Merge branch 'main' into fix/_should_force_answer

.gitignore

@@ -21,4 +21,3 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log

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
 ````

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.

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)

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"
       ]
     },
     {

pyproject.toml

@@ -11,27 +11,27 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm>=1.44.22",
+    "litellm>=1.56.4",
     "instructor>=1.3.3",
-
+    
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
-
+    
     # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
-
+    
     # 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"
 ]

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -26,7 +26,7 @@ class CrewAgentExecutorMixin:
 
     def _should_force_answer(self) -> bool:
         """Determine if a forced answer is required based on iteration count."""
-        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+        return self.iterations >= self.max_iter
 
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""

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)

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:

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)

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))

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()

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)

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]
 

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")

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,

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:

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
 

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)

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)

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()

tests/agent_test.py

@@ -1457,7 +1457,7 @@ def test_agent_with_ollama_llama3():
     assert agent.llm.model == "ollama/llama3.2:3b"
     assert agent.llm.base_url == "http://localhost:11434"
 
-    task = "Respond in 20 words. Which model are you?"
+    task = "Respond in 20 words. Who are you?"
     response = agent.llm.call([{"role": "user", "content": task}])
 
     assert response
@@ -1473,9 +1473,7 @@ def test_llm_call_with_ollama_llama3():
         temperature=0.7,
         max_tokens=30,
     )
-    messages = [
-        {"role": "user", "content": "Respond in 20 words. Which model are you?"}
-    ]
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
 
     response = llm.call(messages)
 

tests/cassettes/test_agent_execute_task_with_ollama.yaml

@@ -12,34 +12,862 @@ interactions:
       available and give your best Final Answer, your job depends on it!\n\nThought:\n\n",
       "options": {"stop": ["\nObservation:"]}, "stream": false}'
     headers:
-      Accept:
+      accept:
       - '*/*'
-      Accept-Encoding:
+      accept-encoding:
       - gzip, deflate
-      Connection:
+      connection:
       - keep-alive
-      Content-Length:
+      content-length:
       - '839'
-      Content-Type:
-      - application/json
-      User-Agent:
-      - python-requests/2.32.3
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    body:
-      string: '{"model":"llama3.2:3b","created_at":"2025-01-02T20:05:52.24992Z","response":"Final
-        Answer: Artificial Intelligence (AI) refers to the development of computer
-        systems capable of performing tasks that typically require human intelligence,
-        such as learning, problem-solving, decision-making, and perception.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,2675,527,1296,3560,13,1296,93371,198,7927,4443,5915,374,25,1296,5915,198,1271,3041,856,1888,4686,1620,4320,311,279,3465,1005,279,4839,2768,3645,1473,85269,25,358,1457,649,3041,264,2294,4320,198,19918,22559,25,4718,1620,4320,2011,387,279,2294,323,279,1455,4686,439,3284,11,433,2011,387,15632,7633,382,40,28832,1005,1521,20447,11,856,2683,14117,389,433,2268,14711,2724,1473,5520,5546,25,83017,1148,15592,374,304,832,11914,271,2028,374,279,1755,13186,369,701,1620,4320,25,362,832,1355,18886,16540,315,15592,198,9514,28832,471,279,5150,4686,2262,439,279,1620,4320,11,539,264,12399,382,11382,0,1115,374,48174,3062,311,499,11,1005,279,7526,2561,323,3041,701,1888,13321,22559,11,701,2683,14117,389,433,2268,85269,1473,128009,128006,78191,128007,271,19918,22559,25,59294,22107,320,15836,8,19813,311,279,4500,315,6500,6067,13171,315,16785,9256,430,11383,1397,3823,11478,11,1778,439,6975,11,3575,99246,11,5597,28846,11,323,21063,13],"total_duration":1461909875,"load_duration":39886208,"prompt_eval_count":181,"prompt_eval_duration":701000000,"eval_count":39,"eval_duration":719000000}'
+    content: '{"model":"llama3.2:3b","created_at":"2024-12-31T16:56:15.759718Z","response":"Final
+      Answer: Artificial Intelligence (AI) refers to the development of computer systems
+      able to perform tasks that typically require human intelligence, including learning,
+      problem-solving, decision-making, and perception.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,2675,527,1296,3560,13,1296,93371,198,7927,4443,5915,374,25,1296,5915,198,1271,3041,856,1888,4686,1620,4320,311,279,3465,1005,279,4839,2768,3645,1473,85269,25,358,1457,649,3041,264,2294,4320,198,19918,22559,25,4718,1620,4320,2011,387,279,2294,323,279,1455,4686,439,3284,11,433,2011,387,15632,7633,382,40,28832,1005,1521,20447,11,856,2683,14117,389,433,2268,14711,2724,1473,5520,5546,25,83017,1148,15592,374,304,832,11914,271,2028,374,279,1755,13186,369,701,1620,4320,25,362,832,1355,18886,16540,315,15592,198,9514,28832,471,279,5150,4686,2262,439,279,1620,4320,11,539,264,12399,382,11382,0,1115,374,48174,3062,311,499,11,1005,279,7526,2561,323,3041,701,1888,13321,22559,11,701,2683,14117,389,433,2268,85269,1473,128009,128006,78191,128007,271,19918,22559,25,59294,22107,320,15836,8,19813,311,279,4500,315,6500,6067,3025,311,2804,9256,430,11383,1397,3823,11478,11,2737,6975,11,3575,99246,11,5597,28846,11,323,21063,13],"total_duration":1156303250,"load_duration":35999125,"prompt_eval_count":181,"prompt_eval_duration":408000000,"eval_count":38,"eval_duration":711000000}'
     headers:
       Content-Length:
-      - '1537'
+      - '1528'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Thu, 02 Jan 2025 20:05:52 GMT
-    status:
-      code: 200
-      message: OK
+      - Tue, 31 Dec 2024 16:56:15 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '23'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
+      Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms
+      and conditions for use, reproduction, distribution \\nand modification of the
+      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
+      manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D
+      or \u201Cyou\u201D means you, or your employer or any other person or entity
+      (if you are \\nentering into this Agreement on such person or entity\u2019s
+      behalf), of the age required under\\napplicable laws, rules or regulations to
+      provide legal consent and that has legal authority\\nto bind your employer or
+      such other person or entity if you are entering in this Agreement\\non their
+      behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models
+      and software and algorithms, including\\nmachine-learning model code, trained
+      model weights, inference-enabling code, training-enabling code,\\nfine-tuning
+      enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama
+      Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation
+      (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
+      or, \\nif you are an entity, your principal place of business is in the EEA
+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
+      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
+      a related website, user interface, blogpost, about page, or product documentation.
+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
+      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
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+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
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+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
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+      \   a. No trademark licenses are granted under this Agreement, and in connection
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+      owned by or associated with the other or any of its affiliates, \\nexcept as
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+      \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date:
+      September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
+      for use, reproduction, distribution \\nand modification of the Llama Materials
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+      or \u201Cyou\u201D means you, or your employer or any other person or entity
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+      \       iii. You must retain in all copies of the Llama Materials that you distribute
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+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
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+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
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+      \   a. No trademark licenses are granted under this Agreement, and in connection
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+      owned by or associated with the other or any of its affiliates, \\nexcept as
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+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
+      monthly active users\\nof the products or services made available by or for
+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
+      you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
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+      brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
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+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
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+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
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+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
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+      The term of this Agreement will commence upon your acceptance of this Agreement
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+      until terminated in accordance with the terms\\nand conditions herein. Meta
+      may terminate this Agreement if you are in breach of any term or condition of
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+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\\"\\nLICENSE \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta
+      is committed to promoting safe and fair use of its tools and features, including
+      Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use
+      Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be
+      found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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+      or harm to children, including the solicitation, creation, acquisition, or dissemination
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+      \       3. Human trafficking, exploitation, and sexual violence\\n        4.
+      The illegal distribution of information or materials to minors, including obscene
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+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
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+      models, or software are associated with Meta or Llama 3.2\\n\\nWith respect
+      to any multimodal models included in Llama 3.2, the rights granted under Section
+      1(a) of the Llama 3.2 Community License Agreement are not being granted to you
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+      business in, the European Union. This restriction does not apply to end users
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+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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+    headers:
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Tue, 31 Dec 2024 16:56:15 GMT
+      Transfer-Encoding:
+      - chunked
+    http_version: HTTP/1.1
+    status_code: 200
 version: 1

tests/cassettes/test_agent_with_ollama_llama3.yaml

@@ -1,36 +1,863 @@
 interactions:
 - request:
     body: '{"model": "llama3.2:3b", "prompt": "### User:\nRespond in 20 words. Who
-      which model are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream":
-      false}'
+      are you?\n\n", "options": {"stop": ["\nObservation:"]}, "stream": false}'
     headers:
-      Accept:
+      accept:
       - '*/*'
-      Accept-Encoding:
+      accept-encoding:
       - gzip, deflate
-      Connection:
+      connection:
       - keep-alive
-      Content-Length:
-      - '156'
-      Content-Type:
-      - application/json
-      User-Agent:
-      - python-requests/2.32.3
+      content-length:
+      - '144'
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    body:
-      string: '{"model":"llama3.2:3b","created_at":"2025-01-02T20:07:07.623404Z","response":"I''m
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+    content: '{"model":"llama3.2:3b","created_at":"2024-12-31T16:57:54.063894Z","response":"I''m
+      an AI designed to provide information and assist with inquiries, while maintaining
+      a neutral and respectful tone always.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,2724,512,66454,304,220,508,4339,13,10699,527,499,1980,128009,128006,78191,128007,271,40,2846,459,15592,6319,311,3493,2038,323,7945,449,44983,11,1418,20958,264,21277,323,49150,16630,2744,13],"total_duration":651386042,"load_duration":41061917,"prompt_eval_count":38,"prompt_eval_duration":204000000,"eval_count":23,"eval_duration":405000000}'
     headers:
       Content-Length:
-      - '690'
+      - '692'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Thu, 02 Jan 2025 20:07:07 GMT
-    status:
-      code: 200
-      message: OK
+      - Tue, 31 Dec 2024 16:57:54 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '23'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
+      Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms
+      and conditions for use, reproduction, distribution \\nand modification of the
+      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
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+      or \u201Cyou\u201D means you, or your employer or any other person or entity
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+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
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+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
+      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
+      a related website, user interface, blogpost, about page, or product documentation.
