.mdx

@@ -200,6 +200,22 @@ Follow the steps below to get Crewing! 🚣‍♂️
         ```
       </CodeGroup>
   </Step>
+
+  <Step title="Enterprise Alternative: Create in Crew Studio">
+  For CrewAI Enterprise users, you can create the same crew without writing code:
+
+  1. Log in to your CrewAI Enterprise account (create a free account at [app.crewai.com](https://app.crewai.com))
+  2. Open Crew Studio
+  3. Type what is the automation you're tryign to build
+  4. Create your tasks visually and connect them in sequence
+  5. Configure your inputs and click "Download Code" or "Deploy"
+
+  ![Crew Studio Quickstart](../images/enterprise/crew-studio-quickstart.png)
+
+  <Card title="Try CrewAI Enterprise" icon="rocket" href="https://app.crewai.com">
+    Start your free account at CrewAI Enterprise
+  </Card>
+  </Step>
   <Step title="View your final report">
   You should see the output in the console and the `report.md` file should be created in the root of your project with the final report.
 
@@ -271,7 +287,7 @@ Follow the steps below to get Crewing! 🚣‍♂️
 </Steps>
 
 <Check>
-Congratulations! 
+Congratulations!
 
 You have successfully set up your crew project and are ready to start building your own agentic workflows!
 </Check>

docs/telemetry.mdx

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

pyproject.toml

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

src/crewai/__init__.py

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

src/crewai/agent.py

@@ -1,7 +1,6 @@
-import re
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Union
+from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -11,6 +10,7 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
+from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
@@ -18,6 +18,11 @@ 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 (
@@ -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."
     )
@@ -205,6 +207,7 @@ class Agent(BaseAgent):
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
                 self.crew._user_memory,
+                self.crew._external_memory,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
@@ -300,12 +303,12 @@ 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 +330,12 @@ 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 +370,6 @@ 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 +415,6 @@ 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 +434,6 @@ 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 +446,77 @@ class Agent(BaseAgent):
             Fingerprint: The agent's fingerprint
         """
         return self.security_config.fingerprint
+
+    def set_fingerprint(self, fingerprint: Fingerprint):
+        self.security_config.fingerprint = fingerprint
+
+    def kickoff(
+        self,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
+    ) -> LiteAgentOutput:
+        """
+        Execute the agent with the given messages using a LiteAgent instance.
+
+        This method is useful when you want to use the Agent configuration but
+        with the simpler and more direct execution flow of LiteAgent.
+
+        Args:
+            messages: Either a string query or a list of message dictionaries.
+                     If a string is provided, it will be converted to a user message.
+                     If a list is provided, each dict should have 'role' and 'content' keys.
+            response_format: Optional Pydantic model for structured output.
+
+        Returns:
+            LiteAgentOutput: The result of the agent execution.
+        """
+        lite_agent = LiteAgent(
+            role=self.role,
+            goal=self.goal,
+            backstory=self.backstory,
+            llm=self.llm,
+            tools=self.tools or [],
+            max_iterations=self.max_iter,
+            max_execution_time=self.max_execution_time,
+            respect_context_window=self.respect_context_window,
+            verbose=self.verbose,
+            response_format=response_format,
+            i18n=self.i18n,
+        )
+
+        return lite_agent.kickoff(messages)
+
+    async def kickoff_async(
+        self,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
+    ) -> LiteAgentOutput:
+        """
+        Execute the agent asynchronously with the given messages using a LiteAgent instance.
+
+        This is the async version of the kickoff method.
+
+        Args:
+            messages: Either a string query or a list of message dictionaries.
+                     If a string is provided, it will be converted to a user message.
+                     If a list is provided, each dict should have 'role' and 'content' keys.
+            response_format: Optional Pydantic model for structured output.
+
+        Returns:
+            LiteAgentOutput: The result of the agent execution.
+        """
+        lite_agent = LiteAgent(
+            role=self.role,
+            goal=self.goal,
+            backstory=self.backstory,
+            llm=self.llm,
+            tools=self.tools or [],
+            max_iterations=self.max_iter,
+            max_execution_time=self.max_execution_time,
+            respect_context_window=self.respect_context_window,
+            verbose=self.verbose,
+            response_format=response_format,
+            i18n=self.i18n,
+        )
+
+        return await lite_agent.kickoff_async(messages)

src/crewai/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,
@@ -72,8 +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:
@@ -91,9 +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")
@@ -135,6 +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."
     )
@@ -153,6 +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
@@ -254,10 +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."""
@@ -356,9 +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
@@ -47,6 +47,27 @@ class CrewAgentExecutorMixin:
                 print(f"Failed to add to short term memory: {e}")
                 pass
 
+    def _create_external_memory(self, output) -> None:
+        """Create and save a external-term memory item if conditions are met."""
+        if (
+            self.crew
+            and self.agent
+            and self.task
+            and hasattr(self.crew, "_external_memory")
+            and self.crew._external_memory
+        ):
+            try:
+                self.crew._external_memory.save(
+                    value=output.text,
+                    metadata={
+                        "description": self.task.description,
+                    },
+                    agent=self.agent.role,
+                )
+            except Exception as e:
+                print(f"Failed to add to external memory: {e}")
+                pass
+
     def _create_long_term_memory(self, output) -> None:
         """Create and save long-term and entity memory items based on evaluation."""
         if (

src/crewai/agents/crew_agent_executor.py

@@ -1,41 +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 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,
-    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()
 
@@ -47,7 +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,
@@ -83,7 +82,7 @@ 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
         }
         existing_stop = self.llm.stop or []
@@ -99,11 +98,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         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()
 
@@ -118,7 +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
@@ -130,6 +129,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
+        self._create_external_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
     def _invoke_loop(self) -> AgentFinish:
@@ -140,16 +140,25 @@ 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):
                     # Extract agent fingerprint if available
@@ -165,8 +174,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                             )
                         }
 
-                    tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer, fingerprint_context=fingerprint_context
+                    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
@@ -176,17 +194,30 @@ 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
@@ -199,89 +230,27 @@ 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."""
@@ -290,175 +259,33 @@ 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,
-        fingerprint_context: Optional[Dict[str, str]] = None,
-    ) -> ToolResult:
-        try:
-            fingerprint_context = fingerprint_context or {}
-
-            if self.agent:
-                # Create tool usage event with fingerprint information
-                event_data = {
-                    "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,
-                    "agent": self.agent,  # Pass the agent object for fingerprint extraction
-                }
-
-                # Include fingerprint context
-                if fingerprint_context:
-                    event_data.update(fingerprint_context)
-
-                # Emit the tool usage started event with agent information
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageStartedEvent(**event_data),
-                )
-
-            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,
-                fingerprint_context=fingerprint_context,  # Pass fingerprint context
-            )
-            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:
-                error_event_data = {
-                    "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),
-                    "agent": self.agent,  # Pass the agent object for fingerprint extraction
-                }
-
-                # Include fingerprint context
-                if fingerprint_context:
-                    error_event_data.update(fingerprint_context)
-
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageErrorEvent(**error_event_data),
-                )
-            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 = []
@@ -466,47 +293,33 @@ 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:
@@ -559,13 +372,6 @@ 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.
 
