feat: centralize default memory path logic & add path validation tests

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

README.md

@@ -4,7 +4,7 @@
 
 # **CrewAI**
 
-🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
+🤖 **CrewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
 
 <h3>
 
@@ -22,17 +22,13 @@
 - [Why CrewAI?](#why-crewai)
 - [Getting Started](#getting-started)
 - [Key Features](#key-features)
-- [Understanding Flows and Crews](#understanding-flows-and-crews)
-- [CrewAI vs LangGraph](#how-crewai-compares)
 - [Examples](#examples)
   - [Quick Tutorial](#quick-tutorial)
   - [Write Job Descriptions](#write-job-descriptions)
   - [Trip Planner](#trip-planner)
   - [Stock Analysis](#stock-analysis)
-  - [Using Crews and Flows Together](#using-crews-and-flows-together)
 - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
 - [How CrewAI Compares](#how-crewai-compares)
-- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)
 - [Contribution](#contribution)
 - [Telemetry](#telemetry)
 - [License](#license)
@@ -40,40 +36,10 @@
 ## Why CrewAI?
 
 The power of AI collaboration has too much to offer.
-CrewAI is a standalone framework, built from the ground up without dependencies on Langchain or other agent frameworks. It's designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
+CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
 
 ## Getting Started
 
-### Learning Resources
-
-Learn CrewAI through our comprehensive courses:
-- [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems
-- [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations
-
-### Understanding Flows and Crews
-
-CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
-
-1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
-   - Natural, autonomous decision-making between agents
-   - Dynamic task delegation and collaboration
-   - Specialized roles with defined goals and expertise
-   - Flexible problem-solving approaches
-
-2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
-   - Fine-grained control over execution paths for real-world scenarios
-   - Secure, consistent state management between tasks
-   - Clean integration of AI agents with production Python code
-   - Conditional branching for complex business logic
-
-The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
-- Build complex, production-grade applications
-- Balance autonomy with precise control
-- Handle sophisticated real-world scenarios
-- Maintain clean, maintainable code structure
-
-### Getting Started with Installation
-
 To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
@@ -298,16 +264,13 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-**Note**: CrewAI is a standalone framework built from the ground up, without dependencies on Langchain or other agent frameworks.
-
-- **Deep Customization**: Build sophisticated agents with full control over the system - from overriding inner prompts to accessing low-level APIs. Customize roles, goals, tools, and behaviors while maintaining clean abstractions.
-- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enabling complex problem-solving in real-world scenarios.
-- **Flexible Task Management**: Define and customize tasks with granular control, from simple operations to complex multi-step processes.
-- **Production-Grade Architecture**: Support for both high-level abstractions and low-level customization, with robust error handling and state management.
-- **Predictable Results**: Ensure consistent, accurate outputs through programmatic guardrails, agent training capabilities, and flow-based execution control. See our [documentation on guardrails](https://docs.crewai.com/how-to/guardrails/) for implementation details.
-- **Model Flexibility**: Run your crew using OpenAI or open source models with production-ready integrations. See [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) for detailed configuration options.
-- **Event-Driven Flows**: Build complex, real-world workflows with precise control over execution paths, state management, and conditional logic.
-- **Process Orchestration**: Achieve any workflow pattern through flows - from simple sequential and hierarchical processes to complex, custom orchestration patterns with conditional branching and parallel execution.
+- **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
+- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
+- **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
+- **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
+- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
+- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
+- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
 
 ![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")
 