+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
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+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
+      monthly active users\\nof the products or services made available by or for
+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
+      you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+      \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
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+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
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+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
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+      The term of this Agreement will commence upon your acceptance of this Agreement
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+      Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be
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+      or harm to children, including the solicitation, creation, acquisition, or dissemination
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+      \       3. Human trafficking, exploitation, and sexual violence\\n        4.
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+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
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+      Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
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+    headers:
+      Content-Type:
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+      Date:
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+      Transfer-Encoding:
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 version: 1

tests/cassettes/test_llm_call_with_ollama_llama3.yaml

@@ -1,36 +1,449 @@
 interactions:
 - request:
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-      model are you??\n\n", "options": {"num_predict": 30, "temperature": 0.7}, "stream":
+    body: '{"model": "llama3.2:3b", "prompt": "### User:\nRespond in 20 words. Who
+      are you?\n\n", "options": {"temperature": 0.7, "num_predict": 30}, "stream":
       false}'
     headers:
-      Accept:
+      accept:
       - '*/*'
-      Accept-Encoding:
+      accept-encoding:
       - gzip, deflate
-      Connection:
+      connection:
       - keep-alive
-      Content-Length:
-      - '164'
-      Content-Type:
-      - application/json
-      User-Agent:
-      - python-requests/2.32.3
+      content-length:
+      - '155'
+      host:
+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    body:
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+    content: '{"model":"llama3.2:3b","created_at":"2024-12-31T17:00:06.295261Z","response":"I''m
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     headers:
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+      - '675'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Thu, 02 Jan 2025 20:24:24 GMT
-    status:
-      code: 200
-      message: OK
+      - Tue, 31 Dec 2024 17:00:06 GMT
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"name": "llama3.2:3b"}'
+    headers:
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+      accept-encoding:
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+      connection:
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+      content-type:
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+      - localhost:11434
+      user-agent:
+      - litellm/1.56.4
+    method: POST
+    uri: http://localhost:11434/api/show
+  response:
+    content: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version
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+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
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+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
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+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
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+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
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+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
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+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
+      AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY
+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
+      ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT,
+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
+      \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,
+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
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+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
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+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
+      to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
+      The term of this Agreement will commence upon your acceptance of this Agreement
+      or access\\nto the Llama Materials and will continue in full force and effect
+      until terminated in accordance with the terms\\nand conditions herein. Meta
+      may terminate this Agreement if you are in breach of any term or condition of
+      this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
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+      of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will
+      be governed and construed under the laws of the State of \\nCalifornia without
+      regard to choice of law principles, and the UN Convention on Contracts for the
+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed
+      to promoting safe and fair use of its tools and features, including Llama 3.2.
+      If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D).
+      The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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+      Any other criminal activity\\n    1. Engage in, promote, incite, or facilitate
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+      and services\\n    3. Engage in the unauthorized or unlicensed practice of any
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+      or infer private or sensitive information about individuals, including information
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+      Engage in or facilitate any action or generate any content that infringes, misappropriates,
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+      or facilitate the creation of malicious code, malware, computer viruses or do
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+      \   7. Engage in any action, or facilitate any action, to intentionally circumvent
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tests/cli/tools/test_main.py

@@ -28,10 +28,9 @@ def test_create_success(mock_subprocess):
     with in_temp_dir():
         tool_command = ToolCommand()
 
-        with (
-            patch.object(tool_command, "login") as mock_login,
-            patch("sys.stdout", new=StringIO()) as fake_out,
-        ):
+        with patch.object(tool_command, "login") as mock_login, patch(
+            "sys.stdout", new=StringIO()
+        ) as fake_out:
             tool_command.create("test-tool")
             output = fake_out.getvalue()
 
@@ -83,7 +82,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY,
+        env=unittest.mock.ANY
     )
 
     assert "Successfully installed sample-tool" in output

tests/crew_test.py

@@ -333,16 +333,16 @@ def test_manager_agent_delegating_to_assigned_task_agent():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -350,20 +350,12 @@ def test_manager_agent_delegating_to_assigned_task_agent():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Researcher"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Researcher"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Researcher" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Researcher" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -412,7 +404,7 @@ def test_manager_agent_delegates_with_varied_role_cases():
         backstory="A researcher with spaces in role name",
         allow_delegation=False,
     )
-
+    
     writer_caps = Agent(
         role="SENIOR WRITER",  # All caps
         goal="Write with caps in role",
@@ -434,13 +426,13 @@ def test_manager_agent_delegates_with_varied_role_cases():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -448,32 +440,20 @@ def test_manager_agent_delegates_with_varied_role_cases():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly and can handle case/whitespace variations
         assert len(tools) == 2
-
+        
         # Check delegation tool descriptions (should work despite case/whitespace differences)
         delegation_tool = tools[0]
         question_tool = tools[1]
-
-        assert (
-            "Delegate a specific task to one of the following coworkers:"
-            in delegation_tool.description
-        )
-        assert (
-            " Researcher " in delegation_tool.description
-            or "SENIOR WRITER" in delegation_tool.description
-        )
-
-        assert (
-            "Ask a specific question to one of the following coworkers:"
-            in question_tool.description
-        )
-        assert (
-            " Researcher " in question_tool.description
-            or "SENIOR WRITER" in question_tool.description
-        )
+        
+        assert "Delegate a specific task to one of the following coworkers:" in delegation_tool.description
+        assert " Researcher " in delegation_tool.description or "SENIOR WRITER" in delegation_tool.description
+        
+        assert "Ask a specific question to one of the following coworkers:" in question_tool.description
+        assert " Researcher " in question_tool.description or "SENIOR WRITER" in question_tool.description
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -499,7 +479,6 @@ def test_crew_with_delegating_agents():
         == "In the rapidly evolving landscape of technology, AI agents have emerged as formidable tools, revolutionizing how we interact with data and automate tasks. These sophisticated systems leverage machine learning and natural language processing to perform a myriad of functions, from virtual personal assistants to complex decision-making companions in industries such as finance, healthcare, and education. By mimicking human intelligence, AI agents can analyze massive data sets at unparalleled speeds, enabling businesses to uncover valuable insights, enhance productivity, and elevate user experiences to unprecedented levels.\n\nOne of the most striking aspects of AI agents is their adaptability; they learn from their interactions and continuously improve their performance over time. This feature is particularly valuable in customer service where AI agents can address inquiries, resolve issues, and provide personalized recommendations without the limitations of human fatigue. Moreover, with intuitive interfaces, AI agents enhance user interactions, making technology more accessible and user-friendly, thereby breaking down barriers that have historically hindered digital engagement.\n\nDespite their immense potential, the deployment of AI agents raises important ethical and practical considerations. Issues related to privacy, data security, and the potential for job displacement necessitate thoughtful dialogue and proactive measures. Striking a balance between technological innovation and societal impact will be crucial as organizations integrate these agents into their operations. Additionally, ensuring transparency in AI decision-making processes is vital to maintain public trust as AI agents become an integral part of daily life.\n\nLooking ahead, the future of AI agents appears bright, with ongoing advancements promising even greater capabilities. As we continue to harness the power of AI, we can expect these agents to play a transformative role in shaping various sectors—streamlining workflows, enabling smarter decision-making, and fostering more personalized experiences. Embracing this technology responsibly can lead to a future where AI agents not only augment human effort but also inspire creativity and efficiency across the board, ultimately redefining our interaction with the digital world."
     )
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_task_tools():
     from typing import Type
@@ -510,7 +489,6 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -538,29 +516,24 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     tasks[0].output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Execute the task and verify both tools are present
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
-
-        assert any(
-            isinstance(tool, TestTool) for tool in tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        tools = kwargs['tools']
 
+        assert any(isinstance(tool, TestTool) for tool in tools), "TestTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_agent_tools():
@@ -572,7 +545,6 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -591,7 +563,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
         Task(
             description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
             expected_output="A 4 paragraph article about AI.",
-            agent=new_ceo,
+            agent=new_ceo
         )
     ]
 
@@ -602,29 +574,24 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     tasks[0].output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Execute the task and verify both tools are present
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
-
-        assert any(
-            isinstance(tool, TestTool) for tool in new_ceo.tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        tools = kwargs['tools']
 
+        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), "TestTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_tools_override_agent_tools():
@@ -636,7 +603,6 @@ def test_task_tools_override_agent_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -664,10 +630,14 @@ def test_task_tools_override_agent_tools():
         description="Write a test task",
         expected_output="Test output",
         agent=new_researcher,
-        tools=[AnotherTestTool()],
+        tools=[AnotherTestTool()]
     )
 
-    crew = Crew(agents=[new_researcher], tasks=[task], process=Process.sequential)
+    crew = Crew(
+        agents=[new_researcher],
+        tasks=[task],
+        process=Process.sequential
+    )
 
     crew.kickoff()
 
@@ -680,7 +650,6 @@ def test_task_tools_override_agent_tools():
     assert len(new_researcher.tools) == 1
     assert isinstance(new_researcher.tools[0], TestTool)
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_tools_override_agent_tools_with_allow_delegation():
     """
@@ -733,13 +702,13 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # We mock execute_sync to verify which tools get used at runtime
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Inspect the call kwargs to verify the actual tools passed to execution
@@ -747,23 +716,16 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(
-            isinstance(tool, AnotherTestTool) for tool in used_tools
-        ), "AnotherTestTool should be present"
-        assert not any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "TestTool should not be present among used tools"
+        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), "AnotherTestTool should be present"
+        assert not any(isinstance(tool, TestTool) for tool in used_tools), "TestTool should not be present among used tools"
 
         # Confirm delegation tool(s) are present
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present"
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
     assert isinstance(researcher_with_delegation.tools[0], TestTool)
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_verbose_output(capsys):
     tasks = [
@@ -1050,8 +1012,8 @@ def test_three_task_with_async_execution():
     )
 
 
-@pytest.mark.asyncio
 @pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.asyncio
 async def test_crew_async_kickoff():
     inputs = [
         {"topic": "dog"},
@@ -1098,9 +1060,8 @@ async def test_crew_async_kickoff():
             assert result[0].token_usage.successful_requests > 0  # type: ignore
 
 
-@pytest.mark.asyncio
 @pytest.mark.vcr(filter_headers=["authorization"])
-async def test_async_task_execution_call_count():
+def test_async_task_execution_call_count():
     from unittest.mock import MagicMock, patch
 
     list_ideas = Task(
@@ -1227,6 +1188,7 @@ def test_kickoff_for_each_empty_input():
     assert results == []
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_invalid_input():
     """Tests if kickoff_for_each raises TypeError for invalid input types."""
 
@@ -1249,6 +1211,7 @@ def test_kickoff_for_each_invalid_input():
         crew.kickoff_for_each("invalid input")
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_error_handling():
     """Tests error handling in kickoff_for_each when kickoff raises an error."""
     from unittest.mock import patch
@@ -1285,6 +1248,7 @@ def test_kickoff_for_each_error_handling():
             crew.kickoff_for_each(inputs=inputs)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_kickoff_async_basic_functionality_and_output():
     """Tests the basic functionality and output of kickoff_async."""
@@ -1319,6 +1283,7 @@ async def test_kickoff_async_basic_functionality_and_output():
         mock_kickoff.assert_called_once_with(inputs)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_basic_functionality_and_output():
     """Tests the basic functionality and output of kickoff_for_each_async."""
@@ -1365,6 +1330,7 @@ async def test_async_kickoff_for_each_async_basic_functionality_and_output():
             mock_kickoff_async.assert_any_call(inputs=input_data)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_empty_input():
     """Tests if akickoff_for_each_async handles an empty input list."""
@@ -1548,12 +1514,12 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
     crew = Crew(agents=[programmer], tasks=[task])
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -1562,10 +1528,7 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
         # Verify that exactly one tool was used and it was a CodeInterpreterTool
         assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(
-            used_tools[0], CodeInterpreterTool
-        ), "Tool should be CodeInterpreterTool"
-
+        assert isinstance(used_tools[0], CodeInterpreterTool), "Tool should be CodeInterpreterTool"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegation_is_not_enabled_if_there_are_only_one_agent():
@@ -1676,16 +1639,16 @@ def test_hierarchical_crew_creation_tasks_with_agents():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Verify execute_sync was called once
@@ -1693,20 +1656,12 @@ def test_hierarchical_crew_creation_tasks_with_agents():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Senior Writer"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Senior Writer"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Senior Writer" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Senior Writer" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1729,7 +1684,9 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Create a mock Future that returns our TaskOutput
@@ -1740,9 +1697,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
     # which sets the output attribute of the task
     task.output = mock_task_output
 
-    with patch.object(
-        Task, "execute_async", return_value=mock_future
-    ) as mock_execute_async:
+    with patch.object(Task, 'execute_async', return_value=mock_future) as mock_execute_async:
         crew.kickoff()
 
         # Verify execute_async was called once
@@ -1750,20 +1705,12 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
 
         # Get the tools argument from the call
         _, kwargs = mock_execute_async.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the delegation tools were passed correctly
         assert len(tools) == 2
-        assert any(
-            "Delegate a specific task to one of the following coworkers: Senior Writer\n"
-            in tool.description
-            for tool in tools
-        )
-        assert any(
-            "Ask a specific question to one of the following coworkers: Senior Writer\n"
-            in tool.description
-            for tool in tools
-        )
+        assert any("Delegate a specific task to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools)
+        assert any("Ask a specific question to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -2092,6 +2039,7 @@ def test_crew_output_file_end_to_end(tmp_path):
     assert expected_file.exists(), f"Output file {expected_file} was not created"
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_output_file_validation_failures():
     """Test output file validation failures in a crew context."""