@@ -592,7 +398,7 @@ 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)
             )
         )
@@ -621,7 +427,7 @@ 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)
             )
         )
@@ -646,45 +452,3 @@ 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/install_crew.py

@@ -3,6 +3,10 @@ import subprocess
 import click
 
 
+# Be mindful about changing this.
+# on some enviorments we don't use this command but instead uv sync directly
+# so if you expect this to support more things you will need to replicate it there
+# ask @joaomdmoura if you are unsure
 def install_crew(proxy_options: list[str]) -> None:
     """
     Install the crew by running the UV command to lock and install.

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

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

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

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

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

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

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

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

src/crewai/crew.py

@@ -28,6 +28,7 @@ from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM, BaseLLM
 from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.external.external_memory import ExternalMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.memory.user.user_memory import UserMemory
@@ -105,6 +106,7 @@ class Crew(BaseModel):
     _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
     _user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
+    _external_memory: Optional[InstanceOf[ExternalMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
     _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
@@ -145,6 +147,10 @@ class Crew(BaseModel):
         default=None,
         description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
     )
+    external_memory: Optional[InstanceOf[ExternalMemory]] = Field(
+        default=None,
+        description="An Instance of the ExternalMemory to be used by the Crew",
+    )
     embedder: Optional[dict] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
@@ -289,26 +295,25 @@ class Crew(BaseModel):
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
+            self._external_memory = (
+                # External memory doesn’t support a default value since it was designed to be managed entirely externally
+                self.external_memory.set_crew(self)
+                if self.external_memory
+                else None
+            )
             if (
-                self.memory_config and "user_memory" in self.memory_config and self.memory_config.get('provider') == 'mem0'
+                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, dict
                 ):  # Check if it's a configuration dict
-                    self._user_memory = UserMemory(
-                        crew=self
-                    )
+                    self._user_memory = UserMemory(crew=self)
                 else:
-                    raise TypeError(
-                        "user_memory must be a configuration dictionary"
-                    )
+                    raise TypeError("user_memory must be a configuration dictionary")
             else:
-                self._logger.log(
-                    "warning", 
-                    "User memory initialization failed. For setup instructions, please refer to the memory documentation: https://docs.crewai.com/concepts/memory#integrating-mem0-for-enhanced-user-memory", 
-                    color="yellow"
-                )
                 self._user_memory = None  # No user memory if not in config
         return self
 
@@ -1159,7 +1164,7 @@ class Crew(BaseModel):
     def copy(self):
         """
         Creates a deep copy of the Crew instance.
-        
+
         Returns:
             Crew: A new instance with copied components
         """
@@ -1174,6 +1179,7 @@ class Crew(BaseModel):
             "_short_term_memory",
             "_long_term_memory",
             "_entity_memory",
+            "_external_memory",
             "_telemetry",
             "agents",
             "tasks",
@@ -1181,7 +1187,6 @@ class Crew(BaseModel):
             "knowledge",
             "manager_agent",
             "manager_llm",
-
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]
@@ -1321,7 +1326,15 @@ class Crew(BaseModel):
             RuntimeError: If memory reset operation fails.
         """
         VALID_TYPES = frozenset(
-            ["long", "short", "entity", "knowledge", "kickoff_outputs", "all"]
+            [
+                "long",
+                "short",
+                "entity",
+                "knowledge",
+                "kickoff_outputs",
+                "all",
+                "external",
+            ]
         )
 
         if command_type not in VALID_TYPES:
@@ -1347,6 +1360,7 @@ class Crew(BaseModel):
         memory_systems = [
             ("short term", getattr(self, "_short_term_memory", None)),
             ("entity", getattr(self, "_entity_memory", None)),
+            ("external", getattr(self, "_external_memory", None)),
             ("long term", getattr(self, "_long_term_memory", None)),
             ("task output", getattr(self, "_task_output_handler", None)),
             ("knowledge", getattr(self, "knowledge", None)),
@@ -1374,6 +1388,7 @@ class Crew(BaseModel):
             "entity": (self._entity_memory, "entity"),
             "knowledge": (self.knowledge, "knowledge"),
             "kickoff_outputs": (self._task_output_handler, "task output"),
+            "external": (self._external_memory, "external"),
         }
 
         memory_system, name = reset_functions[memory_type]

src/crewai/flow/flow.py

@@ -1043,6 +1043,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             import traceback
 
             traceback.print_exc()
+            raise
 
     def _log_flow_event(
         self, message: str, color: str = "yellow", level: str = "info"

src/crewai/lite_agent.py

@@ -0,0 +1,516 @@
+import asyncio
+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/memory/__init__.py

@@ -2,5 +2,12 @@ from .entity.entity_memory import EntityMemory
 from .long_term.long_term_memory import LongTermMemory
 from .short_term.short_term_memory import ShortTermMemory
 from .user.user_memory import UserMemory
+from .external.external_memory import ExternalMemory
 
-__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]
+__all__ = [
+    "UserMemory",
+    "EntityMemory",
+    "LongTermMemory",
+    "ShortTermMemory",
+    "ExternalMemory",
+]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,6 +1,12 @@
 from typing import Any, Dict, Optional
 
-from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
+from crewai.memory import (
+    EntityMemory,
+    ExternalMemory,
+    LongTermMemory,
+    ShortTermMemory,
+    UserMemory,
+)
 
 
 class ContextualMemory:
@@ -11,6 +17,7 @@ class ContextualMemory:
         ltm: LongTermMemory,
         em: EntityMemory,
         um: UserMemory,
+        exm: ExternalMemory,
     ):
         if memory_config is not None:
             self.memory_provider = memory_config.get("provider")
@@ -20,6 +27,7 @@ class ContextualMemory:
         self.ltm = ltm
         self.em = em
         self.um = um
+        self.exm = exm
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -35,6 +43,7 @@ class ContextualMemory:
         context.append(self._fetch_ltm_context(task.description))
         context.append(self._fetch_stm_context(query))
         context.append(self._fetch_entity_context(query))
+        context.append(self._fetch_external_context(query))
         if self.memory_provider == "mem0":
             context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
@@ -106,3 +115,24 @@ class ContextualMemory:
             f"- {result['memory']}" for result in user_memories
         )
         return f"User memories/preferences:\n{formatted_memories}"
+
+    def _fetch_external_context(self, query: str) -> str:
+        """
+        Fetches and formats relevant information from External Memory.
+        Args:
+            query (str): The search query to find relevant information.
+        Returns:
+            str: Formatted information as bullet points, or an empty string if none found.
+        """
+        if self.exm is None:
+            return ""
+
+        external_memories = self.exm.search(query)
+
+        if not external_memories:
+            return ""
+
+        formatted_memories = "\n".join(
+            f"- {result['memory']}" for result in external_memories
+        )
+        return f"External memories:\n{formatted_memories}"