@@ -342,98 +305,6 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
-### Using Crews and Flows Together
-
-CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
-
-```python
-from crewai.flow.flow import Flow, listen, start, router
-from crewai import Crew, Agent, Task
-from pydantic import BaseModel
-
-# Define structured state for precise control
-class MarketState(BaseModel):
-    sentiment: str = "neutral"
-    confidence: float = 0.0
-    recommendations: list = []
-
-class AdvancedAnalysisFlow(Flow[MarketState]):
-    @start()
-    def fetch_market_data(self):
-        # Demonstrate low-level control with structured state
-        self.state.sentiment = "analyzing"
-        return {"sector": "tech", "timeframe": "1W"}  # These parameters match the task description template
-
-    @listen(fetch_market_data)
-    def analyze_with_crew(self, market_data):
-        # Show crew agency through specialized roles
-        analyst = Agent(
-            role="Senior Market Analyst",
-            goal="Conduct deep market analysis with expert insight",
-            backstory="You're a veteran analyst known for identifying subtle market patterns"
-        )
-        researcher = Agent(
-            role="Data Researcher",
-            goal="Gather and validate supporting market data",
-            backstory="You excel at finding and correlating multiple data sources"
-        )
-        
-        analysis_task = Task(
-            description="Analyze {sector} sector data for the past {timeframe}",
-            expected_output="Detailed market analysis with confidence score",
-            agent=analyst
-        )
-        research_task = Task(
-            description="Find supporting data to validate the analysis",
-            expected_output="Corroborating evidence and potential contradictions",
-            agent=researcher
-        )
-        
-        # Demonstrate crew autonomy
-        analysis_crew = Crew(
-            agents=[analyst, researcher],
-            tasks=[analysis_task, research_task],
-            process=Process.sequential,
-            verbose=True
-        )
-        return analysis_crew.kickoff(inputs=market_data)  # Pass market_data as named inputs
-
-    @router(analyze_with_crew)
-    def determine_next_steps(self):
-        # Show flow control with conditional routing
-        if self.state.confidence > 0.8:
-            return "high_confidence"
-        elif self.state.confidence > 0.5:
-            return "medium_confidence"
-        return "low_confidence"
-
-    @listen("high_confidence")
-    def execute_strategy(self):
-        # Demonstrate complex decision making
-        strategy_crew = Crew(
-            agents=[
-                Agent(role="Strategy Expert",
-                      goal="Develop optimal market strategy")
-            ],
-            tasks=[
-                Task(description="Create detailed strategy based on analysis",
-                     expected_output="Step-by-step action plan")
-            ]
-        )
-        return strategy_crew.kickoff()
-
-    @listen("medium_confidence", "low_confidence")
-    def request_additional_analysis(self):
-        self.state.recommendations.append("Gather more data")
-        return "Additional analysis required"
-```
-
-This example demonstrates how to:
-1. Use Python code for basic data operations
-2. Create and execute Crews as steps in your workflow
-3. Use Flow decorators to manage the sequence of operations
-4. Implement conditional branching based on Crew results
-
 ## Connecting Your Crew to a Model
 
 CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
@@ -442,13 +313,9 @@ Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 
 ## How CrewAI Compares
 
-**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.
+**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
 
-- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.
-
-*P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
-
-- **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
+- **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
@@ -573,8 +440,5 @@ A: CrewAI uses anonymous telemetry to collect usage data for improvement purpose
 ### Q: Where can I find examples of CrewAI in action?
 A: You can find various real-life examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
 
-### Q: What is the difference between Crews and Flows?
-A: Crews and Flows serve different but complementary purposes in CrewAI. Crews are teams of AI agents working together to accomplish specific tasks through role-based collaboration, delivering accurate and predictable results. Flows, on the other hand, are event-driven workflows that can orchestrate both Crews and regular Python code, allowing you to build complex automation pipelines with secure state management and conditional execution paths.
-
 ### Q: How can I contribute to CrewAI?
 A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

docs/concepts/knowledge.mdx

@@ -171,58 +171,6 @@ crewai reset-memories --knowledge
 
 This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
 
-## Agent-Specific Knowledge
-
-While knowledge can be provided at the crew level using `crew.knowledge_sources`, individual agents can also have their own knowledge sources using the `knowledge_sources` parameter:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-# Create agent-specific knowledge about a product
-product_specs = StringKnowledgeSource(
-    content="""The XPS 13 laptop features:
-    - 13.4-inch 4K display
-    - Intel Core i7 processor
-    - 16GB RAM
-    - 512GB SSD storage
-    - 12-hour battery life""",
-    metadata={"category": "product_specs"}
-)
-
-# Create a support agent with product knowledge
-support_agent = Agent(
-    role="Technical Support Specialist",
-    goal="Provide accurate product information and support.",
-    backstory="You are an expert on our laptop products and specifications.",
-    knowledge_sources=[product_specs]  # Agent-specific knowledge
-)
-
-# Create a task that requires product knowledge
-support_task = Task(
-    description="Answer this customer question: {question}",
-    agent=support_agent
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[support_agent],
-    tasks=[support_task]
-)
-
-# Get answer about the laptop's specifications
-result = crew.kickoff(
-    inputs={"question": "What is the storage capacity of the XPS 13?"}
-)
-```
-
-<Info>
-  Benefits of agent-specific knowledge:
-  - Give agents specialized information for their roles
-  - Maintain separation of concerns between agents
-  - Combine with crew-level knowledge for layered information access
-</Info>
-
 ## Custom Knowledge Sources
 
 CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.

src/crewai/agent.py

@@ -294,14 +294,7 @@ class Agent(BaseAgent):
             )
 
         if self.crew and self.crew.memory:
-            contextual_memory = ContextualMemory(
-                self.crew.memory_config,
-                self.crew._short_term_memory,
-                self.crew._long_term_memory,
-                self.crew._entity_memory,
-                self.crew._user_memory,
-            )
-            memory = contextual_memory.build_context_for_task(task, context)
+            memory = self.crew.contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 

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
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
 
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""

src/crewai/agents/crew_agent_executor.py

@@ -358,9 +358,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration][
-                        "improved_output"
-                    ] = result.output
+                    training_data[agent_id][train_iteration]["improved_output"] = (
+                        result.output
+                    )
                     training_handler.save(training_data)
                 else:
                     self._printer.print(

src/crewai/cli/tools/main.py

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

src/crewai/crew.py

@@ -25,6 +25,7 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM
+from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -278,6 +279,13 @@ class Crew(BaseModel):
                 )
             else:
                 self._user_memory = None
+            self.contextual_memory = ContextualMemory(
+                memory_config=self.memory_config,
+                stm=self._short_term_memory,
+                ltm=self._long_term_memory,
+                em=self._entity_memory,
+                um=self._user_memory,
+            )
         return self
 
     @model_validator(mode="after")

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -26,10 +26,9 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     safe_file_paths: List[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
-    def validate_file_path(cls, v, info):
+    def validate_file_path(cls, v, values):
         """Validate that at least one of file_path or file_paths is provided."""
-        # Single check if both are None, O(1) instead of nested conditions
-        if v is None and info.data.get("file_path" if info.field_name == "file_paths" else "file_paths") is None:
+        if v is None and ("file_path" not in values or values.get("file_path") is None):
             raise ValueError("Either file_path or file_paths must be provided")
         return v
 

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+from crewai.task import Task
 
 from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 
@@ -10,7 +11,7 @@ class ContextualMemory:
         stm: ShortTermMemory,
         ltm: LongTermMemory,
         em: EntityMemory,
-        um: UserMemory,
+        um: Optional[UserMemory],
     ):
         if memory_config is not None:
             self.memory_provider = memory_config.get("provider")
@@ -21,7 +22,7 @@ class ContextualMemory:
         self.em = em
         self.um = um
 
-    def build_context_for_task(self, task, context) -> str:
+    def build_context_for_task(self, task: Task, context: str) -> str:
         """
         Automatically builds a minimal, highly relevant set of contextual information
         for a given task.
@@ -39,7 +40,7 @@ class ContextualMemory:
             context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
 
-    def _fetch_stm_context(self, query) -> str:
+    def _fetch_stm_context(self, query: str) -> str:
         """
         Fetches recent relevant insights from STM related to the task's description and expected_output,
         formatted as bullet points.
@@ -53,7 +54,7 @@ class ContextualMemory:
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
-    def _fetch_ltm_context(self, task) -> Optional[str]:
+    def _fetch_ltm_context(self, task: str) -> Optional[str]:
         """
         Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,
         formatted as bullet points.
@@ -72,7 +73,7 @@ class ContextualMemory:
 