     agent = Agent(
@@ -2107,7 +2055,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="../output.txt",
+            output_file="../output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2117,7 +2065,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="output.txt | rm -rf /",
+            output_file="output.txt | rm -rf /"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2127,7 +2075,7 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="~/output.txt",
+            output_file="~/output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
@@ -2137,11 +2085,12 @@ def test_crew_output_file_validation_failures():
             description="Analyze data",
             expected_output="Analysis results",
             agent=agent,
-            output_file="{invalid-name}/output.txt",
+            output_file="{invalid-name}/output.txt"
         )
         Crew(agents=[agent], tasks=[task]).kickoff()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_manager_agent():
     from unittest.mock import patch
 
@@ -3100,7 +3049,6 @@ def test_task_tools_preserve_code_execution_tools():
 
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
-
         query: str = Field(..., description="Query to process")
 
     class TestTool(BaseTool):
@@ -3134,7 +3082,7 @@ def test_task_tools_preserve_code_execution_tools():
         description="Write a program to calculate fibonacci numbers.",
         expected_output="A working fibonacci calculator.",
         agent=programmer,
-        tools=[TestTool()],
+        tools=[TestTool()]
     )
 
     crew = Crew(
@@ -3144,12 +3092,12 @@ def test_task_tools_preserve_code_execution_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -3157,21 +3105,12 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "Task's TestTool should be present"
-        assert any(
-            isinstance(tool, CodeInterpreterTool) for tool in used_tools
-        ), "CodeInterpreterTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in used_tools), "Task's TestTool should be present"
+        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), "CodeInterpreterTool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present"
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert (
-            len(used_tools) == 4
-        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-
+        assert len(used_tools) == 4, "Should have TestTool, CodeInterpreter, and 2 delegation tools"
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_flag_adds_multimodal_tools():
@@ -3200,13 +3139,13 @@ def test_multimodal_flag_adds_multimodal_tools():
     crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential)
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description",
+        raw="mocked output",
+        agent="mocked agent"
     )
 
     # Mock execute_sync to verify the tools passed at runtime
-    with patch.object(
-        Task, "execute_sync", return_value=mock_task_output
-    ) as mock_execute_sync:
+    with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync:
         crew.kickoff()
 
         # Get the tools that were actually used in execution
@@ -3214,14 +3153,13 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(
-            isinstance(tool, AddImageTool) for tool in used_tools
-        ), "AddImageTool should be present when agent is multimodal"
+        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
+            "AddImageTool should be present when agent is multimodal"
+        )
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_image_tool_handling():
     """
@@ -3263,10 +3201,10 @@ def test_multimodal_agent_image_tool_handling():
     mock_task_output = TaskOutput(
         description="Mock description",
         raw="A detailed analysis of the image",
-        agent="Image Analyst",
+        agent="Image Analyst"
     )
 
-    with patch.object(Task, "execute_sync") as mock_execute_sync:
+    with patch.object(Task, 'execute_sync') as mock_execute_sync:
         # Set up the mock to return our task output
         mock_execute_sync.return_value = mock_task_output
 
@@ -3275,7 +3213,7 @@ def test_multimodal_agent_image_tool_handling():
 
         # Get the tools that were passed to execute_sync
         _, kwargs = mock_execute_sync.call_args
-        tools = kwargs["tools"]
+        tools = kwargs['tools']
 
         # Verify the AddImageTool is present and properly configured
         image_tools = [tool for tool in tools if tool.name == "Add image to content"]
@@ -3285,7 +3223,7 @@ def test_multimodal_agent_image_tool_handling():
         image_tool = image_tools[0]
         result = image_tool._run(
             image_url="https://example.com/test-image.jpg",
-            action="Please analyze this image",
+            action="Please analyze this image"
         )
 
         # Verify the tool returns the expected format
@@ -3295,7 +3233,6 @@ def test_multimodal_agent_image_tool_handling():
         assert result["content"][0]["type"] == "text"
         assert result["content"][1]["type"] == "image_url"
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multimodal_agent_live_image_analysis():
     """
@@ -3309,7 +3246,7 @@ def test_multimodal_agent_live_image_analysis():
         allow_delegation=False,
         multimodal=True,
         verbose=True,
-        llm="gpt-4o",
+        llm="gpt-4o"
     )
 
     # Create a task for image analysis
@@ -3320,18 +3257,19 @@ def test_multimodal_agent_live_image_analysis():
         Image: {image_url}
         """,
         expected_output="A comprehensive description of the image contents.",
-        agent=image_analyst,
+        agent=image_analyst
     )
 
     # Create and run the crew
-    crew = Crew(agents=[image_analyst], tasks=[analyze_image])
+    crew = Crew(
+        agents=[image_analyst],
+        tasks=[analyze_image]
+    )
 
     # Execute with an image URL
-    result = crew.kickoff(
-        inputs={
-            "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k="
-        }
-    )
+    result = crew.kickoff(inputs={
+        "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k="
+    })
 
     # Verify we got a meaningful response
     assert isinstance(result.raw, str)

tests/knowledge/knowledge_test.py

@@ -578,6 +578,14 @@ def test_multiple_docling_sources():
     assert docling_source.content is not None
 
 
+def test_docling_source_with_local_file():
+    current_dir = Path(__file__).parent
+    pdf_path = current_dir / "crewai_quickstart.pdf"
+    docling_source = CrewDoclingSource(file_paths=[pdf_path])
+    assert docling_source.file_paths == [pdf_path]
+    assert docling_source.content is not None
+
+
 def test_file_path_validation():
     """Test file path validation for knowledge sources."""
     current_dir = Path(__file__).parent
@@ -598,6 +606,6 @@ def test_file_path_validation():
     # Test neither file_path nor file_paths provided
     with pytest.raises(
         ValueError,
-        match="file_path/file_paths must be a Path, str, or a list of these types",
+        match="file_path/file_paths must be a Path, str, or a list of these types"
     ):
         PDFKnowledgeSource()

tests/task_test.py

@@ -719,7 +719,7 @@ def test_interpolate_inputs():
     task = Task(
         description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",
         expected_output="Bullet point list of 5 interesting ideas about {topic}.",
-        output_file="/tmp/{topic}/output_{date}.txt",
+        output_file="/tmp/{topic}/output_{date}.txt"
     )
 
     task.interpolate_inputs(inputs={"topic": "AI", "date": "2024"})
@@ -742,35 +742,41 @@ def test_interpolate_inputs():
 def test_interpolate_only():
     """Test the interpolate_only method for various scenarios including JSON structure preservation."""
     task = Task(
-        description="Unused in this test", expected_output="Unused in this test"
+        description="Unused in this test",
+        expected_output="Unused in this test"
     )
-
+    
     # Test JSON structure preservation
     json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
     result = task.interpolate_only(
         input_string=json_string,
-        inputs={"placeholder": "the data", "nestedVal": "something else"},
+        inputs={"placeholder": "the data", "nestedVal": "something else"}
     )
     assert '"info": "Look at the data"' in result
     assert '"val": "something else"' in result
     assert "{placeholder}" not in result
     assert "{nestedVal}" not in result
-
+    
     # Test normal string interpolation
     normal_string = "Hello {name}, welcome to {place}!"
     result = task.interpolate_only(
-        input_string=normal_string, inputs={"name": "John", "place": "CrewAI"}
+        input_string=normal_string,
+        inputs={"name": "John", "place": "CrewAI"}
     )
     assert result == "Hello John, welcome to CrewAI!"
-
+    
     # Test empty string
-    result = task.interpolate_only(input_string="", inputs={"unused": "value"})
+    result = task.interpolate_only(
+        input_string="",
+        inputs={"unused": "value"}
+    )
     assert result == ""
-
+    
     # Test string with no placeholders
     no_placeholders = "Hello, this is a test"
     result = task.interpolate_only(
-        input_string=no_placeholders, inputs={"unused": "value"}
+        input_string=no_placeholders,
+        inputs={"unused": "value"}
     )
     assert result == no_placeholders
 
@@ -874,91 +880,56 @@ def test_key():
 def test_output_file_validation():
     """Test output file path validation."""
     # Valid paths
-    assert (
-        Task(
-            description="Test task",
-            expected_output="Test output",
-            output_file="output.txt",
-        ).output_file
-        == "output.txt"
-    )
-    assert (
-        Task(
-            description="Test task",
-            expected_output="Test output",
-            output_file="/tmp/output.txt",
-        ).output_file
-        == "tmp/output.txt"
-    )
-    assert (
-        Task(
-            description="Test task",
-            expected_output="Test output",
-            output_file="{dir}/output_{date}.txt",
-        ).output_file
-        == "{dir}/output_{date}.txt"
-    )
-
+    assert Task(
+        description="Test task",
+        expected_output="Test output",
+        output_file="output.txt"
+    ).output_file == "output.txt"
+    assert Task(
+        description="Test task",
+        expected_output="Test output",
+        output_file="/tmp/output.txt"
+    ).output_file == "tmp/output.txt"
+    assert Task(
+        description="Test task",
+        expected_output="Test output",
+        output_file="{dir}/output_{date}.txt"
+    ).output_file == "{dir}/output_{date}.txt"
+    
     # Invalid paths
     with pytest.raises(ValueError, match="Path traversal"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="../output.txt",
+            output_file="../output.txt"
         )
     with pytest.raises(ValueError, match="Path traversal"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="folder/../output.txt",
+            output_file="folder/../output.txt"
         )
     with pytest.raises(ValueError, match="Shell special characters"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="output.txt | rm -rf /",
+            output_file="output.txt | rm -rf /"
         )
     with pytest.raises(ValueError, match="Shell expansion"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="~/output.txt",
+            output_file="~/output.txt"
         )
     with pytest.raises(ValueError, match="Shell expansion"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="$HOME/output.txt",
+            output_file="$HOME/output.txt"
         )
     with pytest.raises(ValueError, match="Invalid template variable"):
         Task(
             description="Test task",
             expected_output="Test output",
-            output_file="{invalid-name}/output.txt",
+            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()

tests/test_manager_llm_delegation.py

@@ -8,49 +8,48 @@ from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 
 class TestAgentTool(BaseAgentTool):
     """Concrete implementation of BaseAgentTool for testing."""
-
     def _run(self, *args, **kwargs):
         """Implement required _run method."""
         return "Test response"
 
-
-@pytest.mark.parametrize(
-    "role_name,should_match",
-    [
-        ("Futel Official Infopoint", True),  # exact match
-        ('  "Futel Official Infopoint"  ', True),  # extra quotes and spaces
-        ("Futel Official Infopoint\n", True),  # trailing newline
-        ('"Futel Official Infopoint"', True),  # embedded quotes
-        (" FUTEL\nOFFICIAL   INFOPOINT ", True),  # multiple whitespace and newline
-        ("futel official infopoint", True),  # lowercase
-        ("FUTEL OFFICIAL INFOPOINT", True),  # uppercase
-        ("Non Existent Agent", False),  # non-existent agent
-        (None, False),  # None agent name
-    ],
-)
+@pytest.mark.parametrize("role_name,should_match", [
+    ('Futel Official Infopoint', True),                    # exact match
+    ('  "Futel Official Infopoint"  ', True),             # extra quotes and spaces
+    ('Futel Official Infopoint\n', True),                 # trailing newline
+    ('"Futel Official Infopoint"', True),                 # embedded quotes
+    (' FUTEL\nOFFICIAL   INFOPOINT ', True),             # multiple whitespace and newline
+    ('futel official infopoint', True),                   # lowercase
+    ('FUTEL OFFICIAL INFOPOINT', True),                   # uppercase
+    ('Non Existent Agent', False),                        # non-existent agent
+    (None, False),                                        # None agent name
+])
 def test_agent_tool_role_matching(role_name, should_match):
     """Test that agent tools can match roles regardless of case, whitespace, and special characters."""
     # Create test agent
     test_agent = Agent(
-        role="Futel Official Infopoint",
-        goal="Answer questions about Futel",
-        backstory="Futel Football Club info",
-        allow_delegation=False,
+        role='Futel Official Infopoint',
+        goal='Answer questions about Futel',
+        backstory='Futel Football Club info',
+        allow_delegation=False
     )
 
     # Create test agent tool
     agent_tool = TestAgentTool(
-        name="test_tool", description="Test tool", agents=[test_agent]
+        name="test_tool",
+        description="Test tool",
+        agents=[test_agent]
     )
 
     # Test role matching
-    result = agent_tool._execute(agent_name=role_name, task="Test task", context=None)
+    result = agent_tool._execute(
+        agent_name=role_name,
+        task='Test task',
+        context=None
+    )
 
     if should_match:
-        assert (
-            "coworker mentioned not found" not in result.lower()
-        ), f"Should find agent with role name: {role_name}"
+        assert "coworker mentioned not found" not in result.lower(), \
+            f"Should find agent with role name: {role_name}"
     else:
-        assert (
-            "coworker mentioned not found" in result.lower()
-        ), f"Should not find agent with role name: {role_name}"
+        assert "coworker mentioned not found" in result.lower(), \
+            f"Should not find agent with role name: {role_name}"

tests/test_task_guardrails.py

@@ -15,7 +15,10 @@ def test_task_without_guardrail():
     agent.execute_task.return_value = "test result"
     agent.crew = None
 
-    task = Task(description="Test task", expected_output="Output")
+    task = Task(
+        description="Test task",
+        expected_output="Output"
+    )
 
     result = task.execute_sync(agent=agent)
     assert isinstance(result, TaskOutput)
@@ -24,7 +27,6 @@ def test_task_without_guardrail():
 
 def test_task_with_successful_guardrail():
     """Test that successful guardrail validation passes transformed result."""
-
     def guardrail(result: TaskOutput):
         return (True, result.raw.upper())
 
@@ -33,7 +35,11 @@ def test_task_with_successful_guardrail():
     agent.execute_task.return_value = "test result"
     agent.crew = None
 
-    task = Task(description="Test task", expected_output="Output", guardrail=guardrail)
+    task = Task(
+        description="Test task",
+        expected_output="Output",
+        guardrail=guardrail
+    )
 
     result = task.execute_sync(agent=agent)
     assert isinstance(result, TaskOutput)
@@ -42,20 +48,22 @@ def test_task_with_successful_guardrail():
 
 def test_task_with_failing_guardrail():
     """Test that failing guardrail triggers retry with error context."""
-
     def guardrail(result: TaskOutput):
         return (False, "Invalid format")
 
     agent = Mock()
     agent.role = "test_agent"
-    agent.execute_task.side_effect = ["bad result", "good result"]
+    agent.execute_task.side_effect = [
+        "bad result",
+        "good result"
+    ]
     agent.crew = None
 
     task = Task(
         description="Test task",
         expected_output="Output",
         guardrail=guardrail,
-        max_retries=1,
+        max_retries=1
     )
 
     # First execution fails guardrail, second succeeds
@@ -69,7 +77,6 @@ def test_task_with_failing_guardrail():
 
 def test_task_with_guardrail_retries():
     """Test that guardrail respects max_retries configuration."""
-
     def guardrail(result: TaskOutput):
         return (False, "Invalid format")
 
@@ -82,7 +89,7 @@ def test_task_with_guardrail_retries():
         description="Test task",
         expected_output="Output",
         guardrail=guardrail,
-        max_retries=2,
+        max_retries=2
     )
 
     with pytest.raises(Exception) as exc_info:
@@ -95,7 +102,6 @@ def test_task_with_guardrail_retries():
 
 def test_guardrail_error_in_context():
     """Test that guardrail error is passed in context for retry."""
-
     def guardrail(result: TaskOutput):
         return (False, "Expected JSON, got string")
 
@@ -107,12 +113,11 @@ def test_guardrail_error_in_context():
         description="Test task",
         expected_output="Output",
         guardrail=guardrail,
-        max_retries=1,
+        max_retries=1
     )
 
     # Mock execute_task to succeed on second attempt
     first_call = True
-
     def execute_task(task, context, tools):
         nonlocal first_call
         if first_call:

uv.lock

@@ -1,42 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
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+    "python_full_version >= '3.12.4' and platform_system == 'Darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')",
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 [[package]]
@@ -66,7 +42,7 @@ wheels = [
 
 [[package]]
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 dependencies = [
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