drop record new episodes

final clean up
Fixing tests
2025-12-17 12:58:31 +00:00 · 2025-01-02 16:03:51 -05:00 · 2025-01-02 15:55:39 -05:00 · 2025-01-02 15:51:23 -05:00 · 2025-01-02 15:31:16 -05:00 · 2025-01-02 15:24:57 -05:00
24 changed files with 253 additions and 1088 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -21,4 +21,3 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
--- a/docs/concepts/flows.mdx
+++ b/docs/concepts/flows.mdx
@@ -138,7 +138,7 @@ print("---- Final Output ----")
 print(final_output)
 ````

-```text Output
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
 ````
--- a/docs/concepts/knowledge.mdx
+++ b/docs/concepts/knowledge.mdx
@@ -4,6 +4,8 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---

+# Using Knowledge in CrewAI
+
 ## What is Knowledge?

 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
@@ -34,20 +36,7 @@ CrewAI supports various types of knowledge sources out of the box:
  </Card>
 </CardGroup>

-## Supported Knowledge Parameters
-
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
-## Quickstart Example
-
-<Tip>
-For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
-Also, use relative paths from the `knowledge` directory when creating the source.
-</Tip>
+## Quick Start

 Here's an example using string-based knowledge:

@@ -91,8 +80,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```


-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
-
+Here's another example with the `CrewDoclingSource`
 ```python Code
 from crewai import LLM, Agent, Crew, Process, Task
 from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
@@ -140,217 +128,39 @@ result = crew.kickoff(
 )
 ```

-## More Examples
-
-Here are examples of how to use different types of knowledge sources:
-
-### Text File Knowledge Source
-```python
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
-
-# Create a text file knowledge source
-text_source = CrewDoclingSource(
-    file_paths=["document.txt", "another.txt"]
-)
-
-# Create crew with text file source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[text_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[text_source]
-)
-```
-
-### PDF Knowledge Source
-```python
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-
-# Create a PDF knowledge source
-pdf_source = PDFKnowledgeSource(
-    file_paths=["document.pdf", "another.pdf"]
-)
-
-# Create crew with PDF knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[pdf_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[pdf_source]
-)
-```
-
-### CSV Knowledge Source
-```python
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-
-# Create a CSV knowledge source
-csv_source = CSVKnowledgeSource(
-    file_paths=["data.csv"]
-)
-
-# Create crew with CSV knowledge source or on agent level
-agent = Agent(
-    ...
-    knowledge_sources=[csv_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[csv_source]
-)
-```
-
-### Excel Knowledge Source
-```python
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-
-# Create an Excel knowledge source
-excel_source = ExcelKnowledgeSource(
-    file_paths=["spreadsheet.xlsx"]
-)
-
-# Create crew with Excel knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[excel_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[excel_source]
-)
-```
-
-### JSON Knowledge Source
-```python
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-
-# Create a JSON knowledge source
-json_source = JSONKnowledgeSource(
-    file_paths=["data.json"]
-)
-
-# Create crew with JSON knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[json_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[json_source]
-)
-```
-
 ## Knowledge Configuration

 ### Chunking Configuration

-Knowledge sources automatically chunk content for better processing. 
-You can configure chunking behavior in your knowledge sources:
+Control how content is split for processing by setting the chunk size and overlap.