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
-    def _fetch_entity_context(self, query) -> str:
+    def _fetch_entity_context(self, query: str) -> str:
         """
         Fetches relevant entity information from Entity Memory related to the task's description and expected_output,
         formatted as bullet points.
@@ -94,6 +95,8 @@ class ContextualMemory:
         Returns:
             str: Formatted user memories as bullet points, or an empty string if none found.
         """
+        if not self.um:
+            return ""
         user_memories = self.um.search(query)
         if not user_memories:
             return ""

src/crewai/memory/entity/entity_memory.py

@@ -11,7 +11,7 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/long_term/long_term_memory.py

@@ -15,8 +15,17 @@ class LongTermMemory(Memory):
     """
 
     def __init__(self, storage=None, path=None):
+        """Initialize long term memory.
+
+        Args:
+            storage: Optional custom storage instance
+            path: Optional custom path for storage location
+
+        Note:
+            If both storage and path are provided, storage takes precedence
+        """
         if not storage:
-            storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
+            storage = LTMSQLiteStorage(storage_path=path) if path else LTMSQLiteStorage()
         super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"

src/crewai/memory/short_term/short_term_memory.py

@@ -15,7 +15,7 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/storage/base_rag_storage.py

@@ -1,5 +1,11 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from pathlib import Path
+import os
+from typing import Any, Dict, List, Optional, TypeVar
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from crewai.utilities.paths import get_default_storage_path
 
 
 class BaseRAGStorage(ABC):
@@ -12,17 +18,46 @@ class BaseRAGStorage(ABC):
     def __init__(
         self,
         type: str,
+        storage_path: Optional[Path] = None,
         allow_reset: bool = True,
         embedder_config: Optional[Any] = None,
         crew: Any = None,
-    ):
+    ) -> None:
+        """Initialize the BaseRAGStorage.
+
+        Args:
+            type: Type of storage being used
+            storage_path: Optional custom path for storage location
+            allow_reset: Whether storage can be reset
+            embedder_config: Optional configuration for the embedder
+            crew: Optional crew instance this storage belongs to
+        
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
         self.type = type
+        self.storage_path = storage_path if storage_path else get_default_storage_path('rag')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self.allow_reset = allow_reset
         self.embedder_config = embedder_config
         self.crew = crew
         self.agents = self._initialize_agents()
 
     def _initialize_agents(self) -> str:
+        """Initialize agent identifiers for storage.
+        
+        Returns:
+            str: Underscore-joined string of sanitized agent role names
+        """
         if self.crew:
             return "_".join(
                 [self._sanitize_role(agent.role) for agent in self.crew.agents]
@@ -31,12 +66,27 @@ class BaseRAGStorage(ABC):
 
     @abstractmethod
     def _sanitize_role(self, role: str) -> str:
-        """Sanitizes agent roles to ensure valid directory names."""
+        """Sanitizes agent roles to ensure valid directory names.
+        
+        Args:
+            role: The agent role name to sanitize
+            
+        Returns:
+            str: Sanitized role name safe for use in paths
+        """
         pass
 
     @abstractmethod
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """Save a value with metadata to the storage."""
+        """Save a value with metadata to the storage.
+        
+        Args:
+            value: The value to store
+            metadata: Additional metadata to store with the value
+            
+        Raises:
+            OSError: If there is an error writing to storage
+        """
         pass
 
     @abstractmethod
@@ -46,25 +96,55 @@ class BaseRAGStorage(ABC):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Any]:
-        """Search for entries in the storage."""
+    ) -> List[Dict[str, Any]]:
+        """Search for entries in the storage.
+        
+        Args:
+            query: The search query string
+            limit: Maximum number of results to return
+            filter: Optional filter criteria
+            score_threshold: Minimum similarity score threshold
+            
+        Returns:
+            List[Dict[str, Any]]: List of matching entries with their metadata
+        """
         pass
 
     @abstractmethod
     def reset(self) -> None:
-        """Reset the storage."""
+        """Reset the storage.
+        
+        Raises:
+            OSError: If there is an error clearing storage
+            PermissionError: If reset is not allowed
+        """
         pass
 
     @abstractmethod
     def _generate_embedding(
         self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> Any:
-        """Generate an embedding for the given text and metadata."""
+    ) -> List[float]:
+        """Generate an embedding for the given text and metadata.
+        
+        Args:
+            text: Text to generate embedding for
+            metadata: Optional metadata to include in embedding
+            
+        Returns:
+            List[float]: Vector embedding of the text
+            
+        Raises:
+            ValueError: If text is empty or invalid
+        """
         pass
 
