Update pyproject.toml and uv.lock to drop crewai-tools as a default requirement in crewai repo

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,6 +65,7 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
+        - '3.13'
     validations:
       required: true
   - type: input
@@ -112,4 +113,4 @@ body:
       label: Additional context
       description: Add any other context about the problem here.
     validations:
-      required: true
+      required: true

.github/workflows/linter.yml

@@ -13,4 +13,4 @@ jobs:
           pip install ruff
 
       - name: Run Ruff Linter
-        run: ruff check
+        run: ruff check --exclude "templates","__init__.py"

.github/workflows/stale.yml

@@ -1,10 +1,5 @@
 name: Mark stale issues and pull requests
 
-permissions:
-  contents: write
-  issues: write
-  pull-requests: write
-
 on:
   schedule:
     - cron: '10 12 * * *'
@@ -13,6 +8,9 @@ on:
 jobs:
   stale:
     runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
     steps:
     - uses: actions/stale@v9
       with:

.github/workflows/tests.yml

@@ -23,7 +23,7 @@ jobs:
 
 
       - name: Set up Python
-        run: uv python install 3.12.8
+        run: uv python install 3.11.9
 
       - name: Install the project
         run: uv sync --dev --all-extras

.pre-commit-config.yaml

@@ -1,7 +1,9 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+    rev: v0.4.4
     hooks:
       - id: ruff
         args: ["--fix"]
+        exclude: "templates"
       - id: ruff-format
+        exclude: "templates"

.ruff.toml

@@ -1,9 +0,0 @@
-exclude = [
-    "templates",
-    "__init__.py",
-]
-
-[lint]
-select = [
-    "I", # isort rules
-]

README.md

@@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 

docs/concepts/crews.mdx

@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
 | **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
+| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
@@ -40,155 +41,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
 </Tip>
 
-## Creating Crews
-
-There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
-
-After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
-
-#### Example Crew Class with Decorators
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-
-
-@CrewBase
-class YourCrewName:
-    """Description of your crew"""
-
-    # Paths to your YAML configuration files
-    # To see an example agent and task defined in YAML, checkout the following:
-    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
-
-    @before_kickoff
-    def prepare_inputs(self, inputs):
-        # Modify inputs before the crew starts
-        inputs['additional_data'] = "Some extra information"
-        return inputs
-
-    @after_kickoff
-    def process_output(self, output):
-        # Modify output after the crew finishes
-        output.raw += "\nProcessed after kickoff."
-        return output
-
-    @agent
-    def agent_one(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_one'],
-            verbose=True
-        )
-
-    @agent
-    def agent_two(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_two'],
-            verbose=True
-        )
-
-    @task
-    def task_one(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_one']
-        )
-
-    @task
-    def task_two(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_two']
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Note>
-Tasks will be executed in the order they are defined.
-</Note>
-
-The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
-
-#### Decorators overview from `annotations.py`
-
-CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
-
-- `@CrewBase`: Marks the class as a crew base class.
-- `@agent`: Denotes a method that returns an `Agent` object.
-- `@task`: Denotes a method that returns a `Task` object.
-- `@crew`: Denotes the method that returns the `Crew` object.
-- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
-- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
-
-These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
-
-### Direct Code Definition (Alternative)
-
-Alternatively, you can define the crew directly in code without using YAML configuration files.
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai_tools import YourCustomTool
-
-class YourCrewName:
-    def agent_one(self) -> Agent:
-        return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
-            verbose=True,
-            tools=[YourCustomTool()]
-        )
-
-    def agent_two(self) -> Agent:
-        return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
-            verbose=True
-        )
-
-    def task_one(self) -> Task:
-        return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
-            agent=self.agent_one()
-        )
-
-    def task_two(self) -> Task:
-        return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
-            agent=self.agent_two()
-        )
-
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.agent_one(), self.agent_two()],
-            tasks=[self.task_one(), self.task_two()],
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-In this example:
-
-- Agents and tasks are defined directly within the class without decorators.
-- We manually create and manage the list of agents and tasks.
-- This approach provides more control but can be less maintainable for larger projects.
 
 ## Crew Output
 
@@ -336,4 +188,4 @@ Then, to replay from a specific task, use:
 crewai replay -t <task_id>
 ```
 
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/concepts/knowledge.mdx

@@ -8,8 +8,8 @@ icon: book
 
 ## What is Knowledge?
 
-Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
-Think of it as giving your agents a reference library they can consult while working.
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. 
+Think of it as giving your agents a reference library they can consult while working. 
 
 <Info>
   Key benefits of using Knowledge:
@@ -47,7 +47,8 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc
 # Create a knowledge source
 content = "Users name is John. He is 30 years old and lives in San Francisco."
 string_source = StringKnowledgeSource(
-    content=content,
+    content=content, 
+    metadata={"preference": "personal"}
 )
 
 # Create an LLM with a temperature of 0 to ensure deterministic outputs
@@ -73,63 +74,28 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+    knowledge={
+      "sources": [string_source], 
+      "metadata": {"preference": "personal"}
+    }, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
 )
 
 result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
 
+## Knowledge Configuration
+
+### Metadata and Filtering
+
+Knowledge sources support metadata for better organization and filtering. Metadata is used to filter the knowledge sources when querying the knowledge store.
 
-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
-
-# Create a knowledge source
-content_source = CrewDoclingSource(
-    file_paths=[
-        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
-        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
-    ],
-)
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-llm = LLM(model="gpt-4o-mini", temperature=0)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About papers",
-    goal="You know everything about the papers.",
-    backstory="""You are a master at understanding papers and their content.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=llm,
-)
-task = Task(
-    description="Answer the following questions about the papers: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
-    knowledge_sources=[
-        content_source
-    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
-)
-
-result = crew.kickoff(
-    inputs={
-        "question": "What is the reward hacking paper about? Be sure to provide sources."
-    }
+knowledge_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"} # Metadata is used to filter the knowledge sources
 )
 ```
 
-## Knowledge Configuration
-
 ### Chunking Configuration
 
 Control how content is split for processing by setting the chunk size and overlap.
@@ -150,27 +116,21 @@ You can also configure the embedder for the knowledge store. This is useful if y
 ...
 string_source = StringKnowledgeSource(
     content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"}
 )
 crew = Crew(
     ...
-    knowledge_sources=[string_source],
-    embedder={
-        "provider": "openai",
-        "config": {"model": "text-embedding-3-small"},
+    knowledge={
+        "sources": [string_source],
+        "metadata": {"preference": "personal"},
+        "embedder_config": {
+          "provider": "openai", # Default embedder provider; can be "ollama", "gemini", e.t.c.
+          "config": {"model": "text-embedding-3-small"} # Default embedder model; can be "mxbai-embed-large", "nomic-embed-tex", e.t.c.
+        },
     },
 )
 ```
 
-## Clearing Knowledge
-
-If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
-
-```bash Command
-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.
-
 ## 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.
@@ -189,10 +149,10 @@ from pydantic import BaseModel, Field
 
 class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
     """Knowledge source that fetches data from Space News API."""
-
+    
     api_endpoint: str = Field(description="API endpoint URL")
     limit: int = Field(default=10, description="Number of articles to fetch")
-
+    
     def load_content(self) -> Dict[Any, str]:
         """Fetch and format space news articles."""
         try:
@@ -200,26 +160,26 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
                 f"{self.api_endpoint}?limit={self.limit}"
             )
             response.raise_for_status()
-
+            
             data = response.json()
             articles = data.get('results', [])
-
+            
             formatted_data = self._format_articles(articles)
             return {self.api_endpoint: formatted_data}
         except Exception as e:
             raise ValueError(f"Failed to fetch space news: {str(e)}")
-
+    
     def _format_articles(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
         for article in articles:
             formatted += f"""
-                Title: {article['title']}
-                Published: {article['published_at']}
-                Summary: {article['summary']}
-                News Site: {article['news_site']}
-                URL: {article['url']}
-                -------------------"""
+Title: {article['title']}
+Published: {article['published_at']}
+Summary: {article['summary']}
+News Site: {article['news_site']}
+URL: {article['url']}
+-------------------"""
         return formatted
 
     def add(self) -> None:
@@ -228,20 +188,25 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
         for _, text in content.items():
             chunks = self._chunk_text(text)
             self.chunks.extend(chunks)
-
-        self._save_documents()
+        
+        self.save_documents(metadata={
+            "source": "space_news_api",
+            "timestamp": datetime.now().isoformat(),
+            "article_count": self.limit
+        })
 
 # Create knowledge source
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=10,
+    metadata={"category": "recent_news", "source": "spaceflight_news"}
 )
 
 # Create specialized agent
 space_analyst = Agent(
     role="Space News Analyst",
     goal="Answer questions about space news accurately and comprehensively",
-    backstory="""You are a space industry analyst with expertise in space exploration,
+    backstory="""You are a space industry analyst with expertise in space exploration, 
     satellite technology, and space industry trends. You excel at answering questions
     about space news and providing detailed, accurate information.""",
     knowledge_sources=[recent_news],
@@ -268,14 +233,13 @@ result = crew.kickoff(
     inputs={"user_question": "What are the latest developments in space exploration?"}
 )
 ```
-
 ```output Output
 # Agent: Space News Analyst
 ## Task: Answer this question about space news: What are the latest developments in space exploration?
 
 
 # Agent: Space News Analyst
-## Final Answer:
+## Final Answer: 
 The latest developments in space exploration, based on recent space news articles, include the following:
 
 1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
@@ -291,27 +255,23 @@ The latest developments in space exploration, based on recent space news article
 6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
 
 7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
-```
-
+``` 
 </CodeGroup>
 #### Key Components Explained
 
 1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
-
    - Extends `BaseKnowledgeSource` for integration with CrewAI
    - Configurable API endpoint and article limit
    - Implements three key methods:
      - `load_content()`: Fetches articles from the API
      - `_format_articles()`: Structures the articles into readable text
-     - `add()`: Processes and stores the content
+     - `add()`: Processes and stores the content with metadata
 
 2. **Agent Configuration**:
-
    - Specialized role as a Space News Analyst
    - Uses the knowledge source to access space news
 
 3. **Task Setup**:
-
    - Takes a user question as input through `{user_question}`
    - Designed to provide detailed answers based on the knowledge source
 
@@ -320,7 +280,6 @@ The latest developments in space exploration, based on recent space news article
    - Handles input/output through the kickoff method
 
 This example demonstrates how to:
-
 - Create a custom knowledge source that fetches real-time data
 - Process and format external data for AI consumption
 - Use the knowledge source to answer specific user questions
@@ -328,26 +287,26 @@ This example demonstrates how to:
 
 #### About the Spaceflight News API
 
-The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
-
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/documentation), which:
 - Provides free access to space-related news articles
 - Requires no authentication
 - Returns structured data about space news
 - Supports pagination and filtering
 
 You can customize the API query by modifying the endpoint URL:
-
 ```python
 # Fetch more articles
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=20,  # Increase the number of articles
+    metadata={"category": "recent_news"}
 )
 