-```python
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-source = StringKnowledgeSource(
-    content="Your content here",
-    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
-    chunk_overlap=200     # Overlap between chunks (default: 200)
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Long content...",
+    chunk_size=4000,     # Characters per chunk (default)
+    chunk_overlap=200    # Overlap between chunks (default)
 )
 ```

-The chunking configuration helps in:
- Breaking down large documents into manageable pieces
- Maintaining context through chunk overlap
- Optimizing retrieval accuracy
+## Embedder Configuration

-### Embeddings Configuration
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.

-You can also configure the embedder for the knowledge store. 
-This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
-The `embedder` parameter supports various embedding model providers that include:
- `openai`: OpenAI's embedding models
- `google`: Google's text embedding models
- `azure`: Azure OpenAI embeddings
- `ollama`: Local embeddings with Ollama
- `vertexai`: Google Cloud VertexAI embeddings
- `cohere`: Cohere's embedding models
- `bedrock`: AWS Bedrock embeddings
- `huggingface`: Hugging Face models
- `watson`: IBM Watson embeddings
-
-Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
-<CodeGroup>
-```python Example
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-import os
-
-# Get the GEMINI API key
-GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
+```python Code
+...
 string_source = StringKnowledgeSource(
-    content=content,
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
 )
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-gemini_llm = LLM(
-    model="gemini/gemini-1.5-pro-002",
-    api_key=GEMINI_API_KEY,
-    temperature=0,
-)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=gemini_llm,
-)
-
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
 crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
+    ...
    knowledge_sources=[string_source],
    embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"},
+    },
 )
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
-```text Output
-# Agent: About User
-## Task: Answer the following questions about the user: What city does John live in and how old is he?

-# Agent: About User
-## Final Answer: 
-John is 30 years old and lives in San Francisco.
-```
-</CodeGroup>
 ## Clearing Knowledge

 If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
--- a/docs/how-to/portkey-observability-and-guardrails.mdx
+++ b/docs/how-to/portkey-observability-and-guardrails.mdx
--- a/docs/how-to/portkey-observability.mdx
+++ b/docs/how-to/portkey-observability.mdx
@@ -1,202 +0,0 @@
---
-title: Portkey Observability and Guardrails
-description: How to use Portkey with CrewAI
-icon: key
---
-
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-## Getting Started
-
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="Configure the LLM Client">
-    To build CrewAI Agents with Portkey, you'll need two keys:
-    - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-    - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-    ```python
-    from crewai import LLM
-    from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-    gpt_llm = LLM(
-        model="gpt-4",
-        base_url=PORTKEY_GATEWAY_URL,
-        api_key="dummy", # We are using Virtual key
-        extra_headers=createHeaders(
-            api_key="YOUR_PORTKEY_API_KEY",
-            virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-        )
-    )
-    ```
-    </Step>
-    <Step title="Create and Run Your First Agent">
-    ```python
-    from crewai import Agent, Task, Crew
-
-    # Define your agents with roles and goals
-    coder = Agent(
-        role='Software developer',
-        goal='Write clear, concise code on demand',
-        backstory='An expert coder with a keen eye for software trends.',
-        llm=gpt_llm
-    )
-
-    # Create tasks for your agents
-    task1 = Task(
-        description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-        expected_output="A clear and concise HTML code",
-        agent=coder
-    )
-
-    # Instantiate your crew
-    crew = Crew(
-        agents=[coder],
-        tasks=[task1],
-    )
-
-    result = crew.kickoff()
-    print(result)
-    ```
-    </Step>
-</Steps>
-
-## Key Features
-
-| Feature | Description |
-|:--------|:------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
- **Simple Cache**: Perfect for exact matches
- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
- **Automatic Retries**: Handle temporary failures gracefully
- **Request Timeouts**: Prevent hanging operations
- **Conditional Routing**: Route requests based on specific conditions
- **Fallbacks**: Set up automatic provider failovers
- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
- Cost per agent interaction
- Response times and latency
- Token usage and efficiency
- Success/failure rates
- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
- Set budget limit and rate limts per Virtual Key (disposable API keys)
- Implement role-based access control
- Track system changes with audit logs
- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
- [📘 Portkey Documentation](https://docs.portkey.ai)
- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
- [🐦 Twitter](https://twitter.com/portkeyai)
- [💬 Discord Community](https://discord.gg/DD7vgKK299)
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -100,8 +100,7 @@
        "how-to/conditional-tasks",
        "how-to/agentops-observability",
        "how-to/langtrace-observability",
-        "how-to/openlit-observability",
-        "how-to/portkey-observability"
+        "how-to/openlit-observability"
      ]
    },
    {
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,25 +13,25 @@ dependencies = [
    "openai>=1.13.3",
    "litellm>=1.44.22",
    "instructor>=1.3.3",
-
+    
    # Text Processing
    "pdfplumber>=0.11.4",
    "regex>=2024.9.11",
-
+    
    # Telemetry and Monitoring
    "opentelemetry-api>=1.22.0",
    "opentelemetry-sdk>=1.22.0",
    "opentelemetry-exporter-otlp-proto-http>=1.22.0",
-
+    
    # Data Handling
    "chromadb>=0.5.23",
    "openpyxl>=3.1.5",
    "pyvis>=0.3.2",
-
+    
    # Authentication and Security
    "auth0-python>=4.7.1",
    "python-dotenv>=1.0.0",
-
+    
    # Configuration and Utils
    "click>=8.1.7",
    "appdirs>=1.4.4",
@@ -40,7 +40,7 @@ dependencies = [
    "uv>=0.4.25",
    "tomli-w>=1.1.0",
    "tomli>=2.0.2",
-    "blinker>=1.9.0"
+    "blinker>=1.9.0",
 ]