src/crewai/memory/external/external_memory.py

@@ -0,0 +1,61 @@
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+from crewai.memory.external.external_memory_item import ExternalMemoryItem
+from crewai.memory.memory import Memory
+from crewai.memory.storage.interface import Storage
+
+if TYPE_CHECKING:
+    from crewai.memory.storage.mem0_storage import Mem0Storage
+
+
+class ExternalMemory(Memory):
+    def __init__(self, storage: Optional[Storage] = None, **data: Any):
+        super().__init__(storage=storage, **data)
+
+    @staticmethod
+    def _configure_mem0(crew: Any, config: Dict[str, Any]) -> "Mem0Storage":
+        from crewai.memory.storage.mem0_storage import Mem0Storage
+
+        return Mem0Storage(type="external", crew=crew, config=config)
+
+    @staticmethod
+    def external_supported_storages() -> Dict[str, Any]:
+        return {
+            "mem0": ExternalMemory._configure_mem0,
+        }
+
+    @staticmethod
+    def create_storage(crew: Any, embedder_config: Optional[Dict[str, Any]]) -> Storage:
+        if not embedder_config:
+            raise ValueError("embedder_config is required")
+
+        if "provider" not in embedder_config:
+            raise ValueError("embedder_config must include a 'provider' key")
+
+        provider = embedder_config["provider"]
+        supported_storages = ExternalMemory.external_supported_storages()
+        if provider not in supported_storages:
+            raise ValueError(f"Provider {provider} not supported")
+
+        return supported_storages[provider](crew, embedder_config.get("config", {}))
+
+    def save(
+        self,
+        value: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
+    ) -> None:
+        """Saves a value into the external storage."""
+        item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
+        super().save(value=item.value, metadata=item.metadata, agent=item.agent)
+
+    def reset(self) -> None:
+        self.storage.reset()
+
+    def set_crew(self, crew: Any) -> "ExternalMemory":
+        super().set_crew(crew)
+
+        if not self.storage:
+            self.storage = self.create_storage(crew, self.embedder_config)
+
+        return self

src/crewai/memory/external/external_memory_item.py

@@ -0,0 +1,13 @@
+from typing import Any, Dict, Optional
+
+
+class ExternalMemoryItem:
+    def __init__(
+        self,
+        value: Any,
+        metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
+    ):
+        self.value = value
+        self.metadata = metadata
+        self.agent = agent

src/crewai/memory/memory.py

@@ -9,6 +9,7 @@ class Memory(BaseModel):
     """
 
     embedder_config: Optional[Dict[str, Any]] = None
+    crew: Optional[Any] = None
 
     storage: Any
 
@@ -36,3 +37,7 @@ class Memory(BaseModel):
         return self.storage.search(
             query=query, limit=limit, score_threshold=score_threshold
         )
+
+    def set_crew(self, crew: Any) -> "Memory":
+        self.crew = crew
+        return self

src/crewai/memory/storage/mem0_storage.py

@@ -11,15 +11,20 @@ class Mem0Storage(Storage):
     Extends Storage to handle embedding and searching across entities using Mem0.
     """
 
-    def __init__(self, type, crew=None):
+    def __init__(self, type, crew=None, config=None):
         super().__init__()
-
-        if type not in ["user", "short_term", "long_term", "entities"]:
-            raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
+        supported_types = ["user", "short_term", "long_term", "entities", "external"]
+        if type not in supported_types:
+            raise ValueError(
+                f"Invalid type '{type}' for Mem0Storage. Must be one of: "
+                + ", ".join(supported_types)
+            )
 
         self.memory_type = type
         self.crew = crew
-        self.memory_config = crew.memory_config
+        self.config = config or {}
+        # TODO: Memory config will be removed in the future the config will be passed as a parameter
+        self.memory_config = self.config or getattr(crew, "memory_config", {}) or {}
 
         # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
         user_id = self._get_user_id()
@@ -27,7 +32,7 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        config = self.memory_config.get("config", {})
+        config = self._get_config()
         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")
@@ -56,26 +61,34 @@ class Mem0Storage(Storage):
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         user_id = self._get_user_id()
         agent_name = self._get_agent_name()
-        if self.memory_type == "user":
-            self.memory.add(value, user_id=user_id, metadata={**metadata})
-        elif self.memory_type == "short_term":
-            agent_name = self._get_agent_name()
-            self.memory.add(
-                value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
-            )
+        params = None
+        if self.memory_type == "short_term":
+            params = {
+                "agent_id": agent_name,
+                "infer": False,
+                "metadata": {"type": "short_term", **metadata},
+            }
         elif self.memory_type == "long_term":
-            agent_name = self._get_agent_name()
-            self.memory.add(
-                value,
-                agent_id=agent_name,
-                infer=False,
-                metadata={"type": "long_term", **metadata},
-            )
+            params = {
+                "agent_id": agent_name,
+                "infer": False,
+                "metadata": {"type": "long_term", **metadata},
+            }
         elif self.memory_type == "entities":
-            entity_name = self._get_agent_name()
-            self.memory.add(
-                value, user_id=entity_name, metadata={"type": "entity", **metadata}
-            )
+            params = {
+                "agent_id": agent_name,
+                "infer": False,
+                "metadata": {"type": "entity", **metadata},
+            }
+        elif self.memory_type == "external":
+            params = {
+                "user_id": user_id,
+                "agent_id": agent_name,
+                "metadata": {"type": "external", **metadata},
+            }
+
+        if params:
+            self.memory.add(value, **params | {"output_format": "v1.1"})
 
     def search(
         self,
@@ -84,41 +97,43 @@ class Mem0Storage(Storage):
         score_threshold: float = 0.35,
     ) -> List[Any]:
         params = {"query": query, "limit": limit}
-        if self.memory_type == "user":
-            user_id = self._get_user_id()
+        if user_id := self._get_user_id():
             params["user_id"] = user_id
-        elif self.memory_type == "short_term":
-            agent_name = self._get_agent_name()
+
+        agent_name = self._get_agent_name()
+        if self.memory_type == "short_term":
             params["agent_id"] = agent_name
             params["metadata"] = {"type": "short_term"}
         elif self.memory_type == "long_term":
-            agent_name = self._get_agent_name()
             params["agent_id"] = agent_name
             params["metadata"] = {"type": "long_term"}
         elif self.memory_type == "entities":
-            agent_name = self._get_agent_name()
             params["agent_id"] = agent_name
             params["metadata"] = {"type": "entity"}
+        elif self.memory_type == "external":
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "external"}
 
         # Discard the filters for now since we create the filters
         # automatically when the crew is created.
         results = self.memory.search(**params)
         return [r for r in results if r["score"] >= score_threshold]
 
-    def _get_user_id(self):
-        if self.memory_type == "user":
-            if hasattr(self, "memory_config") and self.memory_config is not None:
-                return self.memory_config.get("config", {}).get("user_id")
-            else:
-                return None
-        return None
+    def _get_user_id(self) -> str:
+        return self._get_config().get("user_id", "")
 
