Fix lint error and validation error in test_markdown_task.py

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

docs/concepts/cli.mdx

@@ -110,6 +110,8 @@ crewai reset-memories [OPTIONS]
 - `-s, --short`: Reset SHORT TERM memory
 - `-e, --entities`: Reset ENTITIES memory
 - `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-kn, --knowledge`: Reset KNOWLEDGE storage
+- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
 - `-a, --all`: Reset ALL memories
 
 Example:

docs/concepts/crews.mdx

@@ -117,6 +117,12 @@ class YourCrewName:
         )
 ```
 
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={"any": "input here"})
+```
+
 <Note>
 Tasks will be executed in the order they are defined.
 </Note>
@@ -184,6 +190,11 @@ class YourCrewName:
             verbose=True
         )
 ```
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={})
+```
 
 In this example:
 

docs/concepts/flows.mdx

@@ -75,11 +75,12 @@ class ExampleFlow(Flow):
 
 
 flow = ExampleFlow()
+flow.plot()
 result = flow.kickoff()
 
 print(f"Generated fun fact: {result}")
 ```
-
+![Flow Visual image](/images/crewai-flow-1.png)
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
 
 Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
@@ -146,6 +147,7 @@ class OutputExampleFlow(Flow):
 
 
 flow = OutputExampleFlow()
+flow.plot("my_flow_plot")
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
@@ -158,9 +160,10 @@ Second method received: Output from first_method
 ```
 
 </CodeGroup>
+![Flow Visual image](/images/crewai-flow-2.png)
 
 In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
-The `kickoff()` method will return the final output, which is then printed to the console.
+The `kickoff()` method will return the final output, which is then printed to the console. The `plot()` method will generate the HTML file, which will help you understand the flow.
 
 #### Accessing and Updating State
 
@@ -192,6 +195,7 @@ class StateExampleFlow(Flow[ExampleState]):
         return self.state.message
 
 flow = StateExampleFlow()
+flow.plot("my_flow_plot")
 final_output = flow.kickoff()
 print(f"Final Output: {final_output}")
 print("Final State:")
@@ -206,6 +210,8 @@ counter=2 message='Hello from first_method - updated by second_method'
 
 </CodeGroup>
 
+![Flow Visual image](/images/crewai-flow-2.png)
+
 In this example, the state is updated by both `first_method` and `second_method`.
 After the Flow has run, you can access the final state to see the updates made by these methods.
 
@@ -249,9 +255,12 @@ class UnstructuredExampleFlow(Flow):
 
 
 flow = UnstructuredExampleFlow()
+flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
+![Flow Visual image](/images/crewai-flow-3.png)
+
 **Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
 
 **Key Points:**
@@ -302,6 +311,8 @@ flow = StructuredExampleFlow()
 flow.kickoff()
 ```
 
+![Flow Visual image](/images/crewai-flow-3.png)
+
 **Key Points:**
 
 - **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
@@ -436,6 +447,7 @@ class OrExampleFlow(Flow):
 
 
 flow = OrExampleFlow()
+flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -446,6 +458,8 @@ Logger: Hello from the second method
 
 </CodeGroup>
 
+![Flow Visual image](/images/crewai-flow-4.png)
+
 When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
 The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
 
@@ -474,6 +488,7 @@ class AndExampleFlow(Flow):
         print(self.state)
 
 flow = AndExampleFlow()
+flow.plot()
 flow.kickoff()
 ```
 
@@ -484,6 +499,8 @@ flow.kickoff()
 
 </CodeGroup>
 
+![Flow Visual image](/images/crewai-flow-5.png)
+
 When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
 The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
 
@@ -527,6 +544,7 @@ class RouterFlow(Flow[ExampleState]):
 
 
 flow = RouterFlow()
+flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -538,6 +556,8 @@ Fourth method running
 
 </CodeGroup>
 
+![Flow Visual image](/images/crewai-flow-6.png)
+
 In the above example, the `start_method` generates a random boolean value and sets it in the state.
 The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
 If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
@@ -641,6 +661,7 @@ class MarketResearchFlow(Flow[MarketResearchState]):
 # Usage example
 async def run_flow():
     flow = MarketResearchFlow()
+    flow.plot("MarketResearchFlowPlot")
     result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
     return result
 
@@ -650,6 +671,8 @@ if __name__ == "__main__":
     asyncio.run(run_flow())
 ```
 
+![Flow Visual image](/images/crewai-flow-7.png)
+
 This example demonstrates several key features of using Agents in flows:
 
 1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
@@ -746,13 +769,16 @@ def kickoff():
 
 def plot():
     poem_flow = PoemFlow()
-    poem_flow.plot()
+    poem_flow.plot("PoemFlowPlot")
 
 if __name__ == "__main__":
     kickoff()
+    plot()
 ```
 
-In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
+In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method. The PoemFlowPlot will be generated by `plot()` method.
+
+![Flow Visual image](/images/crewai-flow-8.png)
 
