fix: use isinstance for proper type narrowing in Knowledge.query

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

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/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

pyproject.toml

@@ -67,6 +67,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
+    "crewai-tools>=0.17.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/agent.py

@@ -23,7 +23,6 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
 from crewai.utilities.converter import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
-from crewai.utilities.typing import AgentConfig
 
 agentops = None
 
@@ -89,7 +88,6 @@ class Agent(BaseAgent):
     function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    config: Optional[Union[Dict[str, Any], AgentConfig]] = Field(default=None)
     system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
     )

src/crewai/knowledge/knowledge.py

@@ -14,13 +14,13 @@ 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: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+        storage: Optional[KnowledgeStorage] = Field(default=None)
         embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
@@ -49,9 +49,15 @@ class Knowledge(BaseModel):
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
-        """
 
-        results = self.storage.search(
+        Raises:
+            ValueError: If no storage is configured for querying.
+        """
+        storage = self.storage
+        if not isinstance(storage, KnowledgeStorage):
+            raise ValueError("No storage found to perform query.")
+        # Using isinstance check for proper type narrowing
+        results = storage.search(
             query,
             limit,
         )

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -22,7 +22,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         default_factory=list, description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
@@ -62,7 +62,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _save_documents(self):
         """Save the documents to the storage."""
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -16,7 +16,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
     collection_name: Optional[str] = Field(default=None)
 
@@ -46,4 +46,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")

src/crewai/project/annotations.py

@@ -16,12 +16,6 @@ def after_kickoff(func):
 
 
 def task(func):
-    """Decorator to mark a method as a task creator.
-    
-    When applied to a method in a class decorated with @CrewBase,
-    this makes the method's return value accessible as an element
-    of the self.tasks list.
-    """
     func.is_task = True
 
     @wraps(func)
@@ -35,12 +29,6 @@ def task(func):
 
 
 def agent(func):
-    """Decorator to mark a method as an agent creator.
-    
-    When applied to a method in a class decorated with @CrewBase,
-    this makes the method's return value accessible as an element
-    of the self.agents list.
-    """
     func.is_agent = True
     func = memoize(func)
     return func

src/crewai/project/crew_base.py

@@ -1,6 +1,6 @@
 import inspect
 from pathlib import Path
-from typing import Any, Callable, Dict, List, TypeVar, cast
+from typing import Any, Callable, Dict, TypeVar, cast
 
 import yaml
 from dotenv import load_dotenv
@@ -66,9 +66,6 @@ def CrewBase(cls: T) -> T:
             self._kickoff = self._filter_functions(
                 self._original_functions, "is_kickoff"
             )
-            
-            self.agents = []  # type: List[Any]
-            self.tasks = []   # type: List[Any]
 
         @staticmethod
         def load_yaml(config_path: Path):

src/crewai/task.py

@@ -41,7 +41,6 @@ from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.i18n import I18N
-from crewai.utilities.typing import TaskConfig
 
 
 class Task(BaseModel):
@@ -75,7 +74,7 @@ class Task(BaseModel):
     expected_output: str = Field(
         description="Clear definition of expected output for the task."
     )
-    config: Optional[Union[Dict[str, Any], TaskConfig]] = Field(
+    config: Optional[Dict[str, Any]] = Field(
         description="Configuration for the agent",
         default=None,
     )

src/crewai/utilities/typing/__init__.py

@@ -1,14 +0,0 @@
-from typing import Dict, List, Optional, Any, TypedDict, Union
-
-class AgentConfig(TypedDict, total=False):
-    """TypedDict for agent configuration loaded from YAML."""
-    role: str
-    goal: str
-    backstory: str
-    verbose: bool
-
-class TaskConfig(TypedDict, total=False):
-    """TypedDict for task configuration loaded from YAML."""
-    description: str
-    expected_output: str
-    agent: str  # Role of the agent to execute this task

tests/typing_test.py

@@ -1,55 +0,0 @@
-from typing import Dict, Any
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.task import Task
-from crewai.utilities.typing import AgentConfig, TaskConfig
-
-
-def test_agent_with_config_dict():
-    config: AgentConfig = {
-        "role": "Test Agent", 
-        "goal": "Test Goal", 
-        "backstory": "Test Backstory",
-        "verbose": True
-    }
-    
-    agent = Agent(config=config)
-    
-    assert agent.role == "Test Agent"
-    assert agent.goal == "Test Goal"
-    assert agent.backstory == "Test Backstory"
-    assert agent.verbose is True
-
-
-def test_agent_with_yaml_config():
-    config: Dict[str, Any] = {
-        "researcher": {
-            "role": "Researcher",
-            "goal": "Research Goal",
-            "backstory": "Researcher Backstory",
-            "verbose": True
-        }
-    }
-    
-    agent = Agent(config=config["researcher"])
-    
-    assert agent.role == "Researcher"
-    assert agent.goal == "Research Goal"
-    assert agent.backstory == "Researcher Backstory"
-
-
-def test_task_with_config_dict():
-    config: TaskConfig = {
-        "description": "Test Task",
-        "expected_output": "Test Output",
-        "agent": "researcher"
-    }
-    
-    agent = Agent(role="Researcher", goal="Goal", backstory="Backstory")
-    task = Task(config=config, agent=agent)
-    
-    assert task.description == "Test Task"
-    assert task.expected_output == "Test Output"
-    assert task.agent == agent