-    def _get_agent_name(self):
-        agents = self.crew.agents if self.crew else []
+    def _get_agent_name(self) -> str:
+        if not self.crew:
+            return ""
+
+        agents = self.crew.agents
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
         return agents
 
+    def _get_config(self) -> Dict[str, Any]:
+        return self.config or getattr(self, "memory_config", {}).get("config", {}) or {}
+
     def reset(self):
         if self.memory:
             self.memory.reset()

src/crewai/memory/user/user_memory.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import Any, Dict, Optional
 
 from crewai.memory.memory import Memory
@@ -12,6 +13,12 @@ class UserMemory(Memory):
     """
 
     def __init__(self, crew=None):
+        warnings.warn(
+            "UserMemory is deprecated and will be removed in a future version. "
+            "Please use ExternalMemory instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         try:
             from crewai.memory.storage.mem0_storage import Mem0Storage
         except ImportError:
@@ -48,6 +55,4 @@ class UserMemory(Memory):
         try:
             self.storage.reset()
         except Exception as e:
-            raise Exception(
-                f"An error occurred while resetting the user memory: {e}"
-            )
+            raise Exception(f"An error occurred while resetting the user memory: {e}")

src/crewai/project/crew_base.py

@@ -137,13 +137,11 @@ def CrewBase(cls: T) -> T:
                 all_functions, "is_cache_handler"
             )
             callbacks = self._filter_functions(all_functions, "is_callback")
-            agents = self._filter_functions(all_functions, "is_agent")
 
             for agent_name, agent_info in self.agents_config.items():
                 self._map_agent_variables(
                     agent_name,
                     agent_info,
-                    agents,
                     llms,
                     tool_functions,
                     cache_handler_functions,
@@ -154,7 +152,6 @@ def CrewBase(cls: T) -> T:
             self,
             agent_name: str,
             agent_info: Dict[str, Any],
-            agents: Dict[str, Callable],
             llms: Dict[str, Callable],
             tool_functions: Dict[str, Callable],
             cache_handler_functions: Dict[str, Callable],
@@ -172,9 +169,10 @@ def CrewBase(cls: T) -> T:
                 ]
 
             if function_calling_llm := agent_info.get("function_calling_llm"):
-                self.agents_config[agent_name]["function_calling_llm"] = agents[
-                    function_calling_llm
-                ]()
+                try:
+                    self.agents_config[agent_name]["function_calling_llm"] = llms[function_calling_llm]()
+                except KeyError:
+                    self.agents_config[agent_name]["function_calling_llm"] = function_calling_llm
 
             if step_callback := agent_info.get("step_callback"):
                 self.agents_config[agent_name]["step_callback"] = callbacks[

src/crewai/telemetry/telemetry.py

@@ -45,10 +45,10 @@ class Telemetry:
     """
 
     def __init__(self):
-        self.ready = False
-        self.trace_set = False
+        self.ready: bool = False
+        self.trace_set: bool = False
 
-        if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true":
+        if self._is_telemetry_disabled():
             return
 
         try:
@@ -75,6 +75,13 @@ class Telemetry:
             ):
                 raise  # Re-raise the exception to not interfere with system signals
             self.ready = False
+            
+    def _is_telemetry_disabled(self) -> bool:
+        """Check if telemetry should be disabled based on environment variables."""
+        return (
+            os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true" or
+            os.getenv("CREWAI_DISABLE_TELEMETRY", "false").lower() == "true"
+        )
 
     def set_tracer(self):
         if self.ready and not self.trace_set:

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]:
@@ -245,9 +244,13 @@ def to_langchain(
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
-def tool(*args):
+def tool(*args, result_as_answer=False):
     """
     Decorator to create a tool from a function.
+    
+    Args:
+        *args: Positional arguments, either the function to decorate or the tool name.
+        result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.
     """
 
     def _make_with_name(tool_name: str) -> Callable:
@@ -273,6 +276,7 @@ def tool(*args):
                 description=f.__doc__,
                 func=f,
                 args_schema=args_schema,
+                result_as_answer=result_as_answer,
             )
 
         return _make_tool

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
@@ -13,19 +14,25 @@ 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,
     ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
     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",
@@ -61,28 +68,24 @@ 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
@@ -106,17 +109,19 @@ 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
 
@@ -130,8 +135,9 @@ class ToolUsage:
 
             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
 
@@ -140,9 +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(
@@ -157,24 +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",
@@ -183,7 +194,8 @@ 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:
@@ -218,23 +230,25 @@ class ToolUsage:
                     error = ToolUsageErrorException(
                         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
                     )
 
@@ -262,41 +276,46 @@ class ToolUsage:
         )
 
         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(
@@ -315,10 +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,
@@ -351,7 +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()
@@ -373,18 +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:
@@ -409,12 +427,11 @@ class ToolUsage:
         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)
@@ -427,8 +444,9 @@ 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)}"
@@ -458,6 +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
@@ -470,7 +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"
             )
@@ -490,8 +509,8 @@ 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__,
@@ -507,14 +526,19 @@ class ToolUsage:
             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,
+        tool_calling: Union[ToolCalling, InstructorToolCalling],
         from_cache: bool,
         started_at: float,
         result: Any,
@@ -531,16 +555,24 @@ class ToolUsage:
         )
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
-    def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
+    def _prepare_event_data(
+        self, tool: Any, tool_calling: Union[ToolCalling, InstructorToolCalling]
+    ) -> dict:
         event_data = {
-            "agent_key": self.agent.key,
-            "agent_role": (self.agent._original_role or self.agent.role),
             "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": self.agent,  # Adding agent for fingerprint extraction
+            "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
@@ -562,21 +594,31 @@ class ToolUsage:
         arguments = arguments.copy()
 
         # Add security metadata under a designated key
-        if not "security_context" in arguments:
+        if "security_context" not in arguments:
             arguments["security_context"] = {}
 
         security_context = arguments["security_context"]
 
         # Add agent fingerprint if available
-        if hasattr(self, "agent") and hasattr(self.agent, "security_config"):
-            security_context["agent_fingerprint"] = self.agent.security_config.fingerprint.to_dict()
+        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 hasattr(self, "task") and hasattr(self.task, "security_config"):
-            security_context["task_fingerprint"] = self.task.security_config.fingerprint.to_dict()
-
-        # Add crew fingerprint if available
-        if hasattr(self, "crew") and hasattr(self.crew, "security_config"):
-            security_context["crew_fingerprint"] = self.crew.security_config.fingerprint.to_dict()
+        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/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/events/agent_events.py

@@ -1,4 +1,4 @@
-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
@@ -74,3 +74,31 @@ class AgentExecutionErrorEvent(BaseEvent):
                 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/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/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
 
@@ -497,20 +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):
-            if tool_branch in agent_branch.children:
-                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]
@@ -519,11 +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()
@@ -658,3 +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/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/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()
 