 # Add search parameters
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
     limit=10,
+    metadata={"category": "nasa_news"}
 )
 ```
 
@@ -355,14 +314,16 @@ recent_news = SpaceNewsKnowledgeSource(
 
 <AccordionGroup>
   <Accordion title="Content Organization">
+    - Use descriptive metadata for better filtering
     - Keep chunk sizes appropriate for your content type
     - Consider content overlap for context preservation
     - Organize related information into separate knowledge sources
   </Accordion>
-
+  
   <Accordion title="Performance Tips">
-    - Adjust chunk sizes based on content complexity 
+    - Use metadata filtering to narrow search scope
+    - Adjust chunk sizes based on content complexity
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing
   </Accordion>
-</AccordionGroup>
+</AccordionGroup>

docs/concepts/llms.mdx

@@ -29,7 +29,7 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
 
 ## Available Models and Their Capabilities
 
-Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/?leaderboard) and [artificialanalysis.ai](https://artificialanalysis.ai/):
+Here's a detailed breakdown of supported models and their capabilities:
 
 <Tabs>
   <Tab title="OpenAI">
@@ -43,104 +43,13 @@ Here's a detailed breakdown of supported models and their capabilities, you can
       1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
     </Note>
   </Tab>
-  <Tab title="Nvidia NIM">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | nvidia/mistral-nemo-minitron-8b-8k-instruct | 8,192 tokens | State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation. |
-    | nvidia/nemotron-4-mini-hindi-4b-instruct| 4,096 tokens | A bilingual Hindi-English SLM for on-device inference, tailored specifically for Hindi Language. |
-    | "nvidia/llama-3.1-nemotron-70b-instruct | 128k tokens | Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA in order to improve the helpfulness of LLM generated responses. |
-    | nvidia/llama3-chatqa-1.5-8b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
-    | nvidia/llama3-chatqa-1.5-70b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
-    | nvidia/vila | 128k tokens | Multi-modal vision-language model that understands text/img/video and creates informative responses |
-    | nvidia/neva-22| 4,096 tokens | Multi-modal vision-language model that understands text/images and generates informative responses |
-    | nvidia/nemotron-mini-4b-instruct | 8,192 tokens | General-purpose tasks |
-    | nvidia/usdcode-llama3-70b-instruct | 128k tokens | State-of-the-art LLM that answers OpenUSD knowledge queries and generates USD-Python code. |
-    | nvidia/nemotron-4-340b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | meta/codellama-70b | 100k tokens | LLM capable of generating code from natural language and vice versa. |
-    | meta/llama2-70b | 4,096 tokens | Cutting-edge large language AI model capable of generating text and code in response to prompts. |
-    | meta/llama3-8b-instruct | 8,192 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | meta/llama3-70b-instruct | 8,192 tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | meta/llama-3.1-8b-instruct | 128k tokens | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.1-70b-instruct | 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | meta/llama-3.1-405b-instruct | 128k tokens | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
-    | meta/llama-3.2-1b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-3b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-11b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-90b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.1-70b-instruct | 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | google/gemma-7b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/codegemma-7b | 8,192 tokens | Cutting-edge model built on Google's Gemma-7B specialized for code generation and code completion. |
-    | google/codegemma-1.1-7b | 8,192 tokens | Advanced programming model for code generation, completion, reasoning, and instruction following. |
-    | google/recurrentgemma-2b | 8,192 tokens | Novel recurrent architecture based language model for faster inference when generating long sequences. |
-    | google/gemma-2-9b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2-27b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2-2b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/deplot | 512 tokens | One-shot visual language understanding model that translates images of plots into tables. |
-    | google/paligemma | 8,192 tokens | Vision language model adept at comprehending text and visual inputs to produce informative responses. |
-    | mistralai/mistral-7b-instruct-v0.2 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
-    | mistralai/mixtral-8x7b-instruct-v0.1 | 8,192 tokens | An MOE LLM that follows instructions, completes requests, and generates creative text. |
-    | mistralai/mistral-large | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | mistralai/mixtral-8x22b-instruct-v0.1 | 8,192 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | mistralai/mistral-7b-instruct-v0.3 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
-    | nv-mistralai/mistral-nemo-12b-instruct | 128k tokens | Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU. |
-    | mistralai/mamba-codestral-7b-v0.1 | 256k tokens | Model for writing and interacting with code across a wide range of programming languages and tasks. |
-    | microsoft/phi-3-mini-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-mini-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-small-8k-instruct | 8,192 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-small-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-medium-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-medium-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3.5-mini-instruct | 128K tokens | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
-    | microsoft/kosmos-2 | 1,024 tokens | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
-    | microsoft/phi-3-vision-128k-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
-    | microsoft/phi-3.5-vision-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
-    | databricks/dbrx-instruct | 12k tokens | A general-purpose LLM with state-of-the-art performance in language understanding, coding, and RAG. |
-    | snowflake/arctic | 1,024 tokens | Delivers high efficiency inference for enterprise applications focused on SQL generation and coding. |
-    | aisingapore/sea-lion-7b-instruct | 4,096 tokens | LLM to represent and serve the linguistic and cultural diversity of Southeast Asia |
-    | ibm/granite-8b-code-instruct | 4,096 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
-    | ibm/granite-34b-code-instruct | 8,192 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
-    | ibm/granite-3.0-8b-instruct | 4,096 tokens | Advanced Small Language Model supporting RAG, summarization, classification, code, and agentic AI |
-    | ibm/granite-3.0-3b-a800m-instruct | 4,096 tokens | Highly efficient Mixture of Experts model for RAG, summarization, entity extraction, and classification |
-    | mediatek/breeze-7b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | upstage/solar-10.7b-instruct | 4,096 tokens | Excels in NLP tasks, particularly in instruction-following, reasoning, and mathematics. |
-    | writer/palmyra-med-70b-32k | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
-    | writer/palmyra-med-70b | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
-    | writer/palmyra-fin-70b-32k | 32k tokens | Specialized LLM for financial analysis, reporting, and data processing |
-    | 01-ai/yi-large | 32k tokens | Powerful model trained on English and Chinese for diverse tasks including chatbot and creative writing. |
-    | deepseek-ai/deepseek-coder-6.7b-instruct | 2k tokens | Powerful coding model offering advanced capabilities in code generation, completion, and infilling |
-    | rakuten/rakutenai-7b-instruct | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | rakuten/rakutenai-7b-chat | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | baichuan-inc/baichuan2-13b-chat | 4,096 tokens | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
-
-    <Note>
-      NVIDIA's NIM support for models is expanding continuously! For the most up-to-date list of available models, please visit build.nvidia.com.
-    </Note>
-  </Tab>
-  <Tab title="Gemini">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | gemini-2.0-flash-exp | 1M tokens | Higher quality at faster speed, multimodal model, good for most tasks |
-    | gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
-    | gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
-    | gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    <Tip>
-      Google's Gemini models are all multimodal, supporting audio, images, video and text, supporting context caching, json schema, function calling, etc.
-
-      These models are available via API_KEY from 
-      [The Gemini API](https://ai.google.dev/gemini-api/docs) and also from 
-      [Google Cloud Vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai) as part of the
-      [Model Garden](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models).
-    </Tip>
-  </Tab>
   <Tab title="Groq">
     | Model | Context Window | Best For |
     |-------|---------------|-----------|
     | Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks |
     | Llama 3.2 Series | 8,192 tokens | General-purpose tasks |
     | Mixtral 8x7B | 32,768 tokens | Balanced performance and context |
+    | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
     <Tip>
       Groq is known for its fast inference speeds, making it suitable for real-time applications.
@@ -151,7 +60,7 @@ Here's a detailed breakdown of supported models and their capabilities, you can
     |----------|---------------|--------------|
     | Deepseek Chat | 128,000 tokens | Specialized in technical discussions |
     | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding |
-    | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
+    | Gemini | Varies by model | Multimodal capabilities |
 
     <Info>
       Provider selection should consider factors like:
@@ -219,10 +128,10 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: anthropic/claude-2.1
         # llm: anthropic/claude-2.0
 
-        # Google Models - Strong reasoning, large cachable context window, multimodal
+        # Google Models - Good for general tasks
+        # llm: gemini/gemini-pro
         # llm: gemini/gemini-1.5-pro-latest
-        # llm: gemini/gemini-1.5-flash-latest
-        # llm: gemini/gemini-1.5-flash-8b-latest
+        # llm: gemini/gemini-1.0-pro-latest
 
         # AWS Bedrock Models - Enterprise-grade
         # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
@@ -441,18 +350,13 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
-    # Option 1. Gemini accessed with an API key.
-    # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
-
-    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
-    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
     Example usage:
     ```python Code
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini/gemini-pro",
         temperature=0.7
     )
     ```
@@ -508,20 +412,6 @@ Learn how to get the most out of your LLM configuration:
     ```
   </Accordion>
 
-  <Accordion title="Nvidia NIM">
-    ```python Code
-    NVIDIA_API_KEY=<your-api-key>
-    ```
-
-    Example usage:
-    ```python Code
-    llm = LLM(
-        model="nvidia_nim/meta/llama3-70b-instruct",
-        temperature=0.7
-    )
-    ```
-  </Accordion>
-
   <Accordion title="Groq">
     ```python Code
     GROQ_API_KEY=<your-api-key>
@@ -612,6 +502,20 @@ Learn how to get the most out of your LLM configuration:
     ```
   </Accordion>
 
+  <Accordion title="Nvidia NIM">
+    ```python Code
+    NVIDIA_API_KEY=<your-api-key>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="nvidia_nim/meta/llama3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
   <Accordion title="SambaNova">
     ```python Code
     SAMBANOVA_API_KEY=<your-api-key>

docs/concepts/tasks.mdx

@@ -6,7 +6,7 @@ icon: list-check
 
 ## Overview of a Task
 
-In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
+In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`. 
 
 Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
@@ -263,148 +263,8 @@ analysis_task = Task(
 )
 ```
 
-## Task Guardrails
-
-Task guardrails provide a way to validate and transform task outputs before they
-are passed to the next task. This feature helps ensure data quality and provides
-efeedback to agents when their output doesn't meet specific criteria.
-
-### Using Task Guardrails
-
-To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
-
-```python Code
-from typing import Tuple, Union, Dict, Any
-
-def validate_blog_content(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
-    """Validate blog content meets requirements."""
-    try:
-        # Check word count
-        word_count = len(result.split())
-        if word_count > 200:
-            return (False, {
-                "error": "Blog content exceeds 200 words",
-                "code": "WORD_COUNT_ERROR",
-                "context": {"word_count": word_count}
-            })
-
-        # Additional validation logic here
-        return (True, result.strip())
-    except Exception as e:
-        return (False, {
-            "error": "Unexpected error during validation",
-            "code": "SYSTEM_ERROR"
-        })
-
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A blog post under 200 words",
-    agent=blog_agent,
-    guardrail=validate_blog_content  # Add the guardrail function
-)
-```
-
-### Guardrail Function Requirements
-
-1. **Function Signature**:
-   - Must accept exactly one parameter (the task output)
-   - Should return a tuple of `(bool, Any)`
-   - Type hints are recommended but optional
-
-2. **Return Values**:
-   - Success: Return `(True, validated_result)`
-   - Failure: Return `(False, error_details)`
-
-### Error Handling Best Practices
-
-1. **Structured Error Responses**:
-```python Code
-def validate_with_context(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
-    try:
-        # Main validation logic
-        validated_data = perform_validation(result)
-        return (True, validated_data)
-    except ValidationError as e:
-        return (False, {
-            "error": str(e),
-            "code": "VALIDATION_ERROR",
-            "context": {"input": result}
-        })
-    except Exception as e:
-        return (False, {
-            "error": "Unexpected error",
-            "code": "SYSTEM_ERROR"
-        })
-```
-
-2. **Error Categories**:
-   - Use specific error codes
-   - Include relevant context
-   - Provide actionable feedback
-
-3. **Validation Chain**:
-```python Code
-from typing import Any, Dict, List, Tuple, Union
-
-def complex_validation(result: str) -> Tuple[bool, Union[str, Dict[str, Any]]]:
-    """Chain multiple validation steps."""
-    # Step 1: Basic validation
-    if not result:
-        return (False, {"error": "Empty result", "code": "EMPTY_INPUT"})
-
-    # Step 2: Content validation
-    try:
-        validated = validate_content(result)
-        if not validated:
-            return (False, {"error": "Invalid content", "code": "CONTENT_ERROR"})
-
-        # Step 3: Format validation
-        formatted = format_output(validated)
-        return (True, formatted)
-    except Exception as e:
-        return (False, {
-            "error": str(e),
-            "code": "VALIDATION_ERROR",
-            "context": {"step": "content_validation"}
-        })
-```
-
-### Handling Guardrail Results
-
-When a guardrail returns `(False, error)`:
-1. The error is sent back to the agent
-2. The agent attempts to fix the issue
-3. The process repeats until:
-   - The guardrail returns `(True, result)`
-   - Maximum retries are reached
-
-Example with retry handling:
-```python Code
-from typing import Optional, Tuple, Union
-
-def validate_json_output(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
-    """Validate and parse JSON output."""
-    try:
-        # Try to parse as JSON
-        data = json.loads(result)
-        return (True, data)
-    except json.JSONDecodeError as e:
-        return (False, {
-            "error": "Invalid JSON format",
-            "code": "JSON_ERROR",
-            "context": {"line": e.lineno, "column": e.colno}
-        })
-
-task = Task(
-    description="Generate a JSON report",
-    expected_output="A valid JSON object",
-    agent=analyst,
-    guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
-)
-```
-
 ## Getting Structured Consistent Outputs from Tasks
+When you need to ensure that a task outputs a structured and consistent format, you can use the `output_pydantic` or `output_json` properties on a task. These properties allow you to define the expected output structure, making it easier to parse and utilize the results in your application. 
 
 <Note>
 It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
@@ -748,114 +608,6 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
-## Task Guardrails
-
-Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
-
-### Basic Usage
-
-```python Code
-from typing import Tuple, Union
-from crewai import Task
-
-def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
-    """Validate that the output is valid JSON."""
-    try:
-        json_data = json.loads(result)
-        return (True, json_data)
-    except json.JSONDecodeError:
-        return (False, "Output must be valid JSON")
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=validate_json_output
-)
-```
-
-### How Guardrails Work
-
-1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
-2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
-3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
-   - If `success` is `True`, `data` is the validated/transformed result
-   - If `success` is `False`, `data` is the error message
-4. **Result Routing**:
-   - On success (`True`), the result is automatically passed to the next task
-   - On failure (`False`), the error is sent back to the agent to generate a new answer
-
-### Common Use Cases
-
-#### Data Format Validation
-```python Code
-def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure the output contains a valid email address."""
-    import re
-    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    if re.match(email_pattern, result.strip()):
-        return (True, result.strip())
-    return (False, "Output must be a valid email address")
-```
-
-#### Content Filtering
-```python Code
-def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
-    """Remove or validate sensitive information."""
-    sensitive_patterns = ['SSN:', 'password:', 'secret:']
-    for pattern in sensitive_patterns:
-        if pattern.lower() in result.lower():
-            return (False, f"Output contains sensitive information ({pattern})")
-    return (True, result)
-```
-
-#### Data Transformation
-```python Code
-def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure phone numbers are in a consistent format."""
-    import re
-    digits = re.sub(r'\D', '', result)
-    if len(digits) == 10:
-        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-        return (True, formatted)
-    return (False, "Output must be a 10-digit phone number")
-```
-
-### Advanced Features
-
-#### Chaining Multiple Validations
-```python Code
-def chain_validations(*validators):
-    """Chain multiple validators together."""
-    def combined_validator(result):
-        for validator in validators:
-            success, data = validator(result)
-            if not success:
-                return (False, data)
-            result = data
-        return (True, result)
-    return combined_validator
-
-# Usage
-task = Task(
-    description="Get user contact info",
-    expected_output="Email and phone",
-    guardrail=chain_validations(
-        validate_email_format,
-        filter_sensitive_info
-    )
-)
-```
-
-#### Custom Retry Logic
-```python Code
-task = Task(
-    description="Generate data",
-    expected_output="Valid data",
-    guardrail=validate_data,
-    max_retries=5  # Override default retry limit
-)
-```
-
 ## Creating Directories when Saving Files
 
 You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
@@ -877,7 +629,7 @@ save_output_task = Task(
 
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in CrewAI.
-By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
-Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential,
+Tasks are the driving force behind the actions of agents in CrewAI. 
+By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. 
+Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, 
 ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/concepts/tools.mdx

@@ -172,48 +172,6 @@ def my_tool(question: str) -> str:
     return "Result from your custom tool"
 ```
 
-### Structured Tools
-
-The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities.
-
-#### Example:
-Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example:
-
-```python
-from crewai.tools.structured_tool import CrewStructuredTool
-from pydantic import BaseModel
-
-# Define the schema for the tool's input using Pydantic
-class APICallInput(BaseModel):
-    endpoint: str
-    parameters: dict
-
-# Wrapper function to execute the API call
-def tool_wrapper(*args, **kwargs):
-    # Here, you would typically call the API using the parameters
-    # For demonstration, we'll return a placeholder string
-    return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}"
-
-# Create and return the structured tool
-def create_structured_tool():
-    return CrewStructuredTool.from_function(
-        name='Wrapper API',
-        description="A tool to wrap API calls with structured input.",
-        args_schema=APICallInput,
-        func=tool_wrapper,
-    )
-
-# Example usage
-structured_tool = create_structured_tool()
-
-# Execute the tool with structured input
-result = structured_tool._run(**{
-    "endpoint": "https://example.com/api",
-    "parameters": {"key1": "value1", "key2": "value2"}
-})
-print(result)  # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'}
-```
-
 ### Custom Caching Mechanism
 