     @abstractmethod
-    def _initialize_app(self):
-        """Initialize the vector db."""
+    def _initialize_app(self) -> None:
+        """Initialize the vector db.
+        
+        Raises:
+            OSError: If vector db initialization fails
+        """
         pass
 
     def setup_config(self, config: Dict[str, Any]):

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,11 +1,13 @@
 import json
+import os
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from crewai.task import Task
 from crewai.utilities import Printer
 from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-from crewai.utilities.paths import db_storage_path
+from crewai.utilities.paths import get_default_storage_path
 
 
 class KickoffTaskOutputsSQLiteStorage:
@@ -13,10 +15,26 @@ class KickoffTaskOutputsSQLiteStorage:
     An updated SQLite storage class for kickoff task outputs storage.
     """
 
-    def __init__(
-        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
-    ) -> None:
-        self.db_path = db_path
+    def __init__(self, storage_path: Optional[Path] = None) -> None:
+        """Initialize kickoff task outputs storage.
+        
+        Args:
+            storage_path: Optional custom path for storage location
+            
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
+        self.storage_path = storage_path if storage_path else get_default_storage_path('kickoff')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -25,7 +43,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -55,9 +73,21 @@ class KickoffTaskOutputsSQLiteStorage:
         task_index: int,
         was_replayed: bool = False,
         inputs: Dict[str, Any] = {},
-    ):
+    ) -> None:
+        """Add a task output to storage.
+        
+        Args:
+            task: The task whose output is being stored
+            output: The output data from the task
+            task_index: Index of this task in the sequence
+            was_replayed: Whether this was from a replay
+            inputs: Optional input data that led to this output
+            
+        Raises:
+            sqlite3.Error: If there is an error saving to database
+        """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -90,7 +120,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
 
                 fields = []
@@ -119,7 +149,7 @@ class KickoffTaskOutputsSQLiteStorage:
 
     def load(self) -> Optional[List[Dict[str, Any]]]:
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("""
                 SELECT *
@@ -155,7 +185,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Deletes all rows from the latest_kickoff_task_outputs table.
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")
                 conn.commit()

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,9 +1,11 @@
 import json
+import os
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.utilities import Printer
-from crewai.utilities.paths import db_storage_path
+from crewai.utilities.paths import get_default_storage_path
 
 
 class LTMSQLiteStorage:
@@ -11,10 +13,26 @@ class LTMSQLiteStorage:
     An updated SQLite storage class for LTM data storage.
     """
 
-    def __init__(
-        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
-    ) -> None:
-        self.db_path = db_path
+    def __init__(self, storage_path: Optional[Path] = None) -> None:
+        """Initialize LTM SQLite storage.
+        
+        Args:
+            storage_path: Optional custom path for storage location
+            
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
+        self.storage_path = storage_path if storage_path else get_default_storage_path('ltm')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -23,7 +41,7 @@ class LTMSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -51,9 +69,20 @@ class LTMSQLiteStorage:
         datetime: str,
         score: Union[int, float],
     ) -> None:
+        """Save a memory entry to long-term memory.
+        
+        Args:
+            task_description: Description of the task this memory relates to
+            metadata: Additional data to store with the memory
+            datetime: Timestamp for when this memory was created
+            score: Relevance score for this memory (higher is more relevant)
+            
+        Raises:
+            sqlite3.Error: If there is an error saving to the database
+        """
         """Saves data to the LTM table with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -74,7 +103,7 @@ class LTMSQLiteStorage:
     ) -> Optional[List[Dict[str, Any]]]:
         """Queries the LTM table by task description with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     f"""
@@ -109,7 +138,7 @@ class LTMSQLiteStorage:
     ) -> None:
         """Resets the LTM table with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM long_term_memories")
                 conn.commit()

src/crewai/memory/storage/mem0_storage.py

@@ -19,7 +19,7 @@ class Mem0Storage(Storage):
 
         self.memory_type = type
         self.crew = crew
-        self.memory_config = crew.memory_config
+        self.memory_config = crew.memory_config if crew else None
 
         # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
         user_id = self._get_user_id()
@@ -27,9 +27,10 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
-            "MEM0_API_KEY"
-        )
+        if self.memory_config and self.memory_config.get("config"):
+            mem0_api_key = self.memory_config.get("config").get("api_key")
+        else:
+            mem0_api_key = os.getenv("MEM0_API_KEY")
         self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:

src/crewai/memory/storage/rag_storage.py

@@ -11,7 +11,6 @@ from chromadb.api import ClientAPI
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
-from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -40,9 +39,15 @@ class RAGStorage(BaseRAGStorage):
     app: ClientAPI | None = None
 
     def __init__(
-        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
+        self,
+        type,
+        storage_path=None,
+        allow_reset=True,
+        embedder_config=None,
+        crew=None,
+        path=None,
     ):
-        super().__init__(type, allow_reset, embedder_config, crew)
+        super().__init__(type, storage_path, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
@@ -90,7 +95,7 @@ class RAGStorage(BaseRAGStorage):
         """
         Ensures file name does not exceed max allowed by OS
         """
-        base_path = f"{db_storage_path()}/{type}"
+        base_path = f"{self.storage_path}/{type}"
 
         if len(file_name) > MAX_FILE_NAME_LENGTH:
             logging.warning(
@@ -152,7 +157,7 @@ class RAGStorage(BaseRAGStorage):
         try:
             if self.app:
                 self.app.reset()
-                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                shutil.rmtree(f"{self.storage_path}/{self.type}")
                 self.app = None
                 self.collection = None
         except Exception as e:

src/crewai/project/annotations.py

@@ -66,7 +66,6 @@ def cache_handler(func):
 
 
 def crew(func) -> Callable[..., Crew]:
-
     @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:
         instantiated_tasks = []

src/crewai/project/crew_base.py

@@ -216,5 +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

@@ -373,7 +373,9 @@ 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)
 

src/crewai/utilities/embedding_configurator.py

@@ -27,7 +27,7 @@ class EmbeddingConfigurator:
         if embedder_config is None:
             return self._create_default_embedding_function()
 
-        provider = embedder_config.get("provider")
+        provider = embedder_config.get("provider", "")
         config = embedder_config.get("config", {})
         model_name = config.get("model")
 
@@ -38,12 +38,13 @@ class EmbeddingConfigurator:
             except Exception as e:
                 raise ValueError(f"Invalid custom embedding function: {str(e)}")
 
-        if provider not in self.embedding_functions:
+        embedding_function = self.embedding_functions.get(provider, None)
+        if not embedding_function:
             raise Exception(
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
 
-        return self.embedding_functions[provider](config, model_name)
+        return embedding_function(config, model_name)
 
     @staticmethod
     def _create_default_embedding_function():

src/crewai/utilities/paths.py

@@ -22,3 +22,26 @@ def get_project_directory_name():
         cwd = Path.cwd()
         project_directory_name = cwd.name
         return project_directory_name
+
+def get_default_storage_path(storage_type: str) -> Path:
+    """Returns the default storage path for a given storage type.
+    
+    Args:
+        storage_type: Type of storage ('ltm', 'kickoff', 'rag')
+        
+    Returns:
+        Path: Default storage path for the specified type
+        
+    Raises:
+        ValueError: If storage_type is not recognized
+    """
+    base_path = db_storage_path()
+    
+    if storage_type == 'ltm':
+        return base_path / 'latest_long_term_memories.db'
+    elif storage_type == 'kickoff':
+        return base_path / 'latest_kickoff_task_outputs.db'
+    elif storage_type == 'rag':
+        return base_path
+    else:
+        raise ValueError(f"Unknown storage type: {storage_type}")

tests/cli/tools/test_main.py

@@ -28,9 +28,10 @@ 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()
 
@@ -82,7 +83,7 @@ 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/knowledge/knowledge_test.py

@@ -584,28 +584,3 @@ def test_docling_source_with_local_file():
     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
-    pdf_path = current_dir / "crewai_quickstart.pdf"
-
-    # Test valid single file_path
-    source = PDFKnowledgeSource(file_path=pdf_path)
-    assert source.safe_file_paths == [pdf_path]
-
-    # Test valid file_paths list
-    source = PDFKnowledgeSource(file_paths=[pdf_path])
-    assert source.safe_file_paths == [pdf_path]
-
-    # Test both file_path and file_paths provided (should use file_paths)
-    source = PDFKnowledgeSource(file_path=pdf_path, file_paths=[pdf_path])
-    assert source.safe_file_paths == [pdf_path]
-
-    # 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"
-    ):
-        PDFKnowledgeSource()

tests/memory/test_storage_paths.py

@@ -0,0 +1,83 @@
+import os
+import tempfile
+from pathlib import Path
+import pytest
+from unittest.mock import patch
+
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage.kickoff_task_outputs_storage import KickoffTaskOutputsSQLiteStorage
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import get_default_storage_path
+
+class MockRAGStorage(BaseRAGStorage):
+    """Mock implementation of BaseRAGStorage for testing."""
+    def _sanitize_role(self, role: str) -> str:
+        return role.lower()
+    
+    def save(self, value, metadata):
+        pass
+    
+    def search(self, query, limit=3, filter=None, score_threshold=0.35):
+        return []
+    
+    def reset(self):
+        pass
+    
+    def _generate_embedding(self, text, metadata=None):
+        return []
+    
+    def _initialize_app(self):
+        pass
+
+def test_default_storage_paths():
+    """Test that default storage paths are created correctly."""
+    ltm_path = get_default_storage_path('ltm')
+    kickoff_path = get_default_storage_path('kickoff')
+    rag_path = get_default_storage_path('rag')
+    
+    assert str(ltm_path).endswith('latest_long_term_memories.db')
+    assert str(kickoff_path).endswith('latest_kickoff_task_outputs.db')
+    assert isinstance(rag_path, Path)
+
+def test_custom_storage_paths():
+    """Test that custom storage paths are respected."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        custom_path = Path(temp_dir) / 'custom.db'
+        
+        ltm = LTMSQLiteStorage(storage_path=custom_path)
+        assert ltm.storage_path == custom_path
+        
+        kickoff = KickoffTaskOutputsSQLiteStorage(storage_path=custom_path)
+        assert kickoff.storage_path == custom_path
+        
+        rag = MockRAGStorage('test', storage_path=custom_path)
+        assert rag.storage_path == custom_path
+
+def test_directory_creation():
+    """Test that storage directories are created automatically."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        test_dir = Path(temp_dir) / 'test_storage'
+        storage_path = test_dir / 'test.db'
+        
+        assert not test_dir.exists()
+        LTMSQLiteStorage(storage_path=storage_path)
+        assert test_dir.exists()
+
+def test_permission_error():
+    """Test that permission errors are handled correctly."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        test_dir = Path(temp_dir) / 'readonly'
+        test_dir.mkdir()
+        os.chmod(test_dir, 0o444)  # Read-only
+        
+        storage_path = test_dir / 'test.db'
+        with pytest.raises((PermissionError, OSError)) as exc_info:
+            LTMSQLiteStorage(storage_path=storage_path)
+        # Verify that the error message mentions permission
+        assert "permission" in str(exc_info.value).lower()
+
+def test_invalid_path():
+    """Test that invalid paths raise appropriate errors."""
+    with pytest.raises(OSError):
+        # Try to create storage in a non-existent root directory
+        LTMSQLiteStorage(storage_path=Path('/nonexistent/dir/test.db'))

uv.lock

@@ -1,10 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
-    "python_full_version >= '3.12.4'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -300,7 +308,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "os_name == 'nt'" },
+    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -535,7 +543,7 @@ name = "click"
 version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
 wheels = [
@@ -642,7 +650,6 @@ tools = [
 [package.dev-dependencies]
 dev = [
     { name = "cairosvg" },
-    { name = "crewai-tools" },
     { name = "mkdocs" },
     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
@@ -696,7 +703,6 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },
@@ -2462,7 +2468,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "platform_system == 'Windows'" },
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 dependencies = [
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-    { name = "pywin32", marker = "platform_system == 'Windows'" },
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-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
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@@ -3480,7 +3486,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
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-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 dependencies = [
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