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/config/agents.yaml

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

tests/crew_test.py

@@ -350,7 +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."
     )
 
 
@@ -2157,14 +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"])
@@ -4072,14 +4078,14 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
         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
+        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
+        allow_delegation=False,
     )
 
     # Define task
@@ -4093,7 +4099,7 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
         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
+        allow_delegation=True,
     )
 
     # Instantiate crew with a custom manager
@@ -4102,7 +4108,7 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
         tasks=[task],
         manager_agent=manager,
         process=Process.hierarchical,
-        verbose=True
+        verbose=True,
     )
 
     crew_copy = crew.copy()
@@ -4113,4 +4119,3 @@ def test_crew_kickoff_for_each_works_with_manager_agent_copy():
     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/imports_test.py

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

tests/memory/external/external_memory_test.py

@@ -0,0 +1,180 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+from mem0.memory.main import Memory
+
+from crewai.agent import Agent
+from crewai.crew import Crew, Process
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.external.external_memory_item import ExternalMemoryItem
+from crewai.memory.storage.interface import Storage
+from crewai.task import Task
+
+
+@pytest.fixture
+def mock_mem0_memory():
+    mock_memory = MagicMock(spec=Memory)
+    return mock_memory
+
+
+@pytest.fixture
+def patch_configure_mem0(mock_mem0_memory):
+    with patch(
+        "crewai.memory.external.external_memory.ExternalMemory._configure_mem0",
+        return_value=mock_mem0_memory,
+    ) as mocked:
+        yield mocked
+
+
+@pytest.fixture
+def external_memory_with_mocked_config(patch_configure_mem0):
+    embedder_config = {"provider": "mem0"}
+    external_memory = ExternalMemory(embedder_config=embedder_config)
+    return external_memory
+
+
+@pytest.fixture
+def crew_with_external_memory(external_memory_with_mocked_config, patch_configure_mem0):
+    agent = Agent(
+        role="Researcher",
+        goal="Search relevant data and provide results",
+        backstory="You are a researcher at a leading tech think tank.",
+        tools=[],
+        verbose=True,
+    )
+
+    task = Task(
+        description="Perform a search on specific topics.",
+        expected_output="A list of relevant URLs based on the search query.",
+        agent=agent,
+    )
+
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+        process=Process.sequential,
+        memory=True,
+        external_memory=external_memory_with_mocked_config,
+    )
+
+    return crew
+
+
+def test_external_memory_initialization(external_memory_with_mocked_config):
+    assert external_memory_with_mocked_config is not None
+    assert isinstance(external_memory_with_mocked_config, ExternalMemory)
+
+
+def test_external_memory_save(external_memory_with_mocked_config):
+    memory_item = ExternalMemoryItem(
+        value="test value", metadata={"task": "test_task"}, agent="test_agent"
+    )
+
+    with patch.object(ExternalMemory, "save") as mock_save:
+        external_memory_with_mocked_config.save(
+            value=memory_item.value,
+            metadata=memory_item.metadata,
+            agent=memory_item.agent,
+        )
+
+        mock_save.assert_called_once_with(
+            value=memory_item.value,
+            metadata=memory_item.metadata,
+            agent=memory_item.agent,
+        )
+
+
+def test_external_memory_reset(external_memory_with_mocked_config):
+    with patch(
+        "crewai.memory.external.external_memory.ExternalMemory.reset"
+    ) as mock_reset:
+        external_memory_with_mocked_config.reset()
+        mock_reset.assert_called_once()
+
+
+def test_external_memory_supported_storages():
+    supported_storages = ExternalMemory.external_supported_storages()
+    assert "mem0" in supported_storages
+    assert callable(supported_storages["mem0"])
+
+
+def test_external_memory_create_storage_invalid_provider():
+    embedder_config = {"provider": "invalid_provider", "config": {}}
+
+    with pytest.raises(ValueError, match="Provider invalid_provider not supported"):
+        ExternalMemory.create_storage(None, embedder_config)
+
+
+def test_external_memory_create_storage_missing_provider():
+    embedder_config = {"config": {}}
+
+    with pytest.raises(
+        ValueError, match="embedder_config must include a 'provider' key"
+    ):
+        ExternalMemory.create_storage(None, embedder_config)
+
+
+def test_external_memory_create_storage_missing_config():
+    with pytest.raises(ValueError, match="embedder_config is required"):
+        ExternalMemory.create_storage(None, None)
+
+
+def test_crew_with_external_memory_initialization(crew_with_external_memory):
+    assert crew_with_external_memory._external_memory is not None
+    assert isinstance(crew_with_external_memory._external_memory, ExternalMemory)
+    assert crew_with_external_memory._external_memory.crew == crew_with_external_memory
+
+
+@pytest.mark.parametrize("mem_type", ["external", "all"])
+def test_crew_external_memory_reset(mem_type, crew_with_external_memory):
+    with patch(
+        "crewai.memory.external.external_memory.ExternalMemory.reset"
+    ) as mock_reset:
+        crew_with_external_memory.reset_memories(mem_type)
+        mock_reset.assert_called_once()
+
+
+@pytest.mark.parametrize("mem_method", ["search", "save"])
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_external_memory_save(mem_method, crew_with_external_memory):
+    with patch(
+        f"crewai.memory.external.external_memory.ExternalMemory.{mem_method}"
+    ) as mock_method:
+        crew_with_external_memory.kickoff()
+        assert mock_method.call_count > 0
+
+
+def test_external_memory_custom_storage(crew_with_external_memory):
+    class CustomStorage(Storage):
+        def __init__(self):
+            self.memories = []
+
+        def save(self, value, metadata=None, agent=None):
+            self.memories.append({"value": value, "metadata": metadata, "agent": agent})
+
+        def search(self, query, limit=10, score_threshold=0.5):
+            return self.memories
+
+        def reset(self):
+            self.memories = []
+
+    custom_storage = CustomStorage()
+    external_memory = ExternalMemory(storage=custom_storage)
+
+    # by ensuring the crew is set, we can test that the storage is used
+    external_memory.set_crew(crew_with_external_memory)
+
+    test_value = "test value"
+    test_metadata = {"source": "test"}
+    test_agent = "test_agent"
+    external_memory.save(value=test_value, metadata=test_metadata, agent=test_agent)
+
+    results = external_memory.search("test")
+    assert len(results) == 1
+    assert results[0]["value"] == test_value
+    assert results[0]["metadata"] == test_metadata | {"agent": test_agent}
+
+    external_memory.reset()
+    results = external_memory.search("test")
+    assert len(results) == 0

tests/project_test.py

@@ -2,7 +2,16 @@ import pytest
 
 from crewai.agent import Agent
 from crewai.crew import Crew
-from crewai.project import CrewBase, after_kickoff, agent, before_kickoff, crew, task
+from crewai.llm import LLM
+from crewai.project import (
+    CrewBase,
+    after_kickoff,
+    agent,
+    before_kickoff,
+    crew,
+    llm,
+    task,
+)
 from crewai.task import Task
 