 [project.urls]
@@ -49,7 +49,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
-tools = ["crewai-tools>=0.25.5"]
+tools = ["crewai-tools>=0.17.0"]
 embeddings = [
    "tiktoken~=0.7.0"
 ]
--- a/src/crewai/knowledge/source/crew_docling_source.py
+++ b/src/crewai/knowledge/source/crew_docling_source.py
@@ -2,16 +2,11 @@ from pathlib import Path
 from typing import Iterator, List, Optional, Union
 from urllib.parse import urlparse

-try:
-    from docling.datamodel.base_models import InputFormat
-    from docling.document_converter import DocumentConverter
-    from docling.exceptions import ConversionError
-    from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
-    from docling_core.types.doc.document import DoclingDocument
-    DOCLING_AVAILABLE = True
-except ImportError:
-    DOCLING_AVAILABLE = False
-
+from docling.datamodel.base_models import InputFormat
+from docling.document_converter import DocumentConverter
+from docling.exceptions import ConversionError
+from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
+from docling_core.types.doc.document import DoclingDocument
 from pydantic import Field

 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -24,14 +19,6 @@ class CrewDoclingSource(BaseKnowledgeSource):
    This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth.
    """

-    def __init__(self, *args, **kwargs):
-        if not DOCLING_AVAILABLE:
-            raise ImportError(
-                "The docling package is required to use CrewDoclingSource. "
-                "Please install it using: uv add docling"
-            )
-        super().__init__(*args, **kwargs)
-
    _logger: Logger = Logger(verbose=True)

    file_path: Optional[List[Union[Path, str]]] = Field(default=None)
--- a/src/crewai/project/annotations.py
+++ b/src/crewai/project/annotations.py
@@ -4,23 +4,18 @@ from typing import Callable
 from crewai import Crew
 from crewai.project.utils import memoize

-"""Decorators for defining crew components and their behaviors."""
-

 def before_kickoff(func):
-    """Marks a method to execute before crew kickoff."""
    func.is_before_kickoff = True
    return func


 def after_kickoff(func):
-    """Marks a method to execute after crew kickoff."""
    func.is_after_kickoff = True
    return func


 def task(func):
-    """Marks a method as a crew task."""
    func.is_task = True

    @wraps(func)
@@ -34,51 +29,43 @@ def task(func):


 def agent(func):
-    """Marks a method as a crew agent."""
    func.is_agent = True
    func = memoize(func)
    return func


 def llm(func):
-    """Marks a method as an LLM provider."""
    func.is_llm = True
    func = memoize(func)
    return func


 def output_json(cls):
-    """Marks a class as JSON output format."""
    cls.is_output_json = True
    return cls


 def output_pydantic(cls):
-    """Marks a class as Pydantic output format."""
    cls.is_output_pydantic = True
    return cls


 def tool(func):
-    """Marks a method as a crew tool."""
    func.is_tool = True
    return memoize(func)


 def callback(func):
-    """Marks a method as a crew callback."""
    func.is_callback = True
    return memoize(func)


 def cache_handler(func):
-    """Marks a method as a cache handler."""
    func.is_cache_handler = True
    return memoize(func)


 def crew(func) -> Callable[..., Crew]:
-    """Marks a method as the main crew execution point."""

    @wraps(func)
    def wrapper(self, *args, **kwargs) -> Crew:
--- a/src/crewai/project/crew_base.py
+++ b/src/crewai/project/crew_base.py
@@ -9,10 +9,8 @@ load_dotenv()

 T = TypeVar("T", bound=type)

-"""Base decorator for creating crew classes with configuration and function management."""

 def CrewBase(cls: T) -> T:
-    """Wraps a class with crew functionality and configuration management."""
    class WrappedClass(cls):  # type: ignore
        is_crew_class: bool = True  # type: ignore

@@ -218,5 +216,5 @@ def CrewBase(cls: T) -> T:
    # Include base class (qual)name in the wrapper class (qual)name.
    WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
    WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
-
+  
    return cast(T, WrappedClass)
--- a/src/crewai/task.py
+++ b/src/crewai/task.py
@@ -127,41 +127,38 @@ class Task(BaseModel):
    processed_by_agents: Set[str] = Field(default_factory=set)
    guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
        default=None,
-        description="Function to validate task output before proceeding to next task",
+        description="Function to validate task output before proceeding to next task"
    )
    max_retries: int = Field(
-        default=3, description="Maximum number of retries when guardrail fails"
+        default=3,
+        description="Maximum number of retries when guardrail fails"
    )
-    retry_count: int = Field(default=0, description="Current number of retries")
-
-    start_time: Optional[datetime.datetime] = Field(
-        default=None, description="Start time of the task execution"
-    )
-    end_time: Optional[datetime.datetime] = Field(
-        default=None, description="End time of the task execution"
+    retry_count: int = Field(
+        default=0,
+        description="Current number of retries"
    )

    @field_validator("guardrail")
    @classmethod
    def validate_guardrail_function(cls, v: Optional[Callable]) -> Optional[Callable]:
        """Validate that the guardrail function has the correct signature and behavior.
-
+        
        While type hints provide static checking, this validator ensures runtime safety by:
        1. Verifying the function accepts exactly one parameter (the TaskOutput)
        2. Checking return type annotations match Tuple[bool, Any] if present
        3. Providing clear, immediate error messages for debugging
-
+        
        This runtime validation is crucial because:
        - Type hints are optional and can be ignored at runtime
        - Function signatures need immediate validation before task execution
        - Clear error messages help users debug guardrail implementation issues
-
+        
        Args:
            v: The guardrail function to validate
-
+            
        Returns:
            The validated guardrail function
-
+            
        Raises:
            ValueError: If the function signature is invalid or return annotation
                       doesn't match Tuple[bool, Any]
@@ -174,13 +171,8 @@ class Task(BaseModel):
            # Check return annotation if present, but don't require it
            return_annotation = sig.return_annotation
            if return_annotation != inspect.Signature.empty:
-                if not (
-                    return_annotation == Tuple[bool, Any]
-                    or str(return_annotation) == "Tuple[bool, Any]"
-                ):
-                    raise ValueError(
-                        "If return type is annotated, it must be Tuple[bool, Any]"
-                    )
+                if not (return_annotation == Tuple[bool, Any] or str(return_annotation) == 'Tuple[bool, Any]'):
+                    raise ValueError("If return type is annotated, it must be Tuple[bool, Any]")
        return v

    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
@@ -189,6 +181,7 @@ class Task(BaseModel):
    _original_expected_output: Optional[str] = PrivateAttr(default=None)
    _original_output_file: Optional[str] = PrivateAttr(default=None)
    _thread: Optional[threading.Thread] = PrivateAttr(default=None)
+    _execution_time: Optional[float] = PrivateAttr(default=None)

    @model_validator(mode="before")
    @classmethod
@@ -213,19 +206,25 @@ class Task(BaseModel):
                "may_not_set_field", "This field is not to be set by the user.", {}
            )

+    def _set_start_execution_time(self) -> float:
+        return datetime.datetime.now().timestamp()
+
+    def _set_end_execution_time(self, start_time: float) -> None:
+        self._execution_time = datetime.datetime.now().timestamp() - start_time
+
    @field_validator("output_file")
    @classmethod
    def output_file_validation(cls, value: Optional[str]) -> Optional[str]:
        """Validate the output file path.
-
+        
        Args:
            value: The output file path to validate. Can be None or a string.
                  If the path contains template variables (e.g. {var}), leading slashes are preserved.
                  For regular paths, leading slashes are stripped.
-
+        
        Returns:
            The validated and potentially modified path, or None if no path was provided.
-
+            
        Raises:
            ValueError: If the path contains invalid characters, path traversal attempts,
                      or other security concerns.
@@ -235,24 +234,18 @@ class Task(BaseModel):

        # Basic security checks
        if ".." in value:
-            raise ValueError(
-                "Path traversal attempts are not allowed in output_file paths"
-            )
-
+            raise ValueError("Path traversal attempts are not allowed in output_file paths")
+        
        # Check for shell expansion first
-        if value.startswith("~") or value.startswith("$"):
-            raise ValueError(
-                "Shell expansion characters are not allowed in output_file paths"
-            )
-
+        if value.startswith('~') or value.startswith('$'):
+            raise ValueError("Shell expansion characters are not allowed in output_file paths")
+            
        # Then check other shell special characters
-        if any(char in value for char in ["|", ">", "<", "&", ";"]):
-            raise ValueError(
-                "Shell special characters are not allowed in output_file paths"
-            )
+        if any(char in value for char in ['|', '>', '<', '&', ';']):
+            raise ValueError("Shell special characters are not allowed in output_file paths")

        # Don't strip leading slash if it's a template path with variables
-        if "{" in value or "}" in value:
+        if "{" in value or "}" in value: 
            # Validate template variable format
            template_vars = [part.split("}")[0] for part in value.split("{")[1:]]
            for var in template_vars:
@@ -309,12 +302,6 @@ class Task(BaseModel):

        return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()

-    @property
-    def execution_duration(self) -> float | None:
-        if not self.start_time or not self.end_time:
-            return None
-        return (self.end_time - self.start_time).total_seconds()
-
    def execute_async(
        self,
        agent: BaseAgent | None = None,
@@ -355,7 +342,7 @@ class Task(BaseModel):
                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
            )

-        self.start_time = datetime.datetime.now()
+        start_time = self._set_start_execution_time()
        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)

        self.prompt_context = context
@@ -405,17 +392,15 @@ class Task(BaseModel):

            if isinstance(guardrail_result.result, str):
                task_output.raw = guardrail_result.result
-                pydantic_output, json_output = self._export_output(
-                    guardrail_result.result
-                )
+                pydantic_output, json_output = self._export_output(guardrail_result.result)
                task_output.pydantic = pydantic_output
                task_output.json_dict = json_output
            elif isinstance(guardrail_result.result, TaskOutput):
                task_output = guardrail_result.result

        self.output = task_output
-        self.end_time = datetime.datetime.now()

+        self._set_end_execution_time(start_time)
        if self.callback:
            self.callback(self.output)

@@ -427,9 +412,7 @@ class Task(BaseModel):
            content = (
                json_output
                if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
            )
            self._save_file(content)

@@ -451,11 +434,11 @@ class Task(BaseModel):

    def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float]]) -> None:
        """Interpolate inputs into the task description, expected output, and output file path.
-
+        
        Args:
            inputs: Dictionary mapping template variables to their values.
                   Supported value types are strings, integers, and floats.
-
+        
        Raises:
            ValueError: If a required template variable is missing from inputs.
        """
@@ -472,9 +455,7 @@ class Task(BaseModel):
        try:
            self.description = self._original_description.format(**inputs)
        except KeyError as e:
-            raise ValueError(
-                f"Missing required template variable '{e.args[0]}' in description"
-            ) from e
+            raise ValueError(f"Missing required template variable '{e.args[0]}' in description") from e
        except ValueError as e:
            raise ValueError(f"Error interpolating description: {str(e)}") from e

@@ -491,26 +472,22 @@ class Task(BaseModel):
                    input_string=self._original_output_file, inputs=inputs
                )
            except (KeyError, ValueError) as e:
-                raise ValueError(
-                    f"Error interpolating output_file path: {str(e)}"
-                ) from e
+                raise ValueError(f"Error interpolating output_file path: {str(e)}") from e

-    def interpolate_only(
-        self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]
-    ) -> str:
+    def interpolate_only(self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]) -> str:
        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
-
+        
        Args:
            input_string: The string containing template variables to interpolate.
                         Can be None or empty, in which case an empty string is returned.
            inputs: Dictionary mapping template variables to their values.
                   Supported value types are strings, integers, and floats.
                   If input_string is empty or has no placeholders, inputs can be empty.
-
+        
        Returns:
            The interpolated string with all template variables replaced with their values.
            Empty string if input_string is None or empty.
-
+            
        Raises:
            ValueError: If a required template variable is missing from inputs.
            KeyError: If a template variable is not found in the inputs dictionary.
@@ -520,17 +497,13 @@ class Task(BaseModel):
        if "{" not in input_string and "}" not in input_string:
            return input_string
        if not inputs:
-            raise ValueError(
-                "Inputs dictionary cannot be empty when interpolating variables"
-            )
+            raise ValueError("Inputs dictionary cannot be empty when interpolating variables")

        try:
            # Validate input types
            for key, value in inputs.items():
                if not isinstance(value, (str, int, float)):
-                    raise ValueError(
-                        f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}"
-                    )
+                    raise ValueError(f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}")

            escaped_string = input_string.replace("{", "{{").replace("}", "}}")

@@ -539,9 +512,7 @@ class Task(BaseModel):

            return escaped_string.format(**inputs)
        except KeyError as e:
-            raise KeyError(
-                f"Template variable '{e.args[0]}' not found in inputs dictionary"
-            ) from e
+            raise KeyError(f"Template variable '{e.args[0]}' not found in inputs dictionary") from e
        except ValueError as e:
            raise ValueError(f"Error during string interpolation: {str(e)}") from e

@@ -626,10 +597,10 @@ class Task(BaseModel):

    def _save_file(self, result: Any) -> None:
        """Save task output to a file.
-
+        
        Args:
            result: The result to save to the file. Can be a dict or any stringifiable object.
-
+            
        Raises:
            ValueError: If output_file is not set
            RuntimeError: If there is an error writing to the file
@@ -647,7 +618,6 @@ class Task(BaseModel):
            with resolved_path.open("w", encoding="utf-8") as file:
                if isinstance(result, dict):
                    import json
-
                    json.dump(result, file, ensure_ascii=False, indent=2)
                else:
                    file.write(str(result))
--- a/src/crewai/tools/tool_usage.py
+++ b/src/crewai/tools/tool_usage.py
@@ -169,7 +169,7 @@ class ToolUsage:

                if calling.arguments:
                    try:
-                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.schema()["properties"].keys()  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
                        arguments = {
                            k: v
                            for k, v in calling.arguments.items()
--- a/src/crewai/utilities/crew_json_encoder.py
+++ b/src/crewai/utilities/crew_json_encoder.py
@@ -1,5 +1,3 @@
-"""JSON encoder for handling CrewAI specific types."""
-
 import json
 from datetime import date, datetime
 from decimal import Decimal
@@ -10,7 +8,6 @@ from pydantic import BaseModel


 class CrewJSONEncoder(json.JSONEncoder):
-    """Custom JSON encoder for CrewAI objects and special types."""
    def default(self, obj):
        if isinstance(obj, BaseModel):
            return self._handle_pydantic_model(obj)
--- a/src/crewai/utilities/crew_pydantic_output_parser.py
+++ b/src/crewai/utilities/crew_pydantic_output_parser.py
@@ -6,10 +6,9 @@ from pydantic import BaseModel, ValidationError

 from crewai.agents.parser import OutputParserException

-"""Parser for converting text outputs into Pydantic models."""

 class CrewPydanticOutputParser:
-    """Parses text outputs into specified Pydantic models."""
+    """Parses the text into pydantic models"""

    pydantic_object: Type[BaseModel]

--- a/src/crewai/utilities/evaluators/crew_evaluator_handler.py
+++ b/src/crewai/utilities/evaluators/crew_evaluator_handler.py
@@ -180,12 +180,12 @@ class CrewEvaluator:
            self._test_result_span = self._telemetry.individual_test_result_span(
                self.crew,
                evaluation_result.pydantic.quality,
-                current_task.execution_duration,
+                current_task._execution_time,
                self.openai_model_name,
            )
            self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
            self.run_execution_times[self.iteration].append(
-                current_task.execution_duration
+                current_task._execution_time
            )
        else:
            raise ValueError("Evaluation result is not in the expected format")
--- a/src/crewai/utilities/i18n.py
+++ b/src/crewai/utilities/i18n.py
@@ -4,10 +4,8 @@ from typing import Dict, Optional, Union

 from pydantic import BaseModel, Field, PrivateAttr, model_validator

-"""Internationalization support for CrewAI prompts and messages."""

 class I18N(BaseModel):
-    """Handles loading and retrieving internationalized prompts."""
    _prompts: Dict[str, Dict[str, str]] = PrivateAttr()
    prompt_file: Optional[str] = Field(
        default=None,
--- a/src/crewai/utilities/paths.py
+++ b/src/crewai/utilities/paths.py
@@ -3,10 +3,8 @@ from pathlib import Path

 import appdirs

-"""Path management utilities for CrewAI storage and configuration."""

 def db_storage_path():
-    """Returns the path for database storage."""
    app_name = get_project_directory_name()
    app_author = "CrewAI"

@@ -16,7 +14,6 @@ def db_storage_path():


 def get_project_directory_name():
-    """Returns the current project directory name."""
    project_directory_name = os.environ.get("CREWAI_STORAGE_DIR")

    if project_directory_name:
--- a/src/crewai/utilities/planning_handler.py
+++ b/src/crewai/utilities/planning_handler.py
@@ -7,11 +7,10 @@ from pydantic import BaseModel, Field
 from crewai.agent import Agent
 from crewai.task import Task

-"""Handles planning and coordination of crew tasks."""
 logger = logging.getLogger(__name__)

+
 class PlanPerTask(BaseModel):
-    """Represents a plan for a specific task."""
    task: str = Field(..., description="The task for which the plan is created")
    plan: str = Field(
        ...,
@@ -20,7 +19,6 @@ class PlanPerTask(BaseModel):


 class PlannerTaskPydanticOutput(BaseModel):
-    """Output format for task planning results."""
    list_of_plans_per_task: List[PlanPerTask] = Field(
        ...,
        description="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
@@ -28,7 +26,6 @@ class PlannerTaskPydanticOutput(BaseModel):


 class CrewPlanner:
-    """Plans and coordinates the execution of crew tasks."""
    def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
        self.tasks = tasks

--- a/src/crewai/utilities/printer.py
+++ b/src/crewai/utilities/printer.py
@@ -1,11 +1,7 @@
-"""Utility for colored console output."""
-
 from typing import Optional


 class Printer:
-    """Handles colored console output formatting."""
-
    def print(self, content: str, color: Optional[str] = None):
        if color == "purple":
            self._print_purple(content)
--- a/src/crewai/utilities/rpm_controller.py
+++ b/src/crewai/utilities/rpm_controller.py
@@ -6,10 +6,8 @@ from pydantic import BaseModel, Field, PrivateAttr, model_validator

 from crewai.utilities.logger import Logger

-"""Controls request rate limiting for API calls."""

 class RPMController(BaseModel):
-    """Manages requests per minute limiting."""
    max_rpm: Optional[int] = Field(default=None)
    logger: Logger = Field(default_factory=lambda: Logger(verbose=False))
    _current_rpm: int = PrivateAttr(default=0)
--- a/src/crewai/utilities/task_output_storage_handler.py
+++ b/src/crewai/utilities/task_output_storage_handler.py
@@ -8,10 +8,8 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 )
 from crewai.task import Task

-"""Handles storage and retrieval of task execution outputs."""

 class ExecutionLog(BaseModel):
-    """Represents a log entry for task execution."""
    task_id: str
    expected_output: Optional[str] = None
    output: Dict[str, Any]
@@ -24,8 +22,6 @@ class ExecutionLog(BaseModel):
        return getattr(self, key)


-"""Manages storage and retrieval of task outputs."""
-
 class TaskOutputStorageHandler:
    def __init__(self) -> None:
        self.storage = KickoffTaskOutputsSQLiteStorage()
--- a/tests/cassettes/test_task_execution_times.yaml
+++ b/tests/cassettes/test_task_execution_times.yaml
@@ -1,146 +0,0 @@
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-      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!"}, {"role": "user", "content": "\nCurrent Task: Give me
-      a list of 5 interesting ideas to explore for na article, what makes them unique
-      and interesting.\n\nThis is the expect criteria for your final answer: Bullet
-      point list of 5 interesting ideas.\nyou MUST return the actual complete content
-      as the final answer, not a summary.\n\nBegin! This is VERY important to you,
-      use the tools available and give your best Final Answer, your job depends on
-      it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"], "stream":
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-    headers:
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--- a/tests/task_test.py
+++ b/tests/task_test.py
@@ -936,29 +936,3 @@ def test_output_file_validation():
            expected_output="Test output",
            output_file="{invalid-name}/output.txt",
        )
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_execution_times():
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 interesting ideas.",
-        agent=researcher,
-    )
-
-    assert task.start_time is None
-    assert task.end_time is None
-    assert task.execution_duration is None
-
-    task.execute_sync(agent=researcher)
-
-    assert task.start_time is not None
-    assert task.end_time is not None
-    assert task.execution_duration == (task.end_time - task.start_time).total_seconds()
--- a/uv.lock
+++ b/uv.lock
@@ -1,42 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
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-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
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+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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 [[package]]
@@ -66,7 +42,7 @@ wheels = [

 [[package]]
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