 ### Running the Flow
 

docs/concepts/knowledge.mdx

@@ -497,6 +497,13 @@ crew = Crew(
 result = crew.kickoff(
     inputs={"question": "What is the storage capacity of the XPS 13?"}
 )
+
+# Resetting the agent specific knowledge via crew object
+crew.reset_memories(command_type = 'agent_knowledge')
+
+# Resetting the agent specific knowledge via CLI
+crewai reset-memories --agent-knowledge 
+crewai reset-memories -akn
 ```
 
 <Info>

docs/concepts/llms.mdx

@@ -677,18 +677,24 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai import LLM
-    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
+    from crewai.utilities.events import (
+      LLMStreamChunkEvent
+    )
+    from crewai.utilities.events.base_event_listener import BaseEventListener
 
-    class MyEventHandler(EventHandler):
-        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
-            # Process each chunk as it arrives
-            print(f"Received chunk: {event.chunk}")
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
+              # Process each chunk as it arrives
+              print(f"Received chunk: {event.chunk}")
 
-    # Register the event handler
-    from crewai.utilities.events import crewai_event_bus
-    crewai_event_bus.register_handler(MyEventHandler())
+    my_listener = MyCustomListener()
     ```
+
+    <Tip>
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+    </Tip>
   </Tab>
 </Tabs>
 
@@ -785,6 +791,24 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
+  
+  <Accordion title="Drop Additional Parameters">
+    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
+    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
+
+    ```python
+    from crewai import LLM
+    import os
+
+    os.environ["OPENAI_API_KEY"] = "<api-key>"
+
+    o3_llm = LLM(
+        model="o3",
+        drop_params=True,
+        additional_drop_params=["stop"]
+    )
+    ```
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/concepts/memory.mdx

@@ -679,6 +679,7 @@ crewai reset-memories [OPTIONS]
 | `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
 | `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
 | `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False   |
+| `-akn`, `--agent-knowledge` | Reset AGENT KNOWLEDGE storage | Flag (boolean) | False   |
 | `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |
 
 Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
@@ -716,9 +717,11 @@ my_crew.reset_memories(command_type = 'all') # Resets all the memory
 | `entities`         | Reset ENTITIES memory.           | 
 | `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
 | `knowledge`        | Reset KNOWLEDGE memory.          |
+| `agent_knowledge`  | Reset AGENT KNOWLEDGE memory.    |
 | `all`              | Reset ALL memories.              |
 
 
+
 ## Benefits of Using CrewAI's Memory System
 
 - 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.

docs/docs.json

@@ -129,6 +129,7 @@
               "tools/seleniumscrapingtool",
               "tools/snowflakesearchtool",
               "tools/spidertool",
+              "tools/stagehandtool",
               "tools/txtsearchtool",
               "tools/visiontool",
               "tools/weaviatevectorsearchtool",

docs/guides/advanced/customizing-prompts.mdx

@@ -4,8 +4,6 @@ description: Dive deeper into low-level prompt customization for CrewAI, enablin
 icon: message-pen
 ---
 
-# Customizing Prompts at a Low Level
-
 ## Why Customize Prompts?
 
 Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here’s why you might want to take advantage of this deeper control:

docs/guides/advanced/fingerprinting.mdx

@@ -4,8 +4,6 @@ description: Learn how to use CrewAI's fingerprinting system to uniquely identif
 icon: fingerprint
 ---
 
-# Fingerprinting in CrewAI
-
 ## Overview
 
 Fingerprints in CrewAI provide a way to uniquely identify and track components throughout their lifecycle. Each `Agent`, `Crew`, and `Task` automatically receives a unique fingerprint when created, which cannot be manually overridden.

docs/guides/agents/crafting-effective-agents.mdx

@@ -4,8 +4,6 @@ description: Learn best practices for designing powerful, specialized AI agents
 icon: robot
 ---
 
-# Crafting Effective Agents
-
 ## The Art and Science of Agent Design
 
 At the heart of CrewAI lies the agent - a specialized AI entity designed to perform specific roles within a collaborative framework. While creating basic agents is simple, crafting truly effective agents that produce exceptional results requires understanding key design principles and best practices.

docs/guides/concepts/evaluating-use-cases.mdx

@@ -4,8 +4,6 @@ description: Learn how to assess your AI application needs and choose the right
 icon: scale-balanced
 ---
 
-# Evaluating Use Cases for CrewAI
-
 ## Understanding the Decision Framework
 
 When building AI applications with CrewAI, one of the most important decisions you'll make is choosing the right approach for your specific use case. Should you use a Crew? A Flow? A combination of both? This guide will help you evaluate your requirements and make informed architectural decisions.

docs/guides/crews/first-crew.mdx

@@ -4,8 +4,6 @@ description: Step-by-step tutorial to create a collaborative AI team that works
 icon: users-gear
 ---
 
-# Build Your First Crew
-
 ## Unleashing the Power of Collaborative AI
 
 Imagine having a team of specialized AI agents working together seamlessly to solve complex problems, each contributing their unique skills to achieve a common goal. This is the power of CrewAI - a framework that enables you to create collaborative AI systems that can accomplish tasks far beyond what a single AI could achieve alone.

docs/guides/flows/first-flow.mdx

@@ -4,8 +4,6 @@ description: Learn how to create structured, event-driven workflows with precise
 icon: diagram-project
 ---
 
-# Build Your First Flow
-
 ## Taking Control of AI Workflows with Flows
 
 CrewAI Flows represent the next level in AI orchestration - combining the collaborative power of AI agent crews with the precision and flexibility of procedural programming. While crews excel at agent collaboration, flows give you fine-grained control over exactly how and when different components of your AI system interact.

docs/guides/flows/mastering-flow-state.mdx

@@ -4,8 +4,6 @@ description: A comprehensive guide to managing, persisting, and leveraging state
 icon: diagram-project
 ---
 
-# Mastering Flow State Management
-
 ## Understanding the Power of State in Flows
 
 State management is the backbone of any sophisticated AI workflow. In CrewAI Flows, the state system allows you to maintain context, share data between steps, and build complex application logic. Mastering state management is essential for creating reliable, maintainable, and powerful AI applications.

docs/tools/stagehandtool.mdx

@@ -0,0 +1,244 @@
+---
+title: Stagehand Tool
+description: Web automation tool that integrates Stagehand with CrewAI for browser interaction and automation
+icon: hand
+---
+
+
+# Overview
+
+The `StagehandTool` integrates the [Stagehand](https://docs.stagehand.dev/get_started/introduction) framework with CrewAI, enabling agents to interact with websites and automate browser tasks using natural language instructions.
+
+## Overview
+
+Stagehand is a powerful browser automation framework built by Browserbase that allows AI agents to:
+
+- Navigate to websites
+- Click buttons, links, and other elements
+- Fill in forms
+- Extract data from web pages
+- Observe and identify elements
+- Perform complex workflows
+
+The StagehandTool wraps the Stagehand Python SDK to provide CrewAI agents with browser control capabilities through three core primitives:
+
+1. **Act**: Perform actions like clicking, typing, or navigating
+2. **Extract**: Extract structured data from web pages
+3. **Observe**: Identify and analyze elements on the page
+
+## Prerequisites
+
+Before using this tool, ensure you have:
+
+1. A [Browserbase](https://www.browserbase.com/) account with API key and project ID
+2. An API key for an LLM (OpenAI or Anthropic Claude)
+3. The Stagehand Python SDK installed
+
+Install the required dependency:
+
+```bash
+pip install stagehand-py
+```
+
+## Usage
+
+### Basic Implementation
+
+The StagehandTool can be implemented in two ways:
+
+#### 1. Using Context Manager (Recommended)
+<Tip>
+  The context manager approach is recommended as it ensures proper cleanup of resources even if exceptions occur.
+</Tip>
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import StagehandTool
+from stagehand.schemas import AvailableModel
+
+# Initialize the tool with your API keys using a context manager
+with StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",  # OpenAI or Anthropic API key
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,  # Optional: specify which model to use
+) as stagehand_tool:
+    # Create an agent with the tool
+    researcher = Agent(
+        role="Web Researcher",
+        goal="Find and summarize information from websites",
+        backstory="I'm an expert at finding information online.",
+        verbose=True,
+        tools=[stagehand_tool],
+    )
+
+    # Create a task that uses the tool
+    research_task = Task(
+        description="Go to https://www.example.com and tell me what you see on the homepage.",
+        agent=researcher,
+    )
+
+    # Run the crew
+    crew = Crew(
+        agents=[researcher],
+        tasks=[research_task],
+        verbose=True,
+    )
+
+    result = crew.kickoff()
+    print(result)
+```
+
+#### 2. Manual Resource Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import StagehandTool
+from stagehand.schemas import AvailableModel
+
+# Initialize the tool with your API keys
+stagehand_tool = StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
+)
+
+try:
+    # Create an agent with the tool
+    researcher = Agent(
+        role="Web Researcher",
+        goal="Find and summarize information from websites",
+        backstory="I'm an expert at finding information online.",
+        verbose=True,
+        tools=[stagehand_tool],
+    )
+
+    # Create a task that uses the tool
+    research_task = Task(
+        description="Go to https://www.example.com and tell me what you see on the homepage.",
+        agent=researcher,
+    )
+
+    # Run the crew
+    crew = Crew(
+        agents=[researcher],
+        tasks=[research_task],
+        verbose=True,
+    )
+
+    result = crew.kickoff()
+    print(result)
+finally:
+    # Explicitly clean up resources
+    stagehand_tool.close()
+```
+
+## Command Types
+
+The StagehandTool supports three different command types for specific web automation tasks:
+
+### 1. Act Command
+
+The `act` command type (default) enables webpage interactions like clicking buttons, filling forms, and navigation.
+
+```python
+# Perform an action (default behavior)
+result = stagehand_tool.run(
+    instruction="Click the login button", 
+    url="https://example.com",
+    command_type="act"  # Default, so can be omitted
+)
+
+# Fill out a form
+result = stagehand_tool.run(
+    instruction="Fill the contact form with name 'John Doe', email 'john@example.com', and message 'Hello world'", 
+    url="https://example.com/contact"
+)
+```
+
+### 2. Extract Command
+
+The `extract` command type retrieves structured data from webpages.
+
+```python
+# Extract all product information
+result = stagehand_tool.run(
+    instruction="Extract all product names, prices, and descriptions", 
+    url="https://example.com/products",
+    command_type="extract"
+)
+
+# Extract specific information with a selector
+result = stagehand_tool.run(
+    instruction="Extract the main article title and content", 
+    url="https://example.com/blog/article",
+    command_type="extract",
+    selector=".article-container"  # Optional CSS selector
+)
+```
+
+### 3. Observe Command
+
+The `observe` command type identifies and analyzes webpage elements.
+
+```python
+# Find interactive elements
+result = stagehand_tool.run(
+    instruction="Find all interactive elements in the navigation menu", 
+    url="https://example.com",
+    command_type="observe"
+)
+
+# Identify form fields
+result = stagehand_tool.run(
+    instruction="Identify all the input fields in the registration form", 
+    url="https://example.com/register",
+    command_type="observe",
+    selector="#registration-form"
+)
+```
+
+## Configuration Options
+
+Customize the StagehandTool behavior with these parameters:
+
+```python
+stagehand_tool = StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
+    dom_settle_timeout_ms=5000,  # Wait longer for DOM to settle
+    headless=True,  # Run browser in headless mode
+    self_heal=True,  # Attempt to recover from errors
+    wait_for_captcha_solves=True,  # Wait for CAPTCHA solving
+    verbose=1,  # Control logging verbosity (0-3)
+)
+```
+
+## Best Practices
+
+1. **Be Specific**: Provide detailed instructions for better results
+2. **Choose Appropriate Command Type**: Select the right command type for your task
+3. **Use Selectors**: Leverage CSS selectors to improve accuracy
+4. **Break Down Complex Tasks**: Split complex workflows into multiple tool calls
+5. **Implement Error Handling**: Add error handling for potential issues
+
+## Troubleshooting
+
+
+Common issues and solutions:
+
+- **Session Issues**: Verify API keys for both Browserbase and LLM provider
+- **Element Not Found**: Increase `dom_settle_timeout_ms` for slower pages
+- **Action Failures**: Use `observe` to identify correct elements first
+- **Incomplete Data**: Refine instructions or provide specific selectors
+
+
+## Additional Resources
+
+For questions about the CrewAI integration:
+- Join Stagehand's [Slack community](https://stagehand.dev/slack)
+- Open an issue in the [Stagehand repository](https://github.com/browserbase/stagehand)
+- Visit [Stagehand documentation](https://docs.stagehand.dev/)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.119.0"
+version = "0.120.1"
 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"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.44.0"]
+tools = ["crewai-tools~=0.45.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.119.0"
+__version__ = "0.120.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/cli/cli.py

@@ -1,6 +1,5 @@
-import os
 from importlib.metadata import version as get_version
-from typing import Optional, Tuple
+from typing import Optional
 
 import click
 
@@ -138,12 +137,8 @@ def log_tasks_outputs() -> None:
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
 @click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
-@click.option(
-    "-k",
-    "--kickoff-outputs",
-    is_flag=True,
-    help="Reset LATEST KICKOFF TASK OUTPUTS",
-)
+@click.option("-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage")
+@click.option("-k","--kickoff-outputs",is_flag=True,help="Reset LATEST KICKOFF TASK OUTPUTS")
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
 def reset_memories(
     long: bool,
@@ -151,18 +146,20 @@ def reset_memories(
     entities: bool,
     knowledge: bool,
     kickoff_outputs: bool,
+    agent_knowledge: bool,
     all: bool,
 ) -> None:
     """
-    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
+    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs, knowledge, agent_knowledge). This will delete all the data saved.
     """
     try:
-        if not all and not (long or short or entities or knowledge or kickoff_outputs):
+        memory_types = [long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all]
+        if not any(memory_types):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 

src/crewai/cli/reset_memories_command.py

@@ -10,6 +10,7 @@ def reset_memories_command(
     short,
     entity,
     knowledge,
+    agent_knowledge,
     kickoff_outputs,
     all,
 ) -> None:
@@ -23,10 +24,11 @@ def reset_memories_command(
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
       knowledge (bool): Whether to reset the knowledge.
+      agent_knowledge (bool): Whether to reset the agents knowledge.
     """
 
     try:
-        if not any([long, short, entity, kickoff_outputs, knowledge, all]):
+        if not any([long, short, entity, kickoff_outputs, knowledge, agent_knowledge, all]):
             click.echo(
                 "No memory type specified. Please specify at least one type to reset."
             )
@@ -67,6 +69,11 @@ def reset_memories_command(
                 click.echo(
                     f"[Crew ({crew.name if crew.name else crew.id})] Knowledge has been reset."
                 )
+            if agent_knowledge:
+                crew.reset_memories(command_type="agent_knowledge")
+                click.echo(
+                    f"[Crew ({crew.name if crew.name else crew.id})] Agents knowledge has been reset."
+                )
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0,<1.0.0"
+    "crewai[tools]>=0.120.1,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0,<1.0.0",
+    "crewai[tools]>=0.120.1,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0"
+    "crewai[tools]>=0.120.1"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -52,7 +52,7 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import TRAINING_DATA_FILE
+from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.events.crew_events import (
@@ -478,7 +478,7 @@ class Crew(FlowTrackable, BaseModel):
         separated by a synchronous task.
         """
         for i, task in enumerate(self.tasks):
-            if task.async_execution and task.context:
+            if task.async_execution and isinstance(task.context, list):
                 for context_task in task.context:
                     if context_task.async_execution:
                         for j in range(i - 1, -1, -1):
@@ -496,7 +496,7 @@ class Crew(FlowTrackable, BaseModel):
         task_indices = {id(task): i for i, task in enumerate(self.tasks)}
 
         for task in self.tasks:
-            if task.context:
+            if isinstance(task.context, list):
                 for context_task in task.context:
                     if id(context_task) not in task_indices:
                         continue  # Skip context tasks not in the main tasks list
@@ -1034,11 +1034,14 @@ class Crew(FlowTrackable, BaseModel):
                 )
         return cast(List[BaseTool], tools)
 
-    def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
+    def _get_context(self, task: Task, task_outputs: List[TaskOutput]) -> str:
+        if not task.context:
+            return ""
+
         context = (
-            aggregate_raw_outputs_from_tasks(task.context)
-            if task.context
-            else aggregate_raw_outputs_from_task_outputs(task_outputs)
+            aggregate_raw_outputs_from_task_outputs(task_outputs)
+            if task.context is NOT_SPECIFIED
+            else aggregate_raw_outputs_from_tasks(task.context)
         )
         return context
 
@@ -1226,7 +1229,7 @@ class Crew(FlowTrackable, BaseModel):
             task_mapping[task.key] = cloned_task
 
         for cloned_task, original_task in zip(cloned_tasks, self.tasks):
-            if original_task.context:
+            if isinstance(original_task.context, list):
                 cloned_context = [
                     task_mapping[context_task.key]
                     for context_task in original_task.context
@@ -1353,7 +1356,7 @@ class Crew(FlowTrackable, BaseModel):
 
         Args:
             command_type: Type of memory to reset.
-                Valid options: 'long', 'short', 'entity', 'knowledge',
+                Valid options: 'long', 'short', 'entity', 'knowledge', 'agent_knowledge'
                 'kickoff_outputs', or 'all'
 
         Raises:
@@ -1366,6 +1369,7 @@ class Crew(FlowTrackable, BaseModel):
                 "short",
                 "entity",
                 "knowledge",
+                "agent_knowledge",
                 "kickoff_outputs",
                 "all",
                 "external",
@@ -1390,19 +1394,14 @@ class Crew(FlowTrackable, BaseModel):
 
     def _reset_all_memories(self) -> None:
         """Reset all available memory systems."""
-        memory_systems = [
-            ("short term", getattr(self, "_short_term_memory", None)),
-            ("entity", getattr(self, "_entity_memory", None)),
-            ("external", getattr(self, "_external_memory", None)),
-            ("long term", getattr(self, "_long_term_memory", None)),
-            ("task output", getattr(self, "_task_output_handler", None)),
-            ("knowledge", getattr(self, "knowledge", None)),
-        ]
+        memory_systems = self._get_memory_systems()
 
-        for name, system in memory_systems:
-            if system is not None:
+        for memory_type, config in memory_systems.items():
+            if (system := config.get('system')) is not None:
+                name = config.get('name')
                 try:
-                    system.reset()
+                    reset_fn: Callable = cast(Callable, config.get('reset'))
+                    reset_fn(system)
                     self._logger.log(
                         "info",
                         f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
@@ -1421,24 +1420,17 @@ class Crew(FlowTrackable, BaseModel):
         Raises:
             RuntimeError: If the specified memory system fails to reset
         """
-        reset_functions = {
-            "long": (getattr(self, "_long_term_memory", None), "long term"),
-            "short": (getattr(self, "_short_term_memory", None), "short term"),
-            "entity": (getattr(self, "_entity_memory", None), "entity"),
-            "knowledge": (getattr(self, "knowledge", None), "knowledge"),
-            "kickoff_outputs": (
-                getattr(self, "_task_output_handler", None),
-                "task output",
-            ),
-            "external": (getattr(self, "_external_memory", None), "external"),
-        }
+        memory_systems = self._get_memory_systems()
+        config = memory_systems[memory_type]
+        system = config.get('system')
+        name = config.get('name')
 
-        memory_system, name = reset_functions[memory_type]
-        if memory_system is None:
+        if system is None:
             raise RuntimeError(f"{name} memory system is not initialized")
-
+        
         try:
-            memory_system.reset()
+            reset_fn: Callable = cast(Callable, config.get('reset'))
+            reset_fn(system)
             self._logger.log(
                 "info",
                 f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
@@ -1447,3 +1439,64 @@ class Crew(FlowTrackable, BaseModel):
             raise RuntimeError(
                 f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
             ) from e
+
+    def _get_memory_systems(self):
+        """Get all available memory systems with their configuration.
+        
+        Returns:
+            Dict containing all memory systems with their reset functions and display names.
+        """
+        def default_reset(memory):
+            return memory.reset()
+        def knowledge_reset(memory):
+            return self.reset_knowledge(memory)
+        
+        # Get knowledge for agents 
+        agent_knowledges = [getattr(agent, "knowledge", None) for agent in self.agents 
+                                    if getattr(agent, "knowledge", None) is not None]
+        # Get knowledge for crew and agents
+        crew_knowledge = getattr(self, "knowledge", None)
+        crew_and_agent_knowledges = ([crew_knowledge] if crew_knowledge is not None else []) + agent_knowledges
+
+        return {
+            'short': {
+                'system': getattr(self, "_short_term_memory", None),
+                'reset': default_reset,
+                'name': 'Short Term'
+            },
+            'entity': {
+                'system': getattr(self, "_entity_memory", None),
+                'reset': default_reset,
+                'name': 'Entity'
+            },
+            'external': {
+                'system': getattr(self, "_external_memory", None),
+                'reset': default_reset,
+                'name': 'External'
+            },
+            'long': {
+                'system': getattr(self, "_long_term_memory", None),
+                'reset': default_reset,
+                'name': 'Long Term'
+            },
+            'kickoff_outputs': {
+                'system': getattr(self, "_task_output_handler", None),
+                'reset': default_reset,
+                'name': 'Task Output'
+            },
+            'knowledge': {
+                'system': crew_and_agent_knowledges if crew_and_agent_knowledges else None,
+                'reset': knowledge_reset,
+                'name': 'Crew Knowledge and Agent Knowledge'
+            },
+            'agent_knowledge': {
+                'system': agent_knowledges if agent_knowledges else None,
+                'reset': knowledge_reset,
+                'name': 'Agent Knowledge'
+            }
+        }
+
+    def reset_knowledge(self, knowledges: List[Knowledge]) -> None:
+        """Reset crew and agent knowledge storage."""
+        for ks in knowledges:
+            ks.reset()

src/crewai/task.py

@@ -2,7 +2,6 @@ import datetime
 import inspect
 import json
 import logging
-import re
 import threading
 import uuid
 from concurrent.futures import Future
@@ -41,6 +40,7 @@ from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
+from crewai.utilities.constants import NOT_SPECIFIED
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.events import (
     TaskCompletedEvent,
@@ -97,7 +97,7 @@ class Task(BaseModel):
     )
     context: Optional[List["Task"]] = Field(
         description="Other tasks that will have their output used as context for this task.",
-        default=None,
+        default=NOT_SPECIFIED,
     )
     async_execution: Optional[bool] = Field(
         description="Whether the task should be executed asynchronously or not.",
@@ -135,6 +135,10 @@ class Task(BaseModel):
         description="Whether the task should have a human review the final answer of the agent",
         default=False,
     )
+    markdown: Optional[bool] = Field(
+        description="Whether the task should instruct the agent to return the final answer formatted in Markdown",
+        default=False,
+    )
     converter_cls: Optional[Type[Converter]] = Field(
         description="A converter class used to export structured output",
         default=None,
@@ -522,10 +526,14 @@ class Task(BaseModel):
         return guardrail_result
 
     def prompt(self) -> str:
-        """Prompt the task.
-
+        """Generates the task prompt with optional markdown formatting.
+        
+        When the markdown attribute is True, instructions for formatting the
+        response in Markdown syntax will be added to the prompt.
+        
         Returns:
-            Prompt of the task.
+            str: The formatted prompt string containing the task description,
+                 expected output, and optional markdown formatting instructions.
         """
         tasks_slices = [self.description]
 
@@ -533,6 +541,17 @@ class Task(BaseModel):
             expected_output=self.expected_output
         )
         tasks_slices = [self.description, output]
+        
+        if self.markdown:
+            markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.
+Follow these guidelines:
+- Use # for headers
+- Use ** for bold text
+- Use * for italic text
+- Use - or * for bullet points
+- Use `code` for inline code
+- Use ```language for code blocks"""
+            tasks_slices.append(markdown_instruction)
         return "\n".join(tasks_slices)
 
     def interpolate_inputs_and_add_conversation_history(
@@ -643,7 +662,7 @@ class Task(BaseModel):
 
         cloned_context = (
             [task_mapping[context_task.key] for context_task in self.context]
-            if self.context
+            if isinstance(self.context, list)
             else None
         )
 

src/crewai/telemetry/telemetry.py

@@ -10,6 +10,18 @@ from contextlib import contextmanager
 from importlib.metadata import version
 from typing import TYPE_CHECKING, Any, Optional
 
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.sdk.resources import SERVICE_NAME, Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (
+    BatchSpanProcessor,
+    SpanExportResult,
+)
+from opentelemetry.trace import Span, Status, StatusCode
+
 from crewai.telemetry.constants import (
     CREWAI_TELEMETRY_BASE_URL,
     CREWAI_TELEMETRY_SERVICE_NAME,
@@ -25,18 +37,6 @@ def suppress_warnings():
         yield
 
 
-from opentelemetry import trace  # 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 (  # noqa: E402
-    BatchSpanProcessor,
-    SpanExportResult,
-)
-from opentelemetry.trace import Span, Status, StatusCode  # noqa: E402
-
 if TYPE_CHECKING:
     from crewai.crew import Crew
     from crewai.task import Task
@@ -232,7 +232,7 @@ class Telemetry:
                                 "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
-                                    if task.context
+                                    if isinstance(task.context, list)
                                     else None
                                 ),
                                 "tools_names": [
@@ -748,7 +748,7 @@ class Telemetry:
                             "agent_key": task.agent.key if task.agent else None,
                             "context": (
                                 [task.description for task in task.context]
-                                if task.context
+                                if isinstance(task.context, list)
                                 else None
                             ),
                             "tools_names": [

src/crewai/utilities/agent_utils.py

@@ -441,6 +441,11 @@ def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
 
         client = PlusAPI(api_key=get_auth_token())
         response = client.get_agent(from_repository)
+        if response.status_code == 404:
+            raise AgentRepositoryError(
+                f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization"
+            )
+
         if response.status_code != 200:
             raise AgentRepositoryError(
                 f"Agent {from_repository} could not be loaded: {response.text}"
@@ -450,14 +455,14 @@ def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
         for key, value in agent.items():
             if key == "tools":
                 attributes[key] = []
-                for tool_name in value:
+                for tool in value:
                     try:
                         module = importlib.import_module("crewai_tools")
-                        tool_class = getattr(module, tool_name)
+                        tool_class = getattr(module, tool["name"])
                         attributes[key].append(tool_class())
                     except Exception as e:
                         raise AgentRepositoryError(
-                            f"Tool {tool_name} could not be loaded: {e}"
+                            f"Tool {tool['name']} could not be loaded: {e}"
                         ) from e
             else:
                 attributes[key] = value

src/crewai/utilities/constants.py

@@ -5,3 +5,14 @@ KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
 MAX_FILE_NAME_LENGTH = 255
 EMITTER_COLOR = "bold_blue"
+
+
+class _NotSpecified:
+    def __repr__(self):
+        return "NOT_SPECIFIED"
+
+
+# Sentinel value used to detect when no value has been explicitly provided.
+# Unlike `None`, which might be a valid value from the user, `NOT_SPECIFIED` allows
+# us to distinguish between "not passed at all" and "explicitly passed None" or "[]".
+NOT_SPECIFIED = _NotSpecified()

src/crewai/utilities/formatter.py

@@ -1,6 +1,6 @@
-import re
 from typing import TYPE_CHECKING, List
 
+
 if TYPE_CHECKING:
     from crewai.task import Task
     from crewai.tasks.task_output import TaskOutput
@@ -17,6 +17,11 @@ def aggregate_raw_outputs_from_task_outputs(task_outputs: List["TaskOutput"]) ->
 
 def aggregate_raw_outputs_from_tasks(tasks: List["Task"]) -> str:
     """Generate string context from the tasks."""
-    task_outputs = [task.output for task in tasks if task.output is not None]
+
+    task_outputs = (
+        [task.output for task in tasks if task.output is not None]
+        if isinstance(tasks, list)
+        else []
+    )
 
     return aggregate_raw_outputs_from_task_outputs(task_outputs)

src/crewai/utilities/string_utils.py

@@ -59,7 +59,7 @@ def interpolate_only(
     # The regex pattern to find valid variable placeholders
     # Matches {variable_name} where variable_name starts with a letter/underscore
     # and contains only letters, numbers, and underscores
-    pattern = r"\{([A-Za-z_][A-Za-z0-9_]*)\}"
+    pattern = r"\{([A-Za-z_][A-Za-z0-9_\-]*)\}"
 
     # Find all matching variables in the input string
     variables = re.findall(pattern, input_string)

tests/agent_test.py

@@ -2044,7 +2044,7 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": ["SerperDevTool"],
+        "tools": [{"name": "SerperDevTool"}],
     }
     mock_get_agent.return_value = mock_get_response
     agent = Agent(from_repository="test_agent")
@@ -2066,7 +2066,7 @@ def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": ["SerperDevTool"],
+        "tools": [{"name": "SerperDevTool"}],
     }
     mock_get_agent.return_value = mock_get_response
     agent = Agent(from_repository="test_agent", role="Custom Role")
@@ -2086,7 +2086,7 @@ def test_agent_from_repository_with_invalid_tools(mock_get_agent, mock_get_auth_
         "role": "test role",
         "goal": "test goal",
         "backstory": "test backstory",
-        "tools": ["DoesNotExist"],
+        "tools": [{"name": "DoesNotExist"}],
     }
     mock_get_agent.return_value = mock_get_response
     with pytest.raises(
@@ -2096,6 +2096,19 @@ def test_agent_from_repository_with_invalid_tools(mock_get_agent, mock_get_auth_
         Agent(from_repository="test_agent")
 
 
+@patch("crewai.cli.plus_api.PlusAPI.get_agent")
+def test_agent_from_repository_internal_error(mock_get_agent, mock_get_auth_token):
+    mock_get_response = MagicMock()
+    mock_get_response.status_code = 500
+    mock_get_response.text = "Internal server error"
+    mock_get_agent.return_value = mock_get_response
+    with pytest.raises(
+        AgentRepositoryError,
+        match="Agent test_agent could not be loaded: Internal server error",
+    ):
+        Agent(from_repository="test_agent")
+
+
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository_agent_not_found(mock_get_agent, mock_get_auth_token):
     mock_get_response = MagicMock()
@@ -2104,6 +2117,6 @@ def test_agent_from_repository_agent_not_found(mock_get_agent, mock_get_auth_tok
     mock_get_agent.return_value = mock_get_response
     with pytest.raises(
         AgentRepositoryError,
-        match="Agent NOT_FOUND could not be loaded: Agent not found",
+        match="Agent test_agent does not exist, make sure the name is correct or the agent is available on your organization",
     ):
-        Agent(from_repository="NOT_FOUND")
+        Agent(from_repository="test_agent")

tests/cassettes/test_task_interpolation_with_hyphens.yaml

@@ -0,0 +1,121 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are Researcher. You''re
+      an expert researcher, specialized in technology, software engineering, AI and
+      startups. You work as a freelancer and is now working on doing research and
+      analysis for a new customer.\nYour personal goal is: be an assistant that responds
+      with say hello world\nTo give my best complete final answer to the task respond
+      using 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!"}, {"role": "user", "content": "\nCurrent Task: be an assistant that responds
+      with say hello world\n\nThis is the expected criteria for your final answer:
+      The response should be addressing: say hello world\nyou MUST return the actual
+      complete content as the final answer, not a summary.\n\nBegin! This is VERY
+      important to you, use the tools available and give your best Final Answer, your
+      job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '1108'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.68.2
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.68.2
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-read-timeout:
+      - '600.0'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.9
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: !!binary |
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+    headers:
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+      message: OK
+version: 1

tests/cli/cli_test.py

@@ -162,8 +162,18 @@ def test_reset_knowledge(mock_get_crews, runner):
     assert call_count == 1, "reset_memories should have been called once"
 
 
-def test_reset_memory_from_many_crews(mock_get_crews, runner):
+def test_reset_agent_knowledge(mock_get_crews, runner):
+    result = runner.invoke(reset_memories, ["--agent-knowledge"])
+    call_count = 0
+    for crew in mock_get_crews.return_value:
+        crew.reset_memories.assert_called_once_with(command_type="agent_knowledge")
+        assert f"[Crew ({crew.name})] Agents knowledge has been reset." in result.output
+        call_count += 1
 
+    assert call_count == 1, "reset_memories should have been called once"
+
+
+def test_reset_memory_from_many_crews(mock_get_crews, runner):
     crews = []
     for crew_id in ["id-1234", "id-5678"]:
         mock_crew = mock.Mock(spec=Crew)

tests/crew_test.py

@@ -2,22 +2,19 @@
 
 import hashlib
 import json
-import os
-import tempfile
 from concurrent.futures import Future
 from unittest import mock
-from unittest.mock import MagicMock, patch
+from unittest.mock import ANY, MagicMock, patch
 
 import pydantic_core
 import pytest
 
 from crewai.agent import Agent
 from crewai.agents import CacheHandler
-from crewai.agents.cache import CacheHandler
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
-from crewai.flow import Flow, listen, start
+from crewai.flow import Flow, start
+from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
@@ -3141,6 +3138,30 @@ def test_replay_with_context():
         assert crew.tasks[1].context[0].output.raw == "context raw output"
 
 
+def test_replay_with_context_set_to_nullable():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(
+        description="Context Task", expected_output="Say Task Output", agent=agent
+    )
+    task2 = Task(
+        description="Test Task", expected_output="Say Hi", agent=agent, context=[]
+    )
+    task3 = Task(
+        description="Test Task 3", expected_output="Say Hi", agent=agent, context=None
+    )
+
+    crew = Crew(agents=[agent], tasks=[task1, task2, task3], process=Process.sequential)
+    with patch("crewai.task.Task.execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Test Task Output",
+            raw="test raw output",
+            agent="test_agent",
+        )
+        crew.kickoff()
+
+    mock_execute_task.assert_called_with(agent=ANY, context="", tools=ANY)
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_replay_with_invalid_task_id():
     agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
@@ -4383,3 +4404,165 @@ def test_sets_parent_flow_when_inside_flow(researcher, writer):
     flow = MyFlow()
     result = flow.kickoff()
     assert result.parent_flow is flow
+
+
+def test_reset_knowledge_with_no_crew_knowledge(researcher,writer):
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.sequential,
+        tasks=[
+            Task(description="Task 1", expected_output="output", agent=researcher),
+            Task(description="Task 2", expected_output="output", agent=writer),
+        ]
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        crew.reset_memories(command_type='knowledge')
+        
+        # Optionally, you can also check the error message
+    assert "Crew Knowledge and Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
+
+
+def test_reset_knowledge_with_only_crew_knowledge(researcher,writer):
+    mock_ks = MagicMock(spec=Knowledge)
+
+    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
+        crew = Crew(
+            agents=[researcher, writer],
+            process=Process.sequential,
+            tasks=[
+                Task(description="Task 1", expected_output="output", agent=researcher),
+                Task(description="Task 2", expected_output="output", agent=writer),
+            ],
+            knowledge=mock_ks
+        )
+
+        crew.reset_memories(command_type='knowledge')
+        mock_reset_agent_knowledge.assert_called_once_with([mock_ks])
+
+
+def test_reset_knowledge_with_crew_and_agent_knowledge(researcher,writer):
+    mock_ks_crew = MagicMock(spec=Knowledge)
+    mock_ks_research = MagicMock(spec=Knowledge)
+    mock_ks_writer = MagicMock(spec=Knowledge)
+
+    researcher.knowledge = mock_ks_research
+    writer.knowledge = mock_ks_writer
+
+    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
+        crew = Crew(
+            agents=[researcher, writer],
+            process=Process.sequential,
+            tasks=[
+                Task(description="Task 1", expected_output="output", agent=researcher),
+                Task(description="Task 2", expected_output="output", agent=writer),
+            ],
+            knowledge=mock_ks_crew
+        )
+
+        crew.reset_memories(command_type='knowledge')
+        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_crew,mock_ks_research,mock_ks_writer])
+
+
+def test_reset_knowledge_with_only_agent_knowledge(researcher,writer):
+    mock_ks_research = MagicMock(spec=Knowledge)
+    mock_ks_writer = MagicMock(spec=Knowledge)
+
+    researcher.knowledge = mock_ks_research
+    writer.knowledge = mock_ks_writer
+
+    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
+        crew = Crew(
+            agents=[researcher, writer],
+            process=Process.sequential,
+            tasks=[
+                Task(description="Task 1", expected_output="output", agent=researcher),
+                Task(description="Task 2", expected_output="output", agent=writer),
+            ],
+        )
+
+        crew.reset_memories(command_type='knowledge')
+        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
+
+
+def test_reset_agent_knowledge_with_no_agent_knowledge(researcher,writer):
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.sequential,
+        tasks=[
+            Task(description="Task 1", expected_output="output", agent=researcher),
+            Task(description="Task 2", expected_output="output", agent=writer),
+        ],
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        crew.reset_memories(command_type='agent_knowledge')
+    
+    # Optionally, you can also check the error message
+    assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
+
+
+def test_reset_agent_knowledge_with_only_crew_knowledge(researcher,writer):
+    mock_ks = MagicMock(spec=Knowledge)
+
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.sequential,
+        tasks=[
+            Task(description="Task 1", expected_output="output", agent=researcher),
+            Task(description="Task 2", expected_output="output", agent=writer),
+        ],
+        knowledge=mock_ks
+    )
+
+    with pytest.raises(RuntimeError) as excinfo:
+        crew.reset_memories(command_type='agent_knowledge')
+    
+    # Optionally, you can also check the error message
+    assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
+
+
+def test_reset_agent_knowledge_with_crew_and_agent_knowledge(researcher,writer):
+    mock_ks_crew = MagicMock(spec=Knowledge)
+    mock_ks_research = MagicMock(spec=Knowledge)
+    mock_ks_writer = MagicMock(spec=Knowledge)
+
+    researcher.knowledge = mock_ks_research
+    writer.knowledge = mock_ks_writer
+
+    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
+        crew = Crew(
+            agents=[researcher, writer],
+            process=Process.sequential,
+            tasks=[
+                Task(description="Task 1", expected_output="output", agent=researcher),
+                Task(description="Task 2", expected_output="output", agent=writer),
+            ],
+            knowledge=mock_ks_crew
+        )
+
+        crew.reset_memories(command_type='agent_knowledge')
+        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
+
+
+def test_reset_agent_knowledge_with_only_agent_knowledge(researcher,writer):
+    mock_ks_research = MagicMock(spec=Knowledge)
+    mock_ks_writer = MagicMock(spec=Knowledge)
+
+    researcher.knowledge = mock_ks_research
+    writer.knowledge = mock_ks_writer
+
+    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
+        crew = Crew(
+            agents=[researcher, writer],
+            process=Process.sequential,
+            tasks=[
+                Task(description="Task 1", expected_output="output", agent=researcher),
+                Task(description="Task 2", expected_output="output", agent=writer),
+            ],
+        )
+
+        crew.reset_memories(command_type='agent_knowledge')
+        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
+
+

tests/task_test.py

@@ -837,9 +837,6 @@ def test_interpolate_inputs():
 
 def test_interpolate_only():
     """Test the interpolate_only method for various scenarios including JSON structure preservation."""
-    task = Task(
-        description="Unused in this test", expected_output="Unused in this test"
-    )
 
     # Test JSON structure preservation
     json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
@@ -871,10 +868,6 @@ def test_interpolate_only():
 
 def test_interpolate_only_with_dict_inside_expected_output():
     """Test the interpolate_only method for various scenarios including JSON structure preservation."""
-    task = Task(
-        description="Unused in this test",
-        expected_output="Unused in this test: {questions}",
-    )
 
     json_string = '{"questions": {"main_question": "What is the user\'s name?", "secondary_question": "What is the user\'s age?"}}'
     result = interpolate_only(
@@ -1094,11 +1087,6 @@ def test_task_execution_times():
 
 
 def test_interpolate_with_list_of_strings():
-    task = Task(
-        description="Test list interpolation",
-        expected_output="List: {items}",
-    )
-
     # Test simple list of strings
     input_str = "Available items: {items}"
     inputs = {"items": ["apple", "banana", "cherry"]}
@@ -1112,11 +1100,6 @@ def test_interpolate_with_list_of_strings():
 
 
 def test_interpolate_with_list_of_dicts():
-    task = Task(
-        description="Test list of dicts interpolation",
-        expected_output="People: {people}",
-    )
-
     input_data = {
         "people": [
             {"name": "Alice", "age": 30, "skills": ["Python", "AI"]},
@@ -1137,11 +1120,6 @@ def test_interpolate_with_list_of_dicts():
 
 
 def test_interpolate_with_nested_structures():
-    task = Task(
-        description="Test nested structures",
-        expected_output="Company: {company}",
-    )
-
     input_data = {
         "company": {
             "name": "TechCorp",
@@ -1165,11 +1143,6 @@ def test_interpolate_with_nested_structures():
 
 
 def test_interpolate_with_special_characters():
-    task = Task(
-        description="Test special characters in dicts",
-        expected_output="Data: {special_data}",
-    )
-
     input_data = {
         "special_data": {
             "quotes": """This has "double" and 'single' quotes""",
@@ -1188,11 +1161,6 @@ def test_interpolate_with_special_characters():
 
 
 def test_interpolate_mixed_types():
-    task = Task(
-        description="Test mixed type interpolation",
-        expected_output="Mixed: {data}",
-    )
-
     input_data = {
         "data": {
             "name": "Test Dataset",
@@ -1214,11 +1182,6 @@ def test_interpolate_mixed_types():
 
 
 def test_interpolate_complex_combination():
-    task = Task(
-        description="Test complex combination",
-        expected_output="Report: {report}",
-    )
-
     input_data = {
         "report": [
             {
@@ -1243,11 +1206,6 @@ def test_interpolate_complex_combination():
 
 
 def test_interpolate_invalid_type_validation():
-    task = Task(
-        description="Test invalid type validation",
-        expected_output="Should never reach here",
-    )
-
     # Test with invalid top-level type
     with pytest.raises(ValueError) as excinfo:
         interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
@@ -1268,11 +1226,6 @@ def test_interpolate_invalid_type_validation():
 
 
 def test_interpolate_custom_object_validation():
-    task = Task(
-        description="Test custom object rejection",
-        expected_output="Should never reach here",
-    )
-
     class CustomObject:
         def __init__(self, value):
             self.value = value
@@ -1304,11 +1257,6 @@ def test_interpolate_custom_object_validation():
 
 
 def test_interpolate_valid_complex_types():
-    task = Task(
-        description="Test valid complex types",
-        expected_output="Validation should pass",
-    )
-
     # Valid complex structure
     valid_data = {
         "name": "Valid Dataset",
@@ -1328,11 +1276,6 @@ def test_interpolate_valid_complex_types():
 
 
 def test_interpolate_edge_cases():
-    task = Task(
-        description="Test edge cases",
-        expected_output="Edge case handling",
-    )
-
     # Test empty dict and list
     assert interpolate_only("{}", {"data": {}}) == "{}"
     assert interpolate_only("[]", {"data": []}) == "[]"
@@ -1347,11 +1290,6 @@ def test_interpolate_edge_cases():
 
 
 def test_interpolate_valid_types():
-    task = Task(
-        description="Test valid types including null and boolean",
-        expected_output="Should pass validation",
-    )
-
     # Test with boolean and null values (valid JSON types)
     valid_data = {
         "name": "Test",
@@ -1373,11 +1311,11 @@ def test_interpolate_valid_types():
 
 def test_task_with_no_max_execution_time():
     researcher = Agent(
-    role="Researcher",
-    goal="Make the best research and analysis on content about AI and AI agents",
-    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-    allow_delegation=False,
-    max_execution_time=None
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+        allow_delegation=False,
+        max_execution_time=None,
     )
 
     task = Task(
@@ -1386,7 +1324,7 @@ def test_task_with_no_max_execution_time():
         agent=researcher,
     )
 
-    with patch.object(Agent, "_execute_without_timeout", return_value = "ok") as execute:
+    with patch.object(Agent, "_execute_without_timeout", return_value="ok") as execute:
         result = task.execute_sync(agent=researcher)
         assert result.raw == "ok"
         execute.assert_called_once()
@@ -1395,6 +1333,7 @@ def test_task_with_no_max_execution_time():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_with_max_execution_time():
     from crewai.tools import tool
+
     """Test that execution raises TimeoutError when max_execution_time is exceeded."""
 
     @tool("what amazing tool", result_as_answer=True)
@@ -1412,7 +1351,7 @@ def test_task_with_max_execution_time():
         ),
         allow_delegation=False,
         tools=[my_tool],
-        max_execution_time=4
+        max_execution_time=4,
     )
 
     task = Task(
@@ -1428,6 +1367,7 @@ def test_task_with_max_execution_time():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_with_max_execution_time_exceeded():
     from crewai.tools import tool
+
     """Test that execution raises TimeoutError when max_execution_time is exceeded."""
 
     @tool("what amazing tool", result_as_answer=True)
@@ -1445,7 +1385,7 @@ def test_task_with_max_execution_time_exceeded():
         ),
         allow_delegation=False,
         tools=[my_tool],
-        max_execution_time=1
+        max_execution_time=1,
     )
 
     task = Task(
@@ -1455,4 +1395,28 @@ def test_task_with_max_execution_time_exceeded():
     )
 
     with pytest.raises(TimeoutError):
-        task.execute_sync(agent=researcher)
+        task.execute_sync(agent=researcher)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_interpolation_with_hyphens():
+    agent = Agent(
+        role="Researcher",
+        goal="be an assistant that responds with {interpolation-with-hyphens}",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+        allow_delegation=False,
+    )
+    task = Task(
+        description="be an assistant that responds with {interpolation-with-hyphens}",
+        expected_output="The response should be addressing: {interpolation-with-hyphens}",
+        agent=agent,
+    )
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+    )
+    result = crew.kickoff(inputs={"interpolation-with-hyphens": "say hello world"})
+    assert "say hello world" in task.prompt()
+
+    assert result.raw == "Hello, World!"

tests/test_markdown_task.py

@@ -0,0 +1,96 @@
+"""Test the markdown attribute in Task class."""
+
+import pytest
+from pydantic import BaseModel
+
+from crewai import Agent, Task
+
+
+@pytest.mark.parametrize(
+    "markdown_enabled,should_contain_instructions",
+    [
+        (True, True),
+        (False, False),
+    ],
+)
+def test_markdown_option_in_task_prompt(markdown_enabled, should_contain_instructions):
+    """Test that markdown flag correctly controls the inclusion of markdown formatting instructions."""
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher specialized in providing well-formatted content.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Research advances in AI in 2023",
+        expected_output="A summary of key AI advances in 2023",
+        markdown=markdown_enabled,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert "Research advances in AI in 2023" in prompt
+    assert "A summary of key AI advances in 2023" in prompt
+    
+    if should_contain_instructions:
+        assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+        assert "Use # for headers" in prompt
+        assert "Use ** for bold text" in prompt
+    else:
+        assert "Your final answer MUST be formatted in Markdown syntax." not in prompt
+
+
+def test_markdown_with_empty_description():
+    """Test markdown formatting with empty description."""
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="",
+        expected_output="A summary",
+        markdown=True,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert prompt.strip() != ""
+    assert "A summary" in prompt
+    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+
+
+def test_markdown_with_complex_output_format():
+    """Test markdown with JSON output format to ensure compatibility."""
+    
+    class ResearchOutput(BaseModel):
+        title: str
+        findings: list[str]
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Research topic",
+        expected_output="Research results",
+        markdown=True,
+        output_json=ResearchOutput,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+    assert "Research topic" in prompt
+    assert "Research results" in prompt

tests/utilities/test_converter.py

@@ -1,5 +1,4 @@
 import json
-import os
 from typing import Dict, List, Optional
 from unittest.mock import MagicMock, Mock, patch
 
@@ -19,6 +18,8 @@ from crewai.utilities.converter import (
     validate_model,
 )
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+# Tests for enums
+from enum import Enum
 
 
 @pytest.fixture(scope="module")
@@ -359,7 +360,7 @@ def test_convert_with_instructions():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_2_model():
-    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
+    llm = LLM(model="openrouter/meta-llama/llama-3.2-3b-instruct")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
@@ -431,7 +432,7 @@ def test_converter_error_handling():
     )
 
     with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
+        converter.to_pydantic()
 
     assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
 
@@ -515,10 +516,6 @@ def test_converter_with_list_field():
     assert output.items == [1, 2, 3]
 
 
-# Tests for enums
-from enum import Enum
-
-
 def test_converter_with_enum():
     class Color(Enum):
         RED = "red"
@@ -565,7 +562,7 @@ def test_converter_with_ambiguous_input():
     )
 
     with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
+        converter.to_pydantic()
 
     assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
 

uv.lock

@@ -738,7 +738,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.119.0"
+version = "0.120.1"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -828,7 +828,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.44.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.45.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -879,7 +879,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.44.0"
+version = "0.45.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -894,9 +894,9 @@ dependencies = [
     { name = "pytube" },
     { name = "requests" },
 ]
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 [[package]]