 
@@ -31,6 +40,13 @@ class InternalCrew:
     agents_config = "config/agents.yaml"
     tasks_config = "config/tasks.yaml"
 
+    @llm
+    def local_llm(self):
+        return LLM(
+            model='openai/model_name',
+            api_key="None",
+            base_url="http://xxx.xxx.xxx.xxx:8000/v1")
+
     @agent
     def researcher(self):
         return Agent(config=self.agents_config["researcher"])
@@ -105,6 +121,20 @@ def test_task_name():
     ), "Custom task name is not being set as expected"
 
 
+def test_agent_function_calling_llm():
+    crew = InternalCrew()
+    llm = crew.local_llm()
+    obj_llm_agent = crew.researcher()
+    assert (
+        obj_llm_agent.function_calling_llm is llm
+    ), "agent's function_calling_llm is incorrect"
+
+    str_llm_agent = crew.reporting_analyst()
+    assert (
+        str_llm_agent.function_calling_llm.model == "online_llm"
+    ), "agent's function_calling_llm is incorrect"
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_modification():
     crew = InternalCrew()

tests/storage/test_mem0_storage.py

@@ -29,41 +29,32 @@ 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):
+    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
-                }
+                "config": {"host": "localhost", "port": 6333},
             },
             "llm": {
                 "provider": "mock_llm",
-                "config": {
-                    "api_key": "mock-api-key",
-                    "model": "mock-model"
-                }
+                "config": {"api_key": "mock-api-key", "model": "mock-model"},
             },
             "embedder": {
                 "provider": "mock_embedder",
-                "config": {
-                    "api_key": "mock-api-key",
-                    "model": "mock-model"
-                }
+                "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"
-                }
+                    "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"
+            "custom_update_memory_prompt": "mock prompt 2",
         }
 
         # Instantiate the class with memory_config
@@ -92,23 +83,73 @@ def mock_mem0_memory_client():
 
 
 @pytest.fixture
-def mem0_storage_with_memory_client(mock_mem0_memory_client):
+def mem0_storage_with_memory_client_using_config_from_crew(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"},
-                }
-            )
+    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
+        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):
+@pytest.fixture
+def mem0_storage_with_memory_client_using_explictly_config(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",
+                },
+            }
+        )
+
+        new_config = {"provider": "mem0", "config": {"api_key": "new-api-key"}}
+
+        mem0_storage = Mem0Storage(type="short_term", crew=crew, config=new_config)
+        return mem0_storage
+
+
+def test_mem0_storage_with_memory_client_initialization(
+    mem0_storage_with_memory_client_using_config_from_crew, 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
+    assert (
+        mem0_storage_with_memory_client_using_config_from_crew.memory_type
+        == "short_term"
+    )
+    assert (
+        mem0_storage_with_memory_client_using_config_from_crew.memory
+        is mock_mem0_memory_client
+    )
+
+
+def test_mem0_storage_with_explict_config(
+    mem0_storage_with_memory_client_using_explictly_config,
+):
+    expected_config = {"provider": "mem0", "config": {"api_key": "new-api-key"}}
+    assert (
+        mem0_storage_with_memory_client_using_explictly_config.config == expected_config
+    )
+    assert (
+        mem0_storage_with_memory_client_using_explictly_config.memory_config
+        == expected_config
+    )

tests/telemetry/test_telemetry_disable.py

@@ -0,0 +1,30 @@
+import os
+from unittest.mock import patch
+
+import pytest
+
+from crewai.telemetry import Telemetry
+
+
+@pytest.mark.parametrize("env_var,value,expected_ready", [
+    ("OTEL_SDK_DISABLED", "true", False),
+    ("OTEL_SDK_DISABLED", "TRUE", False),
+    ("CREWAI_DISABLE_TELEMETRY", "true", False),
+    ("CREWAI_DISABLE_TELEMETRY", "TRUE", False),
+    ("OTEL_SDK_DISABLED", "false", True),
+    ("CREWAI_DISABLE_TELEMETRY", "false", True),
+])
+def test_telemetry_environment_variables(env_var, value, expected_ready):
+    """Test telemetry state with different environment variable configurations."""
+    with patch.dict(os.environ, {env_var: value}):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is expected_ready
+
+
+def test_telemetry_enabled_by_default():
+    """Test that telemetry is enabled by default."""
+    with patch.dict(os.environ, {}, clear=True):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is True

tests/test_lite_agent.py

@@ -0,0 +1,257 @@
+import asyncio
+from typing import cast
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai import LLM, Agent
+from crewai.lite_agent import LiteAgent, LiteAgentOutput
+from crewai.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"])
+@pytest.mark.parametrize("verbose", [True, False])
+def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
+    """Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
+    # Create a test agent with specific parameters
+    llm = LLM(model="gpt-4o-mini")
+    custom_tools = [WebSearchTool(), CalculatorTool()]
+    max_iter = 10
+    max_execution_time = 300
+
+    agent = Agent(
+        role="Test Agent",
+        goal="Test Goal",
+        backstory="Test Backstory",
+        llm=llm,
+        tools=custom_tools,
+        max_iter=max_iter,
+        max_execution_time=max_execution_time,
+        verbose=verbose,
+    )
+
+    # Create a mock to capture the created LiteAgent
+    created_lite_agent = None
+    original_lite_agent = LiteAgent
+
+    # Define a mock LiteAgent class that captures its arguments
+    class MockLiteAgent(original_lite_agent):
+        def __init__(self, **kwargs):
+            nonlocal created_lite_agent
+            created_lite_agent = kwargs
+            super().__init__(**kwargs)
+
+    # Patch the LiteAgent class
+    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
+
+    # Call kickoff to create the LiteAgent
+    agent.kickoff("Test query")
+
+    # Verify all parameters were passed correctly
+    assert created_lite_agent is not None
+    assert created_lite_agent["role"] == "Test Agent"
+    assert created_lite_agent["goal"] == "Test Goal"
+    assert created_lite_agent["backstory"] == "Test Backstory"
+    assert created_lite_agent["llm"] == llm
+    assert len(created_lite_agent["tools"]) == 2
+    assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
+    assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
+    assert created_lite_agent["max_iterations"] == max_iter
+    assert created_lite_agent["max_execution_time"] == max_execution_time
+    assert created_lite_agent["verbose"] == verbose
+    assert created_lite_agent["response_format"] is None
+
+    # Test with a response_format
+    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
+
+    class TestResponse(BaseModel):
+        test_field: str
+
+    agent.kickoff("Test query", response_format=TestResponse)
+    assert created_lite_agent["response_format"] == TestResponse
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_with_tools():
+    """Test that Agent can use tools."""
+    # Create a LiteAgent with tools
+    llm = LLM(model="gpt-4o-mini")
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = 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 Agent 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 = Agent(
+        role="Info Gatherer",
+        goal="Provide brief information",
+        backstory="You gather and summarize information quickly.",
+        llm=llm,
+        tools=[web_search_tool],
+        verbose=True,
+    )
+
+    result = agent.kickoff(
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence",
+        response_format=SimpleOutput,
+    )
+
+    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 = Agent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = 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
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.asyncio
+async def test_lite_agent_returns_usage_metrics_async():
+    """Test that LiteAgent returns usage metrics when run asynchronously."""
+    llm = LLM(model="gpt-4o-mini")
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = await agent.kickoff_async(
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
+    )
+    assert isinstance(result, LiteAgentOutput)
+    assert "21 million" in result.raw or "37 million" in result.raw
+    assert result.usage_metrics is not None
+    assert result.usage_metrics["total_tokens"] > 0