 <Tip>

docs/how-to/Portkey-Observability-and-Guardrails.md

@@ -1,211 +0,0 @@
-# Portkey Integration with CrewAI
-<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
-
-1. **Install Required Packages:**
-
-```bash
-pip install -qU crewai portkey-ai
-```
-
-2. **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
-    )
-)
-```
-
-3. **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)
-```
-
-
-## 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/how-to/agentops-observability.mdx

@@ -57,7 +57,7 @@ This feature is useful for debugging and understanding how agents interact with
    <Step title="Install AgentOps">
       Install AgentOps with:
       ```bash
-      pip install 'crewai[agentops]'
+      pip install crewai[agentops]
       ```
       or
       ```bash

docs/how-to/multimodal-agents.mdx

@@ -1,138 +0,0 @@
----
-title: Using Multimodal Agents
-description: Learn how to enable and use multimodal capabilities in your agents for processing images and other non-text content within the CrewAI framework.
-icon: image
----
-
-# Using Multimodal Agents
-
-CrewAI supports multimodal agents that can process both text and non-text content like images. This guide will show you how to enable and use multimodal capabilities in your agents.
-
-## Enabling Multimodal Capabilities
-
-To create a multimodal agent, simply set the `multimodal` parameter to `True` when initializing your agent:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Image Analyst",
-    goal="Analyze and extract insights from images",
-    backstory="An expert in visual content interpretation with years of experience in image analysis",
-    multimodal=True  # This enables multimodal capabilities
-)
-```
-
-When you set `multimodal=True`, the agent is automatically configured with the necessary tools for handling non-text content, including the `AddImageTool`.
-
-## Working with Images
-
-The multimodal agent comes pre-configured with the `AddImageTool`, which allows it to process images. You don't need to manually add this tool - it's automatically included when you enable multimodal capabilities.
-
-Here's a complete example showing how to use a multimodal agent to analyze an image:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Create a multimodal agent
-image_analyst = Agent(
-    role="Product Analyst",
-    goal="Analyze product images and provide detailed descriptions",
-    backstory="Expert in visual product analysis with deep knowledge of design and features",
-    multimodal=True
-)
-
-# Create a task for image analysis
-task = Task(
-    description="Analyze the product image at https://example.com/product.jpg and provide a detailed description",
-    agent=image_analyst
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[image_analyst],
-    tasks=[task]
-)
-
-result = crew.kickoff()
-```
-
-### Advanced Usage with Context
-
-You can provide additional context or specific questions about the image when creating tasks for multimodal agents. The task description can include specific aspects you want the agent to focus on:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Create a multimodal agent for detailed analysis
-expert_analyst = Agent(
-    role="Visual Quality Inspector",
-    goal="Perform detailed quality analysis of product images",
-    backstory="Senior quality control expert with expertise in visual inspection",
-    multimodal=True  # AddImageTool is automatically included
-)
-
-# Create a task with specific analysis requirements
-inspection_task = Task(
-    description="""
-    Analyze the product image at https://example.com/product.jpg with focus on:
-    1. Quality of materials
-    2. Manufacturing defects
-    3. Compliance with standards
-    Provide a detailed report highlighting any issues found.
-    """,
-    agent=expert_analyst
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[expert_analyst],
-    tasks=[inspection_task]
-)
-
-result = crew.kickoff()
-```
-
-### Tool Details
-
-When working with multimodal agents, the `AddImageTool` is automatically configured with the following schema:
-
-```python
-class AddImageToolSchema:
-    image_url: str  # Required: The URL or path of the image to process
-    action: Optional[str] = None  # Optional: Additional context or specific questions about the image
-```
-
-The multimodal agent will automatically handle the image processing through its built-in tools, allowing it to:
-- Access images via URLs or local file paths
-- Process image content with optional context or specific questions
-- Provide analysis and insights based on the visual information and task requirements
-
-## Best Practices
-
-When working with multimodal agents, keep these best practices in mind:
-
-1. **Image Access**
-   - Ensure your images are accessible via URLs that the agent can reach
-   - For local images, consider hosting them temporarily or using absolute file paths
-   - Verify that image URLs are valid and accessible before running tasks
-
-2. **Task Description**
-   - Be specific about what aspects of the image you want the agent to analyze
-   - Include clear questions or requirements in the task description
-   - Consider using the optional `action` parameter for focused analysis
-
-3. **Resource Management**
-   - Image processing may require more computational resources than text-only tasks
-   - Some language models may require base64 encoding for image data
-   - Consider batch processing for multiple images to optimize performance
-
-4. **Environment Setup**
-   - Verify that your environment has the necessary dependencies for image processing
-   - Ensure your language model supports multimodal capabilities
-   - Test with small images first to validate your setup
-
-5. **Error Handling**
-   - Implement proper error handling for image loading failures
-   - Have fallback strategies for when image processing fails
-   - Monitor and log image processing operations for debugging

docs/installation.mdx

@@ -7,7 +7,7 @@ icon: wrench
 <Note>
   **Python Version Requirements**
   
-  CrewAI requires `Python >=3.10 and <3.13`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <=3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "crewai"
-version = "0.86.0"
+version = "0.85.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<=3.13"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
@@ -26,10 +26,9 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "chromadb>=0.5.23",
+    "chromadb>=0.5.18",
     "pdfplumber>=0.11.4",
     "openpyxl>=3.1.5",
-    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -38,7 +37,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.17.0"]
+tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
 fastembed = ["fastembed>=0.4.1"]
 pdfplumber = [
@@ -51,13 +50,10 @@ openpyxl = [
     "openpyxl>=3.1.5",
 ]
 mem0 = ["mem0ai>=0.1.29"]
-docling = [
-    "docling>=2.12.0",
-]
 
 [tool.uv]
 dev-dependencies = [
-    "ruff>=0.8.2",
+    "ruff>=0.4.10",
     "mypy>=1.10.0",
     "pre-commit>=3.6.0",
     "mkdocs>=1.4.3",
@@ -67,6 +63,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
+    "crewai-tools>=0.14.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.86.0"
+__version__ = "0.85.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -8,7 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
+from crewai.cli.constants import ENV_VARS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -17,26 +17,33 @@ from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.task import Task
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
 
-try:
-    import agentops  # type: ignore # Name "agentops" is already defined
-    from agentops import track_agent  # type: ignore
-except ImportError:
-
-    def track_agent():
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
         def noop(f):
             return f
 
         return noop
 
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
 
 @track_agent()
 class Agent(BaseAgent):
@@ -115,10 +122,6 @@ class Agent(BaseAgent):
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
-    multimodal: bool = Field(
-        default=False,
-        description="Whether the agent is multimodal.",
-    )
     code_execution_mode: Literal["safe", "unsafe"] = Field(
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
@@ -178,11 +181,20 @@ class Agent(BaseAgent):
                         if key_name and key_name not in unaccepted_attributes:
                             env_value = os.environ.get(key_name)
                             if env_value:
-                                key_name = key_name.lower()
-                                for pattern in LITELLM_PARAMS:
-                                    if pattern in key_name:
-                                        key_name = pattern
-                                        break
+                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = (
+                                    "api_key" if "API_KEY" in key_name else key_name
+                                )
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base" if "API_BASE" in key_name else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in key_name
+                                    else key_name
+                                )
                                 llm_params[key_name] = env_value
                         # Check for default values if the environment variable is not set
                         elif env_var.get("default", False):
@@ -294,7 +306,14 @@ class Agent(BaseAgent):
             )
 
         if self.crew and self.crew.memory:
-            memory = self.crew.contextual_memory.build_context_for_task(task, context)
+            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)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
@@ -404,10 +423,6 @@ class Agent(BaseAgent):
         tools = agent_tools.tools()
         return tools
 
-    def get_multimodal_tools(self) -> List[Tool]:
-        from crewai.tools.agent_tools.add_image_tool import AddImageTool
-        return [AddImageTool()]
-
     def get_code_execution_tools(self):
         try:
             from crewai_tools import CodeInterpreterTool

src/crewai/agents/crew_agent_executor.py

@@ -143,20 +143,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         tool_result = self._execute_tool_and_check_finality(
                             formatted_answer
                         )
-
-                        # Directly append the result to the messages if the
-                        # tool is "Add image to content" in case of multimodal
-                        # agents
-                        if formatted_answer.tool == self._i18n.tools("add_image")["name"]:
-                            self.messages.append(tool_result.result)
-                            continue
-
-                        else:
-                            if self.step_callback:
-                                self.step_callback(tool_result)
-
-                            formatted_answer.text += f"\nObservation: {tool_result.result}"
-
+                        formatted_answer.text += f"\nObservation: {tool_result.result}"
                         formatted_answer.result = tool_result.result
                         if tool_result.result_as_answer:
                             return AgentFinish(
@@ -312,7 +299,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.slice("summarize_instruction").format(group=group),
+                        self._i18n.slice("sumamrize_instruction").format(group=group),
                     ),
                 ],
                 callbacks=self.callbacks,
@@ -358,9 +345,9 @@ 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(
@@ -423,6 +410,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         while self.ask_for_human_input:
             human_feedback = self._ask_human_input(formatted_answer.output)
+            print("Human feedback: ", human_feedback)
 
             if self.crew and self.crew._train:
                 self._handle_crew_training_output(formatted_answer, human_feedback)

src/crewai/agents/parser.py

@@ -1,6 +1,5 @@
 import re
 from typing import Any, Union
-
 from json_repair import repair_json
 
 from crewai.utilities import I18N

src/crewai/cli/authentication/main.py

@@ -5,10 +5,9 @@ from typing import Any, Dict
 import requests
 from rich.console import Console
 
-from crewai.cli.tools.main import ToolCommand
-
 from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
 from .utils import TokenManager, validate_token
+from crewai.cli.tools.main import ToolCommand
 
 console = Console()
 
@@ -80,9 +79,7 @@ class AuthenticationCommand:
                         style="yellow",
                     )
 
-                console.print(
-                    "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
-                )
+                console.print("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
                 return
 
             if token_data["error"] not in ("authorization_pending", "slow_down"):

src/crewai/cli/authentication/token.py

@@ -1,9 +1,10 @@
 from .utils import TokenManager
 
-
 def get_auth_token() -> str:
     """Get the authentication token."""
     access_token = TokenManager().get_token()
     if not access_token:
         raise Exception()
     return access_token
+
+

src/crewai/cli/cli.py

@@ -1,7 +1,7 @@
-from importlib.metadata import version as get_version
 from typing import Optional
 
 import click
+import pkg_resources
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
@@ -25,7 +25,6 @@ from .update_crew import update_crew
 
 
 @click.group()
-@click.version_option(get_version("crewai"))
 def crewai():
     """Top-level command group for crewai."""
 
@@ -51,17 +50,14 @@ def create(type, name, provider, skip_provider=False):
 )
 def version(tools):
     """Show the installed version of crewai."""
-    try:
-        crewai_version = get_version("crewai")
-    except Exception:
-        crewai_version = "unknown version"
+    crewai_version = pkg_resources.get_distribution("crewai").version
     click.echo(f"crewai version: {crewai_version}")
 
     if tools:
         try:
-            tools_version = get_version("crewai")
+            tools_version = pkg_resources.get_distribution("crewai-tools").version
             click.echo(f"crewai tools version: {tools_version}")
-        except Exception:
+        except pkg_resources.DistributionNotFound:
             click.echo("crewai tools not installed")
 
 

src/crewai/cli/command.py

@@ -1,9 +1,8 @@
 import requests
 from requests.exceptions import JSONDecodeError
 from rich.console import Console
-
-from crewai.cli.authentication.token import get_auth_token
 from crewai.cli.plus_api import PlusAPI
+from crewai.cli.authentication.token import get_auth_token
 from crewai.telemetry.telemetry import Telemetry
 
 console = Console()

src/crewai/cli/config.py

@@ -1,19 +1,13 @@
 import json
 from pathlib import Path
-from typing import Optional
-
 from pydantic import BaseModel, Field
+from typing import Optional
 
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
-
 class Settings(BaseModel):
-    tool_repository_username: Optional[str] = Field(
-        None, description="Username for interacting with the Tool Repository"
-    )
-    tool_repository_password: Optional[str] = Field(
-        None, description="Password for interacting with the Tool Repository"
-    )
+    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
+    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
     config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):

src/crewai/cli/constants.py

@@ -159,6 +159,3 @@ MODELS = {
 }
 
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-
-
-LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

src/crewai/cli/plus_api.py

@@ -1,10 +1,8 @@
-from os import getenv
 from typing import Optional
-from urllib.parse import urljoin
-
 import requests
-
+from os import getenv
 from crewai.cli.version import get_crewai_version
+from urllib.parse import urljoin
 
 
 class PlusAPI:

src/crewai/cli/reset_memories_command.py

@@ -1,12 +1,11 @@
 import subprocess
-
 import click
 
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 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
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
 def reset_memories_command(

src/crewai/cli/templates/crew/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

src/crewai/cli/templates/crew/config/tasks.yaml

@@ -12,6 +12,6 @@ reporting_task:
     Review the context you got and expand each topic into a full section for a report.
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
-    A fully fledged report with the main topics, each with a full section of information.
+    A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst

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

@@ -1,26 +1,37 @@
 from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
+from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+# Uncomment the following line to use an example of a knowledge source
+# from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
-# If you want to run a snippet of code before or after the crew starts, 
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
+# Check our tools documentations for more information on how to use them
+# from crewai_tools import SerperDevTool
 
 @CrewBase
 class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
-	# Learn more about YAML configuration files here:
-	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
 	agents_config = 'config/agents.yaml'
 	tasks_config = 'config/tasks.yaml'
 
-	# If you would like to add tools to your agents, you can learn more about it here:
-	# https://docs.crewai.com/concepts/agents#agent-tools
+	@before_kickoff # Optional hook to be executed before the crew starts
+	def pull_data_example(self, inputs):
+		# Example of pulling data from an external API, dynamically changing the inputs
+		inputs['extra_data'] = "This is extra data"
+		return inputs
+
+	@after_kickoff # Optional hook to be executed after the crew has finished
+	def log_results(self, output):
+		# Example of logging results, dynamically changing the output
+		print(f"Results: {output}")
+		return output
+
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
 			config=self.agents_config['researcher'],
+			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
 			verbose=True
 		)
 
@@ -31,9 +42,6 @@ class {{crew_name}}():
 			verbose=True
 		)
 
-	# To learn more about structured task outputs, 
-	# task dependencies, and task callbacks, check out the documentation:
-	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
 	@task
 	def research_task(self) -> Task:
 		return Task(
@@ -50,8 +58,14 @@ class {{crew_name}}():
 	@crew
 	def crew(self) -> Crew:
 		"""Creates the {{crew_name}} crew"""
-		# To learn how to add knowledge sources to your crew, check out the documentation:
-		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+		# You can add knowledge sources here
+		# knowledge_path = "user_preference.txt"
+		# sources = [
+		# 	TextFileKnowledgeSource(
+		# 		file_path="knowledge/user_preference.txt",
+		# 		metadata={"preference": "personal"}
+		# 	),
+		# ]
 
 		return Crew(
 			agents=self.agents, # Automatically created by the @agent decorator
@@ -59,4 +73,5 @@ class {{crew_name}}():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
+			# knowledge_sources=sources, # In the case you want to add knowledge sources
 		)

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0"
+    "crewai[tools]>=0.85.0,<1.0.0"
 ]
 
 [project.scripts]
@@ -18,6 +18,3 @@ test = "{{folder_name}}.main:test"
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
-
-[tool.crewai]
-type = "crew"

src/crewai/cli/templates/crew/tools/custom_tool.py

@@ -10,7 +10,7 @@ class MyCustomToolInput(BaseModel):
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
-        "Clear description for what this tool is useful for, your agent will need this information to use it."
+        "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
     args_schema: Type[BaseModel] = MyCustomToolInput
 

src/crewai/cli/templates/flow/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -1,47 +1,31 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
-# If you want to run a snippet of code before or after the crew starts,
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
-
-
 @CrewBase
-class PoemCrew:
-    """Poem Crew"""
+class PoemCrew():
+	"""Poem Crew"""
 
-    # Learn more about YAML configuration files here:
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
 
-    # If you would lik to add tools to your crew, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
-    @agent
-    def poem_writer(self) -> Agent:
-        return Agent(
-            config=self.agents_config["poem_writer"],
-        )
+	@agent
+	def poem_writer(self) -> Agent:
+		return Agent(
+			config=self.agents_config['poem_writer'],
+		)
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
-    @task
-    def write_poem(self) -> Task:
-        return Task(
-            config=self.tasks_config["write_poem"],
-        )
+	@task
+	def write_poem(self) -> Task:
+		return Task(
+			config=self.tasks_config['write_poem'],
+		)
 
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Research Crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Research Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)

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

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, listen, start
 
-from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
+from .crews.poem_crew.poem_crew import PoemCrew
 
 
 class PoemState(BaseModel):

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0",
+    "crewai[tools]>=0.85.0,<1.0.0",
 ]
 
 [project.scripts]
@@ -15,6 +15,3 @@ plot = "{{folder_name}}.main:plot"
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
-
-[tool.crewai]
-type = "flow"

src/crewai/cli/templates/flow/tools/custom_tool.py

@@ -13,7 +13,7 @@ class MyCustomToolInput(BaseModel):
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
-        "Clear description for what this tool is useful for, your agent will need this information to use it."
+        "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
     args_schema: Type[BaseModel] = MyCustomToolInput
 

src/crewai/cli/templates/tool/README.md

@@ -5,7 +5,7 @@ custom tools to power up your crews.
 
 ## Installing
 
-Ensure you have Python >=3.10 <3.13 installed on your system. This project
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project
 uses [UV](https://docs.astral.sh/uv/) for dependency management and package
 handling, offering a seamless setup and execution experience.
 

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

@@ -3,10 +3,8 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0"
+    "crewai[tools]>=0.85.0"
 ]
 
-[tool.crewai]
-type = "tool"

src/crewai/cli/tools/main.py

@@ -117,7 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         published_handle = publish_response.json()["handle"]
         console.print(
-            f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
+            f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
             style="bold green",
         )
 
@@ -138,7 +138,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         self._add_package(get_response.json())
 
-        console.print(f"Successfully installed {handle}", style="bold green")
+        console.print(f"Succesfully installed {handle}", style="bold green")
 
     def login(self):
         login_response = self.plus_api_client.login_to_tool_repository()
@@ -153,12 +153,8 @@ 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(
@@ -183,7 +179,7 @@ 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:
@@ -208,11 +204,7 @@ 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/cli/utils.py

@@ -33,6 +33,26 @@ def copy_template(src, dst, name, class_name, folder_name):
     click.secho(f"  - Created {dst}", fg="green")
 
 
+# Drop the simple_toml_parser when we move to python3.11
+def simple_toml_parser(content):
+    result = {}
+    current_section = result
+    for line in content.split("\n"):
+        line = line.strip()
+        if line.startswith("[") and line.endswith("]"):
+            # New section
+            section = line[1:-1].split(".")
+            current_section = result
+            for key in section:
+                current_section = current_section.setdefault(key, {})
+        elif "=" in line:
+            key, value = line.split("=", 1)
+            key = key.strip()
+            value = value.strip().strip('"')
+            current_section[key] = value
+    return result
+
+
 def read_toml(file_path: str = "pyproject.toml"):
     """Read the content of a TOML file and return it as a dictionary."""
     with open(file_path, "rb") as f:
@@ -43,7 +63,7 @@ def read_toml(file_path: str = "pyproject.toml"):
 def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
-    return tomli.loads(content)
+    return simple_toml_parser(content)
 
 
 def get_project_name(

src/crewai/cli/version.py

@@ -1,6 +1,6 @@
 import importlib.metadata
 
-
 def get_crewai_version() -> str:
     """Get the version number of CrewAI running the CLI"""
     return importlib.metadata.version("crewai")
+

src/crewai/crew.py

@@ -1,5 +1,6 @@
 import asyncio
 import json
+import os
 import uuid
 import warnings
 from concurrent.futures import Future
@@ -25,7 +26,6 @@ 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
@@ -36,7 +36,6 @@ from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -50,10 +49,12 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-try:
-    import agentops  # type: ignore
-except ImportError:
-    agentops = None
+agentops = None
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops  # type: ignore
+    except ImportError:
+        pass
 
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -279,13 +280,6 @@ 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")
@@ -542,6 +536,9 @@ class Crew(BaseModel):
             if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
                 agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
 
+            if agent.allow_code_execution:  # type: ignore # BaseAgent" has no attribute "allow_code_execution"
+                agent.tools += agent.get_code_execution_tools()  # type: ignore # "BaseAgent" has no attribute "get_code_execution_tools"; maybe "get_delegation_tools"?
+
             if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
                 agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
 
@@ -678,6 +675,7 @@ class Crew(BaseModel):
                 )
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
+            manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
             self.manager_llm = (
                 getattr(self.manager_llm, "model_name", None)
@@ -689,7 +687,6 @@ class Crew(BaseModel):
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
                 backstory=i18n.retrieve("hierarchical_manager_agent", "backstory"),
                 tools=AgentTools(agents=self.agents).tools(),
-                allow_delegation=True,
                 llm=self.manager_llm,
                 verbose=self.verbose,
             )
@@ -732,14 +729,7 @@ class Crew(BaseModel):
                     f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
                 )
 
-            # Determine which tools to use - task tools take precedence over agent tools
-            tools_for_task = task.tools or agent_to_use.tools or []
-            tools_for_task = self._prepare_tools(
-                agent_to_use,
-                task,
-                tools_for_task
-            )
-
+            self._prepare_agent_tools(task)
             self._log_task_start(task, agent_to_use.role)
 
             if isinstance(task, ConditionalTask):
@@ -756,7 +746,7 @@ class Crew(BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=tools_for_task,
+                    tools=agent_to_use.tools,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -768,7 +758,7 @@ class Crew(BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=tools_for_task,
+                    tools=agent_to_use.tools,
                 )
                 task_outputs = [task_output]
                 self._process_task_result(task, task_output)
@@ -805,67 +795,45 @@ class Crew(BaseModel):
             return skipped_task_output
         return None
 
-    def _prepare_tools(self, agent: BaseAgent, task: Task, tools: List[Tool]) -> List[Tool]:
-        # Add delegation tools if agent allows delegation
-        if agent.allow_delegation:
-            if self.process == Process.hierarchical:
-                if self.manager_agent:
-                    tools = self._update_manager_tools(task, tools)
-                else:
-                    raise ValueError("Manager agent is required for hierarchical process.")
-
-            elif agent and agent.allow_delegation:
-                tools = self._add_delegation_tools(task, tools)
-
-        # Add code execution tools if agent allows code execution
-        if agent.allow_code_execution:
-            tools = self._add_code_execution_tools(agent, tools)
-
-        if agent and agent.multimodal:
-            tools = self._add_multimodal_tools(agent, tools)
-
-        return tools
+    def _prepare_agent_tools(self, task: Task):
+        if self.process == Process.hierarchical:
+            if self.manager_agent:
+                self._update_manager_tools(task)
+            else:
+                raise ValueError("Manager agent is required for hierarchical process.")
+        elif task.agent and task.agent.allow_delegation:
+            self._add_delegation_tools(task)
 
     def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
             return self.manager_agent
         return task.agent
 
-    def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> List[Tool]:
-        """Merge new tools into existing tools list, avoiding duplicates by tool name."""
-        if not new_tools:
-            return existing_tools
-
-        # Create mapping of tool names to new tools
-        new_tool_map = {tool.name: tool for tool in new_tools}
-
-        # Remove any existing tools that will be replaced
-        tools = [tool for tool in existing_tools if tool.name not in new_tool_map]
-
-        # Add all new tools
-        tools.extend(new_tools)
-
-        return tools
-
-    def _inject_delegation_tools(self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]):
-        delegation_tools = task_agent.get_delegation_tools(agents)
-        return self._merge_tools(tools, delegation_tools)
-
-    def _add_multimodal_tools(self, agent: BaseAgent, tools: List[Tool]):
-        multimodal_tools = agent.get_multimodal_tools()
-        return self._merge_tools(tools, multimodal_tools)
-
-    def _add_code_execution_tools(self, agent: BaseAgent, tools: List[Tool]):
-        code_tools = agent.get_code_execution_tools()
-        return self._merge_tools(tools, code_tools)
-
-    def _add_delegation_tools(self, task: Task, tools: List[Tool]):
+    def _add_delegation_tools(self, task: Task):
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
-            if not tools:
-                tools = []
-            tools = self._inject_delegation_tools(tools, task.agent, agents_for_delegation)
-        return tools
+            delegation_tools = task.agent.get_delegation_tools(agents_for_delegation)
+
+            # Add tools if they are not already in task.tools
+            for new_tool in delegation_tools:
+                # Find the index of the tool with the same name
+                existing_tool_index = next(
+                    (
+                        index
+                        for index, tool in enumerate(task.tools or [])
+                        if tool.name == new_tool.name
+                    ),
+                    None,
+                )
+                if not task.tools:
+                    task.tools = []
+
+                if existing_tool_index is not None:
+                    # Replace the existing tool
+                    task.tools[existing_tool_index] = new_tool
+                else:
+                    # Add the new tool
+                    task.tools.append(new_tool)
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
@@ -873,13 +841,14 @@ class Crew(BaseModel):
                 task_name=task.name, task=task.description, agent=role, status="started"
             )
 
-    def _update_manager_tools(self, task: Task, tools: List[Tool]):
+    def _update_manager_tools(self, task: Task):
         if self.manager_agent:
             if task.agent:
-                tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
+                self.manager_agent.tools = task.agent.get_delegation_tools([task.agent])
             else:
-                tools = self._inject_delegation_tools(tools, self.manager_agent, self.agents)
-        return tools
+                self.manager_agent.tools = self.manager_agent.get_delegation_tools(
+                    self.agents
+                )
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
         context = (
@@ -1063,7 +1032,6 @@ class Crew(BaseModel):
             agentops.end_session(
                 end_state="Success",
                 end_state_reason="Finished Execution",
-                is_auto_end=True,
             )
         self._telemetry.end_crew(self, final_string_output)
 

src/crewai/flow/flow.py

@@ -14,15 +14,8 @@ from typing import (
     cast,
 )
 
-from blinker import Signal
 from pydantic import BaseModel, ValidationError
 
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
@@ -80,27 +73,10 @@ def listen(condition):
     return decorator
 
 
-def router(condition):
+def router(method):
     def decorator(func):
         func.__is_router__ = True
-        # Handle conditions like listen/start
-        if isinstance(condition, str):
-            func.__trigger_methods__ = [condition]
-            func.__condition_type__ = "OR"
-        elif (
-            isinstance(condition, dict)
-            and "type" in condition
-            and "methods" in condition
-        ):
-            func.__trigger_methods__ = condition["methods"]
-            func.__condition_type__ = condition["type"]
-        elif callable(condition) and hasattr(condition, "__name__"):
-            func.__trigger_methods__ = [condition.__name__]
-            func.__condition_type__ = "OR"
-        else:
-            raise ValueError(
-                "Condition must be a method, string, or a result of or_() or and_()"
-            )
+        func.__router_for__ = method.__name__
         return func
 
     return decorator
@@ -140,8 +116,8 @@ class FlowMeta(type):
 
         start_methods = []
         listeners = {}
+        routers = {}
         router_paths = {}
-        routers = set()
 
         for attr_name, attr_value in dct.items():
             if hasattr(attr_value, "__is_start_method__"):
@@ -154,11 +130,18 @@ class FlowMeta(type):
                 methods = attr_value.__trigger_methods__
                 condition_type = getattr(attr_value, "__condition_type__", "OR")
                 listeners[attr_name] = (condition_type, methods)
-                if hasattr(attr_value, "__is_router__") and attr_value.__is_router__:
-                    routers.add(attr_name)
-                    possible_returns = get_possible_return_constants(attr_value)
-                    if possible_returns:
-                        router_paths[attr_name] = possible_returns
+
+            elif hasattr(attr_value, "__is_router__"):
+                routers[attr_value.__router_for__] = attr_name
+                possible_returns = get_possible_return_constants(attr_value)
+                if possible_returns:
+                    router_paths[attr_name] = possible_returns
+
+                # Register router as a listener to its triggering method
+                trigger_method_name = attr_value.__router_for__
+                methods = [trigger_method_name]
+                condition_type = "OR"
+                listeners[attr_name] = (condition_type, methods)
 
         setattr(cls, "_start_methods", start_methods)
         setattr(cls, "_listeners", listeners)
@@ -173,10 +156,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     _start_methods: List[str] = []
     _listeners: Dict[str, tuple[str, List[str]]] = {}
-    _routers: Set[str] = set()
+    _routers: Dict[str, str] = {}
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
-    event_emitter = Signal("event_emitter")
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -220,10 +202,20 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return self._method_outputs
 
     def _initialize_state(self, inputs: Dict[str, Any]) -> None:
-        if isinstance(self._state, BaseModel):
-            # Structured state
-            try:
+        """
+        Initializes or updates the state with the provided inputs.
 
+        Args:
+            inputs: Dictionary of inputs to initialize or update the state.
+
+        Raises:
+            ValueError: If inputs do not match the structured state model.
+            TypeError: If state is neither a BaseModel instance nor a dictionary.
+        """
+        if isinstance(self._state, BaseModel):
+            # Structured state management
+            try:
+                # Define a function to create the dynamic class
                 def create_model_with_extra_forbid(
                     base_model: Type[BaseModel],
                 ) -> Type[BaseModel]:
@@ -233,33 +225,50 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
                     return ModelWithExtraForbid
 
+                # Create the dynamic class
                 ModelWithExtraForbid = create_model_with_extra_forbid(
                     self._state.__class__
                 )
+
+                # Create a new instance using the combined state and inputs
                 self._state = cast(
                     T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
                 )
+
             except ValidationError as e:
                 raise ValueError(f"Invalid inputs for structured state: {e}") from e
         elif isinstance(self._state, dict):
+            # Unstructured state management
             self._state.update(inputs)
         else:
             raise TypeError("State must be a BaseModel instance or a dictionary.")
 
     def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
-        self.event_emitter.send(
-            self,
-            event=FlowStartedEvent(
-                type="flow_started",
-                flow_name=self.__class__.__name__,
-            ),
-        )
+        """
+        Starts the execution of the flow synchronously.
 
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
         if inputs is not None:
             self._initialize_state(inputs)
         return asyncio.run(self.kickoff_async())
 
     async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow asynchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 
@@ -267,23 +276,20 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self.__class__.__name__, list(self._methods.keys())
         )
 
+        # Create tasks for all start methods
         tasks = [
             self._execute_start_method(start_method)
             for start_method in self._start_methods
         ]
+
+        # Run all start methods concurrently
         await asyncio.gather(*tasks)
 
-        final_output = self._method_outputs[-1] if self._method_outputs else None
-
-        self.event_emitter.send(
-            self,
-            event=FlowFinishedEvent(
-                type="flow_finished",
-                flow_name=self.__class__.__name__,
-                result=final_output,
-            ),
-        )
-        return final_output
+        # Return the final output (from the last executed method)
+        if self._method_outputs:
+            return self._method_outputs[-1]
+        else:
+            return None  # Or raise an exception if no methods were executed
 
     async def _execute_start_method(self, start_method_name: str) -> None:
         result = await self._execute_method(
@@ -299,105 +305,70 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if asyncio.iscoroutinefunction(method)
             else method(*args, **kwargs)
         )
-        self._method_outputs.append(result)
+        self._method_outputs.append(result)  # Store the output
+
+        # Track method execution counts
         self._method_execution_counts[method_name] = (
             self._method_execution_counts.get(method_name, 0) + 1
         )
+
         return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
-        # First, handle routers repeatedly until no router triggers anymore
-        while True:
-            routers_triggered = self._find_triggered_methods(
-                trigger_method, router_only=True
+        listener_tasks = []
+
+        if trigger_method in self._routers:
+            router_method = self._methods[self._routers[trigger_method]]
+            path = await self._execute_method(
+                self._routers[trigger_method], router_method
             )
-            if not routers_triggered:
-                break
-            for router_name in routers_triggered:
-                await self._execute_single_listener(router_name, result)
-                # After executing router, the router's result is the path
-                # The last router executed sets the trigger_method
-                # The router result is the last element in self._method_outputs
-                trigger_method = self._method_outputs[-1]
+            trigger_method = path
 
-        # Now that no more routers are triggered by current trigger_method,
-        # execute normal listeners
-        listeners_triggered = self._find_triggered_methods(
-            trigger_method, router_only=False
-        )
-        if listeners_triggered:
-            tasks = [
-                self._execute_single_listener(listener_name, result)
-                for listener_name in listeners_triggered
-            ]
-            await asyncio.gather(*tasks)
-
-    def _find_triggered_methods(
-        self, trigger_method: str, router_only: bool
-    ) -> List[str]:
-        triggered = []
         for listener_name, (condition_type, methods) in self._listeners.items():
-            is_router = listener_name in self._routers
-
-            if router_only != is_router:
-                continue
-
             if condition_type == "OR":
-                # If the trigger_method matches any in methods, run this
                 if trigger_method in methods:
-                    triggered.append(listener_name)
+                    # Schedule the listener without preventing re-execution
+                    listener_tasks.append(
+                        self._execute_single_listener(listener_name, result)
+                    )
             elif condition_type == "AND":
                 # Initialize pending methods for this listener if not already done
                 if listener_name not in self._pending_and_listeners:
                     self._pending_and_listeners[listener_name] = set(methods)
                 # Remove the trigger method from pending methods
-                if trigger_method in self._pending_and_listeners[listener_name]:
-                    self._pending_and_listeners[listener_name].discard(trigger_method)
-
+                self._pending_and_listeners[listener_name].discard(trigger_method)
                 if not self._pending_and_listeners[listener_name]:
                     # All required methods have been executed
-                    triggered.append(listener_name)
+                    listener_tasks.append(
+                        self._execute_single_listener(listener_name, result)
+                    )
                     # Reset pending methods for this listener
                     self._pending_and_listeners.pop(listener_name, None)
 
-        return triggered
+        # Run all listener tasks concurrently and wait for them to complete
+        if listener_tasks:
+            await asyncio.gather(*listener_tasks)
 
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
             method = self._methods[listener_name]
-
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionStartedEvent(
-                    type="method_execution_started",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
+
+            # Exclude 'self' parameter
             method_params = [p for p in params if p.name != "self"]
 
             if method_params:
+                # If listener expects parameters, pass the result
                 listener_result = await self._execute_method(
                     listener_name, method, result
                 )
             else:
+                # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
-            # Execute listeners (and possibly routers) of this listener
+            # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
-
         except Exception as e:
             print(
                 f"[Flow._execute_single_listener] Error in method {listener_name}: {e}"
@@ -410,4 +381,5 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._telemetry.flow_plotting_span(
             self.__class__.__name__, list(self._methods.keys())
         )
+
         plot_flow(self, filename)

src/crewai/flow/flow_events.py

@@ -1,33 +0,0 @@
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Any, Optional
-
-
-@dataclass
-class Event:
-    type: str
-    flow_name: str
-    timestamp: datetime = field(init=False)
-
-    def __post_init__(self):
-        self.timestamp = datetime.now()
-
-
-@dataclass
-class FlowStartedEvent(Event):
-    pass
-
-
-@dataclass
-class MethodExecutionStartedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class MethodExecutionFinishedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class FlowFinishedEvent(Event):
-    result: Optional[Any] = None

src/crewai/flow/utils.py

@@ -31,50 +31,16 @@ def get_possible_return_constants(function):
         print(f"Source code:\n{source}")
         return None
 
-    return_values = set()
-    dict_definitions = {}
-
-    class DictionaryAssignmentVisitor(ast.NodeVisitor):
-        def visit_Assign(self, node):
-            # Check if this assignment is assigning a dictionary literal to a variable
-            if isinstance(node.value, ast.Dict) and len(node.targets) == 1:
-                target = node.targets[0]
-                if isinstance(target, ast.Name):
-                    var_name = target.id
-                    dict_values = []
-                    # Extract string values from the dictionary
-                    for val in node.value.values:
-                        if isinstance(val, ast.Constant) and isinstance(val.value, str):
-                            dict_values.append(val.value)
-                        # If non-string, skip or just ignore
-                    if dict_values:
-                        dict_definitions[var_name] = dict_values
-            self.generic_visit(node)
+    return_values = []
 
     class ReturnVisitor(ast.NodeVisitor):
         def visit_Return(self, node):
-            # Direct string return
-            if isinstance(node.value, ast.Constant) and isinstance(
-                node.value.value, str
-            ):
-                return_values.add(node.value.value)
-            # Dictionary-based return, like return paths[result]
-            elif isinstance(node.value, ast.Subscript):
-                # Check if we're subscripting a known dictionary variable
-                if isinstance(node.value.value, ast.Name):
-                    var_name = node.value.value.id
-                    if var_name in dict_definitions:
-                        # Add all possible dictionary values
-                        for v in dict_definitions[var_name]:
-                            return_values.add(v)
-            self.generic_visit(node)
+            # Check if the return value is a constant (Python 3.8+)
+            if isinstance(node.value, ast.Constant):
+                return_values.append(node.value.value)
 
-    # First pass: identify dictionary assignments
-    DictionaryAssignmentVisitor().visit(code_ast)
-    # Second pass: identify returns
     ReturnVisitor().visit(code_ast)
-
-    return list(return_values) if return_values else None
+    return return_values
 
 
 def calculate_node_levels(flow):
@@ -95,7 +61,10 @@ def calculate_node_levels(flow):
         current_level = levels[current]
         visited.add(current)
 
-        for listener_name, (condition_type, trigger_methods) in flow._listeners.items():
+        for listener_name, (
+            condition_type,
+            trigger_methods,
+        ) in flow._listeners.items():
             if condition_type == "OR":
                 if current in trigger_methods:
                     if (
@@ -120,7 +89,7 @@ def calculate_node_levels(flow):
                             queue.append(listener_name)
 
         # Handle router connections
-        if current in flow._routers:
+        if current in flow._routers.values():
             router_method_name = current
             paths = flow._router_paths.get(router_method_name, [])
             for path in paths:
@@ -136,7 +105,6 @@ def calculate_node_levels(flow):
                             levels[listener_name] = current_level + 1
                             if listener_name not in visited:
                                 queue.append(listener_name)
-
     return levels
 
 
@@ -174,7 +142,7 @@ def dfs_ancestors(node, ancestors, visited, flow):
             dfs_ancestors(listener_name, ancestors, visited, flow)
 
     # Handle router methods separately
-    if node in flow._routers:
+    if node in flow._routers.values():
         router_method_name = node
         paths = flow._router_paths.get(router_method_name, [])
         for path in paths:

src/crewai/flow/visualization_utils.py

@@ -94,14 +94,12 @@ def add_edges(net, flow, node_positions, colors):
     ancestors = build_ancestor_dict(flow)
     parent_children = build_parent_children_dict(flow)
 
-    # Edges for normal listeners
     for method_name in flow._listeners:
         condition_type, trigger_methods = flow._listeners[method_name]
         is_and_condition = condition_type == "AND"
 
         for trigger in trigger_methods:
-            # Check if nodes exist before adding edges
-            if trigger in node_positions and method_name in node_positions:
+            if trigger in flow._methods or trigger in flow._routers.values():
                 is_router_edge = any(
                     trigger in paths for paths in flow._router_paths.values()
                 )
@@ -137,22 +135,7 @@ def add_edges(net, flow, node_positions, colors):
                 }
 
                 net.add_edge(trigger, method_name, **edge_style)
-            else:
-                # Nodes not found in node_positions. Check if it's a known router outcome and a known method.
-                is_router_edge = any(
-                    trigger in paths for paths in flow._router_paths.values()
-                )
-                # Check if method_name is a known method
-                method_known = method_name in flow._methods
 
-                # If it's a known router edge and the method is known, don't warn.
-                # This means the path is legitimate, just not reflected as nodes here.
-                if not (is_router_edge and method_known):
-                    print(
-                        f"Warning: No node found for '{trigger}' or '{method_name}'. Skipping edge."
-                    )
-
-    # Edges for router return paths
     for router_method_name, paths in flow._router_paths.items():
         for path in paths:
             for listener_name, (
@@ -160,49 +143,36 @@ def add_edges(net, flow, node_positions, colors):
                 trigger_methods,
             ) in flow._listeners.items():
                 if path in trigger_methods:
-                    if (
-                        router_method_name in node_positions
-                        and listener_name in node_positions
-                    ):
-                        is_cycle_edge = is_ancestor(
-                            router_method_name, listener_name, ancestors
-                        )
-                        parent_has_multiple_children = (
-                            len(parent_children.get(router_method_name, [])) > 1
-                        )
-                        needs_curvature = is_cycle_edge or parent_has_multiple_children
+                    is_cycle_edge = is_ancestor(trigger, method_name, ancestors)
+                    parent_has_multiple_children = (
+                        len(parent_children.get(router_method_name, [])) > 1
+                    )
+                    needs_curvature = is_cycle_edge or parent_has_multiple_children
 
-                        if needs_curvature:
-                            source_pos = node_positions.get(router_method_name)
-                            target_pos = node_positions.get(listener_name)
+                    if needs_curvature:
+                        source_pos = node_positions.get(router_method_name)
+                        target_pos = node_positions.get(listener_name)
 
-                            if source_pos and target_pos:
-                                dx = target_pos[0] - source_pos[0]
-                                smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
-                                index = get_child_index(
-                                    router_method_name, listener_name, parent_children
-                                )
-                                edge_smooth = {
-                                    "type": smooth_type,
-                                    "roundness": 0.2 + (0.1 * index),
-                                }
-                            else:
-                                edge_smooth = {"type": "cubicBezier"}
-                        else:
-                            edge_smooth = False
-
-                        edge_style = {
-                            "color": colors["router_edge"],
-                            "width": 2,
-                            "arrows": "to",
-                            "dashes": True,
-                            "smooth": edge_smooth,
-                        }
-                        net.add_edge(router_method_name, listener_name, **edge_style)
-                    else:
-                        # Same check here: known router edge and known method?
-                        method_known = listener_name in flow._methods
-                        if not method_known:
-                            print(
-                                f"Warning: No node found for '{router_method_name}' or '{listener_name}'. Skipping edge."
+                        if source_pos and target_pos:
+                            dx = target_pos[0] - source_pos[0]
+                            smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
+                            index = get_child_index(
+                                router_method_name, listener_name, parent_children
                             )
+                            edge_smooth = {
+                                "type": smooth_type,
+                                "roundness": 0.2 + (0.1 * index),
+                            }
+                        else:
+                            edge_smooth = {"type": "cubicBezier"}
+                    else:
+                        edge_smooth = False
+
+                    edge_style = {
+                        "color": colors["router_edge"],
+                        "width": 2,
+                        "arrows": "to",
+                        "dashes": True,
+                        "smooth": edge_smooth,
+                    }
+                    net.add_edge(router_method_name, listener_name, **edge_style)

src/crewai/knowledge/knowledge.py

@@ -1,10 +1,11 @@
 import os
-from typing import Any, Dict, List, Optional
 
+from typing import List, Optional, Dict, Any
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
@@ -14,13 +15,13 @@ class Knowledge(BaseModel):
     Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
-        storage: Optional[KnowledgeStorage] = Field(default=None)
+        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
         embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
@@ -45,20 +46,19 @@ class Knowledge(BaseModel):
             source.storage = self.storage
             source.add()
 
-    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
+    def query(
+        self, query: List[str], limit: int = 3, preference: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
-        
-        Raises:
-            ValueError: If storage is not initialized.
         """
-        if self.storage is None:
-            raise ValueError("Storage is not initialized.")
-            
+
         results = self.storage.search(
             query,
             limit,
+            filter={"preference": preference} if preference else None,
+            score_threshold=DEFAULT_SCORE_THRESHOLD,
         )
         return results
 

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,41 +1,30 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Union, List, Dict, Any
 
-from pydantic import Field, field_validator
+from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.utilities.logger import Logger
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
-from crewai.utilities.logger import Logger
 
 
 class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     """Base class for knowledge sources that load content from files."""
 
     _logger: Logger = Logger(verbose=True)
-    file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field(
-        default=None,
-        description="[Deprecated] The path to the file. Use file_paths instead.",
-    )
-    file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field(
-        default_factory=list, description="The path to the file"
+    file_path: Union[Path, List[Path], str, List[str]] = Field(
+        ..., description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
-    @field_validator("file_path", "file_paths", mode="before")
-    def validate_file_path(cls, v, values):
-        """Validate that at least one of file_path or file_paths is provided."""
-        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
-
     def model_post_init(self, _):
         """Post-initialization method to load content."""
         self.safe_file_paths = self._process_file_paths()
-        self.validate_content()
+        self.validate_paths()
         self.content = self.load_content()
 
     @abstractmethod
@@ -43,13 +32,13 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory."""
         pass
 
-    def validate_content(self):
+    def validate_paths(self):
         """Validate the paths."""
         for path in self.safe_file_paths:
             if not path.exists():
                 self._logger.log(
                     "error",
-                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If its inside the knowledge directory, use the relative path.",
                     color="red",
                 )
                 raise FileNotFoundError(f"File not found: {path}")
@@ -60,12 +49,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
                     color="red",
                 )
 
-    def _save_documents(self):
+    def save_documents(self, metadata: Dict[str, Any]):
         """Save the documents to the storage."""
-        if self.storage:
-            self.storage.save(self.chunks)
-        else:
-            raise ValueError("No storage found to save documents.")
+        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks, chunk_metadatas)
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""
@@ -73,30 +60,13 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _process_file_paths(self) -> List[Path]:
         """Convert file_path to a list of Path objects."""
-
-        if hasattr(self, "file_path") and self.file_path is not None:
-            self._logger.log(
-                "warning",
-                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
-                color="yellow",
-            )
-            self.file_paths = self.file_path
-
-        if self.file_paths is None:
-            raise ValueError("Your source must be provided with a file_paths: []")
-
-        # Convert single path to list
-        path_list: List[Union[Path, str]] = (
-            [self.file_paths]
-            if isinstance(self.file_paths, (str, Path))
-            else list(self.file_paths)
-            if isinstance(self.file_paths, list)
-            else []
+        paths = (
+            [self.file_path]
+            if isinstance(self.file_path, (str, Path))
+            else self.file_path
         )
 
-        if not path_list:
-            raise ValueError(
-                "file_path/file_paths must be a Path, str, or a list of these types"
-            )
+        if not isinstance(paths, list):
+            raise ValueError("file_path must be a Path, str, or a list of these types")
 
-        return [self.convert_to_path(path) for path in path_list]
+        return [self.convert_to_path(path) for path in paths]

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import List, Dict, Any, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
@@ -16,12 +16,12 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
-    metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
     collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
-    def validate_content(self) -> Any:
+    def load_content(self) -> Dict[Any, str]:
         """Load and preprocess content from the source."""
         pass
 
@@ -41,12 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC):
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
 
-    def _save_documents(self):
+    def save_documents(self, metadata: Dict[str, Any]):
         """
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        if self.storage:
-            self.storage.save(self.chunks)
-        else:
-            raise ValueError("No storage found to save documents.")
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/crew_docling_source.py

@@ -1,120 +0,0 @@
-from pathlib import Path
-from typing import Iterator, List, Optional, Union
-from urllib.parse import urlparse
-
-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
-from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
-from crewai.utilities.logger import Logger
-
-
-class CrewDoclingSource(BaseKnowledgeSource):
-    """Default Source class for converting documents to markdown or json
-    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.
-    """
-
-    _logger: Logger = Logger(verbose=True)
-
-    file_path: Optional[List[Union[Path, str]]] = Field(default=None)
-    file_paths: List[Union[Path, str]] = Field(default_factory=list)
-    chunks: List[str] = Field(default_factory=list)
-    safe_file_paths: List[Union[Path, str]] = Field(default_factory=list)
-    content: List[DoclingDocument] = Field(default_factory=list)
-    document_converter: DocumentConverter = Field(
-        default_factory=lambda: DocumentConverter(
-            allowed_formats=[
-                InputFormat.MD,
-                InputFormat.ASCIIDOC,
-                InputFormat.PDF,
-                InputFormat.DOCX,
-                InputFormat.HTML,
-                InputFormat.IMAGE,
-                InputFormat.XLSX,
-                InputFormat.PPTX,
-            ]
-        )
-    )
-
-    def model_post_init(self, _) -> None:
-        if self.file_path:
-            self._logger.log(
-                "warning",
-                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
-                color="yellow",
-            )
-            self.file_paths = self.file_path
-        self.safe_file_paths = self.validate_content()
-        self.content = self._load_content()
-
-    def _load_content(self) -> List[DoclingDocument]:
-        try:
-            return self._convert_source_to_docling_documents()
-        except ConversionError as e:
-            self._logger.log(
-                "error",
-                f"Error loading content: {e}. Supported formats: {self.document_converter.allowed_formats}",
-                "red",
-            )
-            raise e
-        except Exception as e:
-            self._logger.log("error", f"Error loading content: {e}")
-            raise e
-
-    def add(self) -> None:
-        if self.content is None:
-            return
-        for doc in self.content:
-            new_chunks_iterable = self._chunk_doc(doc)
-            self.chunks.extend(list(new_chunks_iterable))
-        self._save_documents()
-
-    def _convert_source_to_docling_documents(self) -> List[DoclingDocument]:
-        conv_results_iter = self.document_converter.convert_all(self.safe_file_paths)
-        return [result.document for result in conv_results_iter]
-
-    def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]:
-        chunker = HierarchicalChunker()
-        for chunk in chunker.chunk(doc):
-            yield chunk.text
-
-    def validate_content(self) -> List[Union[Path, str]]:
-        processed_paths: List[Union[Path, str]] = []
-        for path in self.file_paths:
-            if isinstance(path, str):
-                if path.startswith(("http://", "https://")):
-                    try:
-                        if self._validate_url(path):
-                            processed_paths.append(path)
-                        else:
-                            raise ValueError(f"Invalid URL format: {path}")
-                    except Exception as e:
-                        raise ValueError(f"Invalid URL: {path}. Error: {str(e)}")
-                else:
-                    local_path = Path(KNOWLEDGE_DIRECTORY + "/" + path)
-                    if local_path.exists():
-                        processed_paths.append(local_path)
-                    else:
-                        raise FileNotFoundError(f"File not found: {local_path}")
-            else:
-                # this is an instance of Path
-                processed_paths.append(path)
-        return processed_paths
-
-    def _validate_url(self, url: str) -> bool:
-        try:
-            result = urlparse(url)
-            return all(
-                [
-                    result.scheme in ("http", "https"),
-                    result.netloc,
-                    len(result.netloc.split(".")) >= 2,  # Ensure domain has TLD
-                ]
-            )
-        except Exception:
-            return False

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -1,6 +1,6 @@
 import csv
-from pathlib import Path
 from typing import Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -30,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,6 +1,5 @@
-from pathlib import Path
 from typing import Dict, List
-
+from pathlib import Path
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
 
@@ -45,7 +44,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
 
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/json_knowledge_source.py

@@ -1,6 +1,6 @@
 import json
-from pathlib import Path
 from typing import Any, Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -42,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -1,5 +1,5 @@
+from typing import List, Dict
 from pathlib import Path
-from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -43,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/string_knowledge_source.py

@@ -13,9 +13,9 @@ class StringKnowledgeSource(BaseKnowledgeSource):
 
     def model_post_init(self, _):
         """Post-initialization method to validate content."""
-        self.validate_content()
+        self.load_content()
 
-    def validate_content(self):
+    def load_content(self):
         """Validate string content."""
         if not isinstance(self.content, str):
             raise ValueError("StringKnowledgeSource only accepts string content")
@@ -24,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
         new_chunks = self._chunk_text(self.content)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -1,5 +1,5 @@
-from pathlib import Path
 from typing import Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -24,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import Dict, Any, List, Optional
 
 
 class BaseKnowledgeStorage(ABC):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -1,22 +1,18 @@
 import contextlib
-import hashlib
 import io
 import logging
-import os
-import shutil
-from typing import Any, Dict, List, Optional, Union, cast
-
 import chromadb
-import chromadb.errors
-from chromadb.api import ClientAPI
-from chromadb.api.types import OneOrMany
-from chromadb.config import Settings
+import os
 
-from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
-from crewai.utilities.logger import Logger
+import chromadb.errors
 from crewai.utilities.paths import db_storage_path
+from typing import Optional, List, Dict, Any, Union
+from crewai.utilities import EmbeddingConfigurator
+from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+import hashlib
+from chromadb.config import Settings
+from chromadb.api import ClientAPI
+from crewai.utilities.logger import Logger
 
 
 @contextlib.contextmanager
@@ -107,77 +103,53 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             raise Exception("Failed to create or get collection")
 
     def reset(self):
-        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
-        if not self.app:
+        if self.app:
+            self.app.reset()
+        else:
+            base_path = os.path.join(db_storage_path(), "knowledge")
             self.app = chromadb.PersistentClient(
                 path=base_path,
                 settings=Settings(allow_reset=True),
             )
-
-        self.app.reset()
-        shutil.rmtree(base_path)
-        self.app = None
-        self.collection = None
+            self.app.reset()
 
     def save(
         self,
         documents: List[str],
-        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
+        metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
     ):
-        if not self.collection:
+        if self.collection:
+            try:
+                metadatas = [metadata] if isinstance(metadata, dict) else metadata
+
+                ids = [
+                    hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
+                ]
+
+                self.collection.upsert(
+                    documents=documents,
+                    metadatas=metadatas,
+                    ids=ids,
+                )
+            except chromadb.errors.InvalidDimensionException as e:
+                Logger(verbose=True).log(
+                    "error",
+                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                    "red",
+                )
+                raise ValueError(
+                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                    "across all operations with this collection."
+                    "Try resetting the collection using `crewai reset-memories -a`"
+                ) from e
+            except Exception as e:
+                Logger(verbose=True).log(
+                    "error", f"Failed to upsert documents: {e}", "red"
+                )
+                raise
+        else:
             raise Exception("Collection not initialized")
 
-        try:
-            # Create a dictionary to store unique documents
-            unique_docs = {}
-
-            # Generate IDs and create a mapping of id -> (document, metadata)
-            for idx, doc in enumerate(documents):
-                doc_id = hashlib.sha256(doc.encode("utf-8")).hexdigest()
-                doc_metadata = None
-                if metadata is not None:
-                    if isinstance(metadata, list):
-                        doc_metadata = metadata[idx]
-                    else:
-                        doc_metadata = metadata
-                unique_docs[doc_id] = (doc, doc_metadata)
-
-            # Prepare filtered lists for ChromaDB
-            filtered_docs = []
-            filtered_metadata = []
-            filtered_ids = []
-
-            # Build the filtered lists
-            for doc_id, (doc, meta) in unique_docs.items():
-                filtered_docs.append(doc)
-                filtered_metadata.append(meta)
-                filtered_ids.append(doc_id)
-
-            # If we have no metadata at all, set it to None
-            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
-                None if all(m is None for m in filtered_metadata) else filtered_metadata
-            )
-
-            self.collection.upsert(
-                documents=filtered_docs,
-                metadatas=final_metadata,
-                ids=filtered_ids,
-            )
-        except chromadb.errors.InvalidDimensionException as e:
-            Logger(verbose=True).log(
-                "error",
-                "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
-                "red",
-            )
-            raise ValueError(
-                "Embedding dimension mismatch. Make sure you're using the same embedding model "
-                "across all operations with this collection."
-                "Try resetting the collection using `crewai reset-memories -a`"
-            ) from e
-        except Exception as e:
-            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
-            raise
-
     def _create_default_embedding_function(self):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,

src/crewai/llm.py

@@ -1,5 +1,4 @@
 import logging
-import os
 import sys
 import threading
 import warnings
@@ -43,11 +42,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4-turbo": 128000,
     "o1-preview": 128000,
     "o1-mini": 128000,
-    # gemini
-    "gemini-2.0-flash": 1048576,
-    "gemini-1.5-pro": 2097152,
-    "gemini-1.5-flash": 1048576,
-    "gemini-1.5-flash-8b": 1048576,
     # deepseek
     "deepseek-chat": 128000,
     # groq
@@ -64,13 +58,8 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "llama3-70b-8192": 8192,
     "llama3-8b-8192": 8192,
     "mixtral-8x7b-32768": 32768,
-    "llama-3.3-70b-versatile": 128000,
-    "llama-3.3-70b-instruct": 128000,
 }
 
-DEFAULT_CONTEXT_WINDOW_SIZE = 8192
-CONTEXT_WINDOW_USAGE_RATIO = 0.75
-
 
 @contextmanager
 def suppress_warnings():
@@ -134,13 +123,11 @@ class LLM:
         self.api_version = api_version
         self.api_key = api_key
         self.callbacks = callbacks
-        self.context_window_size = 0
         self.kwargs = kwargs
 
         litellm.drop_params = True
         litellm.set_verbose = False
         self.set_callbacks(callbacks)
-        self.set_env_callbacks()
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
@@ -202,16 +189,7 @@ class LLM:
 
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
-        if self.context_window_size != 0:
-            return self.context_window_size
-
-        self.context_window_size = int(
-            DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
-        )
-        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if self.model.startswith(key):
-                self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
-        return self.context_window_size
+        return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
 
     def set_callbacks(self, callbacks: List[Any]):
         callback_types = [type(callback) for callback in callbacks]
@@ -224,39 +202,3 @@ class LLM:
                 litellm._async_success_callback.remove(callback)
 
         litellm.callbacks = callbacks
-
-    def set_env_callbacks(self):
-        """
-        Sets the success and failure callbacks for the LiteLLM library from environment variables.
-
-        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
-        environment variables, which should contain comma-separated lists of callback names.
-        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
-        respectively.
-
-        If the environment variables are not set or are empty, the corresponding callback lists
-        will be set to empty lists.
-
-        Example:
-            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
-            LITELLM_FAILURE_CALLBACKS="langfuse"
-
-        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
-        `litellm.failure_callback` to ["langfuse"].
-        """
-        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-        success_callbacks = []
-        if success_callbacks_str:
-            success_callbacks = [
-                callback.strip() for callback in success_callbacks_str.split(",")
-            ]
-
-        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-        failure_callbacks = []
-        if failure_callbacks_str:
-            failure_callbacks = [
-                callback.strip() for callback in failure_callbacks_str.split(",")
-            ]
-
-        litellm.success_callback = success_callbacks
-        litellm.failure_callback = failure_callbacks

src/crewai/memory/contextual/contextual_memory.py

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

@@ -15,17 +15,8 @@ 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(storage_path=path) if path else LTMSQLiteStorage()
+            storage = LTMSQLiteStorage(db_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/memory.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional, List
 
 from crewai.memory.storage.rag_storage import RAGStorage
 

src/crewai/memory/short_term/short_term_memory.py

@@ -1,5 +1,4 @@
 from typing import Any, Dict, Optional
-
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -15,7 +14,7 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None
@@ -33,10 +32,7 @@ class ShortTermMemory(Memory):
                 storage
                 if storage
                 else RAGStorage(
-                    type="short_term",
-                    embedder_config=embedder_config,
-                    crew=crew,
-                    path=path,
+                    type="short_term", embedder_config=embedder_config, crew=crew, path=path
                 )
             )
         super().__init__(storage)

src/crewai/memory/storage/base_rag_storage.py

@@ -1,11 +1,5 @@
 from abc import ABC, abstractmethod
-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
+from typing import Any, Dict, List, Optional
 
 
 class BaseRAGStorage(ABC):
@@ -18,46 +12,17 @@ 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]
@@ -66,27 +31,12 @@ class BaseRAGStorage(ABC):
 
     @abstractmethod
     def _sanitize_role(self, role: str) -> str:
-        """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
-        """
+        """Sanitizes agent roles to ensure valid directory names."""
         pass
 
     @abstractmethod
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """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
-        """
+        """Save a value with metadata to the storage."""
         pass
 
     @abstractmethod
@@ -96,55 +46,25 @@ class BaseRAGStorage(ABC):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> 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
-        """
+    ) -> List[Any]:
+        """Search for entries in the storage."""
         pass
 
     @abstractmethod
     def reset(self) -> None:
-        """Reset the storage.
-        
-        Raises:
-            OSError: If there is an error clearing storage
-            PermissionError: If reset is not allowed
-        """
+        """Reset the storage."""
         pass
 
     @abstractmethod
     def _generate_embedding(
         self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> 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
-        """
+    ) -> Any:
+        """Generate an embedding for the given text and metadata."""
         pass
 
     @abstractmethod
-    def _initialize_app(self) -> None:
-        """Initialize the vector db.
-        
-        Raises:
-            OSError: If vector db initialization fails
-        """
+    def _initialize_app(self):
+        """Initialize the vector db."""
         pass
 
     def setup_config(self, config: Dict[str, Any]):

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,13 +1,11 @@
 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 get_default_storage_path
+from crewai.utilities.paths import db_storage_path
 
 
 class KickoffTaskOutputsSQLiteStorage:
@@ -15,26 +13,10 @@ class KickoffTaskOutputsSQLiteStorage:
     An updated SQLite storage class for kickoff task outputs storage.
     """
 
-    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)}")
-            
+    def __init__(
+        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
+    ) -> None:
+        self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -43,7 +25,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -73,21 +55,9 @@ 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(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -120,7 +90,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
 
                 fields = []
@@ -149,7 +119,7 @@ class KickoffTaskOutputsSQLiteStorage:
 
     def load(self) -> Optional[List[Dict[str, Any]]]:
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("""
                 SELECT *
@@ -185,7 +155,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Deletes all rows from the latest_kickoff_task_outputs table.
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")
                 conn.commit()

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,11 +1,9 @@
 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 get_default_storage_path
+from crewai.utilities.paths import db_storage_path
 
 
 class LTMSQLiteStorage:
@@ -13,26 +11,10 @@ class LTMSQLiteStorage:
     An updated SQLite storage class for LTM data storage.
     """
 
-    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)}")
-            
+    def __init__(
+        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
+    ) -> None:
+        self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -41,7 +23,7 @@ class LTMSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -69,20 +51,9 @@ 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(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -103,7 +74,7 @@ class LTMSQLiteStorage:
     ) -> Optional[List[Dict[str, Any]]]:
         """Queries the LTM table by task description with error handling."""
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     f"""
@@ -138,7 +109,7 @@ class LTMSQLiteStorage:
     ) -> None:
         """Resets the LTM table with error handling."""
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM long_term_memories")
                 conn.commit()

src/crewai/memory/storage/mem0_storage.py

@@ -2,7 +2,6 @@ import os
 from typing import Any, Dict, List
 
 from mem0 import MemoryClient
-
 from crewai.memory.storage.interface import Storage
 
 
@@ -19,7 +18,7 @@ class Mem0Storage(Storage):
 
         self.memory_type = type
         self.crew = crew
-        self.memory_config = crew.memory_config if crew else None
+        self.memory_config = crew.memory_config
 
         # 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()
@@ -27,10 +26,9 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        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")
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or 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

@@ -4,13 +4,12 @@ import logging
 import os
 import shutil
 import uuid
+
 from typing import Any, Dict, List, Optional
-
 from chromadb.api import ClientAPI
-
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import db_storage_path
 from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 
 
 @contextlib.contextmanager
@@ -38,21 +37,12 @@ class RAGStorage(BaseRAGStorage):
 
     app: ClientAPI | None = None
 
-    def __init__(
-        self,
-        type,
-        storage_path=None,
-        allow_reset=True,
-        embedder_config=None,
-        crew=None,
-        path=None,
-    ):
-        super().__init__(type, storage_path, allow_reset, embedder_config, crew)
+    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None, path=None):
+        super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
         self.agents = agents
-        self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
 
@@ -70,7 +60,7 @@ class RAGStorage(BaseRAGStorage):
 
         self._set_embedder_config()
         chroma_client = chromadb.PersistentClient(
-            path=self.path if self.path else self.storage_file_name,
+            path=self.path if self.path else f"{db_storage_path()}/{self.type}/{self.agents}",
             settings=Settings(allow_reset=self.allow_reset),
         )
 
@@ -91,20 +81,6 @@ class RAGStorage(BaseRAGStorage):
         """
         return role.replace("\n", "").replace(" ", "_").replace("/", "_")
 
-    def _build_storage_file_name(self, type: str, file_name: str) -> str:
-        """
-        Ensures file name does not exceed max allowed by OS
-        """
-        base_path = f"{self.storage_path}/{type}"
-
-        if len(file_name) > MAX_FILE_NAME_LENGTH:
-            logging.warning(
-                f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
-            )
-            file_name = file_name[:MAX_FILE_NAME_LENGTH]
-
-        return f"{base_path}/{file_name}"
-
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
@@ -155,11 +131,9 @@ class RAGStorage(BaseRAGStorage):
 
     def reset(self) -> None:
         try:
+            shutil.rmtree(f"{db_storage_path()}/{self.type}")
             if self.app:
                 self.app.reset()
-                shutil.rmtree(f"{self.storage_path}/{self.type}")
-                self.app = None
-                self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
                 # Ignore this specific error

src/crewai/memory/user/user_memory.py

@@ -37,7 +37,7 @@ class UserMemory(Memory):
         limit: int = 3,
         score_threshold: float = 0.35,
     ):
-        results = self.storage.search(
+        results = super().search(
             query=query,
             limit=limit,
             score_threshold=score_threshold,

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 = []
         instantiated_agents = []

src/crewai/project/crew_base.py

@@ -213,8 +213,4 @@ def CrewBase(cls: T) -> T:
                     callback_functions[callback]() for callback in callbacks
                 ]
 
-    # 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/project/utils.py

@@ -1,14 +1,11 @@
-from functools import wraps
-
-
 def memoize(func):
     cache = {}
 
-    @wraps(func)
     def memoized_func(*args, **kwargs):
         key = (args, tuple(kwargs.items()))
         if key not in cache:
             cache[key] = func(*args, **kwargs)
         return cache[key]
 
+    memoized_func.__dict__.update(func.__dict__)
     return memoized_func

src/crewai/task.py

@@ -1,25 +1,12 @@
 import datetime
-import inspect
 import json
-import logging
+import os
 import threading
 import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
-from pathlib import Path
-from typing import (
-    Any,
-    Callable,
-    ClassVar,
-    Dict,
-    List,
-    Optional,
-    Set,
-    Tuple,
-    Type,
-    Union,
-)
+from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
 
 from opentelemetry.trace import Span
 from pydantic import (
@@ -33,7 +20,6 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
@@ -63,7 +49,6 @@ class Task(BaseModel):
     """
 
     __hash__ = object.__hash__  # type: ignore
-    logger: ClassVar[logging.Logger] = logging.getLogger(__name__)
     used_tools: int = 0
     tools_errors: int = 0
     delegations: int = 0
@@ -125,55 +110,6 @@ class Task(BaseModel):
         default=None,
     )
     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"
-    )
-    max_retries: int = Field(
-        default=3,
-        description="Maximum number of retries when guardrail fails"
-    )
-    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]
-        """
-        if v is not None:
-            sig = inspect.signature(v)
-            if len(sig.parameters) != 1:
-                raise ValueError("Guardrail function must accept exactly one parameter")
-
-            # 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]")
-        return v
 
     _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
     _execution_span: Optional[Span] = PrivateAttr(default=None)
@@ -318,6 +254,7 @@ class Task(BaseModel):
         )
 
         pydantic_output, json_output = self._export_output(result)
+
         task_output = TaskOutput(
             name=self.name,
             description=self.description,
@@ -328,37 +265,6 @@ class Task(BaseModel):
             agent=agent.role,
             output_format=self._get_output_format(),
         )
-
-        if self.guardrail:
-            guardrail_result = GuardrailResult.from_tuple(self.guardrail(task_output))
-            if not guardrail_result.success:
-                if self.retry_count >= self.max_retries:
-                    raise Exception(
-                        f"Task failed guardrail validation after {self.max_retries} retries. "
-                        f"Last error: {guardrail_result.error}"
-                    )
-
-                self.retry_count += 1
-                context = (
-                    f"### Previous attempt failed validation: {guardrail_result.error}\n\n\n"
-                    f"### Previous result:\n{task_output.raw}\n\n\n"
-                    "Try again, making sure to address the validation error."
-                )
-                return self._execute_core(agent, context, tools)
-
-            if guardrail_result.result is None:
-                raise Exception(
-                    "Task guardrail returned None as result. This is not allowed."
-                )
-
-            if isinstance(guardrail_result.result, str):
-                task_output.raw = 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._set_end_execution_time(start_time)
@@ -373,9 +279,7 @@ 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)
 
@@ -404,18 +308,7 @@ class Task(BaseModel):
 
         if inputs:
             self.description = self._original_description.format(**inputs)
-            self.expected_output = self.interpolate_only(
-                input_string=self._original_expected_output, inputs=inputs
-            )
-
-    def interpolate_only(self, input_string: str, inputs: Dict[str, Any]) -> str:
-        """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched."""
-        escaped_string = input_string.replace("{", "{{").replace("}", "}}")
-
-        for key in inputs.keys():
-            escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}")
-
-        return escaped_string.format(**inputs)
+            self.expected_output = self._original_expected_output.format(**inputs)
 
     def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
@@ -497,33 +390,21 @@ class Task(BaseModel):
         return OutputFormat.RAW
 
     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
-        """
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        try:
-            resolved_path = Path(self.output_file).expanduser().resolve()
-            directory = resolved_path.parent
+        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
 
-            if not directory.exists():
-                directory.mkdir(parents=True, exist_ok=True)
+        if directory and not os.path.exists(directory):
+            os.makedirs(directory)
 
-            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))
-        except (OSError, IOError) as e:
-            raise RuntimeError(f"Failed to save output file: {e}")
+        with open(self.output_file, "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))
         return None
 
     def __repr__(self):

src/crewai/tasks/guardrail_result.py

@@ -1,56 +0,0 @@
-"""
-Module for handling task guardrail validation results.
-
-This module provides the GuardrailResult class which standardizes
-the way task guardrails return their validation results.
-"""
-
-from typing import Any, Optional, Tuple, Union
-
-from pydantic import BaseModel, field_validator
-
-
-class GuardrailResult(BaseModel):
-    """Result from a task guardrail execution.
-
-    This class standardizes the return format of task guardrails,
-    converting tuple responses into a structured format that can
-    be easily handled by the task execution system.
-
-    Attributes:
-        success (bool): Whether the guardrail validation passed
-        result (Any, optional): The validated/transformed result if successful
-        error (str, optional): Error message if validation failed
-    """
-    success: bool
-    result: Optional[Any] = None
-    error: Optional[str] = None
-
-    @field_validator("result", "error")
-    @classmethod
-    def validate_result_error_exclusivity(cls, v: Any, info) -> Any:
-        values = info.data
-        if "success" in values:
-            if values["success"] and v and "error" in values and values["error"]:
-                raise ValueError("Cannot have both result and error when success is True")
-            if not values["success"] and v and "result" in values and values["result"]:
-                raise ValueError("Cannot have both result and error when success is False")
-        return v
-
-    @classmethod
-    def from_tuple(cls, result: Tuple[bool, Union[Any, str]]) -> "GuardrailResult":
-        """Create a GuardrailResult from a validation tuple.
-
-        Args:
-            result: A tuple of (success, data) where data is either
-                   the validated result or error message.
-
-        Returns:
-            GuardrailResult: A new instance with the tuple data.
-        """
-        success, data = result
-        return cls(
-            success=success,
-            result=data if success else None,
-            error=data if not success else None
-        )

src/crewai/telemetry/telemetry.py

@@ -6,7 +6,6 @@ import os
 import platform
 import warnings
 from contextlib import contextmanager
-from importlib.metadata import version
 from typing import TYPE_CHECKING, Any, Optional
 
 
@@ -17,10 +16,12 @@ def suppress_warnings():
         yield
 
 
+with suppress_warnings():
+    import pkg_resources
+
+
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
-    OTLPSpanExporter,  # noqa: E402
-)
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
@@ -103,7 +104,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "python_version", platform.python_version())
             self._add_attribute(span, "crew_key", crew.key)
@@ -305,7 +306,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "tool_name", tool_name)
             self._add_attribute(span, "attempts", attempts)
@@ -325,7 +326,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "tool_name", tool_name)
             self._add_attribute(span, "attempts", attempts)
@@ -345,7 +346,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             if llm:
                 self._add_attribute(span, "llm", llm.model)
@@ -364,7 +365,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -390,7 +391,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -471,7 +472,7 @@ class Telemetry:
             self._add_attribute(
                 span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(span, "crew_key", crew.key)
             self._add_attribute(span, "crew_id", str(crew.id))
@@ -540,7 +541,7 @@ class Telemetry:
             self._add_attribute(
                 crew._execution_span,
                 "crewai_version",
-                version("crewai"),
+                pkg_resources.get_distribution("crewai").version,
             )
             self._add_attribute(
                 crew._execution_span, "crew_output", final_string_output

src/crewai/tools/agent_tools/add_image_tool.py

@@ -1,45 +0,0 @@
-from typing import Dict, Optional, Union
-
-from pydantic import BaseModel, Field
-
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities import I18N
-
-i18n = I18N()
-
-class AddImageToolSchema(BaseModel):
-    image_url: str = Field(..., description="The URL or path of the image to add")
-    action: Optional[str] = Field(
-        default=None,
-        description="Optional context or question about the image"
-    )
-
-
-class AddImageTool(BaseTool):
-    """Tool for adding images to the content"""
-
-    name: str = Field(default_factory=lambda: i18n.tools("add_image")["name"])  # type: ignore
-    description: str = Field(default_factory=lambda: i18n.tools("add_image")["description"])  # type: ignore
-    args_schema: type[BaseModel] = AddImageToolSchema
-
-    def _run(
-        self,
-        image_url: str,
-        action: Optional[str] = None,
-        **kwargs,
-    ) -> dict:
-        action = action or i18n.tools("add_image")["default_action"]  # type: ignore
-        content = [
-            {"type": "text", "text": action},
-            {
-                "type": "image_url",
-                "image_url": {
-                    "url": image_url,
-                },
-            }
-        ]
-
-        return {
-            "role": "user",
-            "content": content
-        }

src/crewai/tools/agent_tools/agent_tools.py

@@ -1,9 +1,9 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.utilities import I18N
 
-from .ask_question_tool import AskQuestionTool
 from .delegate_work_tool import DelegateWorkTool
+from .ask_question_tool import AskQuestionTool
 
 
 class AgentTools:
@@ -20,13 +20,13 @@ class AgentTools:
         delegate_tool = DelegateWorkTool(
             agents=self.agents,
             i18n=self.i18n,
-            description=self.i18n.tools("delegate_work").format(coworkers=coworkers),  # type: ignore
+            description=self.i18n.tools("delegate_work").format(coworkers=coworkers),
         )
 
         ask_tool = AskQuestionTool(
             agents=self.agents,
             i18n=self.i18n,
-            description=self.i18n.tools("ask_question").format(coworkers=coworkers),  # type: ignore
+            description=self.i18n.tools("ask_question").format(coworkers=coworkers),
         )
 
         return [delegate_tool, ask_tool]

src/crewai/tools/agent_tools/ask_question_tool.py

@@ -1,8 +1,6 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
 from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+from typing import Optional
+from pydantic import BaseModel, Field
 
 
 class AskQuestionToolSchema(BaseModel):

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,10 +1,9 @@
 from typing import Optional, Union
-
 from pydantic import Field
 
+from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
-from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
 
@@ -45,14 +44,14 @@ class BaseAgentTool(BaseTool):
                 if available_agent.role.casefold().replace("\n", "") == agent_name
             ]
         except Exception as _:
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )
             )
 
         if not agent:
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )

src/crewai/tools/agent_tools/delegate_work_tool.py

@@ -1,9 +1,8 @@
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
-
 
 class DelegateWorkToolSchema(BaseModel):
     task: str = Field(..., description="The task to delegate")

src/crewai/tools/tool_usage.py

@@ -1,5 +1,6 @@
 import ast
 import datetime
+import os
 import time
 from difflib import SequenceMatcher
 from textwrap import dedent
@@ -10,16 +11,18 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools import BaseTool
-from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
 
-try:
-    import agentops  # type: ignore
-except ImportError:
-    agentops = None
-OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini", "o1", "o3", "o3-mini"]
+agentops = None
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        import agentops  # type: ignore
+    except ImportError:
+        pass
+
+OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
 
 
 class ToolUsageErrorException(Exception):
@@ -103,19 +106,6 @@ class ToolUsage:
             if self.agent.verbose:
                 self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
-
-        if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]:  # type: ignore
-            try:
-                result = self._use(tool_string=tool_string, tool=tool, calling=calling)
-                return result
-
-            except Exception as e:
-                error = getattr(e, "message", str(e))
-                self.task.increment_tools_errors()
-                if self.agent.verbose:
-                    self._printer.print(content=f"\n\n{error}\n", color="red")
-                return error
-
         return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"  # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
 
     def _use(
@@ -432,10 +422,9 @@ class ToolUsage:
                 elif value.lower() in [
                     "true",
                     "false",
+                    "null",
                 ]:  # Check for boolean and null values
-                    value = value.lower().capitalize()
-                elif value.lower() == "null":
-                    value = "None"
+                    value = value.lower()
                 else:
                     # Assume the value is a string and needs quotes
                     value = '"' + value.replace('"', '\\"') + '"'

src/crewai/tools/tool_usage_events.py

@@ -1,7 +1,6 @@
-from datetime import datetime
 from typing import Any, Dict
-
 from pydantic import BaseModel
+from datetime import datetime
 
 
 class ToolUsageEvent(BaseModel):

src/crewai/translations/en.json

@@ -12,14 +12,14 @@
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
     "no_tools": "\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
     "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
-    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
+    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
     "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",
-    "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
+    "sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
@@ -28,7 +28,7 @@
   "errors": {
     "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
     "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
-    "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
+    "agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
     "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
     "tool_usage_error": "I encountered an error: {error}",
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
@@ -37,11 +37,6 @@
   },
   "tools": {
     "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
-    "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them.",
-    "add_image": {
-      "name": "Add image to content",
-      "description": "See image to understand it's content, you can optionally ask a question about the image",
-      "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe."
-    }
+    "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them."
   }
 }

src/crewai/utilities/constants.py

@@ -3,4 +3,3 @@ TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
 DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
-MAX_FILE_NAME_LENGTH = 255

src/crewai/utilities/crew_json_encoder.py

@@ -1,17 +1,15 @@
+from datetime import datetime, date
 import json
-from datetime import date, datetime
-from decimal import Decimal
-from enum import Enum
 from uuid import UUID
-
 from pydantic import BaseModel
+from decimal import Decimal
 
 
 class CrewJSONEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID) or isinstance(obj, Decimal) or isinstance(obj, Enum):
+        elif isinstance(obj, UUID) or isinstance(obj, Decimal):
             return str(obj)
 
         elif isinstance(obj, datetime) or isinstance(obj, date):

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -1,10 +1,9 @@
 import json
+import regex
 from typing import Any, Type
 
-import regex
-from pydantic import BaseModel, ValidationError
-
 from crewai.agents.parser import OutputParserException
+from pydantic import BaseModel, ValidationError
 
 
 class CrewPydanticOutputParser:

src/crewai/utilities/embedding_configurator.py

@@ -1,7 +1,6 @@
 import os
 from typing import Any, Dict, cast
-
-from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb import EmbeddingFunction, Documents, Embeddings
 from chromadb.api.types import validate_embedding_function
 
 
@@ -27,7 +26,7 @@ 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")
 
@@ -38,13 +37,12 @@ class EmbeddingConfigurator:
             except Exception as e:
                 raise ValueError(f"Invalid custom embedding function: {str(e)}")
 
-        embedding_function = self.embedding_functions.get(provider, None)
-        if not embedding_function:
+        if provider not in self.embedding_functions:
             raise Exception(
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
 
-        return embedding_function(config, model_name)
+        return self.embedding_functions[provider](config, model_name)
 
     @staticmethod
     def _create_default_embedding_function():

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,14 +1,13 @@
 from collections import defaultdict
 
-from pydantic import BaseModel, Field
-from rich.box import HEAVY_EDGE
-from rich.console import Console
-from rich.table import Table
-
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
+from pydantic import BaseModel, Field
+from rich.box import HEAVY_EDGE
+from rich.console import Console
+from rich.table import Table
 
 
 class TaskEvaluationPydanticOutput(BaseModel):

src/crewai/utilities/evaluators/task_evaluator.py

@@ -1,3 +1,4 @@
+import os
 from typing import List
 
 from pydantic import BaseModel, Field
@@ -5,17 +6,27 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
-agentops = None
-try:
-    from agentops import track_agent  # type: ignore
-except ImportError:
 
-    def track_agent(name):
+def mock_agent_ops_provider():
+    def track_agent(*args, **kwargs):
         def noop(f):
             return f
 
         return noop
 
+    return track_agent
+
+
+agentops = None
+
+if os.environ.get("AGENTOPS_API_KEY"):
+    try:
+        from agentops import track_agent
+    except ImportError:
+        track_agent = mock_agent_ops_provider()
+else:
+    track_agent = mock_agent_ops_provider()
+
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")

src/crewai/utilities/events.py

@@ -1,8 +1,8 @@
+from typing import Any, Callable, Generic, List, Dict, Type, TypeVar
 from functools import wraps
-from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
-
 from pydantic import BaseModel
 
+
 T = TypeVar("T")
 EVT = TypeVar("EVT", bound=BaseModel)
 

src/crewai/utilities/i18n.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Dict, Optional, Union
+from typing import Dict, Optional
 
 from pydantic import BaseModel, Field, PrivateAttr, model_validator
 
@@ -41,8 +41,8 @@ class I18N(BaseModel):
     def errors(self, error: str) -> str:
         return self.retrieve("errors", error)
 
-    def tools(self, tool: str) -> Union[str, Dict[str, str]]:
-        return self.retrieve("tools", tool)
+    def tools(self, error: str) -> str:
+        return self.retrieve("tools", error)
 
     def retrieve(self, kind, key) -> str:
         try:

src/crewai/utilities/paths.py

@@ -22,26 +22,3 @@ 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}")

src/crewai/utilities/planning_handler.py

@@ -1,5 +1,4 @@
 from typing import Any, List, Optional
-
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent

src/crewai/utilities/prompts.py

@@ -1,7 +1,5 @@
-from typing import Any, Optional
-
 from pydantic import BaseModel, Field
-
+from typing import Any, Optional
 from crewai.utilities import I18N
 
 

src/crewai/utilities/pydantic_schema_parser.py

@@ -1,4 +1,4 @@
-from typing import Type, Union, get_args, get_origin
+from typing import Type, get_args, get_origin, Union
 
 from pydantic import BaseModel
 

src/crewai/utilities/task_output_storage_handler.py

@@ -1,8 +1,6 @@
-from datetime import datetime
-from typing import Any, Dict, List, Optional
-
 from pydantic import BaseModel, Field
-
+from datetime import datetime
+from typing import Dict, Any, Optional, List
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )