Merge branch 'main' into devin/1735488202-add-tool-documentation

- Added documentation for file operation tools
- Added documentation for search tools
- Added documentation for web scraping tools
- Added documentation for specialized tools (RAG, code interpreter)
- Added documentation for API-based tools (SerpApi, Serply)

Link to Devin run: https://app.devin.ai/sessions/d2f72a2dfb214659aeb3e9f67ed961f7

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

.gitignore

@@ -21,5 +21,3 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
-test_flow.html

README.md

@@ -1,18 +1,10 @@
 <div align="center">
 
-![Logo of CrewAI](./docs/crewai_logo.png)
+![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)
 
 # **CrewAI**
 
-**CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
-
-**CrewAI Enterprise**
-Want to plan, build (+ no code), deploy, monitor and interare your agents: [CrewAI Enterprise](https://www.crewai.com/enterprise). Designed for complex, real-world applications, our enterprise solution offers:
-
-- **Seamless Integrations**
-- **Scalable & Secure Deployment**
-- **Actionable Insights**
-- **24/7 Support**
+🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
 
 <h3>
 
@@ -198,7 +190,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is 2025.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
@@ -400,7 +392,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             goal="Gather and validate supporting market data",
             backstory="You excel at finding and correlating multiple data sources"
         )
-
+        
         analysis_task = Task(
             description="Analyze {sector} sector data for the past {timeframe}",
             expected_output="Detailed market analysis with confidence score",
@@ -411,7 +403,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             expected_output="Corroborating evidence and potential contradictions",
             agent=researcher
         )
-
+        
         # Demonstrate crew autonomy
         analysis_crew = Crew(
             agents=[analyst, researcher],

docs/concepts/agents.mdx

@@ -43,7 +43,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
 | **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
 | **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
-| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Embedder Config** _(optional)_        | `embedder_config`        | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
 
@@ -101,8 +101,6 @@ from crewai_tools import SerperDevTool
 class LatestAiDevelopmentCrew():
   """LatestAiDevelopment crew"""
 
-  agents_config = "config/agents.yaml"
-
   @agent
   def researcher(self) -> Agent:
     return Agent(
@@ -152,7 +150,7 @@ agent = Agent(
     use_system_prompt=True,  # Default: True
     tools=[SerperDevTool()],  # Optional: List of tools
     knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder=None,  # Optional: Custom embedder configuration
+    embedder_config=None,  # Optional: Custom embedder configuration
     system_template=None,  # Optional: Custom system prompt template
     prompt_template=None,  # Optional: Custom prompt template
     response_template=None,  # Optional: Custom response template

docs/concepts/cli.mdx

@@ -12,7 +12,7 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you
 
 To use the CrewAI CLI, make sure you have CrewAI installed:
 
-```shell Terminal
+```shell
 pip install crewai
 ```
 
@@ -20,7 +20,7 @@ pip install crewai
 
 The basic structure of a CrewAI CLI command is:
 
-```shell Terminal
+```shell
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell Terminal
+```shell
 crewai create [OPTIONS] TYPE NAME
 ```
 
@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `NAME`: Name of the crew or flow
 
 Example:
-```shell Terminal
+```shell
 crewai create crew my_new_crew
 crewai create flow my_new_flow
 ```
@@ -47,14 +47,14 @@ crewai create flow my_new_flow
 
 Show the installed version of CrewAI.
 
-```shell Terminal
+```shell
 crewai version [OPTIONS]
 ```
 
 - `--tools`: (Optional) Show the installed version of CrewAI tools
 
 Example:
-```shell Terminal
+```shell
 crewai version
 crewai version --tools
 ```
@@ -63,7 +63,7 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell Terminal
+```shell
 crewai train [OPTIONS]
 ```
 
@@ -71,7 +71,7 @@ crewai train [OPTIONS]
 - `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
 
 Example:
-```shell Terminal
+```shell
 crewai train -n 10 -f my_training_data.pkl
 ```
 
@@ -79,14 +79,14 @@ crewai train -n 10 -f my_training_data.pkl
 
 Replay the crew execution from a specific task.
 
-```shell Terminal
+```shell
 crewai replay [OPTIONS]
 ```
 
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell Terminal    
+```shell    
 crewai replay -t task_123456
 ```
 
@@ -94,7 +94,7 @@ crewai replay -t task_123456
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell Terminal
+```shell
 crewai log-tasks-outputs
 ```
 
@@ -102,7 +102,7 @@ crewai log-tasks-outputs
 
 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
 
-```shell Terminal
+```shell
 crewai reset-memories [OPTIONS]
 ```
 
@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories
 
 Example:
-```shell Terminal
+```shell
 crewai reset-memories --long --short
 crewai reset-memories --all
 ```
@@ -122,7 +122,7 @@ crewai reset-memories --all
 
 Test the crew and evaluate the results.
 
-```shell Terminal
+```shell
 crewai test [OPTIONS]
 ```
 
@@ -130,56 +130,24 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell Terminal    
+```shell
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
 ### 8. Run
 
-Run the crew or flow.
+Run the crew.
 
-```shell Terminal
+```shell
 crewai run
 ```
-
-<Note>
-Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
-</Note>
-
 <Note>
 Make sure to run these commands from the directory where your CrewAI project is set up. 
 Some commands may require additional configuration or setup within your project structure.
 </Note>
 
-### 9. Chat
 
-Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
-
-After receiving the results, you can continue interacting with the assistant for further instructions or questions.
-
-```shell Terminal
-crewai chat
-```
-<Note>
-Ensure you execute these commands from your CrewAI project's root directory.
-</Note>
-<Note>
-IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
-
-```python
-@crew
-def crew(self) -> Crew:
-    return Crew(
-        agents=self.agents,
-        tasks=self.tasks,
-        process=Process.sequential,
-        verbose=True,
-        chat_llm="gpt-4o",  # LLM for chat orchestration
-    )
-```
-</Note>
-
-### 10. API Keys
+### 9. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 
@@ -193,7 +161,6 @@ The CLI will initially prompt for API keys for the following services:
 * Groq
 * Anthropic
 * Google Gemini
-* SambaNova
 
 When you select a provider, the CLI will prompt you to enter your API key.
 

docs/concepts/crews.mdx

@@ -23,14 +23,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -240,23 +240,6 @@ print(f"Tasks Output: {crew_output.tasks_output}")
 print(f"Token Usage: {crew_output.token_usage}")
 ```
 
-## Accessing Crew Logs
-
-You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
-In case of `True(Boolean)` will save as `logs.txt`.
-
-In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
-
-```python Code
-# Save crew logs
-crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
-crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
-```
-
-
-
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -296,9 +279,9 @@ print(result)
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
 - `kickoff()`: Starts the execution process according to the defined process flow.
-- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
+- `kickoff_for_each()`: Executes tasks for each agent individually.
 - `kickoff_async()`: Initiates the workflow asynchronously.
-- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
+- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
 
 ```python Code
 # Start the crew's task execution

docs/concepts/event-listner.mdx

@@ -1,350 +0,0 @@
----
-title: 'Event Listeners'
-description: 'Tap into CrewAI events to build custom integrations and monitoring'
----
-
-# Event Listeners
-
-CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
-
-## How It Works
-
-CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
-
-1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **CrewEvent**: Base class for all events in the system
-3. **BaseEventListener**: Abstract base class for creating custom event listeners
-
-When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
-
-## Creating a Custom Event Listener
-
-To create a custom event listener, you need to:
-
-1. Create a class that inherits from `BaseEventListener`
-2. Implement the `setup_listeners` method
-3. Register handlers for the events you're interested in
-4. Create an instance of your listener in the appropriate file
-
-Here's a simple example of a custom event listener class:
-
-```python
-from crewai.utilities.events import (
-    CrewKickoffStartedEvent,
-    CrewKickoffCompletedEvent,
-    AgentExecutionCompletedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-
-class MyCustomListener(BaseEventListener):
-    def __init__(self):
-        super().__init__()
-    
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
-            print(f"Crew '{event.crew_name}' has started execution!")
-        
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
-            print(f"Crew '{event.crew_name}' has completed execution!")
-            print(f"Output: {event.output}")
-        
-        @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed task")
-            print(f"Output: {event.output}")
-```
-
-## Properly Registering Your Listener
-
-Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
-
-1. The event handlers are registered with the event bus
-2. The listener instance remains in memory (not garbage collected)
-3. The listener is active when events are emitted
-
-### Option 1: Import and Instantiate in Your Crew or Flow Implementation
-
-The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
-
-#### For Crew-based Applications
-
-Create and import your listener at the top of your Crew implementation file:
-
-```python
-# In your crew.py file
-from crewai import Agent, Crew, Task
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomCrew:
-    # Your crew implementation...
-    
-    def crew(self):
-        return Crew(
-            agents=[...],
-            tasks=[...],
-            # ...
-        )
-```
-
-#### For Flow-based Applications
-
-Create and import your listener at the top of your Flow implementation file:
-
-```python
-# In your main.py or flow.py file
-from crewai.flow import Flow, listen, start
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomFlow(Flow):
-    # Your flow implementation...
-    
-    @start()
-    def first_step(self):
-        # ...
-```
-
-This ensures that your listener is loaded and active when your Crew or Flow is executed.
-
-### Option 2: Create a Package for Your Listeners
-
-For a more structured approach, especially if you have multiple listeners:
-
-1. Create a package for your listeners:
-
-```
-my_project/
-  ├── listeners/
-  │   ├── __init__.py
-  │   ├── my_custom_listener.py
-  │   └── another_listener.py
-```
-
-2. In `my_custom_listener.py`, define your listener class and create an instance:
-
-```python
-# my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
-# ... import events ...
-
-class MyCustomListener(BaseEventListener):
-    # ... implementation ...
-
-# Create an instance of your listener
-my_custom_listener = MyCustomListener()
-```
-
-3. In `__init__.py`, import the listener instances to ensure they're loaded:
-
-```python
-# __init__.py
-from .my_custom_listener import my_custom_listener
-from .another_listener import another_listener
-
-# Optionally export them if you need to access them elsewhere
-__all__ = ['my_custom_listener', 'another_listener']
-```
-
-4. Import your listeners package in your Crew or Flow file:
-
-```python
-# In your crew.py or flow.py file
-import my_project.listeners  # This loads all your listeners
-
-class MyCustomCrew:
-    # Your crew implementation...
-```
-
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Available Event Types
-
-CrewAI provides a wide range of events that you can listen for:
-
-### Crew Events
-
-- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
-- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
-- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
-- **CrewTestStartedEvent**: Emitted when a Crew starts testing
-- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
-- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
-- **CrewTrainStartedEvent**: Emitted when a Crew starts training
-- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
-- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
-
-### Agent Events
-
-- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
-- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
-- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
-
-### Task Events
-
-- **TaskStartedEvent**: Emitted when a Task starts execution
-- **TaskCompletedEvent**: Emitted when a Task completes execution
-- **TaskFailedEvent**: Emitted when a Task fails to complete execution
-- **TaskEvaluationEvent**: Emitted when a Task is evaluated
-
-### Tool Usage Events
-
-- **ToolUsageStartedEvent**: Emitted when a tool execution is started
-- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
-- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
-- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
-
-### Flow Events
-
-- **FlowCreatedEvent**: Emitted when a Flow is created
-- **FlowStartedEvent**: Emitted when a Flow starts execution
-- **FlowFinishedEvent**: Emitted when a Flow completes execution
-- **FlowPlotEvent**: Emitted when a Flow is plotted
-- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
-- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
-- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
-
-### LLM Events
-
-- **LLMCallStartedEvent**: Emitted when an LLM call starts
-- **LLMCallCompletedEvent**: Emitted when an LLM call completes
-- **LLMCallFailedEvent**: Emitted when an LLM call fails
-- **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
-
-## Event Handler Structure
-
-Each event handler receives two parameters:
-
-1. **source**: The object that emitted the event
-2. **event**: The event instance, containing event-specific data
-
-The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
-
-- **timestamp**: The time when the event was emitted
-- **type**: A string identifier for the event type
-
-Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
-
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Advanced Usage: Scoped Handlers
-
-For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
-
-```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
-
-with crewai_event_bus.scoped_handlers():
-    @crewai_event_bus.on(CrewKickoffStartedEvent)
-    def temp_handler(source, event):
-        print("This handler only exists within this context")
-    
-    # Do something that emits events
-    
-# Outside the context, the temporary handler is removed
-```
-
-## Use Cases
-
-Event listeners can be used for a variety of purposes:
-
-1. **Logging and Monitoring**: Track the execution of your Crew and log important events
-2. **Analytics**: Collect data about your Crew's performance and behavior
-3. **Debugging**: Set up temporary listeners to debug specific issues
-4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
-5. **Custom Behavior**: Trigger custom actions based on specific events
-
-## Best Practices
-
-1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
-2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
-3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
-4. **Selective Listening**: Only listen for events you actually need to handle
-5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
-
-By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

docs/concepts/flows.mdx

@@ -35,8 +35,6 @@ class ExampleFlow(Flow):
     @start()
     def generate_city(self):
         print("Starting flow")
-        # Each flow state automatically gets a unique ID
-        print(f"Flow State ID: {self.state['id']}")
 
         response = completion(
             model=self.model,
@@ -49,8 +47,6 @@ class ExampleFlow(Flow):
         )
 
         random_city = response["choices"][0]["message"]["content"]
-        # Store the city in our state
-        self.state["city"] = random_city
         print(f"Random City: {random_city}")
 
         return random_city
@@ -68,8 +64,6 @@ class ExampleFlow(Flow):
         )
 
         fun_fact = response["choices"][0]["message"]["content"]
-        # Store the fun fact in our state
-        self.state["fun_fact"] = fun_fact
         return fun_fact
 
 
@@ -82,15 +76,7 @@ print(f"Generated fun fact: {result}")
 
 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.
-
-When you run the Flow, it will:
-1. Generate a unique ID for the flow state
-2. Generate a random city and store it in the state
-3. Generate a fun fact about that city and store it in the state
-4. Print the results to the console
-
-The state's unique ID and stored data can be useful for tracking flow executions and maintaining context between tasks.
+When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console.
 
 **Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
 
@@ -150,12 +136,12 @@ final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-```
+````
 
-```text Output
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
-```
+````
 
 </CodeGroup>
 
@@ -221,39 +207,34 @@ allowing developers to choose the approach that best fits their application's ne
 
 In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
 This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
-Even with unstructured states, CrewAI Flows automatically generates and maintains a unique identifier (UUID) for each state instance.
 
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 
-class UnstructuredExampleFlow(Flow):
+class UntructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
-        # The state automatically includes an 'id' field
-        print(f"State ID: {self.state['id']}")
-        self.state['counter'] = 0
-        self.state['message'] = "Hello from structured flow"
+        self.state.message = "Hello from structured flow"
+        self.state.counter = 0
 
     @listen(first_method)
     def second_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated"
+        self.state.counter += 1
+        self.state.message += " - updated"
 
     @listen(second_method)
     def third_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated again"
+        self.state.counter += 1
+        self.state.message += " - updated again"
 
         print(f"State after third_method: {self.state}")
 
 
-flow = UnstructuredExampleFlow()
+flow = UntructuredExampleFlow()
 flow.kickoff()
 ```
 
-**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:**
 
 - **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
@@ -264,15 +245,12 @@ flow.kickoff()
 Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
 By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
 
-Each state in CrewAI Flows automatically receives a unique identifier (UUID) to help track and manage state instances. This ID is automatically generated and managed by the Flow system.
-
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
 
 class ExampleState(BaseModel):
-    # Note: 'id' field is automatically added to all states
     counter: int = 0
     message: str = ""
 
@@ -281,8 +259,6 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
     def first_method(self):
-        # Access the auto-generated ID if needed
-        print(f"State ID: {self.state.id}")
         self.state.message = "Hello from structured flow"
 
     @listen(first_method)
@@ -323,91 +299,6 @@ flow.kickoff()
 
 By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
 
-## Flow Persistence
-
-The @persist decorator enables automatic state persistence in CrewAI Flows, allowing you to maintain flow state across restarts or different workflow executions. This decorator can be applied at either the class level or method level, providing flexibility in how you manage state persistence.
-
-### Class-Level Persistence
-
-When applied at the class level, the @persist decorator automatically persists all flow method states:
-
-```python
-@persist  # Using SQLiteFlowPersistence by default
-class MyFlow(Flow[MyState]):
-    @start()
-    def initialize_flow(self):
-        # This method will automatically have its state persisted
-        self.state.counter = 1
-        print("Initialized flow. State ID:", self.state.id)
-
-    @listen(initialize_flow)
-    def next_step(self):
-        # The state (including self.state.id) is automatically reloaded
-        self.state.counter += 1
-        print("Flow state is persisted. Counter:", self.state.counter)
-```
-
-### Method-Level Persistence
-
-For more granular control, you can apply @persist to specific methods:
-
-```python
-class AnotherFlow(Flow[dict]):
-    @persist  # Persists only this method's state
-    @start()
-    def begin(self):
-        if "runs" not in self.state:
-            self.state["runs"] = 0
-        self.state["runs"] += 1
-        print("Method-level persisted runs:", self.state["runs"])
-```
-
-### How It Works
-
-1. **Unique State Identification**
-   - Each flow state automatically receives a unique UUID
-   - The ID is preserved across state updates and method calls
-   - Supports both structured (Pydantic BaseModel) and unstructured (dictionary) states
-
-2. **Default SQLite Backend**
-   - SQLiteFlowPersistence is the default storage backend
-   - States are automatically saved to a local SQLite database
-   - Robust error handling ensures clear messages if database operations fail
-
-3. **Error Handling**
-   - Comprehensive error messages for database operations
-   - Automatic state validation during save and load
-   - Clear feedback when persistence operations encounter issues
-
-### Important Considerations
-
-- **State Types**: Both structured (Pydantic BaseModel) and unstructured (dictionary) states are supported
-- **Automatic ID**: The `id` field is automatically added if not present
-- **State Recovery**: Failed or restarted flows can automatically reload their previous state
-- **Custom Implementation**: You can provide your own FlowPersistence implementation for specialized storage needs
-
-### Technical Advantages
-
-1. **Precise Control Through Low-Level Access**
-   - Direct access to persistence operations for advanced use cases
-   - Fine-grained control via method-level persistence decorators
-   - Built-in state inspection and debugging capabilities
-   - Full visibility into state changes and persistence operations
-
-2. **Enhanced Reliability**
-   - Automatic state recovery after system failures or restarts
-   - Transaction-based state updates for data integrity
-   - Comprehensive error handling with clear error messages
-   - Robust validation during state save and load operations
-
-3. **Extensible Architecture**
-   - Customizable persistence backend through FlowPersistence interface
-   - Support for specialized storage solutions beyond SQLite
-   - Compatible with both structured (Pydantic) and unstructured (dict) states
-   - Seamless integration with existing CrewAI flow patterns
-
-The persistence system's architecture emphasizes technical precision and customization options, allowing developers to maintain full control over state management while benefiting from built-in reliability features.
-
 ## Flow Control
 
 ### Conditional Logic: `or`
@@ -737,35 +628,4 @@ Also, check out our YouTube video on how to use flows in CrewAI below!
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
   allowfullscreen
-></iframe>
-
-## Running Flows
-
-There are two ways to run a flow:
-
-### Using the Flow API
-
-You can run a flow programmatically by creating an instance of your flow class and calling the `kickoff()` method:
-
-```python
-flow = ExampleFlow()
-result = flow.kickoff()
-```
-
-### Using the CLI
-
-Starting from version 0.103.0, you can run flows using the `crewai run` command:
-
-```shell
-crewai run
-```
-
-This command automatically detects if your project is a flow (based on the `type = "flow"` setting in your pyproject.toml) and runs it accordingly. This is the recommended way to run flows from the command line.
-
-For backward compatibility, you can also use:
-
-```shell
-crewai flow kickoff
-```
-
-However, the `crewai run` command is now the preferred method as it works for both crews and flows.
+></iframe>

docs/concepts/knowledge.mdx

@@ -4,6 +4,8 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---
 
+# Using Knowledge in CrewAI
+
 ## What is Knowledge?
 
 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
@@ -34,20 +36,7 @@ CrewAI supports various types of knowledge sources out of the box:
   </Card>
 </CardGroup>
 
-## Supported Knowledge Parameters
-
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
-## Quickstart Example
-
-<Tip>
-For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
-Also, use relative paths from the `knowledge` directory when creating the source.
-</Tip>
+## Quick Start
 
 Here's an example using string-based knowledge:
 
@@ -91,14 +80,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```
 
 
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 
-
-<Note>
-  You need to install `docling` for the following example to work: `uv add docling`
-</Note>
-
-
-
+Here's another example with the `CrewDoclingSource`
 ```python Code
 from crewai import LLM, Agent, Crew, Process, Task
 from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
@@ -146,225 +128,39 @@ result = crew.kickoff(
 )
 ```
 
-## More Examples
-
-Here are examples of how to use different types of knowledge sources:
-
-### Text File Knowledge Source
-```python
-from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
-
-# Create a text file knowledge source
-text_source = TextFileKnowledgeSource(
-    file_paths=["document.txt", "another.txt"]
-)
-
-# Create crew with text file source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[text_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[text_source]
-)
-```
-
-### PDF Knowledge Source
-```python
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-
-# Create a PDF knowledge source
-pdf_source = PDFKnowledgeSource(
-    file_paths=["document.pdf", "another.pdf"]
-)
-
-# Create crew with PDF knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[pdf_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[pdf_source]
-)
-```
-
-### CSV Knowledge Source
-```python
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-
-# Create a CSV knowledge source
-csv_source = CSVKnowledgeSource(
-    file_paths=["data.csv"]
-)
-
-# Create crew with CSV knowledge source or on agent level
-agent = Agent(
-    ...
-    knowledge_sources=[csv_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[csv_source]
-)
-```
-
-### Excel Knowledge Source
-```python
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-
-# Create an Excel knowledge source
-excel_source = ExcelKnowledgeSource(
-    file_paths=["spreadsheet.xlsx"]
-)
-
-# Create crew with Excel knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[excel_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[excel_source]
-)
-```
-
-### JSON Knowledge Source
-```python
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-
-# Create a JSON knowledge source
-json_source = JSONKnowledgeSource(
-    file_paths=["data.json"]
-)
-
-# Create crew with JSON knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[json_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[json_source]
-)
-```
-
 ## Knowledge Configuration
 
 ### Chunking Configuration
 
-Knowledge sources automatically chunk content for better processing. 
-You can configure chunking behavior in your knowledge sources:
+Control how content is split for processing by setting the chunk size and overlap.
 
-```python
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-source = StringKnowledgeSource(
-    content="Your content here",
-    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
-    chunk_overlap=200     # Overlap between chunks (default: 200)
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Long content...",
+    chunk_size=4000,     # Characters per chunk (default)
+    chunk_overlap=200    # Overlap between chunks (default)
 )
 ```
 
-The chunking configuration helps in:
-- Breaking down large documents into manageable pieces
-- Maintaining context through chunk overlap
-- Optimizing retrieval accuracy
+## Embedder Configuration
 
-### Embeddings Configuration
+You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
 
-You can also configure the embedder for the knowledge store. 
-This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
-The `embedder` parameter supports various embedding model providers that include:
-- `openai`: OpenAI's embedding models
-- `google`: Google's text embedding models
-- `azure`: Azure OpenAI embeddings
-- `ollama`: Local embeddings with Ollama
-- `vertexai`: Google Cloud VertexAI embeddings
-- `cohere`: Cohere's embedding models
-- `voyageai`: VoyageAI's embedding models
-- `bedrock`: AWS Bedrock embeddings
-- `huggingface`: Hugging Face models
-- `watson`: IBM Watson embeddings
-
-Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
-<CodeGroup>
-```python Example
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-import os
-
-# Get the GEMINI API key
-GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
+```python Code
+...
 string_source = StringKnowledgeSource(
-    content=content,
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
 )
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-gemini_llm = LLM(
-    model="gemini/gemini-1.5-pro-002",
-    api_key=GEMINI_API_KEY,
-    temperature=0,
-)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=gemini_llm,
-    embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
-)
-
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
 crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
+    ...
     knowledge_sources=[string_source],
     embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"},
+    },
 )
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
 ```
-```text Output
-# Agent: About User
-## Task: Answer the following questions about the user: What city does John live in and how old is he?
 
-# Agent: About User
-## Final Answer: 
-John is 30 years old and lives in San Francisco.
-```
-</CodeGroup>
 ## Clearing Knowledge
 
 If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.

docs/concepts/memory.mdx

@@ -58,107 +58,41 @@ my_crew = Crew(
 ### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
 
 ```python Code
-from crewai import Crew, Process
-from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
-from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
-from typing import List, Optional
+from crewai import Crew, Agent, Task, Process
 
 # Assemble your crew with memory capabilities
-my_crew: Crew = Crew(
-    agents = [...],
-    tasks = [...],
-    process = Process.sequential,
-    memory = True,
-    # Long-term memory for persistent storage across sessions
-    long_term_memory = LongTermMemory(
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process="Process.sequential",
+    memory=True,
+    long_term_memory=EnhanceLongTermMemory(
         storage=LTMSQLiteStorage(
-            db_path="/my_crew1/long_term_memory_storage.db"
+            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
         )
     ),
-    # Short-term memory for current context using RAG
-    short_term_memory = ShortTermMemory(
-        storage = RAGStorage(
-                embedder_config={
-                    "provider": "openai",
-                    "config": {
-                        "model": 'text-embedding-3-small'
-                    }
-                },
-                type="short_term",
-                path="/my_crew1/"
-            )
+    short_term_memory=EnhanceShortTermMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="short_term",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
         ),
     ),
-    # Entity memory for tracking key information about entities
-    entity_memory = EntityMemory(
-        storage=RAGStorage(
-            embedder_config={
-                "provider": "openai",
-                "config": {
-                    "model": 'text-embedding-3-small'
-                }
-            },
-            type="short_term",
-            path="/my_crew1/"
-        )
+    entity_memory=EnhanceEntityMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="entities",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
     ),
     verbose=True,
 )
 ```
 
-## Security Considerations
-
-When configuring memory storage:
-- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
-- Never hardcode sensitive information like database credentials
-- Consider access permissions for storage directories
-- Use relative paths when possible to maintain portability
-
-Example using environment variables:
-```python
-import os
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage import LTMSQLiteStorage
-
-# Configure storage path using environment variable
-storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path="{storage_path}/memory.db".format(storage_path=storage_path)
-        )
-    )
-)
-```
-
-## Configuration Examples
-
-### Basic Memory Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-
-# Simple memory configuration
-crew = Crew(memory=True)  # Uses default storage locations
-```
-
-### Custom Storage Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage import LTMSQLiteStorage
-
-# Configure custom storage paths
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(db_path="./memory.db")
-    )
-)
-```
-
 ## Integrating Mem0 for Enhanced User Memory
 
 [Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
@@ -200,23 +134,6 @@ crew = Crew(
 )
 ```
 
-## Memory Configuration Options
-If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
-
-```python Code
-from crewai import Crew
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-    },
-)
-```
 
 ## Additional Embedding Providers
 
@@ -251,12 +168,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
 )
 ```
 
@@ -282,19 +194,6 @@ my_crew = Crew(
 
 ### Using Google AI embeddings
 
-#### Prerequisites
-Before using Google AI embeddings, ensure you have:
-- Access to the Gemini API
-- The necessary API keys and permissions
-
-You will need to update your *pyproject.toml* dependencies:
-```YAML
-dependencies = [
-    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
-    "crewai[tools]>=0.100.0,<1.0.0"
-]
-```
-
 ```python Code
 from crewai import Crew, Agent, Task, Process
 
@@ -308,7 +207,7 @@ my_crew = Crew(
         "provider": "google",
         "config": {
             "api_key": "<YOUR_API_KEY>",
-            "model": "<model_name>"
+            "model_name": "<model_name>"
         }
     }
 )
@@ -326,15 +225,13 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "api_base": "YOUR_API_BASE_PATH",
-            "api_version": "YOUR_API_VERSION",
-            "model_name": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(
+        api_key="YOUR_API_KEY",
+        api_base="YOUR_API_BASE_PATH",
+        api_type="azure",
+        api_version="YOUR_API_VERSION",
+        model_name="text-embedding-3-small"
+    )
 )
 ```
 
@@ -350,15 +247,12 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id"="YOUR_PROJECT_ID",
-            "region"="YOUR_REGION",
-            "api_key"="YOUR_API_KEY",
-            "model_name"="textembedding-gecko"
-        }
-    }
+    embedder=GoogleVertexEmbeddingFunction(
+        project_id="YOUR_PROJECT_ID",
+        region="YOUR_REGION",
+        api_key="YOUR_API_KEY",
+        model_name="textembedding-gecko"
+    )
 )
 ```
 
@@ -377,27 +271,7 @@ my_crew = Crew(
         "provider": "cohere",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model": "<model_name>"
-        }
-    }
-)
-```
-### Using VoyageAI embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "voyageai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "model": "<model_name>"
+            "model_name": "<model_name>"
         }
     }
 )
@@ -447,66 +321,7 @@ my_crew = Crew(
 )
 ```
 
-### Using Amazon Bedrock embeddings
-
-```python Code
-# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
-
-import os
-import boto3
-from crewai import Crew, Agent, Task, Process
-
-boto3_session = boto3.Session(
-    region_name=os.environ.get("AWS_REGION_NAME"),
-    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
-    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
-)
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    embedder={
-    "provider": "bedrock",
-        "config":{
-            "session": boto3_session,
-            "model": "amazon.titan-embed-text-v2:0",
-            "vector_dimension": 1024
-        }
-    }
-    verbose=True
-)
-```
-
-### Adding Custom Embedding Function
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb import Documents, EmbeddingFunction, Embeddings
-
-# Create a custom embedding function
-class CustomEmbedder(EmbeddingFunction):
-    def __call__(self, input: Documents) -> Embeddings:
-        # generate embeddings
-        return [1, 2, 3] # this is a dummy embedding
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-```
-
-### Resetting Memory via cli
+### Resetting Memory
 
 ```shell
 crewai reset-memories [OPTIONS]
@@ -520,46 +335,8 @@ crewai reset-memories [OPTIONS]
 | `-s`, `--short`    | Reset SHORT TERM memory.         | Flag (boolean) | False   |
 | `-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   |
 | `-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.
-
-
-
-
-### Resetting Memory via crew object
-
-```python
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-
-my_crew.reset_memories(command_type = 'all') # Resets all the memory
-```
-
-#### Resetting Memory Options
-
-| Command Type       | Description                      |
-| :----------------- | :------------------------------- |
-| `long`             | Reset LONG TERM memory.          | 
-| `short`            | Reset SHORT TERM memory.         | 
-| `entities`         | Reset ENTITIES memory.           | 
-| `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
-| `knowledge`        | Reset KNOWLEDGE memory.          |
-| `all`              | Reset ALL memories.              |
-
 
 ## Benefits of Using CrewAI's Memory System
 

docs/concepts/planning.mdx

@@ -31,7 +31,7 @@ From this point on, your crew will have planning enabled, and the tasks will be
 
 #### Planning LLM
 
-Now you can define the LLM that will be used to plan the tasks. 
+Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
 
 When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner` 
 responsible for creating the step-by-step logic to add to the Agents' tasks.
@@ -39,6 +39,7 @@ responsible for creating the step-by-step logic to add to the Agents' tasks.
 <CodeGroup>
 ```python Code
 from crewai import Crew, Agent, Task, Process
+from langchain_openai import ChatOpenAI
 
 # Assemble your crew with planning capabilities and custom LLM
 my_crew = Crew(
@@ -46,7 +47,7 @@ my_crew = Crew(
     tasks=self.tasks,
     process=Process.sequential,
     planning=True,
-    planning_llm="gpt-4o"
+    planning_llm=ChatOpenAI(model="gpt-4o")
 )
 
 # Run the crew
@@ -81,8 +82,8 @@ my_crew.kickoff()
 
 3. **Collect Data:**
 
-   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
-   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
+   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
+   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
 
 4. **Analyze Findings:**
 

docs/concepts/processes.mdx

@@ -23,7 +23,9 @@ Processes enable individual agents to operate as a cohesive unit, streamlining t
 To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
 
 ```python
-from crewai import Crew, Process
+from crewai import Crew
+from crewai.process import Process
+from langchain_openai import ChatOpenAI
 
 # Example: Creating a crew with a sequential process
 crew = Crew(
@@ -38,7 +40,7 @@ crew = Crew(
     agents=my_agents,
     tasks=my_tasks,
     process=Process.hierarchical,
-    manager_llm="gpt-4o"
+    manager_llm=ChatOpenAI(model="gpt-4")
     # or
     # manager_agent=my_manager_agent
 )

docs/concepts/tasks.mdx

@@ -33,12 +33,11 @@ crew = Crew(
 | :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
 | **Description**                  | `description`     | `str`                         | A clear, concise statement of what the task entails.                                                                 |
 | **Expected Output**              | `expected_output` | `str`                         | A detailed description of what the task's completion looks like.                                                     |
-| **Name** _(optional)_            | `name`            | `Optional[str]`               | A name identifier for the task.                                                                                      |
-| **Agent** _(optional)_           | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
-| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`              | The tools/resources the agent is limited to use for this task.                                                       |
+| **Name** _(optional)_           | `name`           | `Optional[str]`               | A name identifier for the task.                                                                                      |
+| **Agent** _(optional)_          | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
+| **Tools** _(optional)_          | `tools`           | `List[BaseTool]`             | The tools/resources the agent is limited to use for this task.                                                       |
 | **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
 | **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
-| **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Whether the task should have a human review the final answer of the agent. Defaults to False.                        |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
@@ -69,7 +68,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is 2025.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
@@ -155,7 +154,7 @@ research_task = Task(
     description="""
         Conduct a thorough research about AI Agents.
         Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        the current year is 2024.
     """,
     expected_output="""
         A list with 10 bullet points of the most relevant information about AI Agents
@@ -268,7 +267,7 @@ analysis_task = Task(
 
 Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
-feedback to agents when their output doesn't meet specific criteria.
+efeedback to agents when their output doesn't meet specific criteria.
 
 ### Using Task Guardrails
 
@@ -876,19 +875,6 @@ save_output_task = Task(
 #...
 ```
 
-Check out the video below to see how to use structured outputs in CrewAI:
-
-<iframe
-  width="560"
-  height="315"
-  src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
-></iframe>
-
 ## Conclusion
 
 Tasks are the driving force behind the actions of agents in CrewAI.

docs/concepts/tools.mdx

@@ -150,20 +150,15 @@ There are two main ways for one to create a CrewAI tool:
 
 ```python Code
 from crewai.tools import BaseTool
-from pydantic import BaseModel, Field
 
-class MyToolInput(BaseModel):
-    """Input schema for MyCustomTool."""
-    argument: str = Field(..., description="Description of the argument.")
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
-    description: str = "What this tool does. It's vital for effective utilization."
-    args_schema: Type[BaseModel] = MyToolInput
+    description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
 
     def _run(self, argument: str) -> str:
-        # Your tool's logic here
-        return "Tool's result"
+        # Implementation goes here
+        return "Result from custom tool"
 ```
 
 ### Utilizing the `tool` Decorator

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

@@ -1,9 +1,4 @@
----
-title: Agent Monitoring with Portkey
-description: How to use Portkey with CrewAI
-icon: key
----
-
+# 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%" />
 
 
@@ -15,69 +10,74 @@ Portkey adds 4 core production capabilities to any CrewAI agent:
 3. Full-stack tracing & cost, performance analytics
 4. Real-time guardrails to enforce behavior
 
+
+
+
+
 ## Getting Started
 
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="Configure the LLM Client">
-    To build CrewAI Agents with Portkey, you'll need two keys:
-    - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-    - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
+1. **Install Required Packages:**
 
-    ```python
-    from crewai import LLM
-    from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
+```bash
+pip install -qU crewai portkey-ai
+```
 
-    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
-        )
+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
     )
-    ```
-    </Step>
-    <Step title="Create and Run Your First Agent">
-    ```python
-    from crewai import Agent, Task, Crew
+)
+```
 
-    # Define your agents with roles and goals
-    coder = Agent(
-        role='Software developer',
-        goal='Write clear, concise code on demand',
-        backstory='An expert coder with a keen eye for software trends.',
-        llm=gpt_llm
-    )
+3. **Create and Run Your First Agent:**
 
-    # 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
-    )
+```python
+from crewai import Agent, Task, Crew
 
-    # Instantiate your crew
-    crew = Crew(
-        agents=[coder],
-        tasks=[task1],
-    )
+# 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)
+```
 
-    result = crew.kickoff()
-    print(result)
-    ```
-    </Step>
-</Steps>
 
 ## Key Features
 
 | Feature | Description |
-|:--------|:------------|
+|---------|-------------|
 | 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
 | 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
 | 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
@@ -200,3 +200,12 @@ For detailed information on creating and managing Configs, visit the [Portkey do
 - [📊 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/custom-manager-agent.mdx

@@ -73,9 +73,9 @@ result = crew.kickoff()
 If you're using the hierarchical process and don't want to set a custom manager agent, you can specify the language model for the manager:
 
 ```python Code
-from crewai import LLM
+from langchain_openai import ChatOpenAI
 
-manager_llm = LLM(model="gpt-4o")
+manager_llm = ChatOpenAI(model_name="gpt-4")
 
 crew = Crew(
     agents=[researcher, writer],

docs/how-to/hierarchical-process.mdx

@@ -48,6 +48,7 @@ Define a crew with a designated manager and establish a clear chain of command.
 </Tip>
 
 ```python Code
+from langchain_openai import ChatOpenAI
 from crewai import Crew, Process, Agent
 
 # Agents are defined with attributes for backstory, cache, and verbose mode
@@ -55,51 +56,38 @@ researcher = Agent(
     role='Researcher',
     goal='Conduct in-depth analysis',
     backstory='Experienced data analyst with a knack for uncovering hidden trends.',
+    cache=True,
+    verbose=False,
+    # tools=[]  # This can be optionally specified; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 writer = Agent(
     role='Writer',
     goal='Create engaging content',
     backstory='Creative writer passionate about storytelling in technical domains.',
+    cache=True,
+    verbose=False,
+    # tools=[]  # Optionally specify tools; defaults to an empty list
+    use_system_prompt=True,  # Enable or disable system prompts for this agent
+    max_rpm=30,  # Limit on the number of requests per minute
+    max_iter=5  # Maximum number of iterations for a final answer
 )
 
 # Establishing the crew with a hierarchical process and additional configurations
 project_crew = Crew(
     tasks=[...],  # Tasks to be delegated and executed under the manager's supervision
     agents=[researcher, writer],
-    manager_llm="gpt-4o",  # Specify which LLM the manager should use
-    process=Process.hierarchical,  
-    planning=True, 
+    manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory if manager_agent is not set
+    process=Process.hierarchical,  # Specifies the hierarchical management approach
+    respect_context_window=True,  # Enable respect of the context window for tasks
+    memory=True,  # Enable memory usage for enhanced task execution
+    manager_agent=None,  # Optional: explicitly set a specific agent as manager instead of the manager_llm
+    planning=True,  # Enable planning feature for pre-execution strategy
 )
 ```
 
-### Using a Custom Manager Agent
-
-Alternatively, you can create a custom manager agent with specific attributes tailored to your project's management needs. This gives you more control over the manager's behavior and capabilities.
-
-```python
-# Define a custom manager agent
-manager = Agent(
-    role="Project Manager",
-    goal="Efficiently manage the crew and ensure high-quality task completion",
-    backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success.",
-    allow_delegation=True,
-)
-
-# Use the custom manager in your crew
-project_crew = Crew(
-    tasks=[...],
-    agents=[researcher, writer],
-    manager_agent=manager,  # Use your custom manager agent
-    process=Process.hierarchical,
-    planning=True,
-)
-```
-
-<Tip>
-    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent).
-</Tip>
-
-
 ### Workflow in Action
 
 1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
@@ -109,4 +97,4 @@ project_crew = Crew(
 ## Conclusion
 
 Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management. 
-Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.
+Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.

docs/how-to/human-input-on-execution.mdx

@@ -60,12 +60,12 @@ writer = Agent(
 # Create tasks for your agents
 task1 = Task(
     description=(
-        "Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
+        "Conduct a comprehensive analysis of the latest advancements in AI in 2024. "
         "Identify key trends, breakthrough technologies, and potential industry impacts. "
         "Compile your findings in a detailed report. "
         "Make sure to check with a human if the draft is good before finalizing your answer."
     ),
-    expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
+    expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
     agent=researcher,
     human_input=True
 )
@@ -76,7 +76,7 @@ task2 = Task(
         "Your post should be informative yet accessible, catering to a tech-savvy audience. "
         "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
     ),
-    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
+    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
     agent=writer,
     human_input=True
 )

docs/how-to/kickoff-async.mdx

@@ -54,8 +54,7 @@ coding_agent = Agent(
 # Create a task that requires code execution
 data_analysis_task = Task(
     description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent,
-    expected_output="The average age of the participants."
+    agent=coding_agent
 )
 
 # Create a crew and add the task
@@ -117,4 +116,4 @@ async def async_multiple_crews():
 
 # Run the async function
 asyncio.run(async_multiple_crews())
-```
+```

docs/how-to/langfuse-observability.mdx

@@ -1,100 +0,0 @@
----
-title: Agent Monitoring with Langfuse
-description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
-icon: magnifying-glass-chart
----
-
-# Integrate Langfuse with CrewAI
-
-This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
-
-> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
-
-[![Langfuse Overview Video](https://github.com/user-attachments/assets/3926b288-ff61-4b95-8aa1-45d041c70866)](https://langfuse.com/watch-demo)
-
-## Get Started
-
-We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
-
-### Step 1: Install Dependencies
-
-
-```python
-%pip install langfuse openlit crewai crewai_tools
-```
-
-### Step 2: Set Up Environment Variables
-
-Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
-
-
-```python
-import os
-import base64
-
-LANGFUSE_PUBLIC_KEY="pk-lf-..."
-LANGFUSE_SECRET_KEY="sk-lf-..."
-LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
-
-os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
-# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
-os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
-
-# your openai key
-os.environ["OPENAI_API_KEY"] = "sk-..."
-```
-
-### Step 3: Initialize OpenLit
-
-Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
-
-
-```python
-import openlit
-
-openlit.init()
-```
-
-### Step 4: Create a Simple CrewAI Application
-
-We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
-
-
-```python
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-        role="Writer",
-        goal="You make math engaging and understandable for young children through poetry",
-        backstory="You're an expert in writing haikus but you know nothing of math.",
-        tools=[web_rag_tool],  
-    )
-
-task = Task(description=("What is {multiplication}?"),
-            expected_output=("Compose a haiku that includes the answer."),
-            agent=writer)
-
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
-  share_crew=False
-)
-```
-
-### Step 5: See Traces in Langfuse
-
-After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
-
-![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
-
-_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
-
-## References
-
-- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)

docs/how-to/llm-connections.mdx

@@ -23,7 +23,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Azure OpenAI
 - AWS (Bedrock, SageMaker)
 - Cohere
-- VoyageAI
 - Hugging Face
 - Ollama
 - Mistral AI
@@ -33,7 +32,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Cloudflare Workers AI
 - DeepInfra
 - Groq
-- SambaNova
 - [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
 - And many more!
 

docs/how-to/mlflow-observability.mdx

@@ -1,206 +0,0 @@
----
-title: Agent Monitoring with MLflow
-description: Quickly start monitoring your Agents with MLflow.
-icon: bars-staggered
----
-
-# MLflow Overview
-
-[MLflow](https://mlflow.org/) is an open-source platform to assist machine learning practitioners and teams in handling the complexities of the machine learning process.
-
-It provides a tracing feature that enhances LLM observability in your Generative AI applications by capturing detailed information about the execution of your application’s services. 
-Tracing provides a way to record the inputs, outputs, and metadata associated with each intermediate step of a request, enabling you to easily pinpoint the source of bugs and unexpected behaviors.
-
-![Overview of MLflow crewAI tracing usage](/images/mlflow-tracing.gif)
-
-### Features
-
-- **Tracing Dashboard**: Monitor activities of your crewAI agents with detailed dashboards that include inputs, outputs and metadata of spans.
-- **Automated Tracing**: A fully automated integration with crewAI, which can be enabled by running `mlflow.crewai.autolog()`. 
-- **Manual Trace Instrumentation with minor efforts**: Customize trace instrumentation through MLflow's high-level fluent APIs such as decorators, function wrappers and context managers.
-- **OpenTelemetry Compatibility**: MLflow Tracing supports exporting traces to an OpenTelemetry Collector, which can then be used to export traces to various backends such as Jaeger, Zipkin, and AWS X-Ray.
-- **Package and Deploy Agents**: Package and deploy your crewAI agents to an inference server with a variety of deployment targets.
-- **Securely Host LLMs**: Host multiple LLM from various providers in one unified endpoint through MFflow gateway.
-- **Evaluation**: Evaluate your crewAI agents with a wide range of metrics using a convenient API `mlflow.evaluate()`.
-
-## Setup Instructions
-
-<Steps>
-    <Step title="Install MLflow package">
-      ```shell
-      # The crewAI integration is available in mlflow>=2.19.0
-      pip install mlflow
-      ```
-    </Step>
-    <Step title="Start MFflow tracking server">
-      ```shell
-      # This process is optional, but it is recommended to use MLflow tracking server for better visualization and broader features.
-      mlflow server
-      ```
-    </Step>
-    <Step title="Initialize MLflow in Your Application">
-      Add the following two lines to your application code:
-
-      ```python
-      import mlflow
-
-      mlflow.crewai.autolog()
-
-      # Optional: Set a tracking URI and an experiment name if you have a tracking server
-      mlflow.set_tracking_uri("http://localhost:5000")
-      mlflow.set_experiment("CrewAI")
-      ```
-      
-      Example Usage for tracing CrewAI Agents:
-
-      ```python
-      from crewai import Agent, Crew, Task
-      from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-      from crewai_tools import SerperDevTool, WebsiteSearchTool
-
-      from textwrap import dedent
-
-      content = "Users name is John. He is 30 years old and lives in San Francisco."
-      string_source = StringKnowledgeSource(
-          content=content, metadata={"preference": "personal"}
-      )
-
-      search_tool = WebsiteSearchTool()
-
-
-      class TripAgents:
-          def city_selection_agent(self):
-              return Agent(
-                  role="City Selection Expert",
-                  goal="Select the best city based on weather, season, and prices",
-                  backstory="An expert in analyzing travel data to pick ideal destinations",
-                  tools=[
-                      search_tool,
-                  ],
-                  verbose=True,
-              )
-
-          def local_expert(self):
-              return Agent(
-                  role="Local Expert at this city",
-                  goal="Provide the BEST insights about the selected city",
-                  backstory="""A knowledgeable local guide with extensive information
-              about the city, it's attractions and customs""",
-                  tools=[search_tool],
-                  verbose=True,
-              )
-
-
-      class TripTasks:
-          def identify_task(self, agent, origin, cities, interests, range):
-              return Task(
-                  description=dedent(
-                      f"""
-                      Analyze and select the best city for the trip based
-                      on specific criteria such as weather patterns, seasonal
-                      events, and travel costs. This task involves comparing
-                      multiple cities, considering factors like current weather
-                      conditions, upcoming cultural or seasonal events, and
-                      overall travel expenses.
-                      Your final answer must be a detailed
-                      report on the chosen city, and everything you found out
-                      about it, including the actual flight costs, weather
-                      forecast and attractions.
-
-                      Traveling from: {origin}
-                      City Options: {cities}
-                      Trip Date: {range}
-                      Traveler Interests: {interests}
-                  """
-                  ),
-                  agent=agent,
-                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
-              )
-
-          def gather_task(self, agent, origin, interests, range):
-              return Task(
-                  description=dedent(
-                      f"""
-                      As a local expert on this city you must compile an
-                      in-depth guide for someone traveling there and wanting
-                      to have THE BEST trip ever!
-                      Gather information about key attractions, local customs,
-                      special events, and daily activity recommendations.
-                      Find the best spots to go to, the kind of place only a
-                      local would know.
-                      This guide should provide a thorough overview of what
-                      the city has to offer, including hidden gems, cultural
-                      hotspots, must-visit landmarks, weather forecasts, and
-                      high level costs.
-                      The final answer must be a comprehensive city guide,
-                      rich in cultural insights and practical tips,
-                      tailored to enhance the travel experience.
-
-                      Trip Date: {range}
-                      Traveling from: {origin}
-                      Traveler Interests: {interests}
-                  """
-                  ),
-                  agent=agent,
-                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
-              )
-
-
-      class TripCrew:
-          def __init__(self, origin, cities, date_range, interests):
-              self.cities = cities
-              self.origin = origin
-              self.interests = interests
-              self.date_range = date_range
-
-          def run(self):
-              agents = TripAgents()
-              tasks = TripTasks()
-
-              city_selector_agent = agents.city_selection_agent()
-              local_expert_agent = agents.local_expert()
-
-              identify_task = tasks.identify_task(
-                  city_selector_agent,
-                  self.origin,
-                  self.cities,
-                  self.interests,
-                  self.date_range,
-              )
-              gather_task = tasks.gather_task(
-                  local_expert_agent, self.origin, self.interests, self.date_range
-              )
-
-              crew = Crew(
-                  agents=[city_selector_agent, local_expert_agent],
-                  tasks=[identify_task, gather_task],
-                  verbose=True,
-                  memory=True,
-                  knowledge={
-                      "sources": [string_source],
-                      "metadata": {"preference": "personal"},
-                  },
-              )
-
-              result = crew.kickoff()
-              return result
-
-
-      trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
-      result = trip_crew.run()
-
-      print(result)
-      ```
-      Refer to [MLflow Tracing Documentation](https://mlflow.org/docs/latest/llms/tracing/index.html) for more configurations and use cases.
-    </Step>
-    <Step title="Visualize Activities of Agents">
-      Now traces for your crewAI agents are captured by MLflow. 
-      Let's visit MLflow tracking server to view the traces and get insights into your Agents.
-
-      Open `127.0.0.1:5000` on your browser to visit MLflow tracking server. 
-      <Frame caption="MLflow Tracing Dashboard">
-        <img src="/images/mlflow1.png" alt="MLflow tracing example with crewai" />
-      </Frame>
-    </Step>
-</Steps> 
-

docs/how-to/multimodal-agents.mdx

@@ -1,14 +1,14 @@
 ---
 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: video
+icon: image
 ---
 
-## Using Multimodal Agents
+# 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
+## Enabling Multimodal Capabilities
 
 To create a multimodal agent, simply set the `multimodal` parameter to `True` when initializing your agent:
 
@@ -25,7 +25,7 @@ agent = Agent(
 
 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
+## 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.
 
@@ -45,7 +45,6 @@ image_analyst = Agent(
 # Create a task for image analysis
 task = Task(
     description="Analyze the product image at https://example.com/product.jpg and provide a detailed description",
-    expected_output="A detailed description of the product image",
     agent=image_analyst
 )
 
@@ -82,7 +81,6 @@ inspection_task = Task(
     3. Compliance with standards
     Provide a detailed report highlighting any issues found.
     """,
-    expected_output="A detailed report highlighting any issues found",
     agent=expert_analyst
 )
 
@@ -110,7 +108,7 @@ The multimodal agent will automatically handle the image processing through its
 - Process image content with optional context or specific questions
 - Provide analysis and insights based on the visual information and task requirements
 
-### Best Practices
+## Best Practices
 
 When working with multimodal agents, keep these best practices in mind:
 

docs/installation.mdx

@@ -15,124 +15,113 @@ icon: wrench
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
-CrewAI uses the `uv` as its dependency management and package handling tool. It simplifies project setup and execution, offering a seamless experience.
+# Installing CrewAI
 
-If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on your system, else you can skip to **step 2**.
+CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
+Let's get you set up! 🚀
 
 <Steps>
-    <Step title="Install uv">
-      - **On macOS/Linux:**
-
-        Use `curl` to download the script and execute it with `sh`:
-
-        ```shell
-        curl -LsSf https://astral.sh/uv/install.sh | sh
+    <Step title="Install CrewAI">
+        Install CrewAI with all recommended tools using either method:
+        ```shell Terminal
+        pip install 'crewai[tools]'
         ```
-        If your system doesn't have `curl`, you can use `wget`:
-
-        ```shell
-        wget -qO- https://astral.sh/uv/install.sh | sh
+        or
+        ```shell Terminal
+        pip install crewai crewai-tools
         ```
 
-      - **On Windows:**
-
-        Use `irm` to download the script and `iex` to execute it:
-
-        ```shell
-        powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-        ```
-        If you run into any issues, refer to [UV's installation guide](https://docs.astral.sh/uv/getting-started/installation/) for more information.
+        <Note>
+          Both methods install the core package and additional tools needed for most use cases.
+        </Note>
     </Step>
 
-    <Step title="Install CrewAI 🚀">
-      - Run the following command to install `crewai` CLI:
-        ```shell
-        uv tool install crewai
+    <Step title="Upgrade CrewAI (Existing Installations Only)">
+        If you have an older version of CrewAI installed, you can upgrade it:
+        ```shell Terminal
+        pip install --upgrade crewai crewai-tools
         ```
-          <Warning>
-            If you encounter a `PATH` warning, run this command to update your shell:
-            ```shell
-            uv tool update-shell
-            ```
-          </Warning>
 
-      - To verify that `crewai` is installed, run:
-        ```shell
-        uv tools list
+        <Warning>
+            If you see a Poetry-related warning, you'll need to migrate to our new dependency manager:
+            ```shell Terminal
+            crewai update
+            ```
+            This will update your project to use [UV](https://github.com/astral-sh/uv), our new faster dependency manager.
+        </Warning>
+
+        <Note>
+            Skip this step if you're doing a fresh installation.
+        </Note>
+    </Step>
+
+    <Step title="Verify Installation">
+        Check your installed versions:
+        ```shell Terminal
+        pip freeze | grep crewai
         ```
-      - You should see something like:
-        ```markdown
-        crewai v0.102.0
-        - crewai
+
+        You should see something like:
+        ```markdown Output
+        crewai==X.X.X
+        crewai-tools==X.X.X
         ```
-      <Check>Installation successful! You're ready to create your first crew! 🎉</Check>
+        <Check>Installation successful! You're ready to create your first crew.</Check>
     </Step>
 </Steps>
 
-# Creating a CrewAI Project
+# Creating a New Project
 
-We recommend using the `YAML` template scaffolding for a structured approach to defining agents and tasks. Here's how to get started:
+<Info>
+  We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
+</Info>
 
 <Steps>
-  <Step title="Generate Project Scaffolding">
-    - Run the `crewai` CLI command:
-      ```shell
-      crewai create crew <your_project_name>
-      ```
+  <Step title="Generate Project Structure">
+    Run the CrewAI CLI command:
+    ```shell Terminal
+    crewai create crew <project_name>
+    ```
 
-    - This creates a new project with the following structure:
-      <Frame>
-      ```
-      my_project/
-      ├── .gitignore
-      ├── knowledge/
-      ├── pyproject.toml
-      ├── README.md
-      ├── .env
-      └── src/
-          └── my_project/
-              ├── __init__.py
-              ├── main.py
-              ├── crew.py
-              ├── tools/
-              │   ├── custom_tool.py
-              │   └── __init__.py
-              └── config/
-                  ├── agents.yaml
-                  └── tasks.yaml
-      ```
-      </Frame>
-  </Step>
+    This creates a new project with the following structure:
+    <Frame>
+    ```
+    my_project/
+    ├── .gitignore
+    ├── pyproject.toml
+    ├── README.md
+    ├── .env
+    └── src/
+        └── my_project/
+            ├── __init__.py
+            ├── main.py
+            ├── crew.py
+            ├── tools/
+            │   ├── custom_tool.py
+            │   └── __init__.py
+            └── config/
+                ├── agents.yaml
+                └── tasks.yaml
+    ```
+    </Frame>
+  </Step>   
 
   <Step title="Customize Your Project">
-    - Your project will contain these essential files:
-      | File | Purpose |
-      | --- | --- |
-      | `agents.yaml` | Define your AI agents and their roles |
-      | `tasks.yaml` | Set up agent tasks and workflows |
-      | `.env` | Store API keys and environment variables |
-      | `main.py` | Project entry point and execution flow |
-      | `crew.py` | Crew orchestration and coordination |
-      | `tools/` | Directory for custom agent tools |
-      | `knowledge/` | Directory for knowledge base |
+    Your project will contain these essential files:
 
-    - Start by editing `agents.yaml` and `tasks.yaml` to define your crew's behavior.
-    - Keep sensitive information like API keys in `.env`.
-  </Step>
+    | File | Purpose |
+    | --- | --- |
+    | `agents.yaml` | Define your AI agents and their roles |
+    | `tasks.yaml` | Set up agent tasks and workflows |
+    | `.env` | Store API keys and environment variables |
+    | `main.py` | Project entry point and execution flow |
+    | `crew.py` | Crew orchestration and coordination |
+    | `tools/` | Directory for custom agent tools |
 
-  <Step title="Run your Crew">
-    - Before you run your crew, make sure to run:
-      ```bash
-      crewai install
-      ```
-    - If you need to install additional packages, use:
-      ```shell
-      uv add <package-name>
-      ```
-    - To run your crew, execute the following command in the root of your project:
-      ```bash
-      crewai run
-      ```
+    <Tip>
+      Start by editing `agents.yaml` and `tasks.yaml` to define your crew's behavior.
+      Keep sensitive information like API keys in `.env`.
+    </Tip>
   </Step>
 </Steps>
 

docs/mint.json

@@ -91,7 +91,6 @@
         "how-to/custom-manager-agent",
         "how-to/llm-connections",
         "how-to/customizing-agents",
-        "how-to/multimodal-agents",
         "how-to/coding-agents",
         "how-to/force-tool-output-as-result",
         "how-to/human-input-on-execution",
@@ -101,10 +100,7 @@
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
         "how-to/langtrace-observability",
-        "how-to/mlflow-observability",
-        "how-to/openlit-observability",
-        "how-to/portkey-observability",
-        "how-to/langfuse-observability"
+        "how-to/openlit-observability"
       ]
     },
     {
@@ -116,8 +112,6 @@
     {
       "group": "Tools",
       "pages": [
-        "tools/aimindtool",
-        "tools/bravesearchtool",
         "tools/browserbaseloadtool",
         "tools/codedocssearchtool",
         "tools/codeinterpretertool",
@@ -134,32 +128,18 @@
         "tools/firecrawlscrapewebsitetool",
         "tools/firecrawlsearchtool",
         "tools/githubsearchtool",
-        "tools/hyperbrowserloadtool",
-        "tools/linkupsearchtool",
-        "tools/llamaindextool",
         "tools/serperdevtool",
-        "tools/s3readertool",
-        "tools/s3writertool",
-        "tools/scrapegraphscrapetool",
-        "tools/scrapeelementfromwebsitetool",
         "tools/jsonsearchtool",
         "tools/mdxsearchtool",
         "tools/mysqltool",
-        "tools/multiontool",
         "tools/nl2sqltool",
-        "tools/patronustools",
         "tools/pdfsearchtool",
         "tools/pgsearchtool",
-        "tools/qdrantvectorsearchtool",
-        "tools/ragtool",
         "tools/scrapewebsitetool",
-        "tools/scrapflyscrapetool",
         "tools/seleniumscrapingtool",
-        "tools/snowflakesearchtool",
         "tools/spidertool",
         "tools/txtsearchtool",
         "tools/visiontool",
-        "tools/weaviatevectorsearchtool",
         "tools/websitesearchtool",
         "tools/xmlsearchtool",
         "tools/youtubechannelsearchtool",

docs/quickstart.mdx

@@ -8,10 +8,10 @@ icon: rocket
 
 Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.
 
-Before we proceed, make sure you have finished installing CrewAI.
+Before we proceed, make sure you have `crewai` and `crewai-tools` installed.
 If you haven't installed them yet, you can do so by following the [installation guide](/installation).
 
-Follow the steps below to get Crewing! 🚣‍♂️
+Follow the steps below to get crewing! 🚣‍♂️
 
 <Steps>
   <Step title="Create your crew">
@@ -23,13 +23,6 @@ Follow the steps below to get Crewing! 🚣‍♂️
       ```
     </CodeGroup>
   </Step>
-  <Step title="Navigate to your new crew project">
-    <CodeGroup>
-      ```shell Terminal
-      cd latest-ai-development
-      ```
-    </CodeGroup>
-  </Step>
   <Step title="Modify your `agents.yaml` file">
   <Tip>
   You can also modify the agents as needed to fit your use case or copy and paste as is to your project.
@@ -65,7 +58,7 @@ Follow the steps below to get Crewing! 🚣‍♂️
       description: >
         Conduct a thorough research about {topic}
         Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        the current year is 2024.
       expected_output: >
         A list with 10 bullet points of the most relevant information about {topic}
       agent: researcher
@@ -179,26 +172,21 @@ Follow the steps below to get Crewing! 🚣‍♂️
     - A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
   </Step>
   <Step title="Lock and install the dependencies">
-    - Lock the dependencies and install them by using the CLI command:
-      <CodeGroup>
-        ```shell Terminal
-        crewai install
-        ```
-      </CodeGroup>
-    - If you have additional packages that you want to install, you can do so by running:
-      <CodeGroup>
-        ```shell Terminal
-        uv add <package-name>
-        ```
-      </CodeGroup>
+    Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
+    <CodeGroup>
+      ```shell Terminal
+      cd latest-ai-development
+      crewai install
+      ```
+    </CodeGroup>
   </Step>
   <Step title="Run your crew">
-    - To run your crew, execute the following command in the root of your project:
-      <CodeGroup>
-        ```bash Terminal
-        crewai run
-        ```
-      </CodeGroup>
+  To run your crew, execute the following command in the root of your project:
+    <CodeGroup>
+      ```bash Terminal
+      crewai run
+      ```
+    </CodeGroup>
   </Step>
   <Step title="View your final report">
   You should see the output in the console and the `report.md` file should be created in the root of your project with the final report.
@@ -207,10 +195,10 @@ Follow the steps below to get Crewing! 🚣‍♂️
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # Comprehensive Report on the Rise and Impact of AI Agents in 2024
 
     ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    In 2024, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
 
     ## 2. Benefits of AI Agents
     AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
@@ -264,18 +252,12 @@ Follow the steps below to get Crewing! 🚣‍♂️
     To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
 
     ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    The emergence of AI agents is undeniably reshaping the workplace landscape in 2024. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
     ```
   </CodeGroup>
   </Step>
 </Steps>
 
-<Check>
-Congratulations! 
-
-You have successfully set up your crew project and are ready to start building your own agentic workflows!
-</Check>
-
 ### Note on Consistency in Naming
 
 The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
@@ -296,7 +278,7 @@ email_summarizer:
       Summarize emails into a concise and clear summary
     backstory: >
       You will create a 5 bullet point summary of the report
-    llm: openai/gpt-4o
+    llm: mixtal_llm
 ```
 
 <Tip>
@@ -315,9 +297,66 @@ email_summarizer_task:
       - research_task
 ```
 
+Use the annotations to properly reference the agent and task in the `crew.py` file.
+
+### Annotations include:
+
+* `@agent`
+* `@task`
+* `@crew`
+* `@tool`
+* `@before_kickoff`
+* `@after_kickoff`
+* `@callback`
+* `@output_json`
+* `@output_pydantic`
+* `@cache_handler`
+
+```python crew.py
+# ...
+@agent
+def email_summarizer(self) -> Agent:
+    return Agent(
+        config=self.agents_config["email_summarizer"],
+    )
+
+@task
+def email_summarizer_task(self) -> Task:
+    return Task(
+        config=self.tasks_config["email_summarizer_task"],
+    )
+# ...
+```
+
+<Tip>
+In addition to the [sequential process](../how-to/sequential-process), you can use the [hierarchical process](../how-to/hierarchical-process),
+which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results.
+You can learn more about the core concepts [here](/concepts).
+</Tip>
+
+### Replay Tasks from Latest Crew Kickoff
+
+CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run.
+
+```shell
+crewai replay <task_id>
+```
+
+Replace `<task_id>` with the ID of the task you want to replay.
+
+### Reset Crew Memory
+
+If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
+
+```shell
+crewai reset-memories --all
+```
+
+This will clear the crew's memory, allowing for a fresh start.
+
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through CrewAI Enterprise, where you can deploy your crew in a few clicks.
 
 <CardGroup cols={2}>
   <Card

docs/tools/aimindtool.mdx

@@ -1,118 +0,0 @@
----
-title: AI Mind Tool
-description: The `AIMindTool` is designed to query data sources in natural language.
-icon: brain
----
-
-# `AIMindTool`
-
-## Description
-
-The `AIMindTool` is a wrapper around [AI-Minds](https://mindsdb.com/minds) provided by [MindsDB](https://mindsdb.com/). It allows you to query data sources in natural language by simply configuring their connection parameters. This tool is useful when you need answers to questions from your data stored in various data sources including PostgreSQL, MySQL, MariaDB, ClickHouse, Snowflake, and Google BigQuery.
-
-Minds are AI systems that work similarly to large language models (LLMs) but go beyond by answering any question from any data. This is accomplished by:
-- Selecting the most relevant data for an answer using parametric search
-- Understanding the meaning and providing responses within the correct context through semantic search
-- Delivering precise answers by analyzing data and using machine learning (ML) models
-
-## Installation
-
-To incorporate this tool into your project, you need to install the Minds SDK:
-
-```shell
-uv add minds-sdk
-```
-
-## Steps to Get Started
-
-To effectively use the `AIMindTool`, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` and `minds-sdk` packages are installed in your Python environment.
-2. **API Key Acquisition**: Sign up for a Minds account [here](https://mdb.ai/register), and obtain an API key.
-3. **Environment Configuration**: Store your obtained API key in an environment variable named `MINDS_API_KEY` to facilitate its use by the tool.
-
-## Example
-
-The following example demonstrates how to initialize the tool and execute a query:
-
-```python Code
-from crewai_tools import AIMindTool
-
-# Initialize the AIMindTool
-aimind_tool = AIMindTool(
-    datasources=[
-        {
-            "description": "house sales data",
-            "engine": "postgres",
-            "connection_data": {
-                "user": "demo_user",
-                "password": "demo_password",
-                "host": "samples.mindsdb.com",
-                "port": 5432,
-                "database": "demo",
-                "schema": "demo_data"
-            },
-            "tables": ["house_sales"]
-        }
-    ]
-)
-
-# Run a natural language query
-result = aimind_tool.run("How many 3 bedroom houses were sold in 2008?")
-print(result)
-```
-
-## Parameters
-
-The `AIMindTool` accepts the following parameters:
-
-- **api_key**: Optional. Your Minds API key. If not provided, it will be read from the `MINDS_API_KEY` environment variable.
-- **datasources**: A list of dictionaries, each containing the following keys:
-  - **description**: A description of the data contained in the datasource.
-  - **engine**: The engine (or type) of the datasource.
-  - **connection_data**: A dictionary containing the connection parameters for the datasource.
-  - **tables**: A list of tables that the data source will use. This is optional and can be omitted if all tables in the data source are to be used.
-
-A list of supported data sources and their connection parameters can be found [here](https://docs.mdb.ai/docs/data_sources).
-
-## Agent Integration Example
-
-Here's how to integrate the `AIMindTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent
-from crewai.project import agent
-from crewai_tools import AIMindTool
-
-# Initialize the tool
-aimind_tool = AIMindTool(
-    datasources=[
-        {
-            "description": "sales data",
-            "engine": "postgres",
-            "connection_data": {
-                "user": "your_user",
-                "password": "your_password",
-                "host": "your_host",
-                "port": 5432,
-                "database": "your_db",
-                "schema": "your_schema"
-            },
-            "tables": ["sales"]
-        }
-    ]
-)
-
-# Define an agent with the AIMindTool
-@agent
-def data_analyst(self) -> Agent:
-    return Agent(
-        config=self.agents_config["data_analyst"],
-        allow_delegation=False,
-        tools=[aimind_tool]
-    )
-```
-
-## Conclusion
-
-The `AIMindTool` provides a powerful way to query your data sources using natural language, making it easier to extract insights without writing complex SQL queries. By connecting to various data sources and leveraging AI-Minds technology, this tool enables agents to access and analyze data efficiently. 

docs/tools/brave-search-tool.mdx

@@ -0,0 +1,222 @@
+---
+title: BraveSearchTool
+description: A tool for performing web searches using the Brave Search API
+icon: search
+---
+
+## BraveSearchTool
+
+The BraveSearchTool enables web searches using the Brave Search API, providing customizable result counts, country-specific searches, and rate-limited operations. It formats search results with titles, URLs, and snippets for easy consumption.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Authentication
+
+Set up your Brave Search API key:
+```bash
+export BRAVE_API_KEY='your-brave-api-key'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import BraveSearchTool
+
+# Basic initialization
+search_tool = BraveSearchTool()
+
+# Advanced initialization with custom parameters
+search_tool = BraveSearchTool(
+    country="US",        # Country-specific search
+    n_results=5,         # Number of results to return
+    save_file=True       # Save results to file
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Search and analyze web content',
+    backstory='Expert at finding relevant information online.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class BraveSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the internet"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    country: Optional[str] = "",
+    n_results: int = 10,
+    save_file: bool = False,
+    *args,
+    **kwargs
+):
+    """
+    Initialize the Brave search tool.
+
+    Args:
+        country (Optional[str]): Country code for region-specific search
+        n_results (int): Number of results to return (default: 10)
+        save_file (bool): Whether to save results to file (default: False)
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Execute web search using Brave Search API.
+
+    Args:
+        search_query (str): Query to search
+        save_file (bool, optional): Override save_file setting
+        n_results (int, optional): Override n_results setting
+
+    Returns:
+        str: Formatted search results with titles, URLs, and snippets
+    """
+```
+
+## Best Practices
+
+1. API Authentication:
+   - Securely store BRAVE_API_KEY
+   - Keep API key confidential
+   - Handle authentication errors
+
+2. Rate Limiting:
+   - Tool automatically handles rate limiting
+   - Minimum 1-second interval between requests
+   - Consider implementing additional rate limits
+
+3. Search Optimization:
+   - Use specific search queries
+   - Adjust result count based on needs
+   - Consider regional search requirements
+
+4. Error Handling:
+   - Handle API request failures
+   - Manage parsing errors
+   - Monitor rate limit errors
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import BraveSearchTool
+
+# Initialize tool with custom configuration
+search_tool = BraveSearchTool(
+    country="GB",    # UK-specific search
+    n_results=3,     # Limit to 3 results
+    save_file=True   # Save results to file
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Researcher',
+    goal='Research latest AI developments',
+    backstory='Expert at finding and analyzing tech news.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find the latest news about artificial 
+    intelligence developments in quantum computing.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "latest quantum computing AI developments"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Country-Specific Search
+```python
+# Initialize tools for different regions
+us_search = BraveSearchTool(country="US")
+uk_search = BraveSearchTool(country="GB")
+jp_search = BraveSearchTool(country="JP")
+
+# Compare results across regions
+us_results = us_search.run(
+    search_query="local news"
+)
+uk_results = uk_search.run(
+    search_query="local news"
+)
+jp_results = jp_search.run(
+    search_query="local news"
+)
+```
+
+### Result Management
+```python
+# Save results to file
+archival_search = BraveSearchTool(
+    save_file=True,
+    n_results=20
+)
+
+# Search and save
+results = archival_search.run(
+    search_query="historical events 2023"
+)
+# Results saved to search_results_YYYY-MM-DD_HH-MM-SS.txt
+```
+
+### Error Handling Example
+```python
+try:
+    search_tool = BraveSearchTool()
+    results = search_tool.run(
+        search_query="important topic"
+    )
+    print(results)
+except ValueError as e: # API key missing
+    print(f"Authentication error: {str(e)}")
+except Exception as e:
+    print(f"Search error: {str(e)}")
+```
+
+## Notes
+
+- Requires Brave Search API key
+- Implements automatic rate limiting
+- Supports country-specific searches
+- Customizable result count
+- Optional file saving feature
+- Thread-safe operations
+- Efficient result formatting
+- Handles API errors gracefully
+- Supports parallel searches
+- Maintains search context

docs/tools/bravesearchtool.mdx

@@ -1,96 +0,0 @@
----
-title: Brave Search
-description: The `BraveSearchTool` is designed to search the internet using the Brave Search API.
-icon: searchengin
----
-
-# `BraveSearchTool`
-
-## Description
-
-This tool is designed to perform web searches using the Brave Search API. It allows you to search the internet with a specified query and retrieve relevant results. The tool supports customizable result counts and country-specific searches.
-
-## Installation
-
-To incorporate this tool into your project, follow the installation instructions below:
-
-```shell
-pip install 'crewai[tools]'
-```
-
-## Steps to Get Started
-
-To effectively use the `BraveSearchTool`, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a Brave Search API key by registering at [Brave Search API](https://api.search.brave.com/app/keys).
-3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool.
-
-## Example
-
-The following example demonstrates how to initialize the tool and execute a search with a given query:
-
-```python Code
-from crewai_tools import BraveSearchTool
-
-# Initialize the tool for internet searching capabilities
-tool = BraveSearchTool()
-
-# Execute a search
-results = tool.run(search_query="CrewAI agent framework")
-print(results)
-```
-
-## Parameters
-
-The `BraveSearchTool` accepts the following parameters:
-
-- **search_query**: Mandatory. The search query you want to use to search the internet.
-- **country**: Optional. Specify the country for the search results. Default is empty string.
-- **n_results**: Optional. Number of search results to return. Default is `10`.
-- **save_file**: Optional. Whether to save the search results to a file. Default is `False`.
-
-## Example with Parameters
-
-Here is an example demonstrating how to use the tool with additional parameters:
-
-```python Code
-from crewai_tools import BraveSearchTool
-
-# Initialize the tool with custom parameters
-tool = BraveSearchTool(
-    country="US",
-    n_results=5,
-    save_file=True
-)
-
-# Execute a search
-results = tool.run(search_query="Latest AI developments")
-print(results)
-```
-
-## Agent Integration Example
-
-Here's how to integrate the `BraveSearchTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent
-from crewai.project import agent
-from crewai_tools import BraveSearchTool
-
-# Initialize the tool
-brave_search_tool = BraveSearchTool()
-
-# Define an agent with the BraveSearchTool
-@agent
-def researcher(self) -> Agent:
-    return Agent(
-        config=self.agents_config["researcher"],
-        allow_delegation=False,
-        tools=[brave_search_tool]
-    )
-```
-
-## Conclusion
-
-By integrating the `BraveSearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The tool provides a simple interface to the powerful Brave Search API, making it easy to retrieve and process search results programmatically. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. 

docs/tools/code-docs-search-tool.mdx

@@ -0,0 +1,164 @@
+---
+title: CodeDocsSearchTool
+description: A semantic search tool for code documentation websites using RAG capabilities
+icon: book-open
+---
+
+## CodeDocsSearchTool
+
+The CodeDocsSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within code documentation websites. It inherits from the base RagTool class and provides both fixed and dynamic documentation URL searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CodeDocsSearchTool
+
+# Method 1: Dynamic documentation URL
+docs_search = CodeDocsSearchTool()
+
+# Method 2: Fixed documentation URL
+fixed_docs_search = CodeDocsSearchTool(
+    docs_url="https://docs.example.com"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Documentation Researcher',
+    goal='Search through code documentation semantically',
+    backstory='Expert at finding relevant information in technical documentation.',
+    tools=[docs_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic URL Schema
+```python
+class CodeDocsSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+    docs_url: str     # URL of the documentation site to search
+```
+
+### Fixed URL Schema
+```python
+class FixedCodeDocsSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, docs_url: Optional[str] = None, **kwargs):
+    """
+    Initialize the documentation search tool.
+
+    Args:
+        docs_url (Optional[str]): Fixed URL to a documentation site. If provided, 
+            the tool will only search this documentation.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the documentation site.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'docs_url' for dynamic mode)
+
+    Returns:
+        str: Relevant documentation passages based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed URL when repeatedly searching the same documentation
+   - Use dynamic URL when searching different documentation sites
+2. Write clear, semantic search queries
+3. Ensure documentation sites are accessible
+4. Consider documentation structure and size
+5. Handle potential URL access errors in agent prompts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CodeDocsSearchTool
+
+# Example 1: Fixed documentation search
+api_docs_search = CodeDocsSearchTool(
+    docs_url="https://api.example.com/docs"
+)
+
+# Example 2: Dynamic documentation search
+flexible_docs_search = CodeDocsSearchTool()
+
+# Create agents
+api_analyst = Agent(
+    role='API Documentation Analyst',
+    goal='Find relevant API endpoints and usage examples',
+    backstory='Expert at analyzing API documentation.',
+    tools=[api_docs_search]
+)
+
+docs_researcher = Agent(
+    role='Documentation Researcher',
+    goal='Search through various documentation sites',
+    backstory='Specialist in finding information across multiple docs.',
+    tools=[flexible_docs_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all authentication-related endpoints 
+    in the API documentation.""",
+    agent=api_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "authentication endpoints and methods"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the Python documentation at 
+    docs.python.org for information about async/await.""",
+    agent=docs_researcher
+)
+
+# The agent will use:
+# {
+#   "search_query": "async await syntax and usage",
+#   "docs_url": "https://docs.python.org"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[api_analyst, docs_researcher],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic documentation URLs
+- Uses embeddings for semantic search
+- Thread-safe operations
+- Automatically handles documentation loading and embedding
+- Optimized for technical documentation search

docs/tools/code-interpreter-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: CodeInterpreterTool
+description: A tool for secure Python code execution in isolated Docker environments
+icon: code
+---
+
+## CodeInterpreterTool
+
+The CodeInterpreterTool provides secure Python code execution capabilities using Docker containers. It supports dynamic library installation and offers both safe (Docker-based) and unsafe (direct) execution modes.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CodeInterpreterTool
+
+# Initialize the tool
+code_tool = CodeInterpreterTool()
+
+# Create an agent with the tool
+programmer = Agent(
+    role='Code Executor',
+    goal='Execute and analyze Python code',
+    backstory='Expert at writing and executing Python code.',
+    tools=[code_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class CodeInterpreterSchema(BaseModel):
+    code: str = Field(
+        description="Python3 code used to be interpreted in the Docker container. ALWAYS PRINT the final result and the output of the code"
+    )
+    libraries_used: List[str] = Field(
+        description="List of libraries used in the code with proper installing names separated by commas. Example: numpy,pandas,beautifulsoup4"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    code: Optional[str] = None,
+    user_dockerfile_path: Optional[str] = None,
+    user_docker_base_url: Optional[str] = None,
+    unsafe_mode: bool = False,
+    **kwargs
+):
+    """
+    Initialize the code interpreter tool.
+
+    Args:
+        code (Optional[str]): Default code to execute
+        user_dockerfile_path (Optional[str]): Custom Dockerfile path
+        user_docker_base_url (Optional[str]): Custom Docker daemon URL
+        unsafe_mode (bool): Enable direct code execution
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    code: str,
+    libraries_used: List[str],
+    **kwargs: Any
+) -> str:
+    """
+    Execute Python code in Docker container or directly.
+
+    Args:
+        code (str): Python code to execute
+        libraries_used (List[str]): Required libraries
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Execution output or error message
+    """
+```
+
+## Best Practices
+
+1. Security Considerations:
+   - Use Docker mode by default
+   - Validate input code
+   - Control library access
+   - Monitor execution time
+
+2. Docker Configuration:
+   - Use custom Dockerfile when needed
+   - Handle container lifecycle
+   - Manage resource limits
+   - Clean up after execution
+
+3. Library Management:
+   - Specify exact versions
+   - Use trusted packages
+   - Handle dependencies
+   - Verify installations
+
+4. Error Handling:
+   - Catch execution errors
+   - Handle timeouts
+   - Manage Docker errors
+   - Provide clear messages
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CodeInterpreterTool
+
+# Initialize tool
+code_tool = CodeInterpreterTool()
+
+# Create agent
+programmer = Agent(
+    role='Code Executor',
+    goal='Execute data analysis code',
+    backstory='Expert Python programmer specializing in data analysis.',
+    tools=[code_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Analyze the dataset using pandas and 
+    create a summary visualization with matplotlib.""",
+    agent=programmer
+)
+
+# The tool will use:
+# {
+#   "code": """
+#   import pandas as pd
+#   import matplotlib.pyplot as plt
+#   
+#   # Load and analyze data
+#   df = pd.read_csv('data.csv')
+#   summary = df.describe()
+#   
+#   # Create visualization
+#   plt.figure(figsize=(10, 6))
+#   df['column'].hist()
+#   plt.savefig('output.png')
+#   
+#   print(summary)
+#   """,
+#   "libraries_used": "pandas,matplotlib"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[programmer],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Docker Configuration
+```python
+# Use custom Dockerfile
+tool = CodeInterpreterTool(
+    user_dockerfile_path="/path/to/Dockerfile"
+)
+
+# Use custom Docker daemon
+tool = CodeInterpreterTool(
+    user_docker_base_url="tcp://remote-docker:2375"
+)
+```
+
+### Direct Execution Mode
+```python
+# Enable unsafe mode (not recommended)
+tool = CodeInterpreterTool(unsafe_mode=True)
+
+# Execute code directly
+result = tool.run(
+    code="print('Hello, World!')",
+    libraries_used=[]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    code_tool = CodeInterpreterTool()
+    result = code_tool.run(
+        code="""
+        import numpy as np
+        arr = np.array([1, 2, 3])
+        print(f"Array mean: {arr.mean()}")
+        """,
+        libraries_used=["numpy"]
+    )
+    print(result)
+except Exception as e:
+    print(f"Error executing code: {str(e)}")
+```
+
+## Notes
+
+- Inherits from BaseTool
+- Docker-based isolation
+- Dynamic library installation
+- Secure code execution
+- Custom Docker support
+- Comprehensive error handling
+- Resource management
+- Container cleanup
+- Library dependency handling
+- Execution output capture

docs/tools/codeinterpretertool.mdx

@@ -8,15 +8,18 @@ icon: code-simple
 
 ## Description
 
-The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
+This tool enables the Agent to execute Python 3 code that it has generated autonomously. The code is run in a secure, isolated environment, ensuring safety regardless of the content. 
+
+This functionality is particularly valuable as it allows the Agent to create code, execute it within the same ecosystem, 
+obtain the results, and utilize that information to inform subsequent decisions and actions.
 
 ## Requirements
 
-- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
+- Docker
 
 ## Installation
 
-To use this tool, you need to install the CrewAI tools package:
+Install the `crewai_tools` package
 
 ```shell
 pip install 'crewai[tools]'
@@ -24,153 +27,27 @@ pip install 'crewai[tools]'
 
 ## Example
 
-The following example demonstrates how to use the `CodeInterpreterTool` with a CrewAI agent:
+Remember that when using this tool, the code must be generated by the Agent itself. 
+The code must be a Python3 code. And it will take some time for the first time to run 
+because it needs to build the Docker image.
 
 ```python Code
-from crewai import Agent, Task, Crew, Process
+from crewai import Agent
 from crewai_tools import CodeInterpreterTool
 
-# Initialize the tool
-code_interpreter = CodeInterpreterTool()
-
-# Define an agent that uses the tool
-programmer_agent = Agent(
-    role="Python Programmer",
-    goal="Write and execute Python code to solve problems",
-    backstory="An expert Python programmer who can write efficient code to solve complex problems.",
-    tools=[code_interpreter],
-    verbose=True,
+Agent(
+    ...
+    tools=[CodeInterpreterTool()],
 )
-
-# Example task to generate and execute code
-coding_task = Task(
-    description="Write a Python function to calculate the Fibonacci sequence up to the 10th number and print the result.",
-    expected_output="The Fibonacci sequence up to the 10th number.",
-    agent=programmer_agent,
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[programmer_agent],
-    tasks=[coding_task],
-    verbose=True,
-    process=Process.sequential,
-)
-result = crew.kickoff()
 ```
 
-You can also enable code execution directly when creating an agent:
+We also provide a simple way to use it directly from the Agent.
 
 ```python Code
 from crewai import Agent
 
-# Create an agent with code execution enabled
-programmer_agent = Agent(
-    role="Python Programmer",
-    goal="Write and execute Python code to solve problems",
-    backstory="An expert Python programmer who can write efficient code to solve complex problems.",
-    allow_code_execution=True,  # This automatically adds the CodeInterpreterTool
-    verbose=True,
+agent = Agent(
+    ...
+    allow_code_execution=True,
 )
 ```
-
-## Parameters
-
-The `CodeInterpreterTool` accepts the following parameters during initialization:
-
-- **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
-- **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
-- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
-
-When using the tool with an agent, the agent will need to provide:
-
-- **code**: Required. The Python 3 code to execute.
-- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
-
-## Agent Integration Example
-
-Here's a more detailed example of how to integrate the `CodeInterpreterTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import CodeInterpreterTool
-
-# Initialize the tool
-code_interpreter = CodeInterpreterTool()
-
-# Define an agent that uses the tool
-data_analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data using Python code",
-    backstory="""You are an expert data analyst who specializes in using Python 
-    to analyze and visualize data. You can write efficient code to process 
-    large datasets and extract meaningful insights.""",
-    tools=[code_interpreter],
-    verbose=True,
-)
-
-# Create a task for the agent
-analysis_task = Task(
-    description="""
-    Write Python code to:
-    1. Generate a random dataset of 100 points with x and y coordinates
-    2. Calculate the correlation coefficient between x and y
-    3. Create a scatter plot of the data
-    4. Print the correlation coefficient and save the plot as 'scatter.png'
-    
-    Make sure to handle any necessary imports and print the results.
-    """,
-    expected_output="The correlation coefficient and confirmation that the scatter plot has been saved.",
-    agent=data_analyst,
-)
-
-# Run the task
-crew = Crew(
-    agents=[data_analyst],
-    tasks=[analysis_task],
-    verbose=True,
-    process=Process.sequential,
-)
-result = crew.kickoff()
-```
-
-## Implementation Details
-
-The `CodeInterpreterTool` uses Docker to create a secure environment for code execution:
-
-```python Code
-class CodeInterpreterTool(BaseTool):
-    name: str = "Code Interpreter"
-    description: str = "Interprets Python3 code strings with a final print statement."
-    args_schema: Type[BaseModel] = CodeInterpreterSchema
-    default_image_tag: str = "code-interpreter:latest"
-    
-    def _run(self, **kwargs) -> str:
-        code = kwargs.get("code", self.code)
-        libraries_used = kwargs.get("libraries_used", [])
-
-        if self.unsafe_mode:
-            return self.run_code_unsafe(code, libraries_used)
-        else:
-            return self.run_code_in_docker(code, libraries_used)
-```
-
-The tool performs the following steps:
-1. Verifies that the Docker image exists or builds it if necessary
-2. Creates a Docker container with the current working directory mounted
-3. Installs any required libraries specified by the agent
-4. Executes the Python code in the container
-5. Returns the output of the code execution
-6. Cleans up by stopping and removing the container
-
-## Security Considerations
-
-By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
-
-1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
-2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
-3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
-
-## Conclusion
-
-The `CodeInterpreterTool` provides a powerful way for CrewAI agents to execute Python code in a relatively secure environment. By enabling agents to write and run code, it significantly expands their problem-solving capabilities, especially for tasks involving data analysis, calculations, or other computational work. This tool is particularly useful for agents that need to perform complex operations that are more efficiently expressed in code than in natural language.

docs/tools/composiotool.mdx

@@ -1,118 +1,78 @@
 ---
 title: Composio Tool
-description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
+description: The `ComposioTool` is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
 icon: gear-code
 ---
 
-# `ComposioToolSet`
+# `ComposioTool`
 
 ## Description
-Composio is an integration platform that allows you to connect your AI agents to 250+ tools. Key features include:
 
-- **Enterprise-Grade Authentication**: Built-in support for OAuth, API Keys, JWT with automatic token refresh
-- **Full Observability**: Detailed tool usage logs, execution timestamps, and more
+This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
 
 ## Installation
 
-To incorporate Composio tools into your project, follow the instructions below:
+To incorporate this tool into your project, follow the installation instructions below:
 
 ```shell
-pip install composio-crewai
-pip install crewai
+pip install composio-core
+pip install 'crewai[tools]'
 ```
 
-After the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://app.composio.dev)
+after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
 
 ## Example
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio toolset
+1. Initialize Composio tools
 
 ```python Code
-from composio_crewai import ComposioToolSet, App, Action
-from crewai import Agent, Task, Crew
+from composio import App
+from crewai_tools import ComposioTool
+from crewai import Agent, Task
 
-toolset = ComposioToolSet()
+
+tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
 ```
 
-2. Connect your GitHub account
-<CodeGroup>
-```shell CLI
-composio add github
-```
+If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
+
 ```python Code
-request = toolset.initiate_connection(app=App.GITHUB)
-print(f"Open this URL to authenticate: {request.redirectUrl}")
+tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
 ```
-</CodeGroup>
 
-3. Get Tools
+or use `use_case` to search relevant actions
 
-- Retrieving all the tools from an app (not recommended for production):
 ```python Code
-tools = toolset.get_tools(apps=[App.GITHUB])
+tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
 ```
 
-- Filtering tools based on tags:
-```python Code
-tag = "users"
-
-filtered_action_enums = toolset.find_actions_by_tags(
-    App.GITHUB,
-    tags=[tag], 
-)
-
-tools = toolset.get_tools(actions=filtered_action_enums)
-```
-
-- Filtering tools based on use case:
-```python Code
-use_case = "Star a repository on GitHub"
-
-filtered_action_enums = toolset.find_actions_by_use_case(
-    App.GITHUB, use_case=use_case, advanced=False
-)
-
-tools = toolset.get_tools(actions=filtered_action_enums)
-```
-<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
-
-- Using specific tools:
-
-In this demo, we will use the `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` action from the GitHub app.
-```python Code
-tools = toolset.get_tools(
-    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
-)
-```
-Learn more about filtering actions [here](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)
-
-4. Define agent
+2. Define agent
 
 ```python Code
 crewai_agent = Agent(
-    role="GitHub Agent",
-    goal="You take action on GitHub using GitHub APIs",
-    backstory="You are AI agent that is responsible for taking actions on GitHub on behalf of users using GitHub APIs",
+    role="Github Agent",
+    goal="You take action on Github using Github APIs",
+    backstory=(
+        "You are AI agent that is responsible for taking actions on Github "
+        "on users behalf. You need to take action on Github using Github APIs"
+    ),
     verbose=True,
     tools=tools,
-    llm= # pass an llm
 )
 ```
 
-5. Execute task
+3. Execute task
 
 ```python Code
 task = Task(
-    description="Star a repo composiohq/composio on GitHub",
+    description="Star a repo ComposioHQ/composio on GitHub",
     agent=crewai_agent,
-    expected_output="Status of the operation",
+    expected_output="if the star happened",
 )
 
-crew = Crew(agents=[crewai_agent], tasks=[task])
-
-crew.kickoff()
+task.execute()
 ```
 
-* More detailed list of tools can be found [here](https://app.composio.dev)
+* More detailed list of tools can be found [here](https://app.composio.dev)

docs/tools/csv-search-tool.mdx

@@ -0,0 +1,207 @@
+---
+title: CSVSearchTool
+description: A tool for semantic search within CSV files using RAG capabilities
+icon: table
+---
+
+## CSVSearchTool
+
+The CSVSearchTool enables semantic search capabilities for CSV files using Retrieval-Augmented Generation (RAG). It can process CSV files either specified during initialization or at runtime, making it flexible for various use cases.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import CSVSearchTool
+
+# Method 1: Initialize with specific CSV file
+csv_tool = CSVSearchTool(csv="path/to/data.csv")
+
+# Method 2: Initialize without CSV (specify at runtime)
+flexible_csv_tool = CSVSearchTool()
+
+# Create an agent with the tool
+data_analyst = Agent(
+    role='Data Analyst',
+    goal='Search and analyze CSV data semantically',
+    backstory='Expert at analyzing and extracting insights from CSV data.',
+    tools=[csv_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed CSV Schema (when CSV path provided during initialization)
+```python
+class FixedCSVSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the CSV's content"
+    )
+```
+
+### Flexible CSV Schema (when CSV path provided at runtime)
+```python
+class CSVSearchToolSchema(FixedCSVSearchToolSchema):
+    csv: str = Field(
+        description="Mandatory csv path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    csv: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the CSV search tool.
+
+    Args:
+        csv (Optional[str]): Path to CSV file (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on CSV content.
+
+    Args:
+        search_query (str): Query to search in the CSV
+        **kwargs: Additional arguments including csv path if not initialized
+
+    Returns:
+        str: Relevant content from the CSV matching the query
+    """
+```
+
+## Best Practices
+
+1. CSV File Handling:
+   - Ensure CSV files are properly formatted
+   - Use absolute paths for reliability
+   - Verify file permissions before processing
+
+2. Search Optimization:
+   - Use specific, focused search queries
+   - Consider column names and data structure
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize with CSV for repeated searches
+   - Handle large CSV files appropriately
+   - Monitor memory usage with big datasets
+
+4. Error Handling:
+   - Verify CSV file existence
+   - Handle malformed CSV data
+   - Manage file access permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CSVSearchTool
+
+# Initialize tool with specific CSV
+csv_tool = CSVSearchTool(csv="/path/to/sales_data.csv")
+
+# Create agent
+analyst = Agent(
+    role='Data Analyst',
+    goal='Extract insights from sales data',
+    backstory='Expert at analyzing sales data and trends.',
+    tools=[csv_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all sales records from the CSV 
+    that relate to product returns in Q4 2023.""",
+    agent=analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "product returns Q4 2023"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[analyst],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic CSV Selection
+```python
+# Initialize without CSV
+flexible_tool = CSVSearchTool()
+
+# Search different CSVs
+result1 = flexible_tool.run(
+    search_query="revenue 2023",
+    csv="/path/to/finance.csv"
+)
+
+result2 = flexible_tool.run(
+    search_query="customer feedback",
+    csv="/path/to/surveys.csv"
+)
+```
+
+### Multiple CSV Analysis
+```python
+# Create tools for different CSVs
+sales_tool = CSVSearchTool(csv="/path/to/sales.csv")
+inventory_tool = CSVSearchTool(csv="/path/to/inventory.csv")
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Business Analyst',
+    goal='Cross-reference sales and inventory data',
+    tools=[sales_tool, inventory_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    csv_tool = CSVSearchTool(csv="/path/to/data.csv")
+    result = csv_tool.run(
+        search_query="important metrics"
+    )
+    print(result)
+except Exception as e:
+    print(f"Error processing CSV: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search
+- Supports dynamic CSV file specification
+- Uses embedchain for data processing
+- Maintains search context across queries
+- Thread-safe operations
+- Efficient semantic search capabilities
+- Supports various CSV formats
+- Handles large datasets effectively
+- Preserves CSV structure in search
+- Enables natural language queries

docs/tools/directory-read-tool.mdx

@@ -0,0 +1,217 @@
+---
+title: Directory Read Tool
+description: A tool for recursively listing directory contents
+---
+
+# Directory Read Tool
+
+The Directory Read Tool provides functionality to recursively list all files within a directory. It supports both fixed and dynamic directory path modes, allowing you to specify the directory at initialization or runtime.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+You can use the Directory Read Tool in two ways:
+
+### 1. Fixed Directory Path
+
+Initialize the tool with a specific directory path:
+
+```python
+from crewai import Agent
+from crewai_tools import DirectoryReadTool
+
+# Initialize with a fixed directory
+tool = DirectoryReadTool(directory="/path/to/your/directory")
+
+# Create an agent with the tool
+agent = Agent(
+    role='File System Analyst',
+    goal='Analyze directory contents',
+    backstory='I help analyze and organize file systems',
+    tools=[tool]
+)
+
+# Use in a task
+task = Task(
+    description="List all files in the project directory",
+    agent=agent
+)
+```
+
+### 2. Dynamic Directory Path
+
+Initialize the tool without a specific directory path to provide it at runtime:
+
+```python
+from crewai import Agent
+from crewai_tools import DirectoryReadTool
+
+# Initialize without a fixed directory
+tool = DirectoryReadTool()
+
+# Create an agent with the tool
+agent = Agent(
+    role='File System Explorer',
+    goal='Explore different directories',
+    backstory='I analyze various directory structures',
+    tools=[tool]
+)
+
+# Use in a task with dynamic directory path
+task = Task(
+    description="List all files in the specified directory",
+    agent=agent,
+    context={
+        "directory": "/path/to/explore"
+    }
+)
+```
+
+## Input Schema
+
+### Fixed Directory Mode
+```python
+class FixedDirectoryReadToolSchema(BaseModel):
+    pass  # No additional parameters needed when directory is fixed
+```
+
+### Dynamic Directory Mode
+```python
+class DirectoryReadToolSchema(BaseModel):
+    directory: str  # The path to the directory to list contents
+```
+
+## Function Signatures
+
+```python
+def __init__(self, directory: Optional[str] = None, **kwargs):
+    """
+    Initialize the Directory Read Tool.
+    
+    Args:
+        directory (Optional[str]): Path to the directory (optional)
+        **kwargs: Additional arguments passed to BaseTool
+    """
+
+def _run(
+    self,
+    **kwargs: Any,
+) -> str:
+    """
+    Execute the directory listing.
+    
+    Args:
+        **kwargs: Arguments including 'directory' for dynamic mode
+    
+    Returns:
+        str: A formatted string containing all file paths in the directory
+    """
+```
+
+## Best Practices
+
+1. **Path Handling**:
+   - Use absolute paths to avoid path resolution issues
+   - Handle trailing slashes appropriately
+   - Verify directory existence before listing
+
+2. **Performance Considerations**:
+   - Be mindful of directory size when listing large directories
+   - Consider implementing pagination for large directories
+   - Handle symlinks appropriately
+
+3. **Error Handling**:
+   - Handle directory not found errors gracefully
+   - Manage permission issues appropriately
+   - Validate input parameters before processing
+
+## Example Integration
+
+Here's a complete example showing how to integrate the Directory Read Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DirectoryReadTool
+
+# Initialize the tool
+dir_tool = DirectoryReadTool()
+
+# Create an agent with the tool
+file_analyst = Agent(
+    role='File System Analyst',
+    goal='Analyze and report on directory structures',
+    backstory='I am an expert at analyzing file system organization',
+    tools=[dir_tool]
+)
+
+# Create tasks
+analysis_task = Task(
+    description="""
+    Analyze the project directory structure:
+    1. List all files recursively
+    2. Identify key file types
+    3. Report on directory organization
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=file_analyst,
+    context={
+        "directory": "/path/to/project"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[file_analyst],
+    tasks=[analysis_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Directory Not Found**:
+   ```python
+   try:
+       tool = DirectoryReadTool(directory="/nonexistent/path")
+   except FileNotFoundError:
+       print("Directory not found. Please verify the path.")
+   ```
+
+2. **Permission Issues**:
+   ```python
+   try:
+       tool = DirectoryReadTool(directory="/restricted/path")
+   except PermissionError:
+       print("Insufficient permissions to access the directory.")
+   ```
+
+3. **Invalid Path**:
+   ```python
+   try:
+       result = tool._run(directory="invalid/path")
+   except ValueError:
+       print("Invalid directory path provided.")
+   ```
+
+## Output Format
+
+The tool returns a formatted string containing all file paths in the directory:
+
+```
+File paths:
+- /path/to/directory/file1.txt
+- /path/to/directory/subdirectory/file2.txt
+- /path/to/directory/subdirectory/file3.py
+```
+
+
+Each file path is listed on a new line with a hyphen prefix, making it easy to parse and read the output.

docs/tools/directory-search-tool.mdx

@@ -0,0 +1,214 @@
+---
+title: DirectorySearchTool
+description: A tool for semantic search within directory contents using RAG capabilities
+icon: folder-search
+---
+
+## DirectorySearchTool
+
+The DirectorySearchTool enables semantic search capabilities for directory contents using Retrieval-Augmented Generation (RAG). It processes files recursively within a directory and allows searching through their contents using natural language queries.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import DirectorySearchTool
+
+# Method 1: Initialize with specific directory
+dir_tool = DirectorySearchTool(directory="/path/to/documents")
+
+# Method 2: Initialize without directory (specify at runtime)
+flexible_dir_tool = DirectorySearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Directory Researcher',
+    goal='Search and analyze directory contents',
+    backstory='Expert at finding relevant information in document collections.',
+    tools=[dir_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Directory Schema (when path provided during initialization)
+```python
+class FixedDirectorySearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the directory's content"
+    )
+```
+
+### Flexible Directory Schema (when path provided at runtime)
+```python
+class DirectorySearchToolSchema(FixedDirectorySearchToolSchema):
+    directory: str = Field(
+        description="Mandatory directory you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    directory: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the directory search tool.
+
+    Args:
+        directory (Optional[str]): Path to directory (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on directory contents.
+
+    Args:
+        search_query (str): Query to search in the directory
+        **kwargs: Additional arguments including directory if not initialized
+
+    Returns:
+        str: Relevant content from the directory matching the query
+    """
+```
+
+## Best Practices
+
+1. Directory Management:
+   - Use absolute paths
+   - Verify directory existence
+   - Handle permissions properly
+
+2. Search Optimization:
+   - Use specific queries
+   - Consider file types
+   - Test with sample queries
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle large directories
+   - Monitor processing time
+
+4. Error Handling:
+   - Verify directory access
+   - Handle missing files
+   - Manage permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DirectorySearchTool
+
+# Initialize tool with specific directory
+dir_tool = DirectorySearchTool(
+    directory="/path/to/documents"
+)
+
+# Create agent
+researcher = Agent(
+    role='Directory Researcher',
+    goal='Extract insights from document collections',
+    backstory='Expert at analyzing document collections.',
+    tools=[dir_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications from the directory contents.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Directory Selection
+```python
+# Initialize without directory path
+flexible_tool = DirectorySearchTool()
+
+# Search different directories
+docs_results = flexible_tool.run(
+    search_query="technical specifications",
+    directory="/path/to/docs"
+)
+
+reports_results = flexible_tool.run(
+    search_query="financial metrics",
+    directory="/path/to/reports"
+)
+```
+
+### Multiple Directory Analysis
+```python
+# Create tools for different directories
+docs_tool = DirectorySearchTool(
+    directory="/path/to/docs"
+)
+reports_tool = DirectorySearchTool(
+    directory="/path/to/reports"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Content Analyst',
+    goal='Cross-reference multiple document collections',
+    tools=[docs_tool, reports_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    dir_tool = DirectorySearchTool()
+    results = dir_tool.run(
+        search_query="key concepts",
+        directory="/path/to/documents"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing directory: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses DirectoryLoader
+- Supports recursive search
+- Dynamic directory specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Processes multiple file types
+- Handles nested directories
+- Memory-efficient processing

docs/tools/docx-search-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: DOCXSearchTool
+description: A tool for semantic search within DOCX documents using RAG capabilities
+icon: file-text
+---
+
+## DOCXSearchTool
+
+The DOCXSearchTool enables semantic search capabilities for Microsoft Word (DOCX) documents using Retrieval-Augmented Generation (RAG). It supports both fixed and dynamic document selection modes.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import DOCXSearchTool
+
+# Method 1: Fixed document (specified at initialization)
+fixed_tool = DOCXSearchTool(
+    docx="path/to/document.docx"
+)
+
+# Method 2: Dynamic document (specified at runtime)
+dynamic_tool = DOCXSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Document Researcher',
+    goal='Search and analyze document contents',
+    backstory='Expert at finding relevant information in documents.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Document Mode
+```python
+class FixedDOCXSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the DOCX's content"
+    )
+```
+
+### Dynamic Document Mode
+```python
+class DOCXSearchToolSchema(BaseModel):
+    docx: str = Field(
+        description="Mandatory docx path you want to search"
+    )
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the DOCX's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    docx: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the DOCX search tool.
+
+    Args:
+        docx (Optional[str]): Path to DOCX file (optional for dynamic mode)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    docx: Optional[str] = None,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on document contents.
+
+    Args:
+        search_query (str): Query to search in the document
+        docx (Optional[str]): Document path (required for dynamic mode)
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the document matching the query
+    """
+```
+
+## Best Practices
+
+1. Document Handling:
+   - Use absolute file paths
+   - Verify file existence
+   - Handle large documents
+   - Monitor memory usage
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider document size
+   - Handle formatting
+   - Monitor performance
+
+3. Error Handling:
+   - Check file access
+   - Validate file format
+   - Handle corrupted files
+   - Log issues
+
+4. Mode Selection:
+   - Choose fixed mode for static documents
+   - Use dynamic mode for runtime selection
+   - Consider memory implications
+   - Manage document lifecycle
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import DOCXSearchTool
+
+# Initialize tool
+docx_tool = DOCXSearchTool(
+    docx="reports/annual_report_2023.docx"
+)
+
+# Create agent
+researcher = Agent(
+    role='Document Analyst',
+    goal='Extract insights from annual report',
+    backstory='Expert at analyzing business documents.',
+    tools=[docx_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all mentions of revenue growth 
+    and market expansion.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Document Analysis
+```python
+# Create tools for different documents
+report_tool = DOCXSearchTool(
+    docx="reports/annual_report.docx"
+)
+
+policy_tool = DOCXSearchTool(
+    docx="policies/compliance.docx"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Document Analyst',
+    goal='Cross-reference reports and policies',
+    tools=[report_tool, policy_tool]
+)
+```
+
+### Dynamic Document Loading
+```python
+# Initialize dynamic tool
+dynamic_tool = DOCXSearchTool()
+
+# Use with different documents
+result1 = dynamic_tool.run(
+    docx="document1.docx",
+    search_query="project timeline"
+)
+
+result2 = dynamic_tool.run(
+    docx="document2.docx",
+    search_query="budget allocation"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    docx_tool = DOCXSearchTool(
+        docx="reports/quarterly_report.docx"
+    )
+    results = docx_tool.run(
+        search_query="Q3 performance metrics"
+    )
+    print(results)
+except FileNotFoundError as e:
+    print(f"Document not found: {str(e)}")
+except Exception as e: 
+    print(f"Error processing document: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Supports fixed/dynamic modes
+- Document path validation
+- Memory management
+- Performance optimization
+- Error handling
+- Search capabilities
+- Content extraction
+- Format handling
+- Security features

docs/tools/file-read-tool.mdx

@@ -0,0 +1,193 @@
+---
+title: FileReadTool
+description: A tool for reading file contents with flexible path specification
+icon: file-text
+---
+
+## FileReadTool
+
+The FileReadTool provides functionality to read file contents with support for both fixed and dynamic file path specification. It includes comprehensive error handling for common file operations and maintains clear descriptions of its configured state.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FileReadTool
+
+# Method 1: Initialize with specific file
+reader = FileReadTool(file_path="/path/to/data.txt")
+
+# Method 2: Initialize without file (specify at runtime)
+flexible_reader = FileReadTool()
+
+# Create an agent with the tool
+file_processor = Agent(
+    role='File Processor',
+    goal='Read and process file contents',
+    backstory='Expert at handling file operations and content processing.',
+    tools=[reader],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FileReadToolSchema(BaseModel):
+    file_path: str = Field(
+        description="Mandatory file full path to read the file"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    file_path: Optional[str] = None,
+    **kwargs: Any
+) -> None:
+    """
+    Initialize the file read tool.
+
+    Args:
+        file_path (Optional[str]): Path to file to read (optional)
+        **kwargs: Additional arguments passed to BaseTool
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Read and return file contents.
+
+    Args:
+        file_path (str, optional): Override default file path
+        **kwargs: Additional arguments
+
+    Returns:
+        str: File contents or error message
+    """
+```
+
+## Best Practices
+
+1. File Path Management:
+   - Use absolute paths for reliability
+   - Verify file existence before operations
+   - Handle path resolution properly
+
+2. Error Handling:
+   - Check for file existence
+   - Handle permission issues
+   - Manage encoding errors
+   - Process file access failures
+
+3. Performance Considerations:
+   - Close files after reading
+   - Handle large files appropriately
+   - Consider memory constraints
+
+4. Security Practices:
+   - Validate file paths
+   - Check file permissions
+   - Avoid path traversal issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FileReadTool
+
+# Initialize tool with specific file
+reader = FileReadTool(file_path="/path/to/config.txt")
+
+# Create agent
+processor = Agent(
+    role='File Processor',
+    goal='Process configuration files',
+    backstory='Expert at reading and analyzing configuration files.',
+    tools=[reader]
+)
+
+# Define task
+read_task = Task(
+    description="""Read and analyze the contents of 
+    the configuration file.""",
+    agent=processor
+)
+
+# The tool will use the default file path
+
+# Create crew
+crew = Crew(
+    agents=[processor],
+    tasks=[read_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic File Selection
+```python
+# Initialize without file path
+flexible_reader = FileReadTool()
+
+# Read different files
+config_content = flexible_reader.run(
+    file_path="/path/to/config.txt"
+)
+
+log_content = flexible_reader.run(
+    file_path="/path/to/logs.txt"
+)
+```
+
+### Multiple File Processing
+```python
+# Create tools for different files
+config_reader = FileReadTool(file_path="/path/to/config.txt")
+log_reader = FileReadTool(file_path="/path/to/logs.txt")
+
+# Create agent with multiple tools
+processor = Agent(
+    role='File Analyst',
+    goal='Analyze multiple file types',
+    tools=[config_reader, log_reader]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    reader = FileReadTool()
+    content = reader.run(
+        file_path="/path/to/file.txt"
+    )
+    print(content)
+except Exception as e:
+    print(f"Error reading file: {str(e)}")
+```
+
+## Notes
+
+- Inherits from BaseTool
+- Supports fixed or dynamic file paths
+- Comprehensive error handling
+- Thread-safe operations
+- Clear error messages
+- Flexible path specification
+- Maintains tool description
+- Handles common file errors
+- Supports various file types
+- Memory-efficient operations

docs/tools/filewritertool.mdx

@@ -0,0 +1,141 @@
+---
+title: FileWriterTool
+description: A tool for writing content to files with support for various file formats.
+icon: file-pen
+---
+
+## FileWriterTool
+
+The FileWriterTool provides agents with the capability to write content to files, supporting various file formats and ensuring proper file handling.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FileWriterTool
+
+# Initialize the tool
+file_writer = FileWriterTool()
+
+# Create an agent with the tool
+writer_agent = Agent(
+    role='Content Writer',
+    goal='Write and save content to files',
+    backstory='Expert at creating and managing file content.',
+    tools=[file_writer],
+    verbose=True
+)
+
+# Use in a task
+task = Task(
+    description='Write a report and save it to report.txt',
+    agent=writer_agent
+)
+```
+
+## Tool Attributes
+
+| Attribute | Type | Description |
+| :-------- | :--- | :---------- |
+| name | str | "File Writer Tool" |
+| description | str | "A tool that writes content to a file." |
+
+## Input Schema
+
+```python
+class FileWriterToolInput(BaseModel):
+    filename: str       # Name of the file to write
+    directory: str = "./"  # Optional directory path, defaults to current directory
+    overwrite: str = "False"  # Whether to overwrite existing file ("True"/"False")
+    content: str       # Content to write to the file
+```
+
+## Function Signature
+
+```python
+def _run(self, **kwargs: Any) -> str:
+    """
+    Write content to a file with specified parameters.
+
+    Args:
+        filename (str): Name of the file to write
+        content (str): Content to write to the file
+        directory (str, optional): Directory path. Defaults to "./".
+        overwrite (str, optional): Whether to overwrite existing file. Defaults to "False".
+
+    Returns:
+        str: Success message with filepath or error message
+    """
+```
+
+## Error Handling
+
+The tool includes error handling for common file operations:
+- FileExistsError: When file exists and overwrite is not allowed
+- KeyError: When required parameters are missing
+- Directory Creation: Automatically creates directories if they don't exist
+- General Exceptions: Catches and reports any other file operation errors
+
+## Best Practices
+
+1. Always provide absolute file paths
+2. Ensure proper file permissions
+3. Handle potential errors in your agent prompts
+4. Verify file contents after writing
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FileWriterTool
+
+# Initialize tool
+file_writer = FileWriterTool()
+
+# Create agent
+writer = Agent(
+    role='Technical Writer',
+    goal='Create and save technical documentation',
+    backstory='Expert technical writer with experience in documentation.',
+    tools=[file_writer]
+)
+
+# Define task
+writing_task = Task(
+    description="""Write a technical guide about Python best practices and save it
+    to the docs directory. The file should be named 'python_guide.md'.
+    Include sections on code style, documentation, and testing.
+    If a file already exists, overwrite it.""",
+    agent=writer
+)
+
+# The agent can use the tool with these parameters:
+# {
+#   "filename": "python_guide.md",
+#   "directory": "docs",
+#   "overwrite": "True",
+#   "content": "# Python Best Practices\n\n## Code Style\n..."
+# }
+
+# Create crew
+crew = Crew(
+    agents=[writer],
+    tasks=[writing_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- The tool automatically creates directories in the file path if they don't exist
+- Supports various file formats (txt, md, json, etc.)
+- Returns descriptive error messages for better debugging
+- Thread-safe file operations

docs/tools/filewritetool.mdx

@@ -8,9 +8,9 @@ icon: file-pen
 
 ## Description
 
-The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files with cross-platform compatibility (Windows, Linux, macOS). 
+The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. 
 It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. 
-This tool handles path differences across operating systems, supports UTF-8 encoding, and automatically creates directories if they don't exist, making it easier to organize your output reliably across different platforms.
+This tool supports creating new directories if they don't exist, making it easier to organize your output.
 
 ## Installation
 
@@ -43,8 +43,6 @@ print(result)
 
 ## Conclusion
 
-By integrating the `FileWriterTool` into your crews, the agents can reliably write content to files across different operating systems. 
-This tool is essential for tasks that require saving output data, creating structured file systems, and handling cross-platform file operations. 
-It's particularly recommended for Windows users who may encounter file writing issues with standard Python file operations.
-
-By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and ensures consistent file writing behavior across all platforms.
+By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. 
+This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, 
+incorporating this tool into projects is straightforward and efficient.

docs/tools/firecrawl-crawl-website-tool.mdx

@@ -0,0 +1,181 @@
+---
+title: FirecrawlCrawlWebsiteTool
+description: A web crawling tool powered by Firecrawl API for comprehensive website content extraction
+icon: spider-web
+---
+
+## FirecrawlCrawlWebsiteTool
+
+The FirecrawlCrawlWebsiteTool provides website crawling capabilities using the Firecrawl API. It allows for customizable crawling with options for polling intervals, idempotency, and URL parameters.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install firecrawl-py  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FirecrawlCrawlWebsiteTool
+
+# Method 1: Using environment variable
+# export FIRECRAWL_API_KEY='your-api-key'
+crawler = FirecrawlCrawlWebsiteTool()
+
+# Method 2: Providing API key directly
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key"
+)
+
+# Method 3: With custom configuration
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key",
+    url="https://example.com",  # Base URL
+    poll_interval=5,            # Custom polling interval
+    idempotency_key="unique-key"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Crawler',
+    goal='Extract and analyze website content',
+    backstory='Expert at crawling and analyzing web content.',
+    tools=[crawler],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FirecrawlCrawlWebsiteToolSchema(BaseModel):
+    url: str = Field(description="Website URL")
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    api_key: Optional[str] = None,
+    url: Optional[str] = None,
+    params: Optional[Dict[str, Any]] = None,
+    poll_interval: Optional[int] = 2,
+    idempotency_key: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the website crawling tool.
+
+    Args:
+        api_key (Optional[str]): Firecrawl API key. If not provided, checks FIRECRAWL_API_KEY env var
+        url (Optional[str]): Base URL to crawl. Can be overridden in _run
+        params (Optional[Dict[str, Any]]): Additional parameters for FirecrawlApp
+        poll_interval (Optional[int]): Poll interval for FirecrawlApp
+        idempotency_key (Optional[str]): Idempotency key for FirecrawlApp
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(self, url: str) -> Any:
+    """
+    Crawl a website using Firecrawl.
+
+    Args:
+        url (str): Website URL to crawl (overrides constructor URL if provided)
+
+    Returns:
+        Any: Crawled website content from Firecrawl API
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   - Use environment variable: `export FIRECRAWL_API_KEY='your-api-key'`
+   - Or provide directly in constructor
+2. Configure crawling parameters:
+   - Set appropriate poll intervals
+   - Use idempotency keys for retry safety
+   - Customize URL parameters as needed
+3. Handle rate limits and quotas
+4. Consider website robots.txt policies
+5. Handle potential crawling errors in agent prompts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FirecrawlCrawlWebsiteTool
+
+# Initialize crawler with configuration
+crawler = FirecrawlCrawlWebsiteTool(
+    api_key="your-firecrawl-api-key",
+    poll_interval=5,
+    params={
+        "max_depth": 3,
+        "follow_links": True
+    }
+)
+
+# Create agent
+web_analyst = Agent(
+    role='Web Content Analyst',
+    goal='Extract and analyze website content comprehensively',
+    backstory='Expert at web crawling and content analysis.',
+    tools=[crawler]
+)
+
+# Define task
+crawl_task = Task(
+    description="""Crawl the documentation website at docs.example.com 
+    and extract all API-related content.""",
+    agent=web_analyst
+)
+
+# The agent will use:
+# {
+#   "url": "https://docs.example.com"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[web_analyst],
+    tasks=[crawl_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Configuration Options
+
+### URL Parameters
+```python
+params = {
+    "max_depth": 3,           # Maximum crawl depth
+    "follow_links": True,     # Follow internal links
+    "exclude_patterns": [],   # URL patterns to exclude
+    "include_patterns": []    # URL patterns to include
+}
+```
+
+### Polling Configuration
+```python
+crawler = FirecrawlCrawlWebsiteTool(
+    poll_interval=5,  # Poll every 5 seconds
+    idempotency_key="unique-key-123"  # For retry safety
+)
+```
+
+## Notes
+
+- Requires valid Firecrawl API key
+- Supports both environment variable and direct API key configuration
+- Configurable polling intervals for crawl status
+- Idempotency support for safe retries
+- Thread-safe operations
+- Customizable crawling parameters
+- Respects robots.txt by default

docs/tools/firecrawl-search-tool.mdx

@@ -0,0 +1,154 @@
+---
+title: FirecrawlSearchTool
+description: A web search tool powered by Firecrawl API for comprehensive web search capabilities
+icon: magnifying-glass-chart
+---
+
+## FirecrawlSearchTool
+
+The FirecrawlSearchTool provides web search capabilities using the Firecrawl API. It allows for customizable search queries with options for result formatting and search parameters.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install firecrawl-py  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import FirecrawlSearchTool
+
+# Initialize the tool with your API key
+search_tool = FirecrawlSearchTool(api_key="your-firecrawl-api-key")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Find relevant information across the web',
+    backstory='Expert at web research and information gathering.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class FirecrawlSearchToolSchema(BaseModel):
+    query: str = Field(description="Search query")
+    page_options: Optional[Dict[str, Any]] = Field(
+        default=None, 
+        description="Options for result formatting"
+    )
+    search_options: Optional[Dict[str, Any]] = Field(
+        default=None, 
+        description="Options for searching"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, api_key: Optional[str] = None, **kwargs):
+    """
+    Initialize the Firecrawl search tool.
+
+    Args:
+        api_key (Optional[str]): Firecrawl API key
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    query: str,
+    page_options: Optional[Dict[str, Any]] = None,
+    result_options: Optional[Dict[str, Any]] = None,
+) -> Any:
+    """
+    Perform a web search using Firecrawl.
+
+    Args:
+        query (str): Search query string
+        page_options (Optional[Dict[str, Any]]): Options for result formatting
+        result_options (Optional[Dict[str, Any]]): Options for search results
+
+    Returns:
+        Any: Search results from Firecrawl API
+    """
+```
+
+## Best Practices
+
+1. Always provide a valid API key
+2. Use specific, focused search queries
+3. Customize page and result options for better results
+4. Handle potential API errors in agent prompts
+5. Consider rate limits and usage quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import FirecrawlSearchTool
+
+# Initialize tool with API key
+search_tool = FirecrawlSearchTool(api_key="your-firecrawl-api-key")
+
+# Create agent
+researcher = Agent(
+    role='Market Researcher',
+    goal='Research market trends and competitor analysis',
+    backstory='Expert market analyst with deep research skills.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in electric vehicles,
+    focusing on market leaders and emerging technologies. Format the results
+    in a structured way.""",
+    agent=researcher
+)
+
+# The agent will use:
+# {
+#   "query": "electric vehicle market leaders emerging technologies",
+#   "page_options": {
+#     "format": "structured",
+#     "maxLength": 1000
+#   },
+#   "result_options": {
+#     "limit": 5,
+#     "sortBy": "relevance"
+#   }
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool includes error handling for:
+- Missing API key
+- Missing firecrawl-py package
+- API request failures
+- Invalid options parameters
+
+## Notes
+
+- Requires valid Firecrawl API key
+- Supports customizable search parameters
+- Provides structured web search results
+- Thread-safe operations
+- Efficient for large-scale web searches
+- Handles rate limiting automatically

docs/tools/github-search-tool.mdx

@@ -0,0 +1,233 @@
+---
+title: GithubSearchTool
+description: A tool for semantic search within GitHub repositories using RAG capabilities
+icon: github
+---
+
+## GithubSearchTool
+
+The GithubSearchTool enables semantic search capabilities for GitHub repositories using Retrieval-Augmented Generation (RAG). It processes various content types including code, repository information, pull requests, and issues, allowing natural language queries across repository content.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import GithubSearchTool
+
+# Method 1: Initialize with specific repository
+github_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue"]
+)
+
+# Method 2: Initialize without repository (specify at runtime)
+flexible_github_tool = GithubSearchTool(
+    gh_token="your_github_token",
+    content_types=["code", "repo"]
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='GitHub Researcher',
+    goal='Search and analyze repository contents',
+    backstory='Expert at finding relevant information in GitHub repositories.',
+    tools=[github_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Repository Schema (when repo provided during initialization)
+```python
+class FixedGithubSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the github repo's content"
+    )
+```
+
+### Flexible Repository Schema (when repo provided at runtime)
+```python
+class GithubSearchToolSchema(FixedGithubSearchToolSchema):
+    github_repo: str = Field(
+        description="Mandatory github you want to search"
+    )
+    content_types: List[str] = Field(
+        description="Mandatory content types you want to be included search, options: [code, repo, pr, issue]"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    github_repo: Optional[str] = None,
+    gh_token: str,
+    content_types: List[str],
+    **kwargs
+):
+    """
+    Initialize the GitHub search tool.
+
+    Args:
+        github_repo (Optional[str]): Repository to search (optional)
+        gh_token (str): GitHub authentication token
+        content_types (List[str]): Content types to search
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on repository contents.
+
+    Args:
+        search_query (str): Query to search in the repository
+        **kwargs: Additional arguments including github_repo and content_types if not initialized
+
+    Returns:
+        str: Relevant content from the repository matching the query
+    """
+```
+
+## Best Practices
+
+1. Authentication:
+   - Secure token management
+   - Use environment variables
+   - Handle token expiration
+
+2. Search Optimization:
+   - Target specific content types
+   - Use focused queries
+   - Consider rate limits
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle large repositories
+   - Monitor API usage
+
+4. Error Handling:
+   - Verify repository access
+   - Handle API limits
+   - Manage authentication errors
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import GithubSearchTool
+
+# Initialize tool with specific repository
+github_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue"]
+)
+
+# Create agent
+researcher = Agent(
+    role='GitHub Researcher',
+    goal='Extract insights from repository content',
+    backstory='Expert at analyzing GitHub repositories.',
+    tools=[github_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all implementations of 
+    machine learning algorithms in the codebase.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning implementation"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Repository Selection
+```python
+# Initialize without repository
+flexible_tool = GithubSearchTool(
+    gh_token="your_github_token",
+    content_types=["code", "repo"]
+)
+
+# Search different repositories
+backend_results = flexible_tool.run(
+    search_query="authentication implementation",
+    github_repo="owner/backend-repo"
+)
+
+frontend_results = flexible_tool.run(
+    search_query="component architecture",
+    github_repo="owner/frontend-repo"
+)
+```
+
+### Multiple Content Type Analysis
+```python
+# Create tool with multiple content types
+multi_tool = GithubSearchTool(
+    github_repo="owner/repo",
+    gh_token="your_github_token",
+    content_types=["code", "pr", "issue", "repo"]
+)
+
+# Search across all content types
+results = multi_tool.run(
+    search_query="feature implementation status"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    github_tool = GithubSearchTool(
+        gh_token="your_github_token",
+        content_types=["code"]
+    )
+    results = github_tool.run(
+        search_query="api endpoints",
+        github_repo="owner/repo"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error searching repository: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses GithubLoader
+- Requires authentication
+- Supports multiple content types
+- Dynamic repository specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles API rate limits
+- Memory-efficient processing

docs/tools/hyperbrowserloadtool.mdx

@@ -1,86 +0,0 @@
----
-title: Hyperbrowser Load Tool
-description: The `HyperbrowserLoadTool` enables web scraping and crawling using Hyperbrowser.
-icon: globe
----
-
-# `HyperbrowserLoadTool`
-
-## Description
-
-The `HyperbrowserLoadTool` enables web scraping and crawling using [Hyperbrowser](https://hyperbrowser.ai), a platform for running and scaling headless browsers. This tool allows you to scrape a single page or crawl an entire site, returning the content in properly formatted markdown or HTML.
-
-Key Features:
-- Instant Scalability - Spin up hundreds of browser sessions in seconds without infrastructure headaches
-- Simple Integration - Works seamlessly with popular tools like Puppeteer and Playwright
-- Powerful APIs - Easy to use APIs for scraping/crawling any site
-- Bypass Anti-Bot Measures - Built-in stealth mode, ad blocking, automatic CAPTCHA solving, and rotating proxies
-
-## Installation
-
-To use this tool, you need to install the Hyperbrowser SDK:
-
-```shell
-uv add hyperbrowser
-```
-
-## Steps to Get Started
-
-To effectively use the `HyperbrowserLoadTool`, follow these steps:
-
-1. **Sign Up**: Head to [Hyperbrowser](https://app.hyperbrowser.ai/) to sign up and generate an API key.
-2. **API Key**: Set the `HYPERBROWSER_API_KEY` environment variable or pass it directly to the tool constructor.
-3. **Install SDK**: Install the Hyperbrowser SDK using the command above.
-
-## Example
-
-The following example demonstrates how to initialize the tool and use it to scrape a website:
-
-```python Code
-from crewai_tools import HyperbrowserLoadTool
-from crewai import Agent
-
-# Initialize the tool with your API key
-tool = HyperbrowserLoadTool(api_key="your_api_key")  # Or use environment variable
-
-# Define an agent that uses the tool
-@agent
-def web_researcher(self) -> Agent:
-    '''
-    This agent uses the HyperbrowserLoadTool to scrape websites
-    and extract information.
-    '''
-    return Agent(
-        config=self.agents_config["web_researcher"],
-        tools=[tool]
-    )
-```
-
-## Parameters
-
-The `HyperbrowserLoadTool` accepts the following parameters:
-
-### Constructor Parameters
-- **api_key**: Optional. Your Hyperbrowser API key. If not provided, it will be read from the `HYPERBROWSER_API_KEY` environment variable.
-
-### Run Parameters
-- **url**: Required. The website URL to scrape or crawl.
-- **operation**: Optional. The operation to perform on the website. Either 'scrape' or 'crawl'. Default is 'scrape'.
-- **params**: Optional. Additional parameters for the scrape or crawl operation.
-
-## Supported Parameters
-
-For detailed information on all supported parameters, visit:
-- [Scrape Parameters](https://docs.hyperbrowser.ai/reference/sdks/python/scrape#start-scrape-job-and-wait)
-- [Crawl Parameters](https://docs.hyperbrowser.ai/reference/sdks/python/crawl#start-crawl-job-and-wait)
-
-## Return Format
-
-The tool returns content in the following format:
-
-- For **scrape** operations: The content of the page in markdown or HTML format.
-- For **crawl** operations: The content of each page separated by dividers, including the URL of each page.
-
-## Conclusion
-
-The `HyperbrowserLoadTool` provides a powerful way to scrape and crawl websites, handling complex scenarios like anti-bot measures, CAPTCHAs, and more. By leveraging Hyperbrowser's platform, this tool enables agents to access and extract web content efficiently. 

docs/tools/jina-scrape-website-tool.mdx

@@ -0,0 +1,220 @@
+---
+title: JinaScrapeWebsiteTool
+description: A tool for scraping website content using Jina.ai's reader service with markdown output
+icon: globe
+---
+
+## JinaScrapeWebsiteTool
+
+The JinaScrapeWebsiteTool provides website content scraping capabilities using Jina.ai's reader service. It converts web content into clean markdown format and supports both fixed and dynamic URL modes with optional authentication.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import JinaScrapeWebsiteTool
+
+# Method 1: Fixed URL (specified at initialization)
+fixed_tool = JinaScrapeWebsiteTool(
+    website_url="https://example.com",
+    api_key="your-jina-api-key"  # Optional
+)
+
+# Method 2: Dynamic URL (specified at runtime)
+dynamic_tool = JinaScrapeWebsiteTool(
+    api_key="your-jina-api-key"  # Optional
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Content Researcher',
+    goal='Extract and analyze website content',
+    backstory='Expert at gathering and processing web information.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class JinaScrapeWebsiteToolInput(BaseModel):
+    website_url: str = Field(
+        description="Mandatory website url to read the file"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    website_url: Optional[str] = None,
+    api_key: Optional[str] = None,
+    custom_headers: Optional[dict] = None,
+    **kwargs
+):
+    """
+    Initialize the website scraping tool.
+
+    Args:
+        website_url (Optional[str]): URL to scrape (optional for dynamic mode)
+        api_key (Optional[str]): Jina.ai API key for authentication
+        custom_headers (Optional[dict]): Custom HTTP headers
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    website_url: Optional[str] = None
+) -> str:
+    """
+    Execute website scraping.
+
+    Args:
+        website_url (Optional[str]): URL to scrape (required for dynamic mode)
+
+    Returns:
+        str: Markdown-formatted website content
+    """
+```
+
+## Best Practices
+
+1. URL Handling:
+   - Use complete URLs
+   - Validate URL format
+   - Handle redirects
+   - Monitor timeouts
+
+2. Authentication:
+   - Secure API key storage
+   - Use environment variables
+   - Manage headers properly
+   - Handle auth errors
+
+3. Content Processing:
+   - Handle large pages
+   - Process markdown output
+   - Manage encoding
+   - Handle errors
+
+4. Mode Selection:
+   - Choose fixed mode for static sites
+   - Use dynamic mode for variable URLs
+   - Consider caching
+   - Manage timeouts
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import JinaScrapeWebsiteTool
+import os
+
+# Initialize tool with API key
+scraper_tool = JinaScrapeWebsiteTool(
+    api_key=os.getenv('JINA_API_KEY'),
+    custom_headers={
+        'User-Agent': 'CrewAI Bot 1.0'
+    }
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Content Analyst',
+    goal='Extract and analyze website content',
+    backstory='Expert at processing web information.',
+    tools=[scraper_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Analyze the content of 
+    https://example.com/blog for key insights.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Site Analysis
+```python
+# Initialize tool
+scraper = JinaScrapeWebsiteTool(
+    api_key=os.getenv('JINA_API_KEY')
+)
+
+# Analyze multiple sites
+results = []
+sites = [
+    "https://site1.com",
+    "https://site2.com",
+    "https://site3.com"
+]
+
+for site in sites:
+    content = scraper.run(
+        website_url=site
+    )
+    results.append(content)
+```
+
+### Custom Headers Configuration
+```python
+# Initialize with custom headers
+tool = JinaScrapeWebsiteTool(
+    custom_headers={
+        'User-Agent': 'Custom Bot 1.0',
+        'Accept-Language': 'en-US,en;q=0.9',
+        'Accept': 'text/html,application/xhtml+xml'
+    }
+)
+
+# Use the tool
+content = tool.run(
+    website_url="https://example.com"
+)
+```
+
+### Error Handling Example
+```python
+try: 
+    scraper = JinaScrapeWebsiteTool()
+    content = scraper.run(
+        website_url="https://example.com"
+    )
+    print(content)
+except requests.exceptions.RequestException as e:
+    print(f"Error accessing website: {str(e)}")
+except Exception as e:
+    print(f"Error processing content: {str(e)}")
+```
+
+## Notes
+
+- Uses Jina.ai reader service
+- Markdown output format
+- API key authentication
+- Custom headers support
+- Error handling
+- Timeout management
+- Content processing
+- URL validation
+- Redirect handling
+- Response formatting

docs/tools/json-search-tool.mdx

@@ -0,0 +1,224 @@
+---
+title: JSONSearchTool
+description: A tool for semantic search within JSON files using RAG capabilities
+icon: braces
+---
+
+## JSONSearchTool
+
+The JSONSearchTool enables semantic search capabilities for JSON files using Retrieval-Augmented Generation (RAG). It supports both fixed and dynamic file path modes, allowing flexible usage patterns.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import JSONSearchTool
+
+# Method 1: Fixed path (specified at initialization)
+fixed_tool = JSONSearchTool(
+    json_path="path/to/data.json"
+)
+
+# Method 2: Dynamic path (specified at runtime)
+dynamic_tool = JSONSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='JSON Data Researcher',
+    goal='Search and analyze JSON data',
+    backstory='Expert at finding relevant information in JSON files.',
+    tools=[fixed_tool],  # or [dynamic_tool]
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Path Mode
+```python
+class FixedJSONSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the JSON's content"
+    )
+```
+
+### Dynamic Path Mode
+```python
+class JSONSearchToolSchema(BaseModel):
+    json_path: str = Field(
+        description="Mandatory json path you want to search"
+    )
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the JSON's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    json_path: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the JSON search tool.
+
+    Args:
+        json_path (Optional[str]): Path to JSON file (optional for dynamic mode)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on JSON contents.
+
+    Args:
+        search_query (str): Query to search in the JSON
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the JSON matching the query
+    """
+```
+
+## Best Practices
+
+1. File Handling:
+   - Use absolute file paths
+   - Verify file existence
+   - Handle large JSON files
+   - Monitor memory usage
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider JSON structure
+   - Handle nested data
+   - Monitor performance
+
+3. Error Handling:
+   - Check file access
+   - Validate JSON format
+   - Handle malformed JSON
+   - Log issues
+
+4. Mode Selection:
+   - Choose fixed mode for static files
+   - Use dynamic mode for runtime selection
+   - Consider caching
+   - Manage file lifecycle
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import JSONSearchTool
+
+# Initialize tool
+json_tool = JSONSearchTool(
+    json_path="data/config.json"
+)
+
+# Create agent
+researcher = Agent(
+    role='JSON Data Analyst',
+    goal='Extract insights from JSON configuration',
+    backstory='Expert at analyzing JSON data structures.',
+    tools=[json_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all configuration settings 
+    related to security.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple File Analysis
+```python
+# Create tools for different JSON files
+config_tool = JSONSearchTool(
+    json_path="config/settings.json"
+)
+
+data_tool = JSONSearchTool(
+    json_path="data/records.json"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='JSON Data Analyst',
+    goal='Cross-reference configuration and data',
+    tools=[config_tool, data_tool]
+)
+```
+
+### Dynamic File Loading
+```python
+# Initialize dynamic tool
+dynamic_tool = JSONSearchTool()
+
+# Use with different JSON files
+result1 = dynamic_tool.run(
+    json_path="file1.json",
+    search_query="security settings"
+)
+
+result2 = dynamic_tool.run(
+    json_path="file2.json",
+    search_query="user preferences"
+)
+```
+
+### Error Handling Example
+```python
+try:
+    json_tool = JSONSearchTool(
+        json_path="config/settings.json"
+    )
+    results = json_tool.run(
+        search_query="encryption settings"
+    )
+    print(results)
+except FileNotFoundError as e:
+    print(f"JSON file not found: {str(e)}")
+except ValueError as e:
+    print(f"Invalid JSON format: {str(e)}")
+except Exception as e:
+    print(f"Error processing JSON: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Supports fixed/dynamic modes
+- JSON path validation
+- Memory management
+- Performance optimization
+- Error handling
+- Search capabilities
+- Content extraction
+- Format validation
+- Security features

docs/tools/linkup-search-tool.mdx

@@ -0,0 +1,184 @@
+---
+title: LinkupSearchTool
+description: A search tool powered by Linkup API for retrieving contextual information
+icon: search
+---
+
+## LinkupSearchTool
+
+The LinkupSearchTool provides search capabilities using the Linkup API. It allows for customizable search depth and output formatting, returning structured results with contextual information.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install linkup  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import LinkupSearchTool
+
+# Initialize the tool with your API key
+search_tool = LinkupSearchTool(api_key="your-linkup-api-key")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Information Researcher',
+    goal='Find relevant contextual information',
+    backstory='Expert at retrieving and analyzing contextual data.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Function Signature
+
+```python
+def __init__(self, api_key: str):
+    """
+    Initialize the Linkup search tool.
+
+    Args:
+        api_key (str): Linkup API key for authentication
+    """
+
+def _run(
+    self,
+    query: str,
+    depth: str = "standard",
+    output_type: str = "searchResults"
+) -> dict:
+    """
+    Perform a search using the Linkup API.
+
+    Args:
+        query (str): The search query
+        depth (str): Search depth ("standard" by default)
+        output_type (str): Desired result type ("searchResults" by default)
+
+    Returns:
+        dict: {
+            "success": bool,
+            "results": List[Dict] | None,
+            "error": str | None
+        }
+        
+        On success, results contains list of:
+        {
+            "name": str,
+            "url": str,
+            "content": str
+        }
+    """
+```
+
+## Best Practices
+
+1. Always provide a valid API key
+2. Use specific, focused search queries
+3. Choose appropriate search depth based on needs
+4. Handle potential API errors in agent prompts
+5. Process structured results effectively
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import LinkupSearchTool
+
+# Initialize tool with API key
+search_tool = LinkupSearchTool(api_key="your-linkup-api-key")
+
+# Create agent
+researcher = Agent(
+    role='Context Researcher',
+    goal='Find detailed contextual information about topics',
+    backstory='Expert at discovering and analyzing contextual data.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in quantum computing,
+    focusing on recent breakthroughs and applications. Use standard depth
+    for comprehensive results.""",
+    agent=researcher
+)
+
+# The tool will use:
+# query: "quantum computing recent breakthroughs applications"
+# depth: "standard"
+# output_type: "searchResults"
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Search Depth Options
+```python
+# Quick surface-level search
+results = search_tool._run(
+    query="quantum computing",
+    depth="basic"
+)
+
+# Standard comprehensive search
+results = search_tool._run(
+    query="quantum computing",
+    depth="standard"
+)
+
+# Deep detailed search
+results = search_tool._run(
+    query="quantum computing",
+    depth="deep"
+)
+```
+
+### Output Type Options
+```python
+# Default search results
+results = search_tool._run(
+    query="quantum computing",
+    output_type="searchResults"
+)
+
+# Custom output format
+results = search_tool._run(
+    query="quantum computing",
+    output_type="customFormat"
+)
+```
+
+### Error Handling
+```python
+results = search_tool._run(query="quantum computing")
+if results["success"]:
+    for result in results["results"]:
+        print(f"Name: {result['name']}")
+        print(f"URL: {result['url']}")
+        print(f"Content: {result['content']}")
+else:
+    print(f"Error: {results['error']}")
+```
+
+## Notes
+
+- Requires valid Linkup API key
+- Returns structured search results
+- Supports multiple search depths
+- Configurable output formats
+- Built-in error handling
+- Thread-safe operations
+- Efficient for contextual searches

docs/tools/linkupsearchtool.mdx

@@ -1,112 +0,0 @@
----
-title: Linkup Search Tool
-description: The `LinkupSearchTool` enables querying the Linkup API for contextual information.
-icon: link
----
-
-# `LinkupSearchTool`
-
-## Description
-
-The `LinkupSearchTool` provides the ability to query the Linkup API for contextual information and retrieve structured results. This tool is ideal for enriching workflows with up-to-date and reliable information from Linkup, allowing agents to access relevant data during their tasks.
-
-## Installation
-
-To use this tool, you need to install the Linkup SDK:
-
-```shell
-uv add linkup-sdk
-```
-
-## Steps to Get Started
-
-To effectively use the `LinkupSearchTool`, follow these steps:
-
-1. **API Key**: Obtain a Linkup API key.
-2. **Environment Setup**: Set up your environment with the API key.
-3. **Install SDK**: Install the Linkup SDK using the command above.
-
-## Example
-
-The following example demonstrates how to initialize the tool and use it in an agent:
-
-```python Code
-from crewai_tools import LinkupSearchTool
-from crewai import Agent
-import os
-
-# Initialize the tool with your API key
-linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
-
-# Define an agent that uses the tool
-@agent
-def researcher(self) -> Agent:
-    '''
-    This agent uses the LinkupSearchTool to retrieve contextual information
-    from the Linkup API.
-    '''
-    return Agent(
-        config=self.agents_config["researcher"],
-        tools=[linkup_tool]
-    )
-```
-
-## Parameters
-
-The `LinkupSearchTool` accepts the following parameters:
-
-### Constructor Parameters
-- **api_key**: Required. Your Linkup API key.
-
-### Run Parameters
-- **query**: Required. The search term or phrase.
-- **depth**: Optional. The search depth. Default is "standard".
-- **output_type**: Optional. The type of output. Default is "searchResults".
-
-## Advanced Usage
-
-You can customize the search parameters for more specific results:
-
-```python Code
-# Perform a search with custom parameters
-results = linkup_tool.run(
-    query="Women Nobel Prize Physics",
-    depth="deep",
-    output_type="searchResults"
-)
-```
-
-## Return Format
-
-The tool returns results in the following format:
-
-```json
-{
-  "success": true,
-  "results": [
-    {
-      "name": "Result Title",
-      "url": "https://example.com/result",
-      "content": "Content of the result..."
-    },
-    // Additional results...
-  ]
-}
-```
-
-If an error occurs, the response will be:
-
-```json
-{
-  "success": false,
-  "error": "Error message"
-}
-```
-
-## Error Handling
-
-The tool gracefully handles API errors and provides structured feedback. If the API request fails, the tool will return a dictionary with `success: false` and an error message.
-
-## Conclusion
-
-The `LinkupSearchTool` provides a seamless way to integrate Linkup's contextual information retrieval capabilities into your CrewAI agents. By leveraging this tool, agents can access relevant and up-to-date information to enhance their decision-making and task execution. 

docs/tools/llamaindex-tool.mdx

@@ -0,0 +1,192 @@
+---
+title: LlamaIndexTool
+description: A wrapper tool for integrating LlamaIndex tools and query engines with CrewAI
+icon: link
+---
+
+## LlamaIndexTool
+
+The LlamaIndexTool serves as a bridge between CrewAI and LlamaIndex, allowing you to use LlamaIndex tools and query engines within your CrewAI agents. It supports both direct tool wrapping and query engine integration.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install llama-index  # Required for LlamaIndex integration
+```
+
+## Usage Examples
+
+### Using with LlamaIndex Tools
+
+```python
+from crewai import Agent
+from crewai_tools import LlamaIndexTool
+from llama_index.core.tools import BaseTool as LlamaBaseTool
+from pydantic import BaseModel, Field
+
+# Create a LlamaIndex tool
+class CustomLlamaSchema(BaseModel):
+    query: str = Field(..., description="Query to process")
+
+class CustomLlamaTool(LlamaBaseTool):
+    name = "Custom Llama Tool"
+    description = "A custom LlamaIndex tool"
+    
+    def __call__(self, query: str) -> str:
+        return f"Processed: {query}"
+
+# Wrap the LlamaIndex tool
+llama_tool = CustomLlamaTool()
+wrapped_tool = LlamaIndexTool.from_tool(llama_tool)
+
+# Create an agent with the tool
+agent = Agent(
+    role='LlamaIndex Integration Agent',
+    goal='Process queries using LlamaIndex tools',
+    backstory='Specialist in integrating LlamaIndex capabilities.',
+    tools=[wrapped_tool]
+)
+```
+
+### Using with Query Engines
+
+```python
+from crewai import Agent
+from crewai_tools import LlamaIndexTool
+from llama_index.core import VectorStoreIndex, Document
+
+# Create a query engine
+documents = [Document(text="Sample document content")]
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+# Create the tool
+query_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="Document Search",
+    description="Search through indexed documents"
+)
+
+# Create an agent with the tool
+agent = Agent(
+    role='Document Researcher',
+    goal='Find relevant information in documents',
+    backstory='Expert at searching through document collections.',
+    tools=[query_tool]
+)
+```
+
+## Tool Creation Methods
+
+### From LlamaIndex Tool
+
+```python
+@classmethod
+def from_tool(cls, tool: Any, **kwargs: Any) -> "LlamaIndexTool":
+    """
+    Create a CrewAI tool from a LlamaIndex tool.
+
+    Args:
+        tool (LlamaBaseTool): A LlamaIndex tool to wrap
+        **kwargs: Additional arguments for tool creation
+
+    Returns:
+        LlamaIndexTool: A CrewAI-compatible tool wrapper
+
+    Raises:
+        ValueError: If tool is not a LlamaBaseTool or lacks fn_schema
+    """
+```
+
+### From Query Engine
+
+```python
+@classmethod
+def from_query_engine(
+    cls,
+    query_engine: Any,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+    return_direct: bool = False,
+    **kwargs: Any
+) -> "LlamaIndexTool":
+    """
+    Create a CrewAI tool from a LlamaIndex query engine.
+
+    Args:
+        query_engine (BaseQueryEngine): The query engine to wrap
+        name (Optional[str]): Custom name for the tool
+        description (Optional[str]): Custom description
+        return_direct (bool): Whether to return query engine response directly
+        **kwargs: Additional arguments for tool creation
+
+    Returns:
+        LlamaIndexTool: A CrewAI-compatible tool wrapper
+
+    Raises:
+        ValueError: If query_engine is not a BaseQueryEngine
+    """
+```
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import LlamaIndexTool
+from llama_index.core import VectorStoreIndex, Document
+from llama_index.core.tools import QueryEngineTool
+
+# Create documents and index
+documents = [
+    Document(text="AI is a technology that simulates human intelligence."),
+    Document(text="Machine learning is a subset of AI.")
+]
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+# Create the tool
+search_tool = LlamaIndexTool.from_query_engine(
+    query_engine,
+    name="AI Knowledge Base",
+    description="Search through AI-related documents"
+)
+
+# Create agent
+researcher = Agent(
+    role='AI Researcher',
+    goal='Research AI concepts',
+    backstory='Expert at finding and explaining AI concepts.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find and explain what AI is and its relationship  
+    with machine learning.""",
+    agent=researcher
+)
+
+# The agent will use:
+# {
+#   "query": "What is AI and how does it relate to machine learning?"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Automatically adapts LlamaIndex tool schemas for CrewAI compatibility
+- Renames 'input' parameter to 'query' for better integration
+- Supports both direct tool wrapping and query engine integration
+- Handles schema validation and error resolution
+- Thread-safe operations
+- Compatible with all LlamaIndex tool types and query engines

docs/tools/llamaindextool.mdx

@@ -1,146 +0,0 @@
----
-title: LlamaIndex Tool
-description: The `LlamaIndexTool` is a wrapper for LlamaIndex tools and query engines.
-icon: address-book
----
-
-# `LlamaIndexTool`
-
-## Description
-
-The `LlamaIndexTool` is designed to be a general wrapper around LlamaIndex tools and query engines, enabling you to leverage LlamaIndex resources in terms of RAG/agentic pipelines as tools to plug into CrewAI agents. This tool allows you to seamlessly integrate LlamaIndex's powerful data processing and retrieval capabilities into your CrewAI workflows.
-
-## Installation
-
-To use this tool, you need to install LlamaIndex:
-
-```shell
-uv add llama-index
-```
-
-## Steps to Get Started
-
-To effectively use the `LlamaIndexTool`, follow these steps:
-
-1. **Install LlamaIndex**: Install the LlamaIndex package using the command above.
-2. **Set Up LlamaIndex**: Follow the [LlamaIndex documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
-3. **Create a Tool or Query Engine**: Create a LlamaIndex tool or query engine that you want to use with CrewAI.
-
-## Example
-
-The following examples demonstrate how to initialize the tool from different LlamaIndex components:
-
-### From a LlamaIndex Tool
-
-```python Code
-from crewai_tools import LlamaIndexTool
-from crewai import Agent
-from llama_index.core.tools import FunctionTool
-
-# Example 1: Initialize from FunctionTool
-def search_data(query: str) -> str:
-    """Search for information in the data."""
-    # Your implementation here
-    return f"Results for: {query}"
-
-# Create a LlamaIndex FunctionTool
-og_tool = FunctionTool.from_defaults(
-    search_data, 
-    name="DataSearchTool",
-    description="Search for information in the data"
-)
-
-# Wrap it with LlamaIndexTool
-tool = LlamaIndexTool.from_tool(og_tool)
-
-# Define an agent that uses the tool
-@agent
-def researcher(self) -> Agent:
-    '''
-    This agent uses the LlamaIndexTool to search for information.
-    '''
-    return Agent(
-        config=self.agents_config["researcher"],
-        tools=[tool]
-    )
-```
-
-### From LlamaHub Tools
-
-```python Code
-from crewai_tools import LlamaIndexTool
-from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
-
-# Initialize from LlamaHub Tools
-wolfram_spec = WolframAlphaToolSpec(app_id="your_app_id")
-wolfram_tools = wolfram_spec.to_tool_list()
-tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
-```
-
-### From a LlamaIndex Query Engine
-
-```python Code
-from crewai_tools import LlamaIndexTool
-from llama_index.core import VectorStoreIndex
-from llama_index.core.readers import SimpleDirectoryReader
-
-# Load documents
-documents = SimpleDirectoryReader("./data").load_data()
-
-# Create an index
-index = VectorStoreIndex.from_documents(documents)
-
-# Create a query engine
-query_engine = index.as_query_engine()
-
-# Create a LlamaIndexTool from the query engine
-query_tool = LlamaIndexTool.from_query_engine(
-    query_engine,
-    name="Company Data Query Tool",
-    description="Use this tool to lookup information in company documents"
-)
-```
-
-## Class Methods
-
-The `LlamaIndexTool` provides two main class methods for creating instances:
-
-### from_tool
-
-Creates a `LlamaIndexTool` from a LlamaIndex tool.
-
-```python Code
-@classmethod
-def from_tool(cls, tool: Any, **kwargs: Any) -> "LlamaIndexTool":
-    # Implementation details
-```
-
-### from_query_engine
-
-Creates a `LlamaIndexTool` from a LlamaIndex query engine.
-
-```python Code
-@classmethod
-def from_query_engine(
-    cls,
-    query_engine: Any,
-    name: Optional[str] = None,
-    description: Optional[str] = None,
-    return_direct: bool = False,
-    **kwargs: Any,
-) -> "LlamaIndexTool":
-    # Implementation details
-```
-
-## Parameters
-
-The `from_query_engine` method accepts the following parameters:
-
-- **query_engine**: Required. The LlamaIndex query engine to wrap.
-- **name**: Optional. The name of the tool.
-- **description**: Optional. The description of the tool.
-- **return_direct**: Optional. Whether to return the response directly. Default is `False`.
-
-## Conclusion
-
-The `LlamaIndexTool` provides a powerful way to integrate LlamaIndex's capabilities into CrewAI agents. By wrapping LlamaIndex tools and query engines, it enables agents to leverage sophisticated data retrieval and processing functionalities, enhancing their ability to work with complex information sources. 

docs/tools/mdx-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: MDX Search Tool
+description: A tool for semantic searching within MDX files using RAG capabilities
+---
+
+# MDX Search Tool
+
+The MDX Search Tool enables semantic searching within MDX (Markdown with JSX) files using Retrieval-Augmented Generation (RAG) capabilities. It supports both fixed and dynamic file path modes, allowing you to specify the MDX file at initialization or runtime.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage
+
+You can use the MDX Search Tool in two ways:
+
+### 1. Fixed MDX File Path
+
+Initialize the tool with a specific MDX file path:
+
+```python
+from crewai import Agent
+from crewai_tools import MDXSearchTool
+
+# Initialize with a fixed MDX file
+tool = MDXSearchTool(mdx="/path/to/your/document.mdx")
+
+# Create an agent with the tool
+agent = Agent(
+    role='Technical Writer',
+    goal='Search through MDX documentation',
+    backstory='I help find relevant information in MDX documentation',
+    tools=[tool]
+)
+
+# Use in a task
+task = Task(
+    description="Find information about API endpoints in the documentation",
+    agent=agent
+)
+```
+
+### 2. Dynamic MDX File Path
+
+Initialize the tool without a specific file path to provide it at runtime:
+
+```python
+from crewai import Agent
+from crewai_tools import MDXSearchTool
+
+# Initialize without a fixed MDX file
+tool = MDXSearchTool()
+
+# Create an agent with the tool
+agent = Agent(
+    role='Documentation Analyst',
+    goal='Search through various MDX files',
+    backstory='I analyze different MDX documentation files',
+    tools=[tool]
+)
+
+# Use in a task with dynamic file path
+task = Task(
+    description="Search for 'authentication' in the API documentation",
+    agent=agent,
+    context={
+        "mdx": "/path/to/api-docs.mdx",
+        "search_query": "authentication"
+    }
+)
+```
+
+## Input Schema
+
+### Fixed MDX File Mode
+```python
+class FixedMDXSearchToolSchema(BaseModel):
+    search_query: str  # The search query to find content in the MDX file
+```
+
+### Dynamic MDX File Mode
+```python
+class MDXSearchToolSchema(BaseModel):
+    search_query: str  # The search query to find content in the MDX file
+    mdx: str  # The path to the MDX file to search
+```
+
+## Function Signatures
+
+```python
+def __init__(self, mdx: Optional[str] = None, **kwargs):
+    """
+    Initialize the MDX Search Tool.
+    
+    Args:
+        mdx (Optional[str]): Path to the MDX file (optional)
+        **kwargs: Additional arguments passed to RagTool
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any,
+) -> str:
+    """
+    Execute the search on the MDX file.
+    
+    Args:
+        search_query (str): The query to search for
+        **kwargs: Additional arguments including 'mdx' for dynamic mode
+    
+    Returns:
+        str: The search results from the MDX content
+    """
+```
+
+## Best Practices
+
+1. **File Path Handling**:
+   - Use absolute paths to avoid path resolution issues
+   - Verify file existence before searching
+   - Handle file permissions appropriately
+
+2. **Query Optimization**:
+   - Use specific, focused search queries
+   - Consider context when formulating queries
+   - Break down complex searches into smaller queries
+
+3. **Error Handling**:
+   - Handle file not found errors gracefully
+   - Manage permission issues appropriately
+   - Validate input parameters before processing
+
+## Example Integration
+
+Here's a complete example showing how to integrate the MDX Search Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import MDXSearchTool
+
+# Initialize the tool
+mdx_tool = MDXSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Documentation Researcher',
+    goal='Find and analyze information in MDX documentation',
+    backstory='I am an expert at finding relevant information in documentation',
+    tools=[mdx_tool]
+)
+
+# Create tasks
+search_task = Task(
+    description="""
+    Search through the API documentation for information about authentication methods.
+    Look for:
+    1. Authentication endpoints
+    2. Security best practices
+    3. Token handling
+    
+    Provide a comprehensive summary of the findings.
+    """,
+    agent=researcher,
+    context={
+        "mdx": "/path/to/api-docs.mdx",
+        "search_query": "authentication security tokens"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[search_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **File Not Found**:
+   ```python
+   try:
+       tool = MDXSearchTool(mdx="/path/to/nonexistent.mdx")
+   except FileNotFoundError:
+       print("MDX file not found. Please verify the file path.")
+   ```
+
+2. **Permission Issues**:
+   ```python
+   try: 
+       tool = MDXSearchTool(mdx="/restricted/docs.mdx")
+   except PermissionError:
+       print("Insufficient permissions to access the MDX file.")
+   ```
+
+3. **Invalid Content**:
+   ```python
+   try:
+       result = tool._run(search_query="query", mdx="/path/to/invalid.mdx")
+   except ValueError:
+       print("Invalid MDX content or format.")
+   ```

docs/tools/multiontool.mdx

@@ -1,128 +0,0 @@
----
-title: MultiOn Tool
-description: The `MultiOnTool` empowers CrewAI agents with the capability to navigate and interact with the web through natural language instructions.
-icon: globe
----
-
-# `MultiOnTool`
-
-## Description
-
-The `MultiOnTool` is designed to wrap [MultiOn's](https://docs.multion.ai/welcome) web browsing capabilities, enabling CrewAI agents to control web browsers using natural language instructions. This tool facilitates seamless web browsing, making it an essential asset for projects requiring dynamic web data interaction and automation of web-based tasks.
-
-## Installation
-
-To use this tool, you need to install the MultiOn package:
-
-```shell
-uv add multion
-```
-
-You'll also need to install the MultiOn browser extension and enable API usage.
-
-## Steps to Get Started
-
-To effectively use the `MultiOnTool`, follow these steps:
-
-1. **Install CrewAI**: Ensure that the `crewai[tools]` package is installed in your Python environment.
-2. **Install and use MultiOn**: Follow [MultiOn documentation](https://docs.multion.ai/learn/browser-extension) for installing the MultiOn Browser Extension.
-3. **Enable API Usage**: Click on the MultiOn extension in the extensions folder of your browser (not the hovering MultiOn icon on the web page) to open the extension configurations. Click the API Enabled toggle to enable the API.
-
-## Example
-
-The following example demonstrates how to initialize the tool and execute a web browsing task:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import MultiOnTool
-
-# Initialize the tool
-multion_tool = MultiOnTool(api_key="YOUR_MULTION_API_KEY", local=False)
-
-# Define an agent that uses the tool
-browser_agent = Agent(
-    role="Browser Agent",
-    goal="Control web browsers using natural language",
-    backstory="An expert browsing agent.",
-    tools=[multion_tool],
-    verbose=True,
-)
-
-# Example task to search and summarize news
-browse_task = Task(
-    description="Summarize the top 3 trending AI News headlines",
-    expected_output="A summary of the top 3 trending AI News headlines",
-    agent=browser_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[browser_agent], tasks=[browse_task])
-result = crew.kickoff()
-```
-
-## Parameters
-
-The `MultiOnTool` accepts the following parameters during initialization:
-
-- **api_key**: Optional. Specifies the MultiOn API key. If not provided, it will look for the `MULTION_API_KEY` environment variable.
-- **local**: Optional. Set to `True` to run the agent locally on your browser. Make sure the MultiOn browser extension is installed and API Enabled is checked. Default is `False`.
-- **max_steps**: Optional. Sets the maximum number of steps the MultiOn agent can take for a command. Default is `3`.
-
-## Usage
-
-When using the `MultiOnTool`, the agent will provide natural language instructions that the tool translates into web browsing actions. The tool returns the results of the browsing session along with a status.
-
-```python Code
-# Example of using the tool with an agent
-browser_agent = Agent(
-    role="Web Browser Agent",
-    goal="Search for and summarize information from the web",
-    backstory="An expert at finding and extracting information from websites.",
-    tools=[multion_tool],
-    verbose=True,
-)
-
-# Create a task for the agent
-search_task = Task(
-    description="Search for the latest AI news on TechCrunch and summarize the top 3 headlines",
-    expected_output="A summary of the top 3 AI news headlines from TechCrunch",
-    agent=browser_agent,
-)
-
-# Run the task
-crew = Crew(agents=[browser_agent], tasks=[search_task])
-result = crew.kickoff()
-```
-
-If the status returned is `CONTINUE`, the agent should be instructed to reissue the same instruction to continue execution.
-
-## Implementation Details
-
-The `MultiOnTool` is implemented as a subclass of `BaseTool` from CrewAI. It wraps the MultiOn client to provide web browsing capabilities:
-
-```python Code
-class MultiOnTool(BaseTool):
-    """Tool to wrap MultiOn Browse Capabilities."""
-
-    name: str = "Multion Browse Tool"
-    description: str = """Multion gives the ability for LLMs to control web browsers using natural language instructions.
-            If the status is 'CONTINUE', reissue the same instruction to continue execution
-        """
-    
-    # Implementation details...
-    
-    def _run(self, cmd: str, *args: Any, **kwargs: Any) -> str:
-        """
-        Run the Multion client with the given command.
-        
-        Args:
-            cmd (str): The detailed and specific natural language instruction for web browsing
-            *args (Any): Additional arguments to pass to the Multion client
-            **kwargs (Any): Additional keyword arguments to pass to the Multion client
-        """
-        # Implementation details...
-```
-
-## Conclusion
-
-The `MultiOnTool` provides a powerful way to integrate web browsing capabilities into CrewAI agents. By enabling agents to interact with websites through natural language instructions, it opens up a wide range of possibilities for web-based tasks, from data collection and research to automated interactions with web services. 

docs/tools/mysql-search-tool.mdx

@@ -0,0 +1,217 @@
+---
+title: MySQLSearchTool
+description: A tool for semantic search within MySQL database tables using RAG capabilities
+icon: database
+---
+
+## MySQLSearchTool
+
+The MySQLSearchTool enables semantic search capabilities for MySQL database tables using Retrieval-Augmented Generation (RAG). It processes table contents and allows natural language queries to search through the data.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import MySQLSearchTool
+
+# Initialize the tool
+mysql_tool = MySQLSearchTool(
+    table_name="users",
+    db_uri="mysql://user:pass@localhost:3306/database"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Database Researcher',
+    goal='Search and analyze database contents',
+    backstory='Expert at finding relevant information in databases.',
+    tools=[mysql_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class MySQLSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory semantic search query you want to use to search the database's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    table_name: str,
+    db_uri: str,
+    **kwargs
+):
+    """
+    Initialize the MySQL search tool.
+
+    Args:
+        table_name (str): Name of the table to search
+        db_uri (str): Database connection URI
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on table contents.
+
+    Args:
+        search_query (str): Query to search in the table
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Relevant content from the table matching the query
+    """
+```
+
+## Best Practices
+
+1. Database Connection:
+   - Use secure connection URIs
+   - Handle authentication properly
+   - Manage connection lifecycle
+   - Monitor timeouts
+
+2. Query Optimization:
+   - Structure queries clearly
+   - Consider table size
+   - Handle large datasets
+   - Monitor performance
+
+3. Security Considerations:
+   - Protect credentials
+   - Use environment variables
+   - Limit table access
+   - Validate inputs
+
+4. Error Handling:
+   - Handle connection errors
+   - Manage query timeouts
+   - Provide clear messages
+   - Log issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import MySQLSearchTool
+
+# Initialize tool
+mysql_tool = MySQLSearchTool(
+    table_name="customers",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+# Create agent
+researcher = Agent(
+    role='Database Analyst',
+    goal='Extract customer insights from database',
+    backstory='Expert at analyzing customer data.',
+    tools=[mysql_tool]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all premium customers 
+    with recent purchases.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "premium customers recent purchases"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Table Analysis
+```python
+# Create tools for different tables
+customers_tool = MySQLSearchTool(
+    table_name="customers",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+orders_tool = MySQLSearchTool(
+    table_name="orders",
+    db_uri="mysql://user:pass@localhost:3306/crm"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Data Analyst',
+    goal='Cross-reference customer and order data',
+    tools=[customers_tool, orders_tool]
+)
+```
+
+### Secure Connection Configuration
+```python
+import os
+
+# Use environment variables for credentials
+db_uri = (
+    f"mysql://{os.getenv('DB_USER')}:{os.getenv('DB_PASS')}"
+    f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}"
+    f"/{os.getenv('DB_NAME')}"
+)
+
+tool = MySQLSearchTool(
+    table_name="sensitive_data",
+    db_uri=db_uri
+)
+```
+
+### Error Handling Example
+```python
+try:
+    mysql_tool = MySQLSearchTool(
+        table_name="users",
+        db_uri="mysql://user:pass@localhost:3306/app"
+    )
+    results = mysql_tool.run(
+        search_query="active users in California"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error querying database: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses MySQLLoader
+- Requires database URI
+- Table-specific search
+- Semantic query support
+- Connection management
+- Error handling
+- Performance optimization
+- Security features
+- Memory efficiency

docs/tools/patronustools.mdx

@@ -1,195 +0,0 @@
----
-title: Patronus Evaluation Tools
-description: The Patronus evaluation tools enable CrewAI agents to evaluate and score model inputs and outputs using the Patronus AI platform.
-icon: check
----
-
-# `Patronus Evaluation Tools`
-
-## Description
-
-The [Patronus evaluation tools](https://patronus.ai) are designed to enable CrewAI agents to evaluate and score model inputs and outputs using the Patronus AI platform. These tools provide different levels of control over the evaluation process, from allowing agents to select the most appropriate evaluator and criteria to using predefined criteria or custom local evaluators.
-
-There are three main Patronus evaluation tools:
-
-1. **PatronusEvalTool**: Allows agents to select the most appropriate evaluator and criteria for the evaluation task.
-2. **PatronusPredefinedCriteriaEvalTool**: Uses predefined evaluator and criteria specified by the user.
-3. **PatronusLocalEvaluatorTool**: Uses custom function evaluators defined by the user.
-
-## Installation
-
-To use these tools, you need to install the Patronus package:
-
-```shell
-uv add patronus
-```
-
-You'll also need to set up your Patronus API key as an environment variable:
-
-```shell
-export PATRONUS_API_KEY="your_patronus_api_key"
-```
-
-## Steps to Get Started
-
-To effectively use the Patronus evaluation tools, follow these steps:
-
-1. **Install Patronus**: Install the Patronus package using the command above.
-2. **Set Up API Key**: Set your Patronus API key as an environment variable.
-3. **Choose the Right Tool**: Select the appropriate Patronus evaluation tool based on your needs.
-4. **Configure the Tool**: Configure the tool with the necessary parameters.
-
-## Examples
-
-### Using PatronusEvalTool
-
-The following example demonstrates how to use the `PatronusEvalTool`, which allows agents to select the most appropriate evaluator and criteria:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import PatronusEvalTool
-
-# Initialize the tool
-patronus_eval_tool = PatronusEvalTool()
-
-# Define an agent that uses the tool
-coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code and verify that the output is code",
-    backstory="An experienced coder who can generate high quality python code.",
-    tools=[patronus_eval_tool],
-    verbose=True,
-)
-
-# Example task to generate and evaluate code
-generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
-    agent=coding_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[coding_agent], tasks=[generate_code_task])
-result = crew.kickoff()
-```
-
-### Using PatronusPredefinedCriteriaEvalTool
-
-The following example demonstrates how to use the `PatronusPredefinedCriteriaEvalTool`, which uses predefined evaluator and criteria:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import PatronusPredefinedCriteriaEvalTool
-
-# Initialize the tool with predefined criteria
-patronus_eval_tool = PatronusPredefinedCriteriaEvalTool(
-    evaluators=[{"evaluator": "judge", "criteria": "contains-code"}]
-)
-
-# Define an agent that uses the tool
-coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
-    tools=[patronus_eval_tool],
-    verbose=True,
-)
-
-# Example task to generate code
-generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
-    agent=coding_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[coding_agent], tasks=[generate_code_task])
-result = crew.kickoff()
-```
-
-### Using PatronusLocalEvaluatorTool
-
-The following example demonstrates how to use the `PatronusLocalEvaluatorTool`, which uses custom function evaluators:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import PatronusLocalEvaluatorTool
-from patronus import Client, EvaluationResult
-import random
-
-# Initialize the Patronus client
-client = Client()
-
-# Register a custom evaluator
-@client.register_local_evaluator("random_evaluator")
-def random_evaluator(**kwargs):
-    score = random.random()
-    return EvaluationResult(
-        score_raw=score,
-        pass_=score >= 0.5,
-        explanation="example explanation",
-    )
-
-# Initialize the tool with the custom evaluator
-patronus_eval_tool = PatronusLocalEvaluatorTool(
-    patronus_client=client,
-    evaluator="random_evaluator",
-    evaluated_model_gold_answer="example label",
-)
-
-# Define an agent that uses the tool
-coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
-    tools=[patronus_eval_tool],
-    verbose=True,
-)
-
-# Example task to generate code
-generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
-    agent=coding_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[coding_agent], tasks=[generate_code_task])
-result = crew.kickoff()
-```
-
-## Parameters
-
-### PatronusEvalTool
-
-The `PatronusEvalTool` does not require any parameters during initialization. It automatically fetches available evaluators and criteria from the Patronus API.
-
-### PatronusPredefinedCriteriaEvalTool
-
-The `PatronusPredefinedCriteriaEvalTool` accepts the following parameters during initialization:
-
-- **evaluators**: Required. A list of dictionaries containing the evaluator and criteria to use. For example: `[{"evaluator": "judge", "criteria": "contains-code"}]`.
-
-### PatronusLocalEvaluatorTool
-
-The `PatronusLocalEvaluatorTool` accepts the following parameters during initialization:
-
-- **patronus_client**: Required. The Patronus client instance.
-- **evaluator**: Optional. The name of the registered local evaluator to use. Default is an empty string.
-- **evaluated_model_gold_answer**: Optional. The gold answer to use for evaluation. Default is an empty string.
-
-## Usage
-
-When using the Patronus evaluation tools, you provide the model input, output, and context, and the tool returns the evaluation results from the Patronus API.
-
-For the `PatronusEvalTool` and `PatronusPredefinedCriteriaEvalTool`, the following parameters are required when calling the tool:
-
-- **evaluated_model_input**: The agent's task description in simple text.
-- **evaluated_model_output**: The agent's output of the task.
-- **evaluated_model_retrieved_context**: The agent's context.
-
-For the `PatronusLocalEvaluatorTool`, the same parameters are required, but the evaluator and gold answer are specified during initialization.
-
-## Conclusion
-
-The Patronus evaluation tools provide a powerful way to evaluate and score model inputs and outputs using the Patronus AI platform. By enabling agents to evaluate their own outputs or the outputs of other agents, these tools can help improve the quality and reliability of CrewAI workflows. 

docs/tools/pdf-search-tool.mdx

@@ -0,0 +1,208 @@
+---
+title: PDFSearchTool
+description: A tool for semantic search within PDF documents using RAG capabilities
+icon: file-search
+---
+
+## PDFSearchTool
+
+The PDFSearchTool enables semantic search capabilities for PDF documents using Retrieval-Augmented Generation (RAG). It leverages embedchain's PDFEmbedchainAdapter for efficient PDF processing and supports both fixed and dynamic PDF path specification.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PDFSearchTool
+
+# Method 1: Initialize with specific PDF
+pdf_tool = PDFSearchTool(pdf="/path/to/document.pdf")
+
+# Method 2: Initialize without PDF (specify at runtime)
+flexible_pdf_tool = PDFSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='PDF Researcher',
+    goal='Search and analyze PDF documents',
+    backstory='Expert at finding relevant information in PDFs.',
+    tools=[pdf_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed PDF Schema (when PDF path provided during initialization)
+```python
+class FixedPDFSearchToolSchema(BaseModel):
+    query: str = Field(
+        description="Mandatory query you want to use to search the PDF's content"
+    )
+```
+
+### Flexible PDF Schema (when PDF path provided at runtime)
+```python
+class PDFSearchToolSchema(FixedPDFSearchToolSchema):
+    pdf: str = Field(
+        description="Mandatory pdf path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    pdf: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the PDF search tool.
+
+    Args:
+        pdf (Optional[str]): Path to PDF file (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on PDF content.
+
+    Args:
+        query (str): Search query for the PDF
+        **kwargs: Additional arguments including pdf path if not initialized
+
+    Returns:
+        str: Relevant content from the PDF matching the query
+    """
+```
+
+## Best Practices
+
+1. PDF File Handling:
+   - Use absolute paths for reliability
+   - Verify PDF file existence
+   - Handle large PDFs appropriately
+
+2. Search Optimization:
+   - Use specific, focused queries
+   - Consider document structure
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize with PDF for repeated searches
+   - Handle large documents efficiently
+   - Monitor memory usage
+
+4. Error Handling:
+   - Verify PDF file existence
+   - Handle malformed PDFs
+   - Manage file access permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PDFSearchTool
+
+# Initialize tool with specific PDF
+pdf_tool = PDFSearchTool(pdf="/path/to/research.pdf")
+
+# Create agent
+researcher = Agent(
+    role='PDF Researcher',
+    goal='Extract insights from research papers',
+    backstory='Expert at analyzing research documents.',
+    tools=[pdf_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications in healthcare from the PDF.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "query": "machine learning applications healthcare"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic PDF Selection
+```python
+# Initialize without PDF
+flexible_tool = PDFSearchTool()
+
+# Search different PDFs
+research_results = flexible_tool.run(
+    query="quantum computing",
+    pdf="/path/to/research.pdf"
+)
+
+report_results = flexible_tool.run(
+    query="financial metrics",
+    pdf="/path/to/report.pdf"
+)
+```
+
+### Multiple PDF Analysis
+```python
+# Create tools for different PDFs
+research_tool = PDFSearchTool(pdf="/path/to/research.pdf")
+report_tool = PDFSearchTool(pdf="/path/to/report.pdf")
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Document Analyst',
+    goal='Cross-reference multiple documents',
+    tools=[research_tool, report_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    pdf_tool = PDFSearchTool()
+    results = pdf_tool.run(
+        query="important findings",
+        pdf="/path/to/document.pdf"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing PDF: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses PDFEmbedchainAdapter
+- Supports semantic search
+- Dynamic PDF specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles large documents
+- Supports various PDF formats
+- Memory-efficient processing

docs/tools/pdf-text-writing-tool.mdx

@@ -0,0 +1,234 @@
+---
+title: PDFTextWritingTool
+description: A tool for adding text to specific positions in PDF documents with custom font support
+icon: file-pdf
+---
+
+## PDFTextWritingTool
+
+The PDFTextWritingTool allows you to add text to specific positions in PDF documents with support for custom fonts, colors, and positioning. It's particularly useful for adding annotations, watermarks, or any text overlay to existing PDFs.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PDFTextWritingTool
+
+# Basic initialization
+pdf_tool = PDFTextWritingTool()
+
+# Create an agent with the tool
+document_processor = Agent(
+    role='Document Processor',
+    goal='Add text annotations to PDF documents',
+    backstory='Expert at PDF document processing and text manipulation.',
+    tools=[pdf_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class PDFTextWritingToolSchema(BaseModel):
+    pdf_path: str = Field(
+        description="Path to the PDF file to modify"
+    )
+    text: str = Field(
+        description="Text to add to the PDF"
+    )
+    position: tuple = Field(
+        description="Tuple of (x, y) coordinates for text placement"
+    )
+    font_size: int = Field(
+        default=12,
+        description="Font size of the text"
+    )
+    font_color: str = Field(
+        default="0 0 0 rg",
+        description="RGB color code for the text"
+    )
+    font_name: Optional[str] = Field(
+        default="F1",
+        description="Font name for standard fonts"
+    )
+    font_file: Optional[str] = Field(
+        default=None,
+        description="Path to a .ttf font file for custom font usage"
+    )
+    page_number: int = Field(
+        default=0,
+        description="Page number to add text to"
+    )
+```
+
+## Function Signature
+
+```python
+def run(
+    self,
+    pdf_path: str,
+    text: str,
+    position: tuple,
+    font_size: int,
+    font_color: str,
+    font_name: str = "F1",
+    font_file: Optional[str] = None,
+    page_number: int = 0,
+    **kwargs
+) -> str:
+    """
+    Add text to a specific position in a PDF document.
+
+    Args:
+        pdf_path (str): Path to the PDF file to modify
+        text (str): Text to add to the PDF
+        position (tuple): (x, y) coordinates for text placement
+        font_size (int): Font size of the text
+        font_color (str): RGB color code for the text (e.g., "0 0 0 rg" for black)
+        font_name (str, optional): Font name for standard fonts (default: "F1")
+        font_file (str, optional): Path to a .ttf font file for custom font
+        page_number (int, optional): Page number to add text to (default: 0)
+
+    Returns:
+        str: Success message with output file path
+    """
+```
+
+## Best Practices
+
+1. File Handling:
+   - Ensure PDF files exist before processing
+   - Use absolute paths for reliability
+   - Handle file permissions appropriately
+
+2. Text Positioning:
+   - Use appropriate coordinates based on PDF dimensions
+   - Consider page orientation and margins
+   - Test positioning with small changes first
+
+3. Font Usage:
+   - Verify custom font files exist
+   - Use standard fonts when possible
+   - Test font rendering before production use
+
+4. Error Handling:
+   - Check page numbers are valid
+   - Verify font file accessibility
+   - Handle file writing permissions
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PDFTextWritingTool
+
+# Initialize tool
+pdf_tool = PDFTextWritingTool()
+
+# Create agent
+document_processor = Agent(
+    role='Document Processor',
+    goal='Process and annotate PDF documents',
+    backstory='Expert at PDF manipulation and text placement.',
+    tools=[pdf_tool]
+)
+
+# Define task
+annotation_task = Task(
+    description="""Add a watermark saying 'CONFIDENTIAL' to 
+    the center of the first page of the document at 
+    '/path/to/document.pdf'.""",
+    agent=document_processor
+)
+
+# The tool will use:
+# {
+#   "pdf_path": "/path/to/document.pdf",
+#   "text": "CONFIDENTIAL",
+#   "position": (300, 400),
+#   "font_size": 24,
+#   "font_color": "1 0 0 rg",  # Red color
+#   "page_number": 0
+# }
+
+# Create crew
+crew = Crew(
+    agents=[document_processor],
+    tasks=[annotation_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Font Example
+```python
+# Using a custom font
+result = pdf_tool.run(
+    pdf_path="/path/to/input.pdf",
+    text="Custom Font Text",
+    position=(100, 500),
+    font_size=16,
+    font_color="0 0 1 rg",  # Blue color
+    font_file="/path/to/custom_font.ttf",
+    page_number=0
+)
+```
+
+### Multiple Text Elements
+```python
+# Add multiple text elements
+positions = [(100, 700), (100, 650), (100, 600)]
+texts = ["Header", "Subheader", "Body Text"]
+font_sizes = [18, 14, 12]
+
+for text, position, size in zip(texts, positions, font_sizes):
+    pdf_tool.run(
+        pdf_path="/path/to/input.pdf",
+        text=text,
+        position=position,
+        font_size=size,
+        font_color="0 0 0 rg"  # Black color
+    )
+```
+
+### Color Text Example
+```python
+# Add colored text
+colors = {
+    "red": "1 0 0 rg",
+    "green": "0 1 0 rg",
+    "blue": "0 0 1 rg"
+}
+
+for y_pos, (color_name, color_code) in enumerate(colors.items()):
+    pdf_tool.run(
+        pdf_path="/path/to/input.pdf",
+        text=f"This text is {color_name}",
+        position=(100, 700 - y_pos * 50),
+        font_size=14,
+        font_color=color_code
+    )
+```
+
+## Notes
+
+- Supports custom TrueType fonts (.ttf)
+- Allows RGB color specifications
+- Handles multi-page PDFs
+- Preserves original PDF content
+- Supports text positioning with x,y coordinates
+- Maintains PDF structure and metadata
+- Creates new output file for safety
+- Thread-safe operations
+- Efficient PDF manipulation
+- Supports various text attributes

docs/tools/pg-search-tool.mdx

@@ -0,0 +1,181 @@
+---
+title: PGSearchTool
+description: A RAG-based semantic search tool for PostgreSQL database content
+icon: database-search
+---
+
+## PGSearchTool
+
+The PGSearchTool provides semantic search capabilities for PostgreSQL database content using RAG (Retrieval-Augmented Generation). It allows for natural language queries over database table content by leveraging embeddings and semantic search.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install embedchain  # Required dependency
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import PGSearchTool
+
+# Initialize the tool with database configuration
+search_tool = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="your_table"
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Database Researcher',
+    goal='Find relevant information in database content',
+    backstory='Expert at searching and analyzing database content.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class PGSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory semantic search query for searching the database's content"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, table_name: str, **kwargs):
+    """
+    Initialize the PostgreSQL search tool.
+
+    Args:
+        table_name (str): Name of the table to search
+        db_uri (str): PostgreSQL database URI (required in kwargs)
+        **kwargs: Additional arguments for RagTool initialization
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> Any:
+    """
+    Perform semantic search on database content.
+
+    Args:
+        search_query (str): Semantic search query
+        **kwargs: Additional search parameters
+
+    Returns:
+        Any: Relevant database content based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Secure database credentials:
+   ```python
+   # Use environment variables for sensitive data
+   import os
+   
+   db_uri = (
+       f"postgresql://{os.getenv('DB_USER')}:{os.getenv('DB_PASS')}"
+       f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}"
+   )
+   ```
+
+2. Optimize table selection
+3. Use specific semantic queries
+4. Handle database connection errors
+5. Consider table size and query performance
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import PGSearchTool
+
+# Initialize tool with database configuration
+db_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="customer_feedback"
+)
+
+# Create agent
+analyst = Agent(
+    role='Database Analyst',
+    goal='Analyze customer feedback data',
+    backstory='Expert at finding insights in customer feedback.',
+    tools=[db_search]
+)
+
+# Define task
+analysis_task = Task(
+    description="""Find all customer feedback related to product usability
+    and ease of use. Focus on common patterns and issues.""",
+    agent=analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "product usability feedback ease of use issues"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[analyst],
+    tasks=[analysis_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Multiple Table Search
+```python
+# Create tools for different tables
+customer_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="customers"
+)
+
+orders_search = PGSearchTool(
+    db_uri="postgresql://user:password@localhost:5432/dbname",
+    table_name="orders"
+)
+
+# Use both tools in an agent
+analyst = Agent(
+    role='Multi-table Analyst',
+    goal='Analyze customer and order data',
+    tools=[customer_search, orders_search]
+)
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="customer satisfaction ratings"
+    )
+    # Process results
+except Exception as e:
+    print(f"Database search error: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search
+- Uses embedchain's PostgresLoader
+- Requires valid PostgreSQL connection
+- Supports semantic natural language queries
+- Thread-safe operations
+- Efficient for large tables
+- Handles connection pooling automatically

docs/tools/qdrantvectorsearchtool.mdx

@@ -1,271 +0,0 @@
----
-title: 'Qdrant Vector Search Tool'
-description: 'Semantic search capabilities for CrewAI agents using Qdrant vector database'
-icon: magnifying-glass-plus
----
-
-# `QdrantVectorSearchTool`
-
-The Qdrant Vector Search Tool enables semantic search capabilities in your CrewAI agents by leveraging [Qdrant](https://qdrant.tech/), a vector similarity search engine. This tool allows your agents to search through documents stored in a Qdrant collection using semantic similarity.
-
-## Installation
-
-Install the required packages:
-
-```bash
-uv add qdrant-client
-```
-
-## Basic Usage
-
-Here's a minimal example of how to use the tool:
-
-```python
-from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
-
-# Initialize the tool
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url="your_qdrant_url",
-    qdrant_api_key="your_qdrant_api_key",
-    collection_name="your_collection"
-)
-
-# Create an agent that uses the tool
-agent = Agent(
-    role="Research Assistant",
-    goal="Find relevant information in documents",
-    tools=[qdrant_tool]
-)
-
-# The tool will automatically use OpenAI embeddings
-# and return the 3 most relevant results with scores > 0.35
-```
-
-## Complete Working Example
-
-Here's a complete example showing how to:
-1. Extract text from a PDF
-2. Generate embeddings using OpenAI
-3. Store in Qdrant
-4. Create a CrewAI agentic RAG workflow for semantic search
-
-```python
-import os
-import uuid
-import pdfplumber
-from openai import OpenAI
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai_tools import QdrantVectorSearchTool
-from qdrant_client import QdrantClient
-from qdrant_client.models import PointStruct, Distance, VectorParams
-
-# Load environment variables
-load_dotenv()
-
-# Initialize OpenAI client
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# Extract text from PDF
-def extract_text_from_pdf(pdf_path):
-    text = []
-    with pdfplumber.open(pdf_path) as pdf:
-        for page in pdf.pages:
-            page_text = page.extract_text()
-            if page_text:
-                text.append(page_text.strip())
-    return text
-
-# Generate OpenAI embeddings
-def get_openai_embedding(text):
-    response = client.embeddings.create(
-        input=text,
-        model="text-embedding-3-small"
-    )
-    return response.data[0].embedding
-
-# Store text and embeddings in Qdrant
-def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
-    # Extract text from PDF
-    text_chunks = extract_text_from_pdf(pdf_path)
-    
-    # Create Qdrant collection
-    if qdrant.collection_exists(collection_name):
-        qdrant.delete_collection(collection_name)
-    qdrant.create_collection(
-        collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
-    )
-
-    # Store embeddings
-    points = []
-    for chunk in text_chunks:
-        embedding = get_openai_embedding(chunk)
-        points.append(PointStruct(
-            id=str(uuid.uuid4()),
-            vector=embedding,
-            payload={"text": chunk}
-        ))
-    qdrant.upsert(collection_name=collection_name, points=points)
-
-# Initialize Qdrant client and load data
-qdrant = QdrantClient(
-    url=os.getenv("QDRANT_URL"),
-    api_key=os.getenv("QDRANT_API_KEY")
-)
-collection_name = "example_collection"
-pdf_path = "path/to/your/document.pdf"
-load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
-
-# Initialize Qdrant search tool
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_url=os.getenv("QDRANT_URL"),
-    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-    collection_name=collection_name,
-    limit=3,
-    score_threshold=0.35
-)
-
-# Create CrewAI agents
-search_agent = Agent(
-    role="Senior Semantic Search Agent",
-    goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant 
-    information using semantic search in a Qdrant database.""",
-    tools=[qdrant_tool],
-    verbose=True
-)
-
-answer_agent = Agent(
-    role="Senior Answer Assistant",
-    goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate 
-    answers to questions based on the context provided.""",
-    tools=[qdrant_tool],
-    verbose=True
-)
-
-# Define tasks
-search_task = Task(
-    description="""Search for relevant documents about the {query}.
-    Your final answer should include:
-    - The relevant information found
-    - The similarity scores of the results
-    - The metadata of the relevant documents""",
-    agent=search_agent
-)
-
-answer_task = Task(
-    description="""Given the context and metadata of relevant documents,
-    generate a final answer based on the context.""",
-    agent=answer_agent
-)
-
-# Run CrewAI workflow
-crew = Crew(
-    agents=[search_agent, answer_agent],
-    tasks=[search_task, answer_task],
-    process=Process.sequential,
-    verbose=True
-)
-
-result = crew.kickoff(
-    inputs={"query": "What is the role of X in the document?"}
-)
-print(result)
-```
-
-## Tool Parameters
-
-### Required Parameters
-- `qdrant_url` (str): The URL of your Qdrant server
-- `qdrant_api_key` (str): API key for authentication with Qdrant
-- `collection_name` (str): Name of the Qdrant collection to search
-
-### Optional Parameters
-- `limit` (int): Maximum number of results to return (default: 3)
-- `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
-- `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
-
-## Search Parameters
-
-The tool accepts these parameters in its schema:
-- `query` (str): The search query to find similar documents
-- `filter_by` (str, optional): Metadata field to filter on
-- `filter_value` (str, optional): Value to filter by
-
-## Return Format
-
-The tool returns results in JSON format:
-
-```json
-[
-  {
-    "metadata": {
-      // Any metadata stored with the document
-    },
-    "context": "The actual text content of the document",
-    "distance": 0.95  // Similarity score
-  }
-]
-```
-
-## Default Embedding
-
-By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
-- OpenAI API key set in environment: `OPENAI_API_KEY`
-
-## Custom Embeddings
-
-Instead of using the default embedding model, you might want to use your own embedding function in cases where you:
-
-1. Want to use a different embedding model (e.g., Cohere, HuggingFace, Ollama models)
-2. Need to reduce costs by using open-source embedding models
-3. Have specific requirements for vector dimensions or embedding quality
-4. Want to use domain-specific embeddings (e.g., for medical or legal text)
-
-Here's an example using a HuggingFace model:
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-
-def custom_embeddings(text: str) -> list[float]:
-    # Tokenize and get model outputs
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-    outputs = model(**inputs)
-    
-    # Use mean pooling to get text embedding
-    embeddings = outputs.last_hidden_state.mean(dim=1)
-    
-    # Convert to list of floats and return
-    return embeddings[0].tolist()
-
-# Use custom embeddings with the tool
-tool = QdrantVectorSearchTool(
-    qdrant_url="your_url",
-    qdrant_api_key="your_key",
-    collection_name="your_collection",
-    custom_embedding_fn=custom_embeddings  # Pass your custom function
-)
-```
-
-## Error Handling
-
-The tool handles these specific errors:
-- Raises ImportError if `qdrant-client` is not installed (with option to auto-install)
-- Raises ValueError if `QDRANT_URL` is not set
-- Prompts to install `qdrant-client` if missing using `uv add qdrant-client`
-
-## Environment Variables
-
-Required environment variables:
-```bash
-export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
-export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
-export OPENAI_API_KEY="your_openai_key"  # If using default embeddings

docs/tools/rag-tool.mdx

@@ -0,0 +1,282 @@
+---
+title: RagTool
+description: Base class for Retrieval-Augmented Generation (RAG) tools with flexible adapter support
+icon: database
+---
+
+## RagTool
+
+The RagTool serves as the base class for all Retrieval-Augmented Generation (RAG) tools in the CrewAI ecosystem. It provides a flexible adapter-based architecture for implementing knowledge base functionality with semantic search capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import RagTool
+from crewai_tools.adapters import EmbedchainAdapter
+from embedchain import App
+
+# Create custom adapter
+class CustomAdapter(RagTool.Adapter):
+    def query(self, question: str) -> str:
+        # Implement custom query logic
+        return "Answer based on knowledge base"
+
+    def add(self, *args, **kwargs) -> None:
+        # Implement custom add logic
+        pass
+
+# Method 1: Use default EmbedchainAdapter
+rag_tool = RagTool(
+    name="Custom Knowledge Base",
+    description="Specialized knowledge base for domain data",
+    summarize=True
+)
+
+# Method 2: Use custom adapter
+custom_tool = RagTool(
+    name="Custom Knowledge Base",
+    adapter=CustomAdapter(),
+    summarize=False
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Knowledge Base Researcher',
+    goal='Search and analyze knowledge base content',
+    backstory='Expert at finding relevant information in specialized datasets.',
+    tools=[rag_tool],
+    verbose=True
+)
+```
+
+## Adapter Interface
+
+```python
+class Adapter(BaseModel, ABC):
+    @abstractmethod
+    def query(self, question: str) -> str:
+        """
+        Query the knowledge base with a question.
+        
+        Args:
+            question (str): Query to search in knowledge base
+            
+        Returns:
+            str: Answer based on knowledge base content
+        """
+        
+    @abstractmethod
+    def add(self, *args: Any, **kwargs: Any) -> None:
+        """
+        Add content to the knowledge base.
+        
+        Args:
+            *args: Variable length argument list
+            **kwargs: Arbitrary keyword arguments
+        """
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    name: str = "Knowledge base",
+    description: str = "A knowledge base that can be used to answer questions.",
+    summarize: bool = False,
+    adapter: Optional[Adapter] = None,
+    config: Optional[dict[str, Any]] = None,
+    **kwargs
+):
+    """
+    Initialize the RAG tool.
+
+    Args:
+        name (str): Tool name
+        description (str): Tool description
+        summarize (bool): Enable answer summarization
+        adapter (Optional[Adapter]): Custom adapter implementation
+        config (Optional[dict]): Configuration for default adapter
+        **kwargs: Additional arguments for base tool
+    """
+
+def _run(
+    self,
+    query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute query against knowledge base.
+
+    Args:
+        query (str): Question to ask
+        **kwargs: Additional arguments
+
+    Returns:
+        str: Answer from knowledge base
+    """
+```
+
+## Best Practices
+
+1. Adapter Implementation:
+   - Define clear interfaces
+   - Handle edge cases
+   - Implement error handling
+   - Document behavior
+
+2. Knowledge Base Management:
+   - Organize content logically
+   - Update content regularly
+   - Monitor performance
+   - Handle large datasets
+
+3. Query Optimization:
+   - Structure queries clearly
+   - Consider context
+   - Handle ambiguity
+   - Validate inputs
+
+4. Error Handling:
+   - Handle missing data
+   - Manage timeouts
+   - Provide clear messages
+   - Log issues
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import RagTool
+from embedchain import App
+
+# Initialize tool with custom configuration
+rag_tool = RagTool(
+    name="Technical Documentation KB",
+    description="Knowledge base for technical documentation",
+    summarize=True,
+    config={
+        "collection_name": "tech_docs",
+        "chunking": {
+            "chunk_size": 500,
+            "chunk_overlap": 50
+        }
+    }
+)
+
+# Add content to knowledge base
+rag_tool.add(
+    "Technical documentation content here...",
+    data_type="text"
+)
+
+# Create agent
+researcher = Agent(
+    role='Documentation Expert',
+    goal='Extract technical information from documentation',
+    backstory='Expert at analyzing technical documentation.',
+    tools=[rag_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of API endpoints 
+    and their authentication requirements.""",
+    agent=researcher
+)
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Custom Adapter Implementation
+```python
+from typing import Any
+from pydantic import BaseModel
+from abc import ABC, abstractmethod
+
+class SpecializedAdapter(RagTool.Adapter):
+    def __init__(self, config: dict):
+        self.config = config
+        self.knowledge_base = {}
+
+    def query(self, question: str) -> str:
+        # Implement specialized query logic
+        return self._process_query(question)
+
+    def add(self, content: str, **kwargs: Any) -> None:
+        # Implement specialized content addition
+        self._process_content(content, **kwargs)
+
+# Use custom adapter
+specialized_tool = RagTool(
+    name="Specialized KB",
+    adapter=SpecializedAdapter(config={"mode": "advanced"})
+)
+```
+
+### Configuration Management
+```python
+# Configure default EmbedchainAdapter
+config = {
+    "collection_name": "custom_collection",
+    "embedding": {
+        "model": "sentence-transformers/all-mpnet-base-v2",
+        "dimensions": 768
+    },
+    "chunking": {
+        "chunk_size": 1000,
+        "chunk_overlap": 100
+    }
+}
+
+tool = RagTool(config=config)
+```
+
+### Error Handling Example
+```python
+try:
+    rag_tool = RagTool()
+    
+    # Add content
+    rag_tool.add(
+        "Documentation content...",
+        data_type="text"
+    )
+    
+    # Query content
+    result = rag_tool.run(
+        query="What are the system requirements?"
+    )
+    print(result)
+except Exception as e:
+    print(f"Error using knowledge base: {str(e)}")
+```
+
+## Notes
+
+- Base class for RAG tools
+- Flexible adapter pattern
+- Default EmbedchainAdapter
+- Custom adapter support
+- Content management
+- Query processing
+- Error handling
+- Configuration options
+- Performance optimization
+- Memory management

docs/tools/ragtool.mdx

@@ -1,154 +0,0 @@
----
-title: RAG Tool
-description: The `RagTool` is a dynamic knowledge base tool for answering questions using Retrieval-Augmented Generation.
-icon: vector-square
----
-
-# `RagTool`
-
-## Description
-
-The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain. 
-It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources. 
-This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
-
-## Example
-
-The following example demonstrates how to initialize the tool and use it with different data sources:
-
-```python Code
-from crewai_tools import RagTool
-
-# Create a RAG tool with default settings
-rag_tool = RagTool()
-
-# Add content from a file
-rag_tool.add(data_type="file", path="path/to/your/document.pdf")
-
-# Add content from a web page
-rag_tool.add(data_type="web_page", url="https://example.com")
-
-# Define an agent with the RagTool
-@agent
-def knowledge_expert(self) -> Agent:
-    '''
-    This agent uses the RagTool to answer questions about the knowledge base.
-    '''
-    return Agent(
-        config=self.agents_config["knowledge_expert"],
-        allow_delegation=False,
-        tools=[rag_tool]
-    )
-```
-
-## Supported Data Sources
-
-The `RagTool` can be used with a wide variety of data sources, including:
-
-- 📰 PDF files
-- 📊 CSV files
-- 📃 JSON files
-- 📝 Text
-- 📁 Directories/Folders
-- 🌐 HTML Web pages
-- 📽️ YouTube Channels
-- 📺 YouTube Videos
-- 📚 Documentation websites
-- 📝 MDX files
-- 📄 DOCX files
-- 🧾 XML files
-- 📬 Gmail
-- 📝 GitHub repositories
-- 🐘 PostgreSQL databases
-- 🐬 MySQL databases
-- 🤖 Slack conversations
-- 💬 Discord messages
-- 🗨️ Discourse forums
-- 📝 Substack newsletters
-- 🐝 Beehiiv content
-- 💾 Dropbox files
-- 🖼️ Images
-- ⚙️ Custom data sources
-
-## Parameters
-
-The `RagTool` accepts the following parameters:
-
-- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
-- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, an EmbedchainAdapter will be used.
-- **config**: Optional. Configuration for the underlying EmbedChain App.
-
-## Adding Content
-
-You can add content to the knowledge base using the `add` method:
-
-```python Code
-# Add a PDF file
-rag_tool.add(data_type="file", path="path/to/your/document.pdf")
-
-# Add a web page
-rag_tool.add(data_type="web_page", url="https://example.com")
-
-# Add a YouTube video
-rag_tool.add(data_type="youtube_video", url="https://www.youtube.com/watch?v=VIDEO_ID")
-
-# Add a directory of files
-rag_tool.add(data_type="directory", path="path/to/your/directory")
-```
-
-## Agent Integration Example
-
-Here's how to integrate the `RagTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent
-from crewai.project import agent
-from crewai_tools import RagTool
-
-# Initialize the tool and add content
-rag_tool = RagTool()
-rag_tool.add(data_type="web_page", url="https://docs.crewai.com")
-rag_tool.add(data_type="file", path="company_data.pdf")
-
-# Define an agent with the RagTool
-@agent
-def knowledge_expert(self) -> Agent:
-    return Agent(
-        config=self.agents_config["knowledge_expert"],
-        allow_delegation=False,
-        tools=[rag_tool]
-    )
-```
-
-## Advanced Configuration
-
-You can customize the behavior of the `RagTool` by providing a configuration dictionary:
-
-```python Code
-from crewai_tools import RagTool
-
-# Create a RAG tool with custom configuration
-config = {
-    "app": {
-        "name": "custom_app",
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4",
-        }
-    },
-    "embedder": {
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-ada-002"
-        }
-    }
-}
-
-rag_tool = RagTool(config=config, summarize=True)
-```
-
-## Conclusion
-
-The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses. 

docs/tools/s3readertool.mdx

@@ -1,144 +0,0 @@
----
-title: S3 Reader Tool
-description: The `S3ReaderTool` enables CrewAI agents to read files from Amazon S3 buckets.
-icon: aws
----
-
-# `S3ReaderTool`
-
-## Description
-
-The `S3ReaderTool` is designed to read files from Amazon S3 buckets. This tool allows CrewAI agents to access and retrieve content stored in S3, making it ideal for workflows that require reading data, configuration files, or any other content stored in AWS S3 storage.
-
-## Installation
-
-To use this tool, you need to install the required dependencies:
-
-```shell
-uv add boto3
-```
-
-## Steps to Get Started
-
-To effectively use the `S3ReaderTool`, follow these steps:
-
-1. **Install Dependencies**: Install the required packages using the command above.
-2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
-3. **Initialize the Tool**: Create an instance of the tool.
-4. **Specify S3 Path**: Provide the S3 path to the file you want to read.
-
-## Example
-
-The following example demonstrates how to use the `S3ReaderTool` to read a file from an S3 bucket:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools.aws.s3 import S3ReaderTool
-
-# Initialize the tool
-s3_reader_tool = S3ReaderTool()
-
-# Define an agent that uses the tool
-file_reader_agent = Agent(
-    role="File Reader",
-    goal="Read files from S3 buckets",
-    backstory="An expert in retrieving and processing files from cloud storage.",
-    tools=[s3_reader_tool],
-    verbose=True,
-)
-
-# Example task to read a configuration file
-read_task = Task(
-    description="Read the configuration file from {my_bucket} and summarize its contents.",
-    expected_output="A summary of the configuration file contents.",
-    agent=file_reader_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[file_reader_agent], tasks=[read_task])
-result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
-```
-
-## Parameters
-
-The `S3ReaderTool` accepts the following parameter when used by an agent:
-
-- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
-
-## AWS Credentials
-
-The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
-
-- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
-- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
-- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
-
-## Usage
-
-When using the `S3ReaderTool` with an agent, the agent will need to provide the S3 file path:
-
-```python Code
-# Example of using the tool with an agent
-file_reader_agent = Agent(
-    role="File Reader",
-    goal="Read files from S3 buckets",
-    backstory="An expert in retrieving and processing files from cloud storage.",
-    tools=[s3_reader_tool],
-    verbose=True,
-)
-
-# Create a task for the agent to read a specific file
-read_config_task = Task(
-    description="Read the application configuration file from {my_bucket} and extract the database connection settings.",
-    expected_output="The database connection settings from the configuration file.",
-    agent=file_reader_agent,
-)
-
-# Run the task
-crew = Crew(agents=[file_reader_agent], tasks=[read_config_task])
-result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/app-config.json"})
-```
-
-## Error Handling
-
-The `S3ReaderTool` includes error handling for common S3 issues:
-
-- Invalid S3 path format
-- Missing or inaccessible files
-- Permission issues
-- AWS credential problems
-
-When an error occurs, the tool will return an error message that includes details about the issue.
-
-## Implementation Details
-
-The `S3ReaderTool` uses the AWS SDK for Python (boto3) to interact with S3:
-
-```python Code
-class S3ReaderTool(BaseTool):
-    name: str = "S3 Reader Tool"
-    description: str = "Reads a file from Amazon S3 given an S3 file path"
-    
-    def _run(self, file_path: str) -> str:
-        try:
-            bucket_name, object_key = self._parse_s3_path(file_path)
-
-            s3 = boto3.client(
-                's3',
-                region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
-                aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
-                aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
-            )
-
-            # Read file content from S3
-            response = s3.get_object(Bucket=bucket_name, Key=object_key)
-            file_content = response['Body'].read().decode('utf-8')
-
-            return file_content
-        except ClientError as e:
-            return f"Error reading file from S3: {str(e)}"
-```
-
-## Conclusion
-
-The `S3ReaderTool` provides a straightforward way to read files from Amazon S3 buckets. By enabling agents to access content stored in S3, it facilitates workflows that require cloud-based file access. This tool is particularly useful for data processing, configuration management, and any task that involves retrieving information from AWS S3 storage. 

docs/tools/s3writertool.mdx

@@ -1,150 +0,0 @@
----
-title: S3 Writer Tool
-description: The `S3WriterTool` enables CrewAI agents to write content to files in Amazon S3 buckets.
-icon: aws
----
-
-# `S3WriterTool`
-
-## Description
-
-The `S3WriterTool` is designed to write content to files in Amazon S3 buckets. This tool allows CrewAI agents to create or update files in S3, making it ideal for workflows that require storing data, saving configuration files, or persisting any other content to AWS S3 storage.
-
-## Installation
-
-To use this tool, you need to install the required dependencies:
-
-```shell
-uv add boto3
-```
-
-## Steps to Get Started
-
-To effectively use the `S3WriterTool`, follow these steps:
-
-1. **Install Dependencies**: Install the required packages using the command above.
-2. **Configure AWS Credentials**: Set up your AWS credentials as environment variables.
-3. **Initialize the Tool**: Create an instance of the tool.
-4. **Specify S3 Path and Content**: Provide the S3 path where you want to write the file and the content to be written.
-
-## Example
-
-The following example demonstrates how to use the `S3WriterTool` to write content to a file in an S3 bucket:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools.aws.s3 import S3WriterTool
-
-# Initialize the tool
-s3_writer_tool = S3WriterTool()
-
-# Define an agent that uses the tool
-file_writer_agent = Agent(
-    role="File Writer",
-    goal="Write content to files in S3 buckets",
-    backstory="An expert in storing and managing files in cloud storage.",
-    tools=[s3_writer_tool],
-    verbose=True,
-)
-
-# Example task to write a report
-write_task = Task(
-    description="Generate a summary report of the quarterly sales data and save it to {my_bucket}.",
-    expected_output="Confirmation that the report was successfully saved to S3.",
-    agent=file_writer_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[file_writer_agent], tasks=[write_task])
-result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/reports/quarterly-summary.txt"})
-```
-
-## Parameters
-
-The `S3WriterTool` accepts the following parameters when used by an agent:
-
-- **file_path**: Required. The S3 file path in the format `s3://bucket-name/file-name`.
-- **content**: Required. The content to write to the file.
-
-## AWS Credentials
-
-The tool requires AWS credentials to access S3 buckets. You can configure these credentials using environment variables:
-
-- **CREW_AWS_REGION**: The AWS region where your S3 bucket is located. Default is `us-east-1`.
-- **CREW_AWS_ACCESS_KEY_ID**: Your AWS access key ID.
-- **CREW_AWS_SEC_ACCESS_KEY**: Your AWS secret access key.
-
-## Usage
-
-When using the `S3WriterTool` with an agent, the agent will need to provide both the S3 file path and the content to write:
-
-```python Code
-# Example of using the tool with an agent
-file_writer_agent = Agent(
-    role="File Writer",
-    goal="Write content to files in S3 buckets",
-    backstory="An expert in storing and managing files in cloud storage.",
-    tools=[s3_writer_tool],
-    verbose=True,
-)
-
-# Create a task for the agent to write a specific file
-write_config_task = Task(
-    description="""
-    Create a configuration file with the following database settings:
-    - host: db.example.com
-    - port: 5432
-    - username: app_user
-    - password: secure_password
-    
-    Save this configuration as JSON to {my_bucket}.
-    """,
-    expected_output="Confirmation that the configuration file was successfully saved to S3.",
-    agent=file_writer_agent,
-)
-
-# Run the task
-crew = Crew(agents=[file_writer_agent], tasks=[write_config_task])
-result = crew.kickoff(inputs={"my_bucket": "s3://my-bucket/config/db-config.json"})
-```
-
-## Error Handling
-
-The `S3WriterTool` includes error handling for common S3 issues:
-
-- Invalid S3 path format
-- Permission issues (e.g., no write access to the bucket)
-- AWS credential problems
-- Bucket does not exist
-
-When an error occurs, the tool will return an error message that includes details about the issue.
-
-## Implementation Details
-
-The `S3WriterTool` uses the AWS SDK for Python (boto3) to interact with S3:
-
-```python Code
-class S3WriterTool(BaseTool):
-    name: str = "S3 Writer Tool"
-    description: str = "Writes content to a file in Amazon S3 given an S3 file path"
-    
-    def _run(self, file_path: str, content: str) -> str:
-        try:
-            bucket_name, object_key = self._parse_s3_path(file_path)
-
-            s3 = boto3.client(
-                's3',
-                region_name=os.getenv('CREW_AWS_REGION', 'us-east-1'),
-                aws_access_key_id=os.getenv('CREW_AWS_ACCESS_KEY_ID'),
-                aws_secret_access_key=os.getenv('CREW_AWS_SEC_ACCESS_KEY')
-            )
-
-            s3.put_object(Bucket=bucket_name, Key=object_key, Body=content.encode('utf-8'))
-            return f"Successfully wrote content to {file_path}"
-        except ClientError as e:
-            return f"Error writing file to S3: {str(e)}"
-```
-
-## Conclusion
-
-The `S3WriterTool` provides a straightforward way to write content to files in Amazon S3 buckets. By enabling agents to create and update files in S3, it facilitates workflows that require cloud-based file storage. This tool is particularly useful for data persistence, configuration management, report generation, and any task that involves storing information in AWS S3 storage. 

docs/tools/scrapeelementfromwebsitetool.mdx

@@ -1,139 +0,0 @@
----
-title: Scrape Element From Website Tool
-description: The `ScrapeElementFromWebsiteTool` enables CrewAI agents to extract specific elements from websites using CSS selectors.
-icon: code
----
-
-# `ScrapeElementFromWebsiteTool`
-
-## Description
-
-The `ScrapeElementFromWebsiteTool` is designed to extract specific elements from websites using CSS selectors. This tool allows CrewAI agents to scrape targeted content from web pages, making it useful for data extraction tasks where only specific parts of a webpage are needed.
-
-## Installation
-
-To use this tool, you need to install the required dependencies:
-
-```shell
-uv add requests beautifulsoup4
-```
-
-## Steps to Get Started
-
-To effectively use the `ScrapeElementFromWebsiteTool`, follow these steps:
-
-1. **Install Dependencies**: Install the required packages using the command above.
-2. **Identify CSS Selectors**: Determine the CSS selectors for the elements you want to extract from the website.
-3. **Initialize the Tool**: Create an instance of the tool with the necessary parameters.
-
-## Example
-
-The following example demonstrates how to use the `ScrapeElementFromWebsiteTool` to extract specific elements from a website:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import ScrapeElementFromWebsiteTool
-
-# Initialize the tool
-scrape_tool = ScrapeElementFromWebsiteTool()
-
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract specific information from websites",
-    backstory="An expert in web scraping who can extract targeted content from web pages.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Example task to extract headlines from a news website
-scrape_task = Task(
-    description="Extract the main headlines from the CNN homepage. Use the CSS selector '.headline' to target the headline elements.",
-    expected_output="A list of the main headlines from CNN.",
-    agent=web_scraper_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[web_scraper_agent], tasks=[scrape_task])
-result = crew.kickoff()
-```
-
-You can also initialize the tool with predefined parameters:
-
-```python Code
-# Initialize the tool with predefined parameters
-scrape_tool = ScrapeElementFromWebsiteTool(
-    website_url="https://www.example.com",
-    css_element=".main-content"
-)
-```
-
-## Parameters
-
-The `ScrapeElementFromWebsiteTool` accepts the following parameters during initialization:
-
-- **website_url**: Optional. The URL of the website to scrape. If provided during initialization, the agent won't need to specify it when using the tool.
-- **css_element**: Optional. The CSS selector for the elements to extract. If provided during initialization, the agent won't need to specify it when using the tool.
-- **cookies**: Optional. A dictionary containing cookies to be sent with the request. This can be useful for websites that require authentication.
-
-## Usage
-
-When using the `ScrapeElementFromWebsiteTool` with an agent, the agent will need to provide the following parameters (unless they were specified during initialization):
-
-- **website_url**: The URL of the website to scrape.
-- **css_element**: The CSS selector for the elements to extract.
-
-The tool will return the text content of all elements matching the CSS selector, joined by newlines.
-
-```python Code
-# Example of using the tool with an agent
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract specific elements from websites",
-    backstory="An expert in web scraping who can extract targeted content using CSS selectors.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Create a task for the agent to extract specific elements
-extract_task = Task(
-    description="""
-    Extract all product titles from the featured products section on example.com.
-    Use the CSS selector '.product-title' to target the title elements.
-    """,
-    expected_output="A list of product titles from the website",
-    agent=web_scraper_agent,
-)
-
-# Run the task through a crew
-crew = Crew(agents=[web_scraper_agent], tasks=[extract_task])
-result = crew.kickoff()
-```
-
-## Implementation Details
-
-The `ScrapeElementFromWebsiteTool` uses the `requests` library to fetch the web page and `BeautifulSoup` to parse the HTML and extract the specified elements:
-
-```python Code
-class ScrapeElementFromWebsiteTool(BaseTool):
-    name: str = "Read a website content"
-    description: str = "A tool that can be used to read a website content."
-    
-    # Implementation details...
-    
-    def _run(self, **kwargs: Any) -> Any:
-        website_url = kwargs.get("website_url", self.website_url)
-        css_element = kwargs.get("css_element", self.css_element)
-        page = requests.get(
-            website_url,
-            headers=self.headers,
-            cookies=self.cookies if self.cookies else {},
-        )
-        parsed = BeautifulSoup(page.content, "html.parser")
-        elements = parsed.select(css_element)
-        return "\n".join([element.get_text() for element in elements])
-```
-
-## Conclusion
-
-The `ScrapeElementFromWebsiteTool` provides a powerful way to extract specific elements from websites using CSS selectors. By enabling agents to target only the content they need, it makes web scraping tasks more efficient and focused. This tool is particularly useful for data extraction, content monitoring, and research tasks where specific information needs to be extracted from web pages. 

docs/tools/scrapegraphscrapetool.mdx

@@ -1,196 +0,0 @@
----
-title: Scrapegraph Scrape Tool
-description: The `ScrapegraphScrapeTool` leverages Scrapegraph AI's SmartScraper API to intelligently extract content from websites.
-icon: chart-area
----
-
-# `ScrapegraphScrapeTool`
-
-## Description
-
-The `ScrapegraphScrapeTool` is designed to leverage Scrapegraph AI's SmartScraper API to intelligently extract content from websites. This tool provides advanced web scraping capabilities with AI-powered content extraction, making it ideal for targeted data collection and content analysis tasks. Unlike traditional web scrapers, it can understand the context and structure of web pages to extract the most relevant information based on natural language prompts.
-
-## Installation
-
-To use this tool, you need to install the Scrapegraph Python client:
-
-```shell
-uv add scrapegraph-py
-```
-
-You'll also need to set up your Scrapegraph API key as an environment variable:
-
-```shell
-export SCRAPEGRAPH_API_KEY="your_api_key"
-```
-
-You can obtain an API key from [Scrapegraph AI](https://scrapegraphai.com).
-
-## Steps to Get Started
-
-To effectively use the `ScrapegraphScrapeTool`, follow these steps:
-
-1. **Install Dependencies**: Install the required package using the command above.
-2. **Set Up API Key**: Set your Scrapegraph API key as an environment variable or provide it during initialization.
-3. **Initialize the Tool**: Create an instance of the tool with the necessary parameters.
-4. **Define Extraction Prompts**: Create natural language prompts to guide the extraction of specific content.
-
-## Example
-
-The following example demonstrates how to use the `ScrapegraphScrapeTool` to extract content from a website:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import ScrapegraphScrapeTool
-
-# Initialize the tool
-scrape_tool = ScrapegraphScrapeTool(api_key="your_api_key")
-
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract specific information from websites",
-    backstory="An expert in web scraping who can extract targeted content from web pages.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Example task to extract product information from an e-commerce site
-scrape_task = Task(
-    description="Extract product names, prices, and descriptions from the featured products section of example.com.",
-    expected_output="A structured list of product information including names, prices, and descriptions.",
-    agent=web_scraper_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[web_scraper_agent], tasks=[scrape_task])
-result = crew.kickoff()
-```
-
-You can also initialize the tool with predefined parameters:
-
-```python Code
-# Initialize the tool with predefined parameters
-scrape_tool = ScrapegraphScrapeTool(
-    website_url="https://www.example.com",
-    user_prompt="Extract all product prices and descriptions",
-    api_key="your_api_key"
-)
-```
-
-## Parameters
-
-The `ScrapegraphScrapeTool` accepts the following parameters during initialization:
-
-- **api_key**: Optional. Your Scrapegraph API key. If not provided, it will look for the `SCRAPEGRAPH_API_KEY` environment variable.
-- **website_url**: Optional. The URL of the website to scrape. If provided during initialization, the agent won't need to specify it when using the tool.
-- **user_prompt**: Optional. Custom instructions for content extraction. If provided during initialization, the agent won't need to specify it when using the tool.
-- **enable_logging**: Optional. Whether to enable logging for the Scrapegraph client. Default is `False`.
-
-## Usage
-
-When using the `ScrapegraphScrapeTool` with an agent, the agent will need to provide the following parameters (unless they were specified during initialization):
-
-- **website_url**: The URL of the website to scrape.
-- **user_prompt**: Optional. Custom instructions for content extraction. Default is "Extract the main content of the webpage".
-
-The tool will return the extracted content based on the provided prompt.
-
-```python Code
-# Example of using the tool with an agent
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract specific information from websites",
-    backstory="An expert in web scraping who can extract targeted content from web pages.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Create a task for the agent to extract specific content
-extract_task = Task(
-    description="Extract the main heading and summary from example.com",
-    expected_output="The main heading and summary from the website",
-    agent=web_scraper_agent,
-)
-
-# Run the task
-crew = Crew(agents=[web_scraper_agent], tasks=[extract_task])
-result = crew.kickoff()
-```
-
-## Error Handling
-
-The `ScrapegraphScrapeTool` may raise the following exceptions:
-
-- **ValueError**: When API key is missing or URL format is invalid.
-- **RateLimitError**: When API rate limits are exceeded.
-- **RuntimeError**: When scraping operation fails (network issues, API errors).
-
-It's recommended to instruct agents to handle potential errors gracefully:
-
-```python Code
-# Create a task that includes error handling instructions
-robust_extract_task = Task(
-    description="""
-    Extract the main heading from example.com.
-    Be aware that you might encounter errors such as:
-    - Invalid URL format
-    - Missing API key
-    - Rate limit exceeded
-    - Network or API errors
-    
-    If you encounter any errors, provide a clear explanation of what went wrong
-    and suggest possible solutions.
-    """,
-    expected_output="Either the extracted heading or a clear error explanation",
-    agent=web_scraper_agent,
-)
-```
-
-## Rate Limiting
-
-The Scrapegraph API has rate limits that vary based on your subscription plan. Consider the following best practices:
-
-- Implement appropriate delays between requests when processing multiple URLs.
-- Handle rate limit errors gracefully in your application.
-- Check your API plan limits on the Scrapegraph dashboard.
-
-## Implementation Details
-
-The `ScrapegraphScrapeTool` uses the Scrapegraph Python client to interact with the SmartScraper API:
-
-```python Code
-class ScrapegraphScrapeTool(BaseTool):
-    """
-    A tool that uses Scrapegraph AI to intelligently scrape website content.
-    """
-    
-    # Implementation details...
-    
-    def _run(self, **kwargs: Any) -> Any:
-        website_url = kwargs.get("website_url", self.website_url)
-        user_prompt = (
-            kwargs.get("user_prompt", self.user_prompt)
-            or "Extract the main content of the webpage"
-        )
-
-        if not website_url:
-            raise ValueError("website_url is required")
-
-        # Validate URL format
-        self._validate_url(website_url)
-
-        try:
-            # Make the SmartScraper request
-            response = self._client.smartscraper(
-                website_url=website_url,
-                user_prompt=user_prompt,
-            )
-
-            return response
-        # Error handling...
-```
-
-## Conclusion
-
-The `ScrapegraphScrapeTool` provides a powerful way to extract content from websites using AI-powered understanding of web page structure. By enabling agents to target specific information using natural language prompts, it makes web scraping tasks more efficient and focused. This tool is particularly useful for data extraction, content monitoring, and research tasks where specific information needs to be extracted from web pages. 

docs/tools/scrapflyscrapetool.mdx

@@ -1,220 +0,0 @@
----
-title: Scrapfly Scrape Website Tool
-description: The `ScrapflyScrapeWebsiteTool` leverages Scrapfly's web scraping API to extract content from websites in various formats.
-icon: spider
----
-
-# `ScrapflyScrapeWebsiteTool`
-
-## Description
-
-The `ScrapflyScrapeWebsiteTool` is designed to leverage [Scrapfly](https://scrapfly.io/)'s web scraping API to extract content from websites. This tool provides advanced web scraping capabilities with headless browser support, proxies, and anti-bot bypass features. It allows for extracting web page data in various formats, including raw HTML, markdown, and plain text, making it ideal for a wide range of web scraping tasks.
-
-## Installation
-
-To use this tool, you need to install the Scrapfly SDK:
-
-```shell
-uv add scrapfly-sdk
-```
-
-You'll also need to obtain a Scrapfly API key by registering at [scrapfly.io/register](https://www.scrapfly.io/register/).
-
-## Steps to Get Started
-
-To effectively use the `ScrapflyScrapeWebsiteTool`, follow these steps:
-
-1. **Install Dependencies**: Install the Scrapfly SDK using the command above.
-2. **Obtain API Key**: Register at Scrapfly to get your API key.
-3. **Initialize the Tool**: Create an instance of the tool with your API key.
-4. **Configure Scraping Parameters**: Customize the scraping parameters based on your needs.
-
-## Example
-
-The following example demonstrates how to use the `ScrapflyScrapeWebsiteTool` to extract content from a website:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import ScrapflyScrapeWebsiteTool
-
-# Initialize the tool
-scrape_tool = ScrapflyScrapeWebsiteTool(api_key="your_scrapfly_api_key")
-
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract information from websites",
-    backstory="An expert in web scraping who can extract content from any website.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Example task to extract content from a website
-scrape_task = Task(
-    description="Extract the main content from the product page at https://web-scraping.dev/products and summarize the available products.",
-    expected_output="A summary of the products available on the website.",
-    agent=web_scraper_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[web_scraper_agent], tasks=[scrape_task])
-result = crew.kickoff()
-```
-
-You can also customize the scraping parameters:
-
-```python Code
-# Example with custom scraping parameters
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract information from websites with custom parameters",
-    backstory="An expert in web scraping who can extract content from any website.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# The agent will use the tool with parameters like:
-# url="https://web-scraping.dev/products"
-# scrape_format="markdown"
-# ignore_scrape_failures=True
-# scrape_config={
-#     "asp": True,  # Bypass scraping blocking solutions, like Cloudflare
-#     "render_js": True,  # Enable JavaScript rendering with a cloud headless browser
-#     "proxy_pool": "public_residential_pool",  # Select a proxy pool
-#     "country": "us",  # Select a proxy location
-#     "auto_scroll": True,  # Auto scroll the page
-# }
-
-scrape_task = Task(
-    description="Extract the main content from the product page at https://web-scraping.dev/products using advanced scraping options including JavaScript rendering and proxy settings.",
-    expected_output="A detailed summary of the products with all available information.",
-    agent=web_scraper_agent,
-)
-```
-
-## Parameters
-
-The `ScrapflyScrapeWebsiteTool` accepts the following parameters:
-
-### Initialization Parameters
-
-- **api_key**: Required. Your Scrapfly API key.
-
-### Run Parameters
-
-- **url**: Required. The URL of the website to scrape.
-- **scrape_format**: Optional. The format in which to extract the web page content. Options are "raw" (HTML), "markdown", or "text". Default is "markdown".
-- **scrape_config**: Optional. A dictionary containing additional Scrapfly scraping configuration options.
-- **ignore_scrape_failures**: Optional. Whether to ignore failures during scraping. If set to `True`, the tool will return `None` instead of raising an exception when scraping fails.
-
-## Scrapfly Configuration Options
-
-The `scrape_config` parameter allows you to customize the scraping behavior with the following options:
-
-- **asp**: Enable anti-scraping protection bypass.
-- **render_js**: Enable JavaScript rendering with a cloud headless browser.
-- **proxy_pool**: Select a proxy pool (e.g., "public_residential_pool", "datacenter").
-- **country**: Select a proxy location (e.g., "us", "uk").
-- **auto_scroll**: Automatically scroll the page to load lazy-loaded content.
-- **js**: Execute custom JavaScript code by the headless browser.
-
-For a complete list of configuration options, refer to the [Scrapfly API documentation](https://scrapfly.io/docs/scrape-api/getting-started).
-
-## Usage
-
-When using the `ScrapflyScrapeWebsiteTool` with an agent, the agent will need to provide the URL of the website to scrape and can optionally specify the format and additional configuration options:
-
-```python Code
-# Example of using the tool with an agent
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract information from websites",
-    backstory="An expert in web scraping who can extract content from any website.",
-    tools=[scrape_tool],
-    verbose=True,
-)
-
-# Create a task for the agent
-scrape_task = Task(
-    description="Extract the main content from example.com in markdown format.",
-    expected_output="The main content of example.com in markdown format.",
-    agent=web_scraper_agent,
-)
-
-# Run the task
-crew = Crew(agents=[web_scraper_agent], tasks=[scrape_task])
-result = crew.kickoff()
-```
-
-For more advanced usage with custom configuration:
-
-```python Code
-# Create a task with more specific instructions
-advanced_scrape_task = Task(
-    description="""
-    Extract content from example.com with the following requirements:
-    - Convert the content to plain text format
-    - Enable JavaScript rendering
-    - Use a US-based proxy
-    - Handle any scraping failures gracefully
-    """,
-    expected_output="The extracted content from example.com",
-    agent=web_scraper_agent,
-)
-```
-
-## Error Handling
-
-By default, the `ScrapflyScrapeWebsiteTool` will raise an exception if scraping fails. Agents can be instructed to handle failures gracefully by specifying the `ignore_scrape_failures` parameter:
-
-```python Code
-# Create a task that instructs the agent to handle errors
-error_handling_task = Task(
-    description="""
-    Extract content from a potentially problematic website and make sure to handle any 
-    scraping failures gracefully by setting ignore_scrape_failures to True.
-    """,
-    expected_output="Either the extracted content or a graceful error message",
-    agent=web_scraper_agent,
-)
-```
-
-## Implementation Details
-
-The `ScrapflyScrapeWebsiteTool` uses the Scrapfly SDK to interact with the Scrapfly API:
-
-```python Code
-class ScrapflyScrapeWebsiteTool(BaseTool):
-    name: str = "Scrapfly web scraping API tool"
-    description: str = (
-        "Scrape a webpage url using Scrapfly and return its content as markdown or text"
-    )
-    
-    # Implementation details...
-    
-    def _run(
-        self,
-        url: str,
-        scrape_format: str = "markdown",
-        scrape_config: Optional[Dict[str, Any]] = None,
-        ignore_scrape_failures: Optional[bool] = None,
-    ):
-        from scrapfly import ScrapeApiResponse, ScrapeConfig
-
-        scrape_config = scrape_config if scrape_config is not None else {}
-        try:
-            response: ScrapeApiResponse = self.scrapfly.scrape(
-                ScrapeConfig(url, format=scrape_format, **scrape_config)
-            )
-            return response.scrape_result["content"]
-        except Exception as e:
-            if ignore_scrape_failures:
-                logger.error(f"Error fetching data from {url}, exception: {e}")
-                return None
-            else:
-                raise e
-```
-
-## Conclusion
-
-The `ScrapflyScrapeWebsiteTool` provides a powerful way to extract content from websites using Scrapfly's advanced web scraping capabilities. With features like headless browser support, proxies, and anti-bot bypass, it can handle complex websites and extract content in various formats. This tool is particularly useful for data extraction, content monitoring, and research tasks where reliable web scraping is required. 

docs/tools/seleniumscrapingtool.mdx

@@ -13,183 +13,64 @@ icon: clipboard-user
 
 ## Description
 
-The `SeleniumScrapingTool` is crafted for high-efficiency web scraping tasks. 
+The SeleniumScrapingTool is crafted for high-efficiency web scraping tasks. 
 It allows for precise extraction of content from web pages by using CSS selectors to target specific elements. 
 Its design caters to a wide range of scraping needs, offering flexibility to work with any provided website URL.
 
 ## Installation
 
-To use this tool, you need to install the CrewAI tools package and Selenium:
+To get started with the SeleniumScrapingTool, install the crewai_tools package using pip:
 
 ```shell
 pip install 'crewai[tools]'
-uv add selenium webdriver-manager
 ```
 
-You'll also need to have Chrome installed on your system, as the tool uses Chrome WebDriver for browser automation.
+## Usage Examples
 
-## Example
-
-The following example demonstrates how to use the `SeleniumScrapingTool` with a CrewAI agent:
+Below are some scenarios where the SeleniumScrapingTool can be utilized:
 
 ```python Code
-from crewai import Agent, Task, Crew, Process
 from crewai_tools import SeleniumScrapingTool
 
-# Initialize the tool
-selenium_tool = SeleniumScrapingTool()
+# Example 1: 
+# Initialize the tool without any parameters to scrape 
+# the current page it navigates to
+tool = SeleniumScrapingTool()
 
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract information from websites using Selenium",
-    backstory="An expert web scraper who can extract content from dynamic websites.",
-    tools=[selenium_tool],
-    verbose=True,
+# Example 2: 
+# Scrape the entire webpage of a given URL
+tool = SeleniumScrapingTool(website_url='https://example.com')
+
+# Example 3: 
+# Target and scrape a specific CSS element from a webpage
+tool = SeleniumScrapingTool(
+    website_url='https://example.com',
+    css_element='.main-content'
 )
 
-# Example task to scrape content from a website
-scrape_task = Task(
-    description="Extract the main content from the homepage of example.com. Use the CSS selector 'main' to target the main content area.",
-    expected_output="The main content from example.com's homepage.",
-    agent=web_scraper_agent,
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[web_scraper_agent],
-    tasks=[scrape_task],
-    verbose=True,
-    process=Process.sequential,
-)
-result = crew.kickoff()
-```
-
-You can also initialize the tool with predefined parameters:
-
-```python Code
-# Initialize the tool with predefined parameters
-selenium_tool = SeleniumScrapingTool(
+# Example 4: 
+# Perform scraping with additional parameters for a customized experience
+tool = SeleniumScrapingTool(
     website_url='https://example.com',
     css_element='.main-content',
-    wait_time=5
-)
-
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract information from websites using Selenium",
-    backstory="An expert web scraper who can extract content from dynamic websites.",
-    tools=[selenium_tool],
-    verbose=True,
+    cookie={'name': 'user', 'value': 'John Doe'},
+    wait_time=10
 )
 ```
 
-## Parameters
+## Arguments
 
-The `SeleniumScrapingTool` accepts the following parameters during initialization:
+The following parameters can be used to customize the SeleniumScrapingTool's scraping process:
 
-- **website_url**: Optional. The URL of the website to scrape. If provided during initialization, the agent won't need to specify it when using the tool.
-- **css_element**: Optional. The CSS selector for the elements to extract. If provided during initialization, the agent won't need to specify it when using the tool.
-- **cookie**: Optional. A dictionary containing cookie information, useful for simulating a logged-in session to access restricted content.
-- **wait_time**: Optional. Specifies the delay (in seconds) before scraping, allowing the website and any dynamic content to fully load. Default is `3` seconds.
-- **return_html**: Optional. Whether to return the HTML content instead of just the text. Default is `False`.
+| Argument       | Type     | Description                                                                                                                         |
+|:---------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------|
+| **website_url**  | `string`   | **Mandatory**. Specifies the URL of the website from which content is to be scraped.                                                |
+| **css_element**  | `string`   | **Mandatory**. The CSS selector for a specific element to target on the website, enabling focused scraping of a particular part of a webpage. |
+| **cookie**       | `object`   | **Optional**. A dictionary containing cookie information, useful for simulating a logged-in session to access restricted content.    |
+| **wait_time**    | `int`      | **Optional**. Specifies the delay (in seconds) before scraping, allowing the website and any dynamic content to fully load.          |
 
-When using the tool with an agent, the agent will need to provide the following parameters (unless they were specified during initialization):
 
-- **website_url**: Required. The URL of the website to scrape.
-- **css_element**: Required. The CSS selector for the elements to extract.
-
-## Agent Integration Example
-
-Here's a more detailed example of how to integrate the `SeleniumScrapingTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import SeleniumScrapingTool
-
-# Initialize the tool
-selenium_tool = SeleniumScrapingTool()
-
-# Define an agent that uses the tool
-web_scraper_agent = Agent(
-    role="Web Scraper",
-    goal="Extract and analyze information from dynamic websites",
-    backstory="""You are an expert web scraper who specializes in extracting 
-    content from dynamic websites that require browser automation. You have 
-    extensive knowledge of CSS selectors and can identify the right selectors 
-    to target specific content on any website.""",
-    tools=[selenium_tool],
-    verbose=True,
-)
-
-# Create a task for the agent
-scrape_task = Task(
-    description="""
-    Extract the following information from the news website at {website_url}:
-    
-    1. The headlines of all featured articles (CSS selector: '.headline')
-    2. The publication dates of these articles (CSS selector: '.pub-date')
-    3. The author names where available (CSS selector: '.author')
-    
-    Compile this information into a structured format with each article's details grouped together.
-    """,
-    expected_output="A structured list of articles with their headlines, publication dates, and authors.",
-    agent=web_scraper_agent,
-)
-
-# Run the task
-crew = Crew(
-    agents=[web_scraper_agent],
-    tasks=[scrape_task],
-    verbose=True,
-    process=Process.sequential,
-)
-result = crew.kickoff(inputs={"website_url": "https://news-example.com"})
-```
-
-## Implementation Details
-
-The `SeleniumScrapingTool` uses Selenium WebDriver to automate browser interactions:
-
-```python Code
-class SeleniumScrapingTool(BaseTool):
-    name: str = "Read a website content"
-    description: str = "A tool that can be used to read a website content."
-    args_schema: Type[BaseModel] = SeleniumScrapingToolSchema
-    
-    def _run(self, **kwargs: Any) -> Any:
-        website_url = kwargs.get("website_url", self.website_url)
-        css_element = kwargs.get("css_element", self.css_element)
-        return_html = kwargs.get("return_html", self.return_html)
-        driver = self._create_driver(website_url, self.cookie, self.wait_time)
-
-        content = self._get_content(driver, css_element, return_html)
-        driver.close()
-
-        return "\n".join(content)
-```
-
-The tool performs the following steps:
-1. Creates a headless Chrome browser instance
-2. Navigates to the specified URL
-3. Waits for the specified time to allow the page to load
-4. Adds any cookies if provided
-5. Extracts content based on the CSS selector
-6. Returns the extracted content as text or HTML
-7. Closes the browser instance
-
-## Handling Dynamic Content
-
-The `SeleniumScrapingTool` is particularly useful for scraping websites with dynamic content that is loaded via JavaScript. By using a real browser instance, it can:
-
-1. Execute JavaScript on the page
-2. Wait for dynamic content to load
-3. Interact with elements if needed
-4. Extract content that would not be available with simple HTTP requests
-
-You can adjust the `wait_time` parameter to ensure that all dynamic content has loaded before extraction.
-
-## Conclusion
-
-The `SeleniumScrapingTool` provides a powerful way to extract content from websites using browser automation. By enabling agents to interact with websites as a real user would, it facilitates scraping of dynamic content that would be difficult or impossible to extract using simpler methods. This tool is particularly useful for research, data collection, and monitoring tasks that involve modern web applications with JavaScript-rendered content.
+<Warning>
+    Since the `SeleniumScrapingTool` is under active development, the parameters and functionality may evolve over time. 
+    Users are encouraged to keep the tool updated and report any issues or suggestions for enhancements.
+</Warning>

docs/tools/serpapi-google-search-tool.mdx

@@ -0,0 +1,229 @@
+---
+title: SerpApi Google Search Tool
+description: A tool for performing Google searches using the SerpApi service
+---
+
+# SerpApi Google Search Tool
+
+The SerpApi Google Search Tool enables performing Google searches using the SerpApi service. It provides location-aware search capabilities with comprehensive result filtering.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install serpapi
+```
+
+## Prerequisites
+
+You need a SerpApi API key to use this tool. You can get one from [SerpApi's website](https://serpapi.com/manage-api-key).
+
+Set your API key as an environment variable:
+```bash
+export SERPAPI_API_KEY="your_api_key_here"
+```
+
+## Usage
+
+Here's how to use the SerpApi Google Search Tool:
+
+```python
+from crewai import Agent
+from crewai_tools import SerpApiGoogleSearchTool
+
+# Initialize the tool
+search_tool = SerpApiGoogleSearchTool()
+
+# Create an agent with the tool
+search_agent = Agent(
+    role='Web Researcher',
+    goal='Find accurate information online',
+    backstory='I help research and analyze online information',
+    tools=[search_tool]
+)
+
+# Use in a task
+task = Task(
+    description="Research recent AI developments",
+    agent=search_agent,
+    context={
+        "search_query": "latest artificial intelligence breakthroughs 2024",
+        "location": "United States"  # Optional
+    }
+)
+```
+
+## Input Schema
+
+```python
+class SerpApiGoogleSearchToolSchema(BaseModel):
+    search_query: str  # The search query for Google Search
+    location: Optional[str] = None  # Optional location for localized results
+```
+
+## Function Signatures
+
+### Base Tool Initialization
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the SerpApi tool with API credentials.
+    
+    Raises:
+        ImportError: If serpapi package is not installed
+        ValueError: If SERPAPI_API_KEY environment variable is not set
+    """
+```
+
+### Search Execution
+```python
+def _run(
+    self,
+    **kwargs: Any,
+) -> dict:
+    """
+    Execute the Google search.
+    
+    Args:
+        search_query (str): The search query
+        location (Optional[str]): Optional location for results
+    
+    Returns:
+        dict: Filtered search results from Google
+        
+    Raises:
+        HTTPError: If the API request fails
+    """
+```
+
+## Best Practices
+
+1. **API Key Management**:
+   - Store the API key securely in environment variables
+   - Never hardcode the API key in your code
+   - Verify API key validity before making requests
+
+2. **Search Optimization**:
+   - Use specific, targeted search queries
+   - Include relevant keywords and time frames
+   - Leverage location parameter for regional results
+
+3. **Error Handling**:
+   - Handle API rate limits gracefully
+   - Implement retry logic for failed requests
+   - Validate input parameters before making requests
+
+## Example Integration
+
+Here's a complete example showing how to integrate the SerpApi Google Search Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerpApiGoogleSearchTool
+
+# Initialize the tool
+search_tool = SerpApiGoogleSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Research Analyst',
+    goal='Find and analyze current information',
+    backstory="""I am an expert at finding and analyzing 
+    information from various online sources.""",
+    tools=[search_tool]
+)
+
+# Create tasks
+research_task = Task(
+    description="""
+    Research the following topic:
+    1. Latest developments in quantum computing
+    2. Focus on practical applications
+    3. Include major company announcements
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=researcher,
+    context={
+        "search_query": "quantum computing breakthroughs applications companies",
+        "location": "United States"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Missing API Key**:
+   ```python
+   try:
+       tool = SerpApiGoogleSearchTool()
+   except ValueError as e:
+       print("API key not found. Set SERPAPI_API_KEY environment variable.")
+   ```
+
+2. **API Request Errors**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="quantum computing",
+           location="United States"
+       )
+   except HTTPError as e:
+       print(f"API request failed: {str(e)}")
+   ```
+
+3. **Invalid Parameters**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="",  # Empty query
+           location="Invalid Location"
+       )
+   except ValueError as e:
+       print("Invalid search parameters provided.")
+   ```
+
+## Response Format
+
+The tool returns a filtered dictionary containing Google search results. Example response structure:
+
+```python
+{
+    "organic_results": [
+        {
+            "title": "Page Title",
+            "link": "https://...",
+            "snippet": "Page description or excerpt...",
+            "position": 1
+        }
+        # Additional results...
+    ],
+    "knowledge_graph": {
+        "title": "Topic Title",
+        "description": "Topic description...",
+        "source": {
+            "name": "Source Name",
+            "link": "https://..."
+        }
+    },
+    "related_questions": [
+        {
+            "question": "Related question?",
+            "answer": "Answer to related question..."
+        }
+        # Additional related questions...
+    ]
+}
+```
+
+The response is automatically filtered to remove metadata and unnecessary fields, focusing on the most relevant search information. Fields like search metadata, parameters, and pagination are omitted for clarity.

docs/tools/serpapi-google-shopping-tool.mdx

@@ -0,0 +1,225 @@
+---
+title: SerpApi Google Shopping Tool
+description: A tool for searching Google Shopping using the SerpApi service
+---
+
+# SerpApi Google Shopping Tool
+
+The SerpApi Google Shopping Tool enables searching Google Shopping results using the SerpApi service. It provides location-aware shopping search capabilities with comprehensive result filtering.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install serpapi
+```
+
+## Prerequisites
+
+You need a SerpApi API key to use this tool. You can get one from [SerpApi's website](https://serpapi.com/manage-api-key).
+
+Set your API key as an environment variable:
+```bash
+export SERPAPI_API_KEY="your_api_key_here"
+```
+
+## Usage
+
+Here's how to use the SerpApi Google Shopping Tool:
+
+```python
+from crewai import Agent
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+shopping_agent = Agent(
+    role='Shopping Researcher',
+    goal='Find the best shopping deals',
+    backstory='I help find and analyze shopping options',
+    tools=[shopping_tool]
+)
+
+# Use in a task
+task = Task(
+    description="Find best deals for gaming laptops",
+    agent=shopping_agent,
+    context={
+        "search_query": "gaming laptop deals",
+        "location": "United States"  # Optional
+    }
+)
+```
+
+## Input Schema
+
+```python
+class SerpApiGoogleShoppingToolSchema(BaseModel):
+    search_query: str  # The search query for Google Shopping
+    location: Optional[str] = None  # Optional location for localized results
+```
+
+## Function Signatures
+
+### Base Tool Initialization
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the SerpApi tool with API credentials.
+    
+    Raises:
+        ImportError: If serpapi package is not installed
+        ValueError: If SERPAPI_API_KEY environment variable is not set
+    """
+```
+
+### Search Execution
+```python
+def _run(
+    self,
+    **kwargs: Any,
+) -> dict:
+    """
+    Execute the Google Shopping search.
+    
+    Args:
+        search_query (str): The search query for Google Shopping
+        location (Optional[str]): Optional location for results
+    
+    Returns:
+        dict: Filtered search results from Google Shopping
+        
+    Raises:
+        HTTPError: If the API request fails
+    """
+```
+
+## Best Practices
+
+1. **API Key Management**:
+   - Store the API key securely in environment variables
+   - Never hardcode the API key in your code
+   - Verify API key validity before making requests
+
+2. **Search Optimization**:
+   - Use specific, targeted search queries
+   - Include relevant product details in queries
+   - Leverage location parameter for regional pricing
+
+3. **Error Handling**:
+   - Handle API rate limits gracefully
+   - Implement retry logic for failed requests
+   - Validate input parameters before making requests
+
+## Example Integration
+
+Here's a complete example showing how to integrate the SerpApi Google Shopping Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Shopping Analyst',
+    goal='Find and analyze the best shopping deals',
+    backstory="""I am an expert at finding the best shopping deals 
+    and analyzing product offerings across different regions.""",
+    tools=[shopping_tool]
+)
+
+# Create tasks
+search_task = Task(
+    description="""
+    Research gaming laptops with the following criteria:
+    1. Price range: $800-$1500
+    2. Released in the last year
+    3. Compare prices across different retailers
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=researcher,
+    context={
+        "search_query": "gaming laptop RTX 4060 2023",
+        "location": "United States"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[search_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Missing API Key**:
+   ```python
+   try:
+       tool = SerpApiGoogleShoppingTool()
+   except ValueError as e:
+       print("API key not found. Set SERPAPI_API_KEY environment variable.")
+   ```
+
+2. **API Request Errors**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="gaming laptop",
+           location="United States"
+       )
+   except HTTPError as e:
+       print(f"API request failed: {str(e)}")
+   ```
+
+3. **Invalid Parameters**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="",  # Empty query
+           location="Invalid Location"
+       )
+   except ValueError as e:
+       print("Invalid search parameters provided.")
+   ```
+
+## Response Format
+
+The tool returns a filtered dictionary containing Google Shopping results. Example response structure:
+
+```python
+{
+    "shopping_results": [
+        {
+            "title": "Product Title",
+            "price": "$999.99",
+            "link": "https://...",
+            "source": "Retailer Name",
+            "rating": 4.5,
+            "reviews": 123,
+            "thumbnail": "https://..."
+        }
+        # Additional results...
+    ],
+    "organic_results": [
+        {
+            "title": "Related Product",
+            "link": "https://...",
+            "snippet": "Product description..."
+        }
+        # Additional organic results...
+    ]
+}
+```
+
+The response is automatically filtered to remove metadata and unnecessary fields, focusing on the most relevant shopping information.

docs/tools/serply-job-search-tool.mdx

@@ -0,0 +1,184 @@
+---
+title: SerplyJobSearchTool
+description: A tool for searching US job postings using the Serply API
+icon: briefcase
+---
+
+## SerplyJobSearchTool
+
+The SerplyJobSearchTool provides job search capabilities using the Serply API. It allows for searching job postings in the US market, returning structured information about positions, employers, locations, and remote work status.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyJobSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Initialize the tool
+search_tool = SerplyJobSearchTool()
+
+# Create an agent with the tool
+job_researcher = Agent(
+    role='Job Market Researcher',
+    goal='Find relevant job opportunities',
+    backstory='Expert at analyzing job market trends and opportunities.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyJobSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching job postings"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the job search tool.
+
+    Args:
+        **kwargs: Additional arguments for RagTool initialization
+    
+    Note:
+        Requires SERPLY_API_KEY environment variable
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform job search using Serply API.
+
+    Args:
+        search_query (str): Job search query
+        **kwargs: Additional search parameters
+
+    Returns:
+        str: Formatted string containing job listings with details:
+            - Position
+            - Employer
+            - Location
+            - Link
+            - Highlights
+            - Remote/Hybrid status
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Use specific search queries
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyJobSearchTool
+
+# Initialize tool
+job_search = SerplyJobSearchTool()
+
+# Create agent
+recruiter = Agent(
+    role='Technical Recruiter',
+    goal='Find relevant job opportunities in tech',
+    backstory='Expert at identifying promising tech positions.',
+    tools=[job_search]
+)
+
+# Define task
+search_task = Task(
+    description="""Search for senior software engineer positions 
+    with remote work options in the US. Focus on positions 
+    requiring Python expertise.""",
+    agent=recruiter
+)
+
+# The tool will use:
+# {
+#   "search_query": "senior software engineer python remote"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[recruiter],
+    tasks=[search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Handling Search Results
+```python
+# Example of processing structured results
+results = search_tool._run(
+    search_query="machine learning engineer"
+)
+
+# Results format:
+"""
+Search results:
+Position: Senior Machine Learning Engineer
+Employer: TechCorp Inc
+Location: San Francisco, CA
+Link: https://example.com/job/123
+Highlights: Python, TensorFlow, 5+ years experience
+Is Remote: True
+Is Hybrid: False
+---
+Position: ML Engineer
+...
+"""
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="data scientist"
+    )
+    if not results:
+        print("No jobs found")
+    else:
+        print(results)
+except Exception as e:
+    print(f"Job search error: {str(e)}")
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Currently supports US job market only
+- Returns structured job information
+- Includes remote/hybrid status
+- Thread-safe operations
+- Efficient job search capabilities
+- Handles API rate limiting automatically
+- Provides detailed job highlights

docs/tools/serply-news-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: SerplyNewsSearchTool
+description: A news article search tool powered by Serply API with configurable search parameters
+icon: newspaper
+---
+
+## SerplyNewsSearchTool
+
+The SerplyNewsSearchTool provides news article search capabilities using the Serply API. It allows for customizable search parameters including result limits and proxy location for region-specific news results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyNewsSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+news_tool = SerplyNewsSearchTool()
+
+# Advanced initialization with custom parameters
+news_tool = SerplyNewsSearchTool(
+    limit=20,             # Return 20 results
+    proxy_location="FR"   # Search from France
+)
+
+# Create an agent with the tool
+news_researcher = Agent(
+    role='News Researcher',
+    goal='Find relevant news articles',
+    backstory='Expert at news research and information gathering.',
+    tools=[news_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyNewsSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching news articles"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    limit: Optional[int] = 10,
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the news search tool.
+
+    Args:
+        limit (int): Maximum number of results [10-100] (default: 10)
+        proxy_location (str): Region for local news results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform news search using Serply API.
+
+    Args:
+        search_query (str): News search query
+
+    Returns:
+        str: Formatted string containing news results:
+            - Title
+            - Link
+            - Source
+            - Published Date
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Set reasonable result limits
+   - Select relevant proxy location for regional news
+   - Consider time sensitivity of news content
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyNewsSearchTool
+
+# Initialize tool with custom configuration
+news_tool = SerplyNewsSearchTool(
+    limit=15,             # 15 results
+    proxy_location="US"   # US news sources
+)
+
+# Create agent
+news_analyst = Agent(
+    role='News Analyst',
+    goal='Research breaking news and developments',
+    backstory='Expert at analyzing news trends and developments.',
+    tools=[news_tool]
+)
+
+# Define task
+news_task = Task(
+    description="""Research the latest developments in renewable 
+    energy technology and investments, focusing on major 
+    announcements and industry trends.""",
+    agent=news_analyst
+)
+
+# The tool will use:
+# {
+#   "search_query": "renewable energy technology investments news"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[news_analyst],
+    tasks=[news_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Regional News Configuration
+```python
+# French news sources
+fr_news = SerplyNewsSearchTool(
+    proxy_location="FR",
+    limit=20
+)
+
+# Japanese news sources
+jp_news = SerplyNewsSearchTool(
+    proxy_location="JP",
+    limit=20
+)
+```
+
+### Result Processing
+```python
+# Get news results
+try:
+    results = news_tool._run(
+        search_query="renewable energy investments"
+    )
+    print(results)
+except Exception as e:
+    print(f"News search error: {str(e)}")
+```
+
+### Multiple Region Search
+```python
+# Search across multiple regions
+regions = ["US", "GB", "DE"]
+all_results = []
+
+for region in regions:
+    regional_tool = SerplyNewsSearchTool(
+        proxy_location=region,
+        limit=5
+    )
+    results = regional_tool._run(
+        search_query="global tech innovations"
+    )
+    all_results.append(f"Results from {region}:\n{results}")
+
+combined_results = "\n\n".join(all_results)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple regions for news sources
+- Configurable result limits (10-100)
+- Returns structured news article data
+- Thread-safe operations
+- Efficient news search capabilities
+- Handles API rate limiting automatically
+- Includes source attribution and publication dates
+- Follows redirects for final article URLs

docs/tools/serply-scholar-search-tool.mdx

@@ -0,0 +1,209 @@
+---
+title: SerplyScholarSearchTool
+description: A scholarly literature search tool powered by Serply API with configurable search parameters
+icon: book
+---
+
+## SerplyScholarSearchTool
+
+The SerplyScholarSearchTool provides scholarly literature search capabilities using the Serply API. It allows for customizable search parameters including language and proxy location for region-specific academic results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyScholarSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+scholar_tool = SerplyScholarSearchTool()
+
+# Advanced initialization with custom parameters
+scholar_tool = SerplyScholarSearchTool(
+    hl="fr",             # French language results
+    proxy_location="FR"  # Search from France
+)
+
+# Create an agent with the tool
+academic_researcher = Agent(
+    role='Academic Researcher',
+    goal='Find relevant scholarly literature',
+    backstory='Expert at academic research and literature review.',
+    tools=[scholar_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyScholarSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for fetching scholarly literature"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    hl: str = "us",
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the scholar search tool.
+
+    Args:
+        hl (str): Host language code for results (default: "us")
+            Reference: https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages
+        proxy_location (str): Region for local results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform scholarly literature search using Serply API.
+
+    Args:
+        search_query (str): Academic search query
+
+    Returns:
+        str: Formatted string containing scholarly results:
+            - Title
+            - Link
+            - Description
+            - Citation
+            - Authors
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Use relevant language codes
+   - Select appropriate proxy location
+   - Provide specific academic search terms
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyScholarSearchTool
+
+# Initialize tool with custom configuration
+scholar_tool = SerplyScholarSearchTool(
+    hl="en",             # English results
+    proxy_location="US"  # US academic sources
+)
+
+# Create agent
+researcher = Agent(
+    role='Academic Researcher',
+    goal='Research recent academic publications',
+    backstory='Expert at analyzing academic literature and research trends.',
+    tools=[scholar_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research recent academic publications on 
+    machine learning applications in healthcare, focusing on 
+    peer-reviewed articles from the last two years.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning healthcare applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Language and Region Configuration
+```python
+# French academic sources
+fr_scholar = SerplyScholarSearchTool(
+    hl="fr",
+    proxy_location="FR"
+)
+
+# German academic sources
+de_scholar = SerplyScholarSearchTool(
+    hl="de",
+    proxy_location="DE"
+)
+```
+
+### Result Processing
+```python
+try:
+    results = scholar_tool._run(
+        search_query="machine learning healthcare applications"
+    )
+    print(results)
+except Exception as e:
+    print(f"Scholar search error: {str(e)}")
+```
+
+### Citation Analysis
+```python
+# Extract and analyze citations
+def analyze_citations(results):
+    citations = []
+    for result in results.split("---"):
+        if "Cite:" in result:
+            citation = result.split("Cite:")[1].split("\n")[0].strip()
+            citations.append(citation)
+    return citations
+
+results = scholar_tool._run(
+    search_query="artificial intelligence ethics"
+)
+citations = analyze_citations(results)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple languages and regions
+- Returns structured academic article data
+- Includes citation information
+- Lists all authors of publications
+- Thread-safe operations
+- Efficient scholarly search capabilities
+- Handles API rate limiting automatically
+- Supports both direct and document links
+- Provides comprehensive article metadata

docs/tools/serply-web-search-tool.mdx

@@ -0,0 +1,213 @@
+---
+title: SerplyWebSearchTool
+description: A Google search tool powered by Serply API with configurable search parameters
+icon: search
+---
+
+## SerplyWebSearchTool
+
+The SerplyWebSearchTool provides Google search capabilities using the Serply API. It allows for customizable search parameters including language, result limits, device type, and proxy location for region-specific results.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyWebSearchTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+search_tool = SerplyWebSearchTool()
+
+# Advanced initialization with custom parameters
+search_tool = SerplyWebSearchTool(
+    hl="fr",              # French language results
+    limit=20,             # Return 20 results
+    device_type="mobile", # Mobile search results
+    proxy_location="FR"   # Search from France
+)
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Web Researcher',
+    goal='Find relevant information online',
+    backstory='Expert at web research and information gathering.',
+    tools=[search_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyWebSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query for Google search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    hl: str = "us",
+    limit: int = 10,
+    device_type: str = "desktop",
+    proxy_location: str = "US",
+    **kwargs
+):
+    """
+    Initialize the Google search tool.
+
+    Args:
+        hl (str): Host language code for results (default: "us")
+            Reference: https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages
+        limit (int): Maximum number of results [10-100] (default: 10)
+        device_type (str): "desktop" or "mobile" results (default: "desktop")
+        proxy_location (str): Region for local results (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Perform Google search using Serply API.
+
+    Args:
+        search_query (str): Search query
+
+    Returns:
+        str: Formatted string containing search results:
+            - Title
+            - Link
+            - Description
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure search parameters appropriately:
+   - Use relevant language codes
+   - Set reasonable result limits
+   - Choose appropriate device type
+   - Select relevant proxy location
+
+3. Handle potential API errors
+4. Process structured results effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyWebSearchTool
+
+# Initialize tool with custom configuration
+search_tool = SerplyWebSearchTool(
+    hl="en",              # English results
+    limit=15,             # 15 results
+    device_type="desktop",
+    proxy_location="US"
+)
+
+# Create agent
+researcher = Agent(
+    role='Web Researcher',
+    goal='Research emerging technology trends',
+    backstory='Expert at finding and analyzing tech trends.',
+    tools=[search_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Research the latest developments in artificial 
+    intelligence and machine learning, focusing on practical 
+    applications in business.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "latest AI ML developments business applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Language and Region Configuration
+```python
+# French search from France
+fr_search = SerplyWebSearchTool(
+    hl="fr",
+    proxy_location="FR"
+)
+
+# Japanese search from Japan
+jp_search = SerplyWebSearchTool(
+    hl="ja",
+    proxy_location="JP"
+)
+```
+
+### Device-Specific Results
+```python
+# Mobile results
+mobile_search = SerplyWebSearchTool(
+    device_type="mobile",
+    limit=20
+)
+
+# Desktop results
+desktop_search = SerplyWebSearchTool(
+    device_type="desktop",
+    limit=20
+)
+```
+
+### Error Handling
+```python
+try:
+    results = search_tool._run(
+        search_query="artificial intelligence trends"
+    )
+    print(results)
+except Exception as e:
+    print(f"Search error: {str(e)}")
+```
+
+## Notes
+
+
+- Requires valid Serply API key
+- Supports multiple languages and regions
+- Configurable result limits (10-100)
+- Device-specific search results
+- Thread-safe operations
+- Efficient search capabilities
+- Handles API rate limiting automatically
+- Returns structured search results

docs/tools/serply-webpage-to-markdown-tool.mdx

@@ -0,0 +1,201 @@
+---
+title: SerplyWebpageToMarkdownTool
+description: A tool for converting web pages to markdown format using Serply API
+icon: markdown
+---
+
+## SerplyWebpageToMarkdownTool
+
+The SerplyWebpageToMarkdownTool converts web pages to markdown format using the Serply API, making it easier for LLMs to process and understand web content. It supports configurable proxy locations for region-specific access.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import SerplyWebpageToMarkdownTool
+
+# Set environment variable
+# export SERPLY_API_KEY='your-api-key'
+
+# Basic initialization
+markdown_tool = SerplyWebpageToMarkdownTool()
+
+# Advanced initialization with custom parameters
+markdown_tool = SerplyWebpageToMarkdownTool(
+    proxy_location="FR"  # Access from France
+)
+
+# Create an agent with the tool
+web_processor = Agent(
+    role='Web Content Processor',
+    goal='Convert web content to markdown format',
+    backstory='Expert at processing and formatting web content.',
+    tools=[markdown_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+```python
+class SerplyWebpageToMarkdownToolSchema(BaseModel):
+    url: str = Field(
+        description="Mandatory URL of the webpage to convert to markdown"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    proxy_location: Optional[str] = "US",
+    **kwargs
+):
+    """
+    Initialize the webpage to markdown conversion tool.
+
+    Args:
+        proxy_location (str): Region for accessing the webpage (default: "US")
+            Options: US, CA, IE, GB, FR, DE, SE, IN, JP, KR, SG, AU, BR
+        **kwargs: Additional arguments for tool creation
+    """
+
+def _run(
+    self,
+    **kwargs: Any
+) -> str:
+    """
+    Convert webpage to markdown using Serply API.
+
+    Args:
+        url (str): URL of the webpage to convert
+
+    Returns:
+        str: Markdown formatted content of the webpage
+    """
+```
+
+## Best Practices
+
+1. Set up API authentication:
+   ```bash
+   export SERPLY_API_KEY='your-serply-api-key'
+   ```
+
+2. Configure proxy location appropriately:
+   - Select relevant region for access
+   - Consider content accessibility
+   - Handle region-specific content
+
+3. Handle potential API errors
+4. Process markdown output effectively
+5. Consider rate limits and quotas
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerplyWebpageToMarkdownTool
+
+# Initialize tool with custom configuration
+markdown_tool = SerplyWebpageToMarkdownTool(
+    proxy_location="US"  # US access point
+)
+
+# Create agent
+processor = Agent(
+    role='Content Processor',
+    goal='Convert web content to structured markdown',
+    backstory='Expert at processing web content into structured formats.',
+    tools=[markdown_tool]
+)
+
+# Define task
+conversion_task = Task(
+    description="""Convert the documentation page at 
+    https://example.com/docs into markdown format for 
+    further processing.""",
+    agent=processor
+)
+
+# The tool will use:
+# {
+#   "url": "https://example.com/docs"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[processor],
+    tasks=[conversion_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Regional Access Configuration
+```python
+# European access points
+fr_processor = SerplyWebpageToMarkdownTool(
+    proxy_location="FR"
+)
+
+de_processor = SerplyWebpageToMarkdownTool(
+    proxy_location="DE"
+)
+```
+
+### Error Handling
+```python
+try:
+    markdown_content = markdown_tool._run(
+        url="https://example.com/page"
+    )
+    print(markdown_content)
+except Exception as e:
+    print(f"Conversion error: {str(e)}")
+```
+
+### Content Processing
+```python
+# Process multiple pages
+urls = [
+    "https://example.com/page1",
+    "https://example.com/page2",
+    "https://example.com/page3"
+]
+
+markdown_contents = []
+for url in urls:
+    try:
+        content = markdown_tool._run(url=url)
+        markdown_contents.append(content)
+    except Exception as e:
+        print(f"Error processing {url}: {str(e)}")
+        continue
+
+# Combine contents
+combined_markdown = "\n\n---\n\n".join(markdown_contents)
+```
+
+## Notes
+
+- Requires valid Serply API key
+- Supports multiple proxy locations
+- Returns markdown-formatted content
+- Simplifies web content for LLM processing
+- Thread-safe operations
+- Efficient content conversion
+- Handles API rate limiting automatically
+- Preserves content structure in markdown
+- Supports various webpage formats
+- Makes web content more accessible to AI agents

docs/tools/snowflakesearchtool.mdx

@@ -1,202 +0,0 @@
----
-title: Snowflake Search Tool
-description: The `SnowflakeSearchTool` enables CrewAI agents to execute SQL queries and perform semantic search on Snowflake data warehouses.
-icon: snowflake
----
-
-# `SnowflakeSearchTool`
-
-## Description
-
-The `SnowflakeSearchTool` is designed to connect to Snowflake data warehouses and execute SQL queries with advanced features like connection pooling, retry logic, and asynchronous execution. This tool allows CrewAI agents to interact with Snowflake databases, making it ideal for data analysis, reporting, and business intelligence tasks that require access to enterprise data stored in Snowflake.
-
-## Installation
-
-To use this tool, you need to install the required dependencies:
-
-```shell
-uv add cryptography snowflake-connector-python snowflake-sqlalchemy
-```
-
-Or alternatively:
-
-```shell
-uv sync --extra snowflake
-```
-
-## Steps to Get Started
-
-To effectively use the `SnowflakeSearchTool`, follow these steps:
-
-1. **Install Dependencies**: Install the required packages using one of the commands above.
-2. **Configure Snowflake Connection**: Create a `SnowflakeConfig` object with your Snowflake credentials.
-3. **Initialize the Tool**: Create an instance of the tool with the necessary configuration.
-4. **Execute Queries**: Use the tool to run SQL queries against your Snowflake database.
-
-## Example
-
-The following example demonstrates how to use the `SnowflakeSearchTool` to query data from a Snowflake database:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SnowflakeSearchTool, SnowflakeConfig
-
-# Create Snowflake configuration
-config = SnowflakeConfig(
-    account="your_account",
-    user="your_username",
-    password="your_password",
-    warehouse="COMPUTE_WH",
-    database="your_database",
-    snowflake_schema="your_schema"
-)
-
-# Initialize the tool
-snowflake_tool = SnowflakeSearchTool(config=config)
-
-# Define an agent that uses the tool
-data_analyst_agent = Agent(
-    role="Data Analyst",
-    goal="Analyze data from Snowflake database",
-    backstory="An expert data analyst who can extract insights from enterprise data.",
-    tools=[snowflake_tool],
-    verbose=True,
-)
-
-# Example task to query sales data
-query_task = Task(
-    description="Query the sales data for the last quarter and summarize the top 5 products by revenue.",
-    expected_output="A summary of the top 5 products by revenue for the last quarter.",
-    agent=data_analyst_agent,
-)
-
-# Create and run the crew
-crew = Crew(agents=[data_analyst_agent], 
-            tasks=[query_task])
-result = crew.kickoff()
-```
-
-You can also customize the tool with additional parameters:
-
-```python Code
-# Initialize the tool with custom parameters
-snowflake_tool = SnowflakeSearchTool(
-    config=config,
-    pool_size=10,
-    max_retries=5,
-    retry_delay=2.0,
-    enable_caching=True
-)
-```
-
-## Parameters
-
-### SnowflakeConfig Parameters
-
-The `SnowflakeConfig` class accepts the following parameters:
-
-- **account**: Required. Snowflake account identifier.
-- **user**: Required. Snowflake username.
-- **password**: Optional*. Snowflake password.
-- **private_key_path**: Optional*. Path to private key file (alternative to password).
-- **warehouse**: Required. Snowflake warehouse name.
-- **database**: Required. Default database.
-- **snowflake_schema**: Required. Default schema.
-- **role**: Optional. Snowflake role.
-- **session_parameters**: Optional. Custom session parameters as a dictionary.
-
-*Either `password` or `private_key_path` must be provided.
-
-### SnowflakeSearchTool Parameters
-
-The `SnowflakeSearchTool` accepts the following parameters during initialization:
-
-- **config**: Required. A `SnowflakeConfig` object containing connection details.
-- **pool_size**: Optional. Number of connections in the pool. Default is 5.
-- **max_retries**: Optional. Maximum retry attempts for failed queries. Default is 3.
-- **retry_delay**: Optional. Delay between retries in seconds. Default is 1.0.
-- **enable_caching**: Optional. Whether to enable query result caching. Default is True.
-
-## Usage
-
-When using the `SnowflakeSearchTool`, you need to provide the following parameters:
-
-- **query**: Required. The SQL query to execute.
-- **database**: Optional. Override the default database specified in the config.
-- **snowflake_schema**: Optional. Override the default schema specified in the config.
-- **timeout**: Optional. Query timeout in seconds. Default is 300.
-
-The tool will return the query results as a list of dictionaries, where each dictionary represents a row with column names as keys.
-
-```python Code
-# Example of using the tool with an agent
-data_analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze sales data from Snowflake",
-    backstory="An expert data analyst with experience in SQL and data visualization.",
-    tools=[snowflake_tool],
-    verbose=True
-)
-
-# The agent will use the tool with parameters like:
-# query="SELECT product_name, SUM(revenue) as total_revenue FROM sales GROUP BY product_name ORDER BY total_revenue DESC LIMIT 5"
-# timeout=600
-
-# Create a task for the agent
-analysis_task = Task(
-    description="Query the sales database and identify the top 5 products by revenue for the last quarter.",
-    expected_output="A detailed analysis of the top 5 products by revenue.",
-    agent=data_analyst
-)
-
-# Run the task
-crew = Crew(
-    agents=[data_analyst], 
-    tasks=[analysis_task]
-)
-result = crew.kickoff()
-```
-
-## Advanced Features
-
-### Connection Pooling
-
-The `SnowflakeSearchTool` implements connection pooling to improve performance by reusing database connections. You can control the pool size with the `pool_size` parameter.
-
-### Automatic Retries
-
-The tool automatically retries failed queries with exponential backoff. You can configure the retry behavior with the `max_retries` and `retry_delay` parameters.
-
-### Query Result Caching
-
-To improve performance for repeated queries, the tool can cache query results. This feature is enabled by default but can be disabled by setting `enable_caching=False`.
-
-### Key-Pair Authentication
-
-In addition to password authentication, the tool supports key-pair authentication for enhanced security:
-
-```python Code
-config = SnowflakeConfig(
-    account="your_account",
-    user="your_username",
-    private_key_path="/path/to/your/private/key.p8",
-    warehouse="COMPUTE_WH",
-    database="your_database",
-    snowflake_schema="your_schema"
-)
-```
-
-## Error Handling
-
-The `SnowflakeSearchTool` includes comprehensive error handling for common Snowflake issues:
-
-- Connection failures
-- Query timeouts
-- Authentication errors
-- Database and schema errors
-
-When an error occurs, the tool will attempt to retry the operation (if configured) and provide detailed error information.
-
-## Conclusion
-
-The `SnowflakeSearchTool` provides a powerful way to integrate Snowflake data warehouses with CrewAI agents. With features like connection pooling, automatic retries, and query caching, it enables efficient and reliable access to enterprise data. This tool is particularly useful for data analysis, reporting, and business intelligence tasks that require access to structured data stored in Snowflake. 

docs/tools/txt-search-tool.mdx

@@ -0,0 +1,158 @@
+---
+title: TXTSearchTool
+description: A semantic search tool for text files using RAG capabilities
+icon: magnifying-glass-document
+---
+
+## TXTSearchTool
+
+The TXTSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within text files. It inherits from the base RagTool class and provides both fixed and dynamic text file searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import TXTSearchTool
+
+# Method 1: Dynamic file path
+txt_search = TXTSearchTool()
+
+# Method 2: Fixed file path
+fixed_txt_search = TXTSearchTool(txt="path/to/fixed/document.txt")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Research Assistant',
+    goal='Search through text documents semantically',
+    backstory='Expert at finding relevant information in documents using semantic search.',
+    tools=[txt_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic File Path Schema
+```python
+class TXTSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+    txt: str          # Path to the text file to search
+```
+
+### Fixed File Path Schema
+```python
+class FixedTXTSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, txt: Optional[str] = None, **kwargs):
+    """
+    Initialize the TXT search tool.
+
+    Args:
+        txt (Optional[str]): Fixed path to a text file. If provided, the tool will only search this file.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the text file.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'txt' for dynamic file path)
+
+    Returns:
+        str: Relevant text passages based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed file path when repeatedly searching the same document
+   - Use dynamic file path when searching different documents
+2. Write clear, semantic search queries
+3. Handle potential file access errors in agent prompts
+4. Consider memory usage for large text files
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import TXTSearchTool
+
+# Example 1: Fixed document search
+documentation_search = TXTSearchTool(txt="api_documentation.txt")
+
+# Example 2: Dynamic document search
+flexible_search = TXTSearchTool()
+
+# Create agents
+doc_analyst = Agent(
+    role='Documentation Analyst',
+    goal='Find relevant API documentation sections',
+    backstory='Expert at analyzing technical documentation.',
+    tools=[documentation_search]
+)
+
+file_analyst = Agent(
+    role='File Analyst',
+    goal='Search through various text files',
+    backstory='Specialist in finding information across multiple documents.',
+    tools=[flexible_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all API endpoints related to user authentication 
+    in the documentation.""",
+    agent=doc_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "user authentication API endpoints"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the logs.txt file for any database 
+    connection errors.""",
+    agent=file_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "database connection errors",
+#   "txt": "logs.txt"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[doc_analyst, file_analyst],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic text file paths
+- Uses embeddings for semantic search
+- Optimized for text file analysis
+- Thread-safe operations
+- Automatically handles file loading and embedding

docs/tools/weaviatevectorsearchtool.mdx

@@ -1,164 +0,0 @@
----
-title: Weaviate Vector Search
-description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents.
-icon: database
----
-
-# `WeaviateVectorSearchTool`
-
-## Description
-
-The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector embeddings for more accurate and contextually relevant search results.
-
-[Weaviate](https://weaviate.io/) is a vector database that stores and queries vector embeddings, enabling semantic search capabilities.
-
-## Installation
-
-To incorporate this tool into your project, you need to install the Weaviate client:
-
-```shell
-uv add weaviate-client
-```
-
-## Steps to Get Started
-
-To effectively use the `WeaviateVectorSearchTool`, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` and `weaviate-client` packages are installed in your Python environment.
-2. **Weaviate Setup**: Set up a Weaviate cluster. You can follow the [Weaviate documentation](https://weaviate.io/developers/wcs/connect) for instructions.
-3. **API Keys**: Obtain your Weaviate cluster URL and API key.
-4. **OpenAI API Key**: Ensure you have an OpenAI API key set in your environment variables as `OPENAI_API_KEY`.
-
-## Example
-
-The following example demonstrates how to initialize the tool and execute a search:
-
-```python Code
-from crewai_tools import WeaviateVectorSearchTool
-
-# Initialize the tool
-tool = WeaviateVectorSearchTool(
-    collection_name='example_collections',
-    limit=3,
-    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
-    weaviate_api_key="your-weaviate-api-key",
-)
-
-@agent
-def search_agent(self) -> Agent:
-    '''
-    This agent uses the WeaviateVectorSearchTool to search for 
-    semantically similar documents in a Weaviate vector database.
-    '''
-    return Agent(
-        config=self.agents_config["search_agent"],
-        tools=[tool]
-    )
-```
-
-## Parameters
-
-The `WeaviateVectorSearchTool` accepts the following parameters:
-
-- **collection_name**: Required. The name of the collection to search within.
-- **weaviate_cluster_url**: Required. The URL of the Weaviate cluster.
-- **weaviate_api_key**: Required. The API key for the Weaviate cluster.
-- **limit**: Optional. The number of results to return. Default is `3`.
-- **vectorizer**: Optional. The vectorizer to use. If not provided, it will use `text2vec_openai` with the `nomic-embed-text` model.
-- **generative_model**: Optional. The generative model to use. If not provided, it will use OpenAI's `gpt-4o`.
-
-## Advanced Configuration
-
-You can customize the vectorizer and generative model used by the tool:
-
-```python Code
-from crewai_tools import WeaviateVectorSearchTool
-from weaviate.classes.config import Configure
-
-# Setup custom model for vectorizer and generative model
-tool = WeaviateVectorSearchTool(
-    collection_name='example_collections',
-    limit=3,
-    vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
-    generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
-    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
-    weaviate_api_key="your-weaviate-api-key",
-)
-```
-
-## Preloading Documents
-
-You can preload your Weaviate database with documents before using the tool:
-
-```python Code
-import os
-from crewai_tools import WeaviateVectorSearchTool
-import weaviate
-from weaviate.classes.init import Auth
-
-# Connect to Weaviate
-client = weaviate.connect_to_weaviate_cloud(
-    cluster_url="https://your-weaviate-cluster-url.com",
-    auth_credentials=Auth.api_key("your-weaviate-api-key"),
-    headers={"X-OpenAI-Api-Key": "your-openai-api-key"}
-)
-
-# Get or create collection
-test_docs = client.collections.get("example_collections")
-if not test_docs:
-    test_docs = client.collections.create(
-        name="example_collections",
-        vectorizer_config=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
-        generative_config=Configure.Generative.openai(model="gpt-4o"),
-    )
-
-# Load documents
-docs_to_load = os.listdir("knowledge")
-with test_docs.batch.dynamic() as batch:
-    for d in docs_to_load:
-        with open(os.path.join("knowledge", d), "r") as f:
-            content = f.read()
-        batch.add_object(
-            {
-                "content": content,
-                "year": d.split("_")[0],
-            }
-        )
-
-# Initialize the tool
-tool = WeaviateVectorSearchTool(
-    collection_name='example_collections', 
-    limit=3,
-    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
-    weaviate_api_key="your-weaviate-api-key",
-)
-```
-
-## Agent Integration Example
-
-Here's how to integrate the `WeaviateVectorSearchTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent
-from crewai_tools import WeaviateVectorSearchTool
-
-# Initialize the tool
-weaviate_tool = WeaviateVectorSearchTool(
-    collection_name='example_collections',
-    limit=3,
-    weaviate_cluster_url="https://your-weaviate-cluster-url.com",
-    weaviate_api_key="your-weaviate-api-key",
-)
-
-# Create an agent with the tool
-rag_agent = Agent(
-    name="rag_agent",
-    role="You are a helpful assistant that can answer questions with the help of the WeaviateVectorSearchTool.",
-    llm="gpt-4o-mini",
-    tools=[weaviate_tool],
-)
-```
-
-## Conclusion
-
-The `WeaviateVectorSearchTool` provides a powerful way to search for semantically similar documents in a Weaviate vector database. By leveraging vector embeddings, it enables more accurate and contextually relevant search results compared to traditional keyword-based searches. This tool is particularly useful for applications that require finding information based on meaning rather than exact matches. 

docs/tools/youtube-channel-search-tool.mdx

@@ -0,0 +1,159 @@
+---
+title: YoutubeChannelSearchTool
+description: A semantic search tool for YouTube channel content using RAG capabilities
+icon: youtube
+---
+
+## YoutubeChannelSearchTool
+
+The YoutubeChannelSearchTool is a specialized Retrieval-Augmented Generation (RAG) tool that enables semantic search within YouTube channel content. It inherits from the base RagTool class and provides both fixed and dynamic YouTube channel searching capabilities.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import YoutubeChannelSearchTool
+
+# Method 1: Dynamic channel handle
+youtube_search = YoutubeChannelSearchTool()
+
+# Method 2: Fixed channel handle
+fixed_channel_search = YoutubeChannelSearchTool(youtube_channel_handle="@example_channel")
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Content Researcher',
+    goal='Search through YouTube channel content semantically',
+    backstory='Expert at finding relevant information in YouTube content.',
+    tools=[youtube_search],
+    verbose=True
+)
+```
+
+## Input Schema
+
+The tool supports two input schemas depending on initialization:
+
+### Dynamic Channel Schema
+```python
+class YoutubeChannelSearchToolSchema(BaseModel):
+    search_query: str           # The semantic search query
+    youtube_channel_handle: str # YouTube channel handle (with or without @)
+```
+
+### Fixed Channel Schema
+```python
+class FixedYoutubeChannelSearchToolSchema(BaseModel):
+    search_query: str  # The semantic search query
+```
+
+## Function Signature
+
+```python
+def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
+    """
+    Initialize the YouTube channel search tool.
+
+    Args:
+        youtube_channel_handle (Optional[str]): Fixed channel handle. If provided, 
+            the tool will only search this channel.
+        **kwargs: Additional arguments passed to the parent RagTool
+    """
+
+def _run(self, search_query: str, **kwargs: Any) -> Any:
+    """
+    Perform semantic search on the YouTube channel content.
+
+    Args:
+        search_query (str): The semantic search query
+        **kwargs: Additional arguments (including 'youtube_channel_handle' for dynamic mode)
+
+    Returns:
+        str: Relevant content from the YouTube channel based on semantic search
+    """
+```
+
+## Best Practices
+
+1. Choose initialization method based on use case:
+   - Use fixed channel handle when repeatedly searching the same channel
+   - Use dynamic handle when searching different channels
+2. Write clear, semantic search queries
+3. Channel handles can be provided with or without '@' prefix
+4. Consider content availability and channel size
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import YoutubeChannelSearchTool
+
+# Example 1: Fixed channel search
+tech_channel_search = YoutubeChannelSearchTool(youtube_channel_handle="@TechChannel")
+
+# Example 2: Dynamic channel search
+flexible_search = YoutubeChannelSearchTool()
+
+# Create agents
+tech_analyst = Agent(
+    role='Tech Content Analyst',
+    goal='Find relevant tech tutorials and explanations',
+    backstory='Expert at analyzing technical YouTube content.',
+    tools=[tech_channel_search]
+)
+
+content_researcher = Agent(
+    role='Content Researcher',
+    goal='Search across multiple YouTube channels',
+    backstory='Specialist in finding information across various channels.',
+    tools=[flexible_search]
+)
+
+# Define tasks
+fixed_search_task = Task(
+    description="""Find all tutorials related to machine learning 
+    basics in the channel.""",
+    agent=tech_analyst
+)
+
+# The agent will use:
+# {
+#   "search_query": "machine learning basics tutorial"
+# }
+
+dynamic_search_task = Task(
+    description="""Search through the @AIResearch channel for 
+    content about neural networks.""",
+    agent=content_researcher
+)
+
+# The agent will use:
+# {
+#   "search_query": "neural networks explanation",
+#   "youtube_channel_handle": "@AIResearch"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[tech_analyst, content_researcher],
+    tasks=[fixed_search_task, dynamic_search_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Notes
+
+- Inherits from RagTool for semantic search capabilities
+- Supports both fixed and dynamic YouTube channel handles
+- Automatically adds '@' prefix to channel handles if missing
+- Uses embeddings for semantic search
+- Thread-safe operations
+- Automatically handles YouTube content loading and embedding

docs/tools/youtube-video-search-tool.mdx

@@ -0,0 +1,216 @@
+---
+title: YoutubeVideoSearchTool
+description: A tool for semantic search within YouTube video content using RAG capabilities
+icon: video
+---
+
+## YoutubeVideoSearchTool
+
+The YoutubeVideoSearchTool enables semantic search capabilities for YouTube video content using Retrieval-Augmented Generation (RAG). It processes video content and allows searching through transcripts and metadata using natural language queries.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+```
+
+## Usage Example
+
+```python
+from crewai import Agent
+from crewai_tools import YoutubeVideoSearchTool
+
+# Method 1: Initialize with specific video
+video_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://www.youtube.com/watch?v=example"
+)
+
+# Method 2: Initialize without video (specify at runtime)
+flexible_video_tool = YoutubeVideoSearchTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Video Researcher',
+    goal='Search and analyze video content',
+    backstory='Expert at finding relevant information in videos.',
+    tools=[video_tool],
+    verbose=True
+)
+```
+
+## Input Schema
+
+### Fixed Video Schema (when URL provided during initialization)
+```python
+class FixedYoutubeVideoSearchToolSchema(BaseModel):
+    search_query: str = Field(
+        description="Mandatory search query you want to use to search the Youtube Video content"
+    )
+```
+
+### Flexible Video Schema (when URL provided at runtime)
+```python
+class YoutubeVideoSearchToolSchema(FixedYoutubeVideoSearchToolSchema):
+    youtube_video_url: str = Field(
+        description="Mandatory youtube_video_url path you want to search"
+    )
+```
+
+## Function Signature
+
+```python
+def __init__(
+    self,
+    youtube_video_url: Optional[str] = None,
+    **kwargs
+):
+    """
+    Initialize the YouTube video search tool.
+
+    Args:
+        youtube_video_url (Optional[str]): URL of YouTube video (optional)
+        **kwargs: Additional arguments for RAG tool configuration
+    """
+
+def _run(
+    self,
+    search_query: str,
+    **kwargs: Any
+) -> str:
+    """
+    Execute semantic search on video content.
+
+    Args:
+        search_query (str): Query to search in the video
+        **kwargs: Additional arguments including youtube_video_url if not initialized
+
+    Returns:
+        str: Relevant content from the video matching the query
+    """
+```
+
+## Best Practices
+
+1. Video URL Management:
+   - Use complete YouTube URLs
+   - Verify video accessibility
+   - Handle region restrictions
+
+2. Search Optimization:
+   - Use specific, focused queries
+   - Consider video context
+   - Test with sample queries first
+
+3. Performance Considerations:
+   - Pre-initialize for repeated searches
+   - Handle long videos appropriately
+   - Monitor processing time
+
+4. Error Handling:
+   - Verify video availability
+   - Handle unavailable videos
+   - Manage API limitations
+
+## Integration Example
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import YoutubeVideoSearchTool
+
+# Initialize tool with specific video
+video_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://www.youtube.com/watch?v=example"
+)
+
+# Create agent
+researcher = Agent(
+    role='Video Researcher',
+    goal='Extract insights from video content',
+    backstory='Expert at analyzing video content.',
+    tools=[video_tool]
+)
+
+# Define task
+research_task = Task(
+    description="""Find all mentions of machine learning 
+    applications from the video content.""",
+    agent=researcher
+)
+
+# The tool will use:
+# {
+#   "search_query": "machine learning applications"
+# }
+
+# Create crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task]
+)
+
+# Execute
+result = crew.kickoff()
+```
+
+## Advanced Usage
+
+### Dynamic Video Selection
+```python
+# Initialize without video URL
+flexible_tool = YoutubeVideoSearchTool()
+
+# Search different videos
+tech_results = flexible_tool.run(
+    search_query="quantum computing",
+    youtube_video_url="https://youtube.com/watch?v=tech123"
+)
+
+science_results = flexible_tool.run(
+    search_query="particle physics",
+    youtube_video_url="https://youtube.com/watch?v=science456"
+)
+```
+
+### Multiple Video Analysis
+```python
+# Create tools for different videos
+tech_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://youtube.com/watch?v=tech123"
+)
+science_tool = YoutubeVideoSearchTool(
+    youtube_video_url="https://youtube.com/watch?v=science456"
+)
+
+# Create agent with multiple tools
+analyst = Agent(
+    role='Content Analyst',
+    goal='Cross-reference multiple videos',
+    tools=[tech_tool, science_tool]
+)
+```
+
+### Error Handling Example
+```python
+try:
+    video_tool = YoutubeVideoSearchTool()
+    results = video_tool.run(
+        search_query="key concepts",
+        youtube_video_url="https://youtube.com/watch?v=example"
+    )
+    print(results)
+except Exception as e:
+    print(f"Error processing video: {str(e)}")
+```
+
+## Notes
+
+- Inherits from RagTool
+- Uses embedchain for processing
+- Supports semantic search
+- Dynamic video specification
+- Efficient content retrieval
+- Thread-safe operations
+- Maintains search context
+- Handles video transcripts
+- Processes video metadata
+- Memory-efficient processing

docs/tools/youtubechannelsearchtool.mdx

@@ -27,73 +27,31 @@ pip install 'crewai[tools]'
 
 ## Example
 
-The following example demonstrates how to use the `YoutubeChannelSearchTool` with a CrewAI agent:
+To begin using the YoutubeChannelSearchTool, follow the example below. 
+This demonstrates initializing the tool with a specific Youtube channel handle and conducting a search within that channel's content.
 
 ```python Code
-from crewai import Agent, Task, Crew
 from crewai_tools import YoutubeChannelSearchTool
 
-# Initialize the tool for general YouTube channel searches
-youtube_channel_tool = YoutubeChannelSearchTool()
+# Initialize the tool to search within any Youtube channel's content the agent learns about during its execution
+tool = YoutubeChannelSearchTool()
 
-# Define an agent that uses the tool
-channel_researcher = Agent(
-    role="Channel Researcher",
-    goal="Extract relevant information from YouTube channels",
-    backstory="An expert researcher who specializes in analyzing YouTube channel content.",
-    tools=[youtube_channel_tool],
-    verbose=True,
-)
+# OR
 
-# Example task to search for information in a specific channel
-research_task = Task(
-    description="Search for information about machine learning tutorials in the YouTube channel {youtube_channel_handle}",
-    expected_output="A summary of the key machine learning tutorials available on the channel.",
-    agent=channel_researcher,
-)
-
-# Create and run the crew
-crew = Crew(agents=[channel_researcher], tasks=[research_task])
-result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleChannel"})
+# Initialize the tool with a specific Youtube channel handle to target your search
+tool = YoutubeChannelSearchTool(youtube_channel_handle='@exampleChannel')
 ```
 
-You can also initialize the tool with a specific YouTube channel handle:
+## Arguments
 
-```python Code
-# Initialize the tool with a specific YouTube channel handle
-youtube_channel_tool = YoutubeChannelSearchTool(
-    youtube_channel_handle='@exampleChannel'
-)
+- `youtube_channel_handle` : A mandatory string representing the Youtube channel handle. This parameter is crucial for initializing the tool to specify the channel you want to search within. The tool is designed to only search within the content of the provided channel handle.
 
-# Define an agent that uses the tool
-channel_researcher = Agent(
-    role="Channel Researcher",
-    goal="Extract relevant information from a specific YouTube channel",
-    backstory="An expert researcher who specializes in analyzing YouTube channel content.",
-    tools=[youtube_channel_tool],
-    verbose=True,
-)
-```
-
-## Parameters
-
-The `YoutubeChannelSearchTool` accepts the following parameters:
-
-- **youtube_channel_handle**: Optional. The handle of the YouTube channel to search within. If provided during initialization, the agent won't need to specify it when using the tool. If the handle doesn't start with '@', it will be automatically added.
-- **config**: Optional. Configuration for the underlying RAG system, including LLM and embedder settings.
-- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
-
-When using the tool with an agent, the agent will need to provide:
-
-- **search_query**: Required. The search query to find relevant information in the channel content.
-- **youtube_channel_handle**: Required only if not provided during initialization. The handle of the YouTube channel to search within.
-
-## Custom Model and Embeddings
+## Custom model and embeddings
 
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
-```python Code  
-youtube_channel_tool = YoutubeChannelSearchTool(
+```python Code
+tool = YoutubeChannelSearchTool(
     config=dict(
         llm=dict(
             provider="ollama", # or google, openai, anthropic, llama2, ...
@@ -114,81 +72,4 @@ youtube_channel_tool = YoutubeChannelSearchTool(
         ),
     )
 )
-```
-
-## Agent Integration Example
-
-Here's a more detailed example of how to integrate the `YoutubeChannelSearchTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import YoutubeChannelSearchTool
-
-# Initialize the tool
-youtube_channel_tool = YoutubeChannelSearchTool()
-
-# Define an agent that uses the tool
-channel_researcher = Agent(
-    role="Channel Researcher",
-    goal="Extract and analyze information from YouTube channels",
-    backstory="""You are an expert channel researcher who specializes in extracting 
-    and analyzing information from YouTube channels. You have a keen eye for detail 
-    and can quickly identify key points and insights from video content across an entire channel.""",
-    tools=[youtube_channel_tool],
-    verbose=True,
-)
-
-# Create a task for the agent
-research_task = Task(
-    description="""
-    Search for information about data science projects and tutorials 
-    in the YouTube channel {youtube_channel_handle}. 
-    
-    Focus on:
-    1. Key data science techniques covered
-    2. Popular tutorial series
-    3. Most viewed or recommended videos
-    
-    Provide a comprehensive summary of these points.
-    """,
-    expected_output="A detailed summary of data science content available on the channel.",
-    agent=channel_researcher,
-)
-
-# Run the task
-crew = Crew(agents=[channel_researcher], tasks=[research_task])
-result = crew.kickoff(inputs={"youtube_channel_handle": "@exampleDataScienceChannel"})
-```
-
-## Implementation Details
-
-The `YoutubeChannelSearchTool` is implemented as a subclass of `RagTool`, which provides the base functionality for Retrieval-Augmented Generation:
-
-```python Code
-class YoutubeChannelSearchTool(RagTool):
-    name: str = "Search a Youtube Channels content"
-    description: str = "A tool that can be used to semantic search a query from a Youtube Channels content."
-    args_schema: Type[BaseModel] = YoutubeChannelSearchToolSchema
-
-    def __init__(self, youtube_channel_handle: Optional[str] = None, **kwargs):
-        super().__init__(**kwargs)
-        if youtube_channel_handle is not None:
-            kwargs["data_type"] = DataType.YOUTUBE_CHANNEL
-            self.add(youtube_channel_handle)
-            self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content."
-            self.args_schema = FixedYoutubeChannelSearchToolSchema
-            self._generate_description()
-
-    def add(
-        self,
-        youtube_channel_handle: str,
-        **kwargs: Any,
-    ) -> None:
-        if not youtube_channel_handle.startswith("@"):
-            youtube_channel_handle = f"@{youtube_channel_handle}"
-        super().add(youtube_channel_handle, **kwargs)
-```
-
-## Conclusion
-
-The `YoutubeChannelSearchTool` provides a powerful way to search and extract information from YouTube channel content using RAG techniques. By enabling agents to search across an entire channel's videos, it facilitates information extraction and analysis tasks that would otherwise be difficult to perform. This tool is particularly useful for research, content analysis, and knowledge extraction from YouTube channels.
+```

docs/tools/youtubevideosearchtool.mdx

@@ -29,73 +29,35 @@ pip install 'crewai[tools]'
 
 ## Example
 
-The following example demonstrates how to use the `YoutubeVideoSearchTool` with a CrewAI agent:
+To integrate the YoutubeVideoSearchTool into your Python projects, follow the example below. 
+This demonstrates how to use the tool both for general Youtube content searches and for targeted searches within a specific video's content.
 
 ```python Code
-from crewai import Agent, Task, Crew
 from crewai_tools import YoutubeVideoSearchTool
 
-# Initialize the tool for general YouTube video searches
-youtube_search_tool = YoutubeVideoSearchTool()
+# General search across Youtube content without specifying a video URL, 
+# so the agent can search within any Youtube video content 
+# it learns about its url during its operation
+tool = YoutubeVideoSearchTool()
 
-# Define an agent that uses the tool
-video_researcher = Agent(
-    role="Video Researcher",
-    goal="Extract relevant information from YouTube videos",
-    backstory="An expert researcher who specializes in analyzing video content.",
-    tools=[youtube_search_tool],
-    verbose=True,
-)
-
-# Example task to search for information in a specific video
-research_task = Task(
-    description="Search for information about machine learning frameworks in the YouTube video at {youtube_video_url}",
-    expected_output="A summary of the key machine learning frameworks mentioned in the video.",
-    agent=video_researcher,
-)
-
-# Create and run the crew
-crew = Crew(agents=[video_researcher], tasks=[research_task])
-result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
-```
-
-You can also initialize the tool with a specific YouTube video URL:
-
-```python Code
-# Initialize the tool with a specific YouTube video URL
-youtube_search_tool = YoutubeVideoSearchTool(
+# Targeted search within a specific Youtube video's content
+tool = YoutubeVideoSearchTool(
     youtube_video_url='https://youtube.com/watch?v=example'
 )
-
-# Define an agent that uses the tool
-video_researcher = Agent(
-    role="Video Researcher",
-    goal="Extract relevant information from a specific YouTube video",
-    backstory="An expert researcher who specializes in analyzing video content.",
-    tools=[youtube_search_tool],
-    verbose=True,
-)
 ```
 
-## Parameters
+## Arguments
 
-The `YoutubeVideoSearchTool` accepts the following parameters:
+The YoutubeVideoSearchTool accepts the following initialization arguments:
 
-- **youtube_video_url**: Optional. The URL of the YouTube video to search within. If provided during initialization, the agent won't need to specify it when using the tool.
-- **config**: Optional. Configuration for the underlying RAG system, including LLM and embedder settings.
-- **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
+- `youtube_video_url`: An optional argument at initialization but required if targeting a specific Youtube video. It specifies the Youtube video URL path you want to search within.
 
-When using the tool with an agent, the agent will need to provide:
-
-- **search_query**: Required. The search query to find relevant information in the video content.
-- **youtube_video_url**: Required only if not provided during initialization. The URL of the YouTube video to search within.
-
-## Custom Model and Embeddings
+## Custom model and embeddings
 
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code  
-youtube_search_tool = YoutubeVideoSearchTool(
+tool = YoutubeVideoSearchTool(
     config=dict(
         llm=dict(
             provider="ollama", # or google, openai, anthropic, llama2, ...
@@ -116,72 +78,4 @@ youtube_search_tool = YoutubeVideoSearchTool(
         ),
     )
 )
-```
-
-## Agent Integration Example
-
-Here's a more detailed example of how to integrate the `YoutubeVideoSearchTool` with a CrewAI agent:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import YoutubeVideoSearchTool
-
-# Initialize the tool
-youtube_search_tool = YoutubeVideoSearchTool()
-
-# Define an agent that uses the tool
-video_researcher = Agent(
-    role="Video Researcher",
-    goal="Extract and analyze information from YouTube videos",
-    backstory="""You are an expert video researcher who specializes in extracting 
-    and analyzing information from YouTube videos. You have a keen eye for detail 
-    and can quickly identify key points and insights from video content.""",
-    tools=[youtube_search_tool],
-    verbose=True,
-)
-
-# Create a task for the agent
-research_task = Task(
-    description="""
-    Search for information about recent advancements in artificial intelligence 
-    in the YouTube video at {youtube_video_url}. 
-    
-    Focus on:
-    1. Key AI technologies mentioned
-    2. Real-world applications discussed
-    3. Future predictions made by the speaker
-    
-    Provide a comprehensive summary of these points.
-    """,
-    expected_output="A detailed summary of AI advancements, applications, and future predictions from the video.",
-    agent=video_researcher,
-)
-
-# Run the task
-crew = Crew(agents=[video_researcher], tasks=[research_task])
-result = crew.kickoff(inputs={"youtube_video_url": "https://youtube.com/watch?v=example"})
-```
-
-## Implementation Details
-
-The `YoutubeVideoSearchTool` is implemented as a subclass of `RagTool`, which provides the base functionality for Retrieval-Augmented Generation:
-
-```python Code
-class YoutubeVideoSearchTool(RagTool):
-    name: str = "Search a Youtube Video content"
-    description: str = "A tool that can be used to semantic search a query from a Youtube Video content."
-    args_schema: Type[BaseModel] = YoutubeVideoSearchToolSchema
-
-    def __init__(self, youtube_video_url: Optional[str] = None, **kwargs):
-        super().__init__(**kwargs)
-        if youtube_video_url is not None:
-            kwargs["data_type"] = DataType.YOUTUBE_VIDEO
-            self.add(youtube_video_url)
-            self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content."
-            self.args_schema = FixedYoutubeVideoSearchToolSchema
-            self._generate_description()
-```
-
-## Conclusion
-
-The `YoutubeVideoSearchTool` provides a powerful way to search and extract information from YouTube video content using RAG techniques. By enabling agents to search within video content, it facilitates information extraction and analysis tasks that would otherwise be difficult to perform. This tool is particularly useful for research, content analysis, and knowledge extraction from video sources.
+```

mkdocs.yml

@@ -152,7 +152,6 @@ nav:
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
     - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md'
-    - Agent Monitoring with MLflow: 'how-to/mlflow-Observability.md'
   - Tools Docs:
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.102.0"
+version = "0.86.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -11,22 +11,27 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.60.2",
+    "litellm>=1.44.22",
     "instructor>=1.3.3",
+    
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
+    
     # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
+    
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
+    
     # Authentication and Security
     "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
+    
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
@@ -36,7 +41,6 @@ dependencies = [
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
     "blinker>=1.9.0",
-    "json5>=0.10.0",
 ]
 
 [project.urls]
@@ -45,7 +49,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.36.0"]
+tools = ["crewai-tools>=0.17.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.102.0"
+__version__ = "0.86.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,13 +1,14 @@
-import re
+import os
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -16,20 +17,28 @@ from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.task import Task
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.utilities.events.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+agentops = None
 
+try:
+    import agentops  # type: ignore # Name "agentops" is already defined
+    from agentops import track_agent  # type: ignore
+except ImportError:
+
+    def track_agent():
+        def noop(f):
+            return f
+
+        return noop
+
+
+@track_agent()
 class Agent(BaseAgent):
     """Represents an agent in a system.
 
@@ -46,13 +55,13 @@ class Agent(BaseAgent):
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
             max_iter: Maximum number of iterations for an agent to execute a task.
+            memory: Whether the agent should have memory or not.
             max_rpm: Maximum number of requests per minute for the agent execution to be respected.
             verbose: Whether the agent execution should be in verbose mode.
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             knowledge_sources: Knowledge sources for the agent.
-            embedder: Embedder configuration for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -62,6 +71,9 @@ class Agent(BaseAgent):
     )
     agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
+    cache_handler: InstanceOf[CacheHandler] = Field(
+        default=None, description="An instance of the CacheHandler class."
+    )
     step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
@@ -73,7 +85,7 @@ class Agent(BaseAgent):
     llm: Union[str, InstanceOf[LLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -95,6 +107,10 @@ class Agent(BaseAgent):
         default=True,
         description="Keep messages under the context window size by summarizing content.",
     )
+    max_iter: int = Field(
+        default=20,
+        description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
+    )
     max_retry_limit: int = Field(
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
@@ -107,18 +123,105 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder_config: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
+    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
+        self._set_knowledge()
         self.agent_ops_agent_name = self.role
+        unaccepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
-        self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # If it's already an LLM instance, keep it as is
+            pass
+        elif self.llm is None:
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            if api_base:
+                llm_params["base_url"] = api_base
+
+            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                if provider == set_provider:
+                    for env_var in env_vars:
+                        # Check if the environment variable is set
+                        key_name = env_var.get("key_name")
+                        if key_name and key_name not in unaccepted_attributes:
+                            env_value = os.environ.get(key_name)
+                            if env_value:
+                                key_name = key_name.lower()
+                                for pattern in LITELLM_PARAMS:
+                                    if pattern in key_name:
+                                        key_name = pattern
+                                        break
+                                llm_params[key_name] = env_value
+                        # Check for default values if the environment variable is not set
+                        elif env_var.get("default", False):
+                            for key, value in env_var.items():
+                                if key not in ["prompt", "key_name", "default"]:
+                                    # Only add default if the key is already set in os.environ
+                                    if key in os.environ:
+                                        llm_params[key] = value
+
+            self.llm = LLM(**llm_params)
+        else:
+            # For any other type, attempt to extract relevant attributes
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "logprobs": getattr(self.llm, "logprobs", None),
+                "timeout": getattr(self.llm, "timeout", None),
+                "max_retries": getattr(self.llm, "max_retries", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
+
+        # Similar handling for function_calling_llm
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -133,22 +236,17 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def _set_knowledge(self):
         try:
-            if self.embedder is None and crew_embedder:
-                self.embedder = crew_embedder
-
             if self.knowledge_sources:
-                full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
-                knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
+                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    self.knowledge = Knowledge(
+                    self._knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder=self.embedder,
+                        embedder_config=self.embedder_config,
                         collection_name=knowledge_agent_name,
-                        storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
@@ -182,15 +280,13 @@ class Agent(BaseAgent):
             if task.output_json:
                 # schema = json.dumps(task.output_json, indent=2)
                 schema = generate_model_description(task.output_json)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
 
             elif task.output_pydantic:
                 schema = generate_model_description(task.output_pydantic)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
+
+            task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
 
         if context:
             task_prompt = self.i18n.slice("task_with_context").format(
@@ -209,8 +305,8 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
+        if self._knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
                 agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
@@ -234,15 +330,6 @@ class Agent(BaseAgent):
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
         try:
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionStartedEvent(
-                    agent=self,
-                    tools=self.tools,
-                    task_prompt=task_prompt,
-                    task=task,
-                ),
-            )
             result = self.agent_executor.invoke(
                 {
                     "input": task_prompt,
@@ -252,27 +339,8 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                crewai_event_bus.emit(
-                    self,
-                    event=AgentExecutionErrorEvent(
-                        agent=self,
-                        task=task,
-                        error=str(e),
-                    ),
-                )
-                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
-                crewai_event_bus.emit(
-                    self,
-                    event=AgentExecutionErrorEvent(
-                        agent=self,
-                        task=task,
-                        error=str(e),
-                    ),
-                )
                 raise e
             result = self.execute_task(task, context, tools)
 
@@ -285,10 +353,7 @@ class Agent(BaseAgent):
         for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
             if tool_result.get("result_as_answer", False):
                 result = tool_result["result"]
-        crewai_event_bus.emit(
-            self,
-            event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
-        )
+
         return result
 
     def create_agent_executor(
@@ -346,14 +411,13 @@ class Agent(BaseAgent):
         tools = agent_tools.tools()
         return tools
 
-    def get_multimodal_tools(self) -> Sequence[BaseTool]:
+    def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
-
         return [AddImageTool()]
 
     def get_code_execution_tools(self):
         try:
-            from crewai_tools import CodeInterpreterTool  # type: ignore
+            from crewai_tools import CodeInterpreterTool
 
             # Set the unsafe_mode based on the code_execution_mode attribute
             unsafe_mode = self.code_execution_mode == "unsafe"

src/crewai/agents/agent_builder/base_agent.py

@@ -18,12 +18,10 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.tools.base_tool import BaseTool, Tool
+from crewai.tools import BaseTool
+from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
-from crewai.utilities.converter import Converter
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -42,7 +40,7 @@ class BaseAgent(ABC, BaseModel):
         max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
         allow_delegation (bool): Allow delegation of tasks to agents.
         tools (Optional[List[Any]]): Tools at the agent's disposal.
-        max_iter (int): Maximum iterations for an agent to execute a task.
+        max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
         agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
         crew (Any): Crew to which the agent belongs.
@@ -50,8 +48,6 @@ class BaseAgent(ABC, BaseModel):
         cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
         tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
         max_tokens: Maximum number of tokens for the agent to generate in a response.
-        knowledge_sources: Knowledge sources for the agent.
-        knowledge_storage: Custom knowledge storage for the agent.
 
 
     Methods:
@@ -111,10 +107,10 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[BaseTool]] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
-    max_iter: int = Field(
+    max_iter: Optional[int] = Field(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
     agent_executor: InstanceOf = Field(
@@ -125,27 +121,15 @@ class BaseAgent(ABC, BaseModel):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
+    cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     tools_handler: InstanceOf[ToolsHandler] = Field(
-        default_factory=ToolsHandler,
-        description="An instance of the ToolsHandler class.",
+        default=None, description="An instance of the ToolsHandler class."
     )
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
     )
-    knowledge: Optional[Knowledge] = Field(
-        default=None, description="Knowledge for the agent."
-    )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
-        default=None,
-        description="Knowledge sources for the agent.",
-    )
-    knowledge_storage: Optional[Any] = Field(
-        default=None,
-        description="Custom knowledge storage for the agent.",
-    )
 
     @model_validator(mode="before")
     @classmethod
@@ -255,7 +239,7 @@ class BaseAgent(ABC, BaseModel):
     @abstractmethod
     def get_output_converter(
         self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
-    ) -> Converter:
+    ):
         """Get the converter class for the agent to create json/pydantic outputs."""
         pass
 
@@ -272,44 +256,13 @@ class BaseAgent(ABC, BaseModel):
             "tools_handler",
             "cache_handler",
             "llm",
-            "knowledge_sources",
-            "knowledge_storage",
-            "knowledge",
         }
 
-        # Copy llm
+        # Copy llm and clear callbacks
         existing_llm = shallow_copy(self.llm)
-        copied_knowledge = shallow_copy(self.knowledge)
-        copied_knowledge_storage = shallow_copy(self.knowledge_storage)
-        # Properly copy knowledge sources if they exist
-        existing_knowledge_sources = None
-        if self.knowledge_sources:
-            # Create a shared storage instance for all knowledge sources
-            shared_storage = (
-                self.knowledge_sources[0].storage if self.knowledge_sources else None
-            )
-
-            existing_knowledge_sources = []
-            for source in self.knowledge_sources:
-                copied_source = (
-                    source.model_copy()
-                    if hasattr(source, "model_copy")
-                    else shallow_copy(source)
-                )
-                # Ensure all copied sources use the same storage instance
-                copied_source.storage = shared_storage
-                existing_knowledge_sources.append(copied_source)
-
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
-        copied_agent = type(self)(
-            **copied_data,
-            llm=existing_llm,
-            tools=self.tools,
-            knowledge_sources=existing_knowledge_sources,
-            knowledge=copied_knowledge,
-            knowledge_storage=copied_knowledge_storage,
-        )
+        copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
 
         return copied_agent
 
@@ -351,6 +304,3 @@ class BaseAgent(ABC, BaseModel):
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
-
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
-        pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -19,10 +19,15 @@ class CrewAgentExecutorMixin:
     agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
+    have_forced_answer: bool
     max_iter: int
     _i18n: I18N
     _printer: Printer = Printer()
 
+    def _should_force_answer(self) -> bool:
+        """Determine if a forced answer is required based on iteration count."""
+        return (self.iterations >= self.max_iter) and not self.have_forced_answer
+
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""
         if (
@@ -95,34 +100,18 @@ class CrewAgentExecutorMixin:
                 pass
 
     def _ask_human_input(self, final_answer: str) -> str:
-        """Prompt human input with mode-appropriate messaging."""
+        """Prompt human input for final decision making."""
         self._printer.print(
             content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
         )
 
-        # Training mode prompt (single iteration)
-        if self.crew and getattr(self.crew, "_train", False):
-            prompt = (
+        self._printer.print(
+            content=(
                 "\n\n=====\n"
-                "## TRAINING MODE: Provide feedback to improve the agent's performance.\n"
-                "This will be used to train better versions of the agent.\n"
-                "Please provide detailed feedback about the result quality and reasoning process.\n"
+                "## Please provide feedback on the Final Result and the Agent's actions. "
+                "Respond with 'looks good' or a similar phrase when you're satisfied.\n"
                 "=====\n"
-            )
-        # Regular human-in-the-loop prompt (multiple iterations)
-        else:
-            prompt = (
-                "\n\n=====\n"
-                "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
-                "Please follow these guidelines:\n"
-                " - If you are happy with the result, simply hit Enter without typing anything.\n"
-                " - Otherwise, provide specific improvement requests.\n"
-                " - You can provide multiple rounds of feedback until satisfied.\n"
-                "=====\n"
-            )
-
-        self._printer.print(content=prompt, color="bold_yellow")
-        response = input()
-        if response.strip() != "":
-            self._printer.print(content="\nProcessing your feedback...", color="cyan")
-        return response
+            ),
+            color="bold_yellow",
+        )
+        return input()

src/crewai/agents/agent_builder/utilities/base_output_converter.py

@@ -25,17 +25,17 @@ class OutputConverter(BaseModel, ABC):
     llm: Any = Field(description="The language model to be used to convert the text.")
     model: Any = Field(description="The model to be used to convert the text.")
     instructions: str = Field(description="Conversion instructions to the LLM.")
-    max_attempts: int = Field(
+    max_attempts: Optional[int] = Field(
         description="Max number of attempts to try to get the output formatted.",
         default=3,
     )
 
     @abstractmethod
-    def to_pydantic(self, current_attempt=1) -> BaseModel:
+    def to_pydantic(self, current_attempt=1):
         """Convert text to pydantic."""
         pass
 
     @abstractmethod
-    def to_json(self, current_attempt=1) -> dict:
+    def to_json(self, current_attempt=1):
         """Convert text to json."""
         pass

src/crewai/agents/agent_builder/utilities/base_token_process.py

@@ -2,26 +2,25 @@ from crewai.types.usage_metrics import UsageMetrics
 
 
 class TokenProcess:
-    def __init__(self) -> None:
-        self.total_tokens: int = 0
-        self.prompt_tokens: int = 0
-        self.cached_prompt_tokens: int = 0
-        self.completion_tokens: int = 0
-        self.successful_requests: int = 0
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    cached_prompt_tokens: int = 0
+    completion_tokens: int = 0
+    successful_requests: int = 0
 
-    def sum_prompt_tokens(self, tokens: int) -> None:
-        self.prompt_tokens += tokens
-        self.total_tokens += tokens
+    def sum_prompt_tokens(self, tokens: int):
+        self.prompt_tokens = self.prompt_tokens + tokens
+        self.total_tokens = self.total_tokens + tokens
 
-    def sum_completion_tokens(self, tokens: int) -> None:
-        self.completion_tokens += tokens
-        self.total_tokens += tokens
+    def sum_completion_tokens(self, tokens: int):
+        self.completion_tokens = self.completion_tokens + tokens
+        self.total_tokens = self.total_tokens + tokens
 
-    def sum_cached_prompt_tokens(self, tokens: int) -> None:
-        self.cached_prompt_tokens += tokens
+    def sum_cached_prompt_tokens(self, tokens: int):
+        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
 
-    def sum_successful_requests(self, requests: int) -> None:
-        self.successful_requests += requests
+    def sum_successful_requests(self, requests: int):
+        self.successful_requests = self.successful_requests + requests
 
     def get_summary(self) -> UsageMetrics:
         return UsageMetrics(

src/crewai/agents/crew_agent_executor.py

@@ -1,7 +1,7 @@
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -13,17 +13,10 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
 from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
-from crewai.utilities.events import (
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-    crewai_event_bus,
-)
-from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -57,11 +50,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         original_tools: List[Any] = [],
         function_calling_llm: Any = None,
         respect_context_window: bool = False,
-        request_within_rpm_limit: Optional[Callable[[], bool]] = None,
+        request_within_rpm_limit: Any = None,
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm: LLM = llm
+        self.llm = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -84,11 +77,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
+        self.have_forced_answer = False
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        self.stop = stop_words
-        self.llm.stop = list(set(self.llm.stop + self.stop))
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -103,22 +99,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-
-        try:
-            formatted_answer = self._invoke_loop()
-        except AssertionError:
-            self._printer.print(
-                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                color="red",
-            )
-            raise
-        except Exception as e:
-            self._handle_unknown_error(e)
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
-            else:
-                raise e
+        formatted_answer = self._invoke_loop()
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -127,178 +108,106 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _invoke_loop(self) -> AgentFinish:
-        """
-        Main loop to invoke the agent's thought process until it reaches a conclusion
-        or the maximum number of iterations is reached.
-        """
-        formatted_answer = None
-        while not isinstance(formatted_answer, AgentFinish):
-            try:
-                if self._has_reached_max_iterations():
-                    formatted_answer = self._handle_max_iterations_exceeded(
-                        formatted_answer
-                    )
-                    break
-
-                self._enforce_rpm_limit()
-
-                answer = self._get_llm_response()
-                formatted_answer = self._process_llm_response(answer)
-
-                if isinstance(formatted_answer, AgentAction):
-                    tool_result = self._execute_tool_and_check_finality(
-                        formatted_answer
-                    )
-                    formatted_answer = self._handle_agent_action(
-                        formatted_answer, tool_result
-                    )
-
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text, role="assistant")
-
-            except OutputParserException as e:
-                formatted_answer = self._handle_output_parser_exception(e)
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    # Do not retry on litellm errors
-                    raise e
-                if self._is_context_length_exceeded(e):
-                    self._handle_context_length()
-                    continue
-                else:
-                    self._handle_unknown_error(e)
-                    raise e
-            finally:
-                self.iterations += 1
-
-        # During the invoke loop, formatted_answer alternates between AgentAction
-        # (when the agent is using tools) and eventually becomes AgentFinish
-        # (when the agent reaches a final answer). This assertion confirms we've
-        # reached a final answer and helps type checking understand this transition.
-        assert isinstance(formatted_answer, AgentFinish)
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
-    def _handle_unknown_error(self, exception: Exception) -> None:
-        """Handle unknown errors by informing the user."""
-        self._printer.print(
-            content="An unknown error occurred. Please check the details below.",
-            color="red",
-        )
-        self._printer.print(
-            content=f"Error details: {exception}",
-            color="red",
-        )
-
-    def _has_reached_max_iterations(self) -> bool:
-        """Check if the maximum number of iterations has been reached."""
-        return self.iterations >= self.max_iter
-
-    def _enforce_rpm_limit(self) -> None:
-        """Enforce the requests per minute (RPM) limit if applicable."""
-        if self.request_within_rpm_limit:
-            self.request_within_rpm_limit()
-
-    def _get_llm_response(self) -> str:
-        """Call the LLM and return the response, handling any invalid responses."""
+    def _invoke_loop(self, formatted_answer