tests/tools/test_base_tool.py

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

tests/tools/test_tool_usage.py

@@ -99,9 +99,6 @@ def test_tool_usage_render():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[tool],
-        original_tools=[tool],
-        tools_description="Sample tool for testing",
-        tools_names="random_number_generator",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -136,9 +133,6 @@ def test_validate_tool_input_booleans_and_none():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -158,9 +152,6 @@ def test_validate_tool_input_mixed_types():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -180,9 +171,6 @@ def test_validate_tool_input_single_quotes():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -202,9 +190,6 @@ def test_validate_tool_input_invalid_json_repairable():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -224,9 +209,6 @@ def test_validate_tool_input_with_special_characters():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=MagicMock(),
         agent=MagicMock(),
@@ -245,9 +227,6 @@ def test_validate_tool_input_none_input():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -262,9 +241,6 @@ def test_validate_tool_input_valid_json():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -282,9 +258,6 @@ def test_validate_tool_input_python_dict():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -302,9 +275,6 @@ def test_validate_tool_input_json5_unquoted_keys():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -322,9 +292,6 @@ def test_validate_tool_input_with_trailing_commas():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -355,9 +322,6 @@ def test_validate_tool_input_invalid_input():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=mock_agent,
@@ -388,9 +352,6 @@ def test_validate_tool_input_complex_structure():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -427,9 +388,6 @@ def test_validate_tool_input_code_content():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -450,9 +408,6 @@ def test_validate_tool_input_with_escaped_quotes():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -470,9 +425,6 @@ def test_validate_tool_input_large_json_content():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
         agent=MagicMock(),
@@ -512,9 +464,6 @@ def test_tool_selection_error_event_direct():
     tool_usage = ToolUsage(
         tools_handler=mock_tools_handler,
         tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
         task=mock_task,
         function_calling_llm=None,
         agent=mock_agent,
@@ -536,7 +485,8 @@ def test_tool_selection_error_event_direct():
     assert event.agent_role == "test_role"
     assert event.tool_name == "Non Existent Tool"
     assert event.tool_args == {}
-    assert event.tool_class == "Test Tool Description"
+    assert "Tool Name: Test Tool" in event.tool_class
+    assert "A test tool" in event.tool_class
     assert "don't exist" in event.error
 
     received_events.clear()
@@ -550,7 +500,7 @@ def test_tool_selection_error_event_direct():
     assert event.agent_role == "test_role"
     assert event.tool_name == ""
     assert event.tool_args == {}
-    assert event.tool_class == "Test Tool Description"
+    assert "Test Tool" in event.tool_class
     assert "forgot the Action name" in event.error
 
 
@@ -591,9 +541,6 @@ def test_tool_validate_input_error_event():
     tool_usage = ToolUsage(
         tools_handler=mock_tools_handler,
         tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
         task=mock_task,
         function_calling_llm=None,
         agent=mock_agent,
@@ -661,9 +608,6 @@ def test_tool_usage_finished_event_with_result():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
         task=mock_task,
         function_calling_llm=None,
         agent=mock_agent,
@@ -740,9 +684,6 @@ def test_tool_usage_finished_event_with_cached_result():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[test_tool],
-        original_tools=[test_tool],
-        tools_description="Test Tool Description",
-        tools_names="Test Tool",
         task=mock_task,
         function_calling_llm=None,
         agent=mock_agent,

tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml

@@ -1,7 +1,7 @@
 interactions:
 - request:
     body: '{"messages": [{"role": "user", "content": "Tell me a short joke"}], "model":
-      "gpt-4o", "stop": [], "stream": false}'
+      "gpt-4o", "stop": []}'
     headers:
       accept:
       - application/json
@@ -10,13 +10,15 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '115'
+      - '98'
       content-type:
       - application/json
+      cookie:
+      - _cfuvid=IY8ppO70AMHr2skDSUsGh71zqHHdCQCZ3OvkPi26NBc-1740424913267-0.0.1.1-604800000
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.65.1
+      - OpenAI/Python 1.68.2
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -26,7 +28,7 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.65.1
+      - 1.68.2
       x-stainless-raw-response:
       - 'true'
       x-stainless-read-timeout:
@@ -40,19 +42,21 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFJBbtswELzrFVteerEKSZbrxpcCDuBTUfSUtigCgSZXEhuKJLirNEbg
-        vxeSHMtBXSAXHmZ2BjPLfU4AhNFiA0K1klUXbLpde/X1tvtW/tnfrW6//Lzb7UraLn8s2+xpJxaD
-        wu9/o+IX1Qflu2CRjXcTrSJKxsE1X5d5kRWrdT4SnddoB1kTOC19WmRFmWaf0uzjSdh6o5DEBn4l
-        AADP4ztEdBqfxAayxQvSIZFsUGzOQwAiejsgQhIZYulYLGZSecfoxtTf2wNo794zkDLo2BATcOyJ
-        QbLv6DNsUcmeELjFA3TyAaEPgI8YD9wa17y7NI5Y9ySHXq639oQfz0mtb0L0ezrxZ7w2zlBbRZTk
-        3ZCK2AcxsscE4H7cSP+qpAjRd4Er9g/oBsO8mOzE/AVXSPYs7YwX5eKKW6WRpbF0sVGhpGpRz8p5
-        /bLXxl8QyUXnf8Nc8556G9e8xX4mlMLAqKsQURv1uvA8FnE40P+NnXc8BhaE8dEorNhgHP5BYy17
-        O92OoAMxdlVtXIMxRDMdUB2qWt3UuV5ny5VIjslfAAAA//8DADx20t9JAwAA
+    content: "{\n  \"id\": \"chatcmpl-BHJ51XXwVMlREjnoe4n4fiA0Ynkab\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1743464619,\n  \"model\": \"gpt-4o-2024-08-06\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"Why don't skeletons fight each other?\\n\\nThey
+      don't have the guts.\",\n        \"refusal\": null,\n        \"annotations\":
+      []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n
+      \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 12,\n    \"completion_tokens\":
+      15,\n    \"total_tokens\": 27,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_de57b65c90\"\n}\n"
     headers:
       CF-RAY:
-      - 91bbfc033e461d6e-ATL
+      - 9293b5d18d3f9450-SJC
       Connection:
       - keep-alive
       Content-Encoding:
@@ -60,14 +64,14 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Wed, 05 Mar 2025 19:22:51 GMT
+      - Mon, 31 Mar 2025 23:43:40 GMT
       Server:
       - cloudflare
       Set-Cookie:
-      - __cf_bm=LecfSlhN6VGr4kTlMiMCqRPInNb1m8zOikTZxtsE_WM-1741202571-1.0.1.1-T8nh2g1PcqyLIV97_HH9Q_nSUyCtaiFAOzvMxlswn6XjJCcSLJhi_fmkbylwppwoRPTxgs4S6VsVH0mp4ZcDTABBbtemKj7vS8QRDpRrmsU;
-        path=/; expires=Wed, 05-Mar-25 19:52:51 GMT; domain=.api.openai.com; HttpOnly;
+      - __cf_bm=.esGqWXxYzwXyi6048Ocr_NZH1IMsgTTuNN0drcWtSI-1743464620-1.0.1.1-YroBLb5o02zaPiXdGGE3YNO3x56olTA3JQos540j.l2aoeOzHIMVubkp2uSSTBHefPb7OPDKFzjpRXoAVof9jgVUDL6C89g4Zu1_SXtWxEE;
+        path=/; expires=Tue, 01-Apr-25 00:13:40 GMT; domain=.api.openai.com; HttpOnly;
         Secure; SameSite=None
-      - _cfuvid=wyMrJP5k5bgWyD8rsK4JPvAJ78JWrsrT0lyV9DP4WZM-1741202571727-0.0.1.1-604800000;
+      - _cfuvid=jrsyZSqr3xLO_beX7x7VEel62eQFToYHZgRqR0eqVNs-1743464620187-0.0.1.1-604800000;
         path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
       Transfer-Encoding:
       - chunked
@@ -82,26 +86,25 @@ interactions:
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '416'
+      - '275'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
       - max-age=31536000; includeSubDomains; preload
       x-ratelimit-limit-requests:
-      - '10000'
+      - '50000'
       x-ratelimit-limit-tokens:
-      - '30000000'
+      - '150000000'
       x-ratelimit-remaining-requests:
-      - '9999'
+      - '49999'
       x-ratelimit-remaining-tokens:
-      - '29999978'
+      - '149999993'
       x-ratelimit-reset-requests:
-      - 6ms
+      - 1ms
       x-ratelimit-reset-tokens:
       - 0s
       x-request-id:
-      - req_f42504d00bda0a492dced0ba3cf302d8
-    status:
-      code: 200
-      message: OK
+      - req_09cc97e978a7a4b57a1c9ebc9c688fb8
+    http_version: HTTP/1.1
+    status_code: 200
 version: 1

tests/utilities/test_events.py

@@ -355,7 +355,7 @@ def test_tools_emits_finished_events():
     assert received_events[0].agent_key == agent.key
     assert received_events[0].agent_role == agent.role
     assert received_events[0].tool_name == SayHiTool().name
-    assert received_events[0].tool_args == {}
+    assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
     assert received_events[0].type == "tool_usage_finished"
     assert isinstance(received_events[0].timestamp, datetime)
 
@@ -385,6 +385,7 @@ def test_tools_emits_error_events():
         goal="Try to use the error tool",
         backstory="You are an assistant that tests error handling",
         tools=[ErrorTool()],
+        llm=LLM(model="gpt-4o-mini"),
     )
 
     task = Task(
@@ -396,11 +397,11 @@ def test_tools_emits_error_events():
     crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
     crew.kickoff()
 
-    assert len(received_events) == 75
+    assert len(received_events) == 48
     assert received_events[0].agent_key == agent.key
     assert received_events[0].agent_role == agent.role
     assert received_events[0].tool_name == "error_tool"
-    assert received_events[0].tool_args == {}
+    assert received_events[0].tool_args == "{}" or received_events[0].tool_args == {}
     assert str(received_events[0].error) == "Simulated tool error"
     assert received_events[0].type == "tool_usage_error"
     assert isinstance(received_events[0].timestamp, datetime)

uv.lock

@@ -1,19 +1,42 @@
 version = 1
-revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
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+    { name = "colorama", marker = "os_name == 'nt'" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -556,7 +579,7 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
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@@ -607,7 +630,7 @@ wheels = [
 
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 name = "crewai"
-version = "0.108.0"
+version = "0.114.0"
 source = { editable = "." }
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@@ -695,7 +718,7 @@ requires-dist = [
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     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.38.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.40.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -722,7 +745,6 @@ requires-dist = [
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-provides-extras = ["tools", "embeddings", "agentops", "fastembed", "pdfplumber", "pandas", "openpyxl", "mem0", "docling", "aisuite"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -745,7 +767,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
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+version = "0.40.1"
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 dependencies = [
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@@ -760,9 +782,9 @@ dependencies = [
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+sdist = { url = "https://files.pythonhosted.org/packages/16/ff/0c16c9943ec1501b12fc72aca7815f191ffe94d5f1fe4e9c353ee8c4ad1d/crewai_tools-0.40.1.tar.gz", hash = "sha256:6af5040b2277df8fd592238a17bf584f95dcc9ef7766236534999c8a9e9d0b52", size = 744094 }
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+    { url = "https://files.pythonhosted.org/packages/35/05/619c00bae2dda038f0d218dd5197120c938e9c9ccef1b9e50cfb037486f6/crewai_tools-0.40.1-py3-none-any.whl", hash = "sha256:8f459f74dee64364bfdbc524c815c4afcfb9ed532b51e6b8b4f616398d46cf1e", size = 573286 },
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
     { name = "ghp-import" },
     { name = "jinja2" },
     { name = "markdown" },
@@ -2677,7 +2699,7 @@ version = "2.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
+    { name = "pywin32", marker = "platform_system == 'Windows'" },
     { name = "tqdm" },
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 sdist = { url = "https://files.pythonhosted.org/packages/3a/93/80ac75c20ce54c785648b4ed363c88f148bf22637e10c9863db4fbe73e74/mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97", size = 271270 }
@@ -2924,7 +2946,7 @@ name = "nvidia-cudnn-cu12"
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 dependencies = [
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+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
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@@ -2951,9 +2973,9 @@ name = "nvidia-cusolver-cu12"
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-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
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@@ -2964,7 +2986,7 @@ name = "nvidia-cusparse-cu12"
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-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
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@@ -2975,6 +2997,7 @@ name = "nvidia-nccl-cu12"
 version = "2.20.5"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/bb/d09dda47c881f9ff504afd6f9ca4f502ded6d8fc2f572cacc5e39da91c28/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01", size = 176238458 },
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+    { url = "https://files.pythonhosted.org/packages/31/db/dc71113d441f208cdfe7ae10d4983884e13f464a6252450693365e166dcf/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41", size = 19270338 },
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 [[package]]
@@ -3501,7 +3525,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
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@@ -5008,19 +5032,19 @@ dependencies = [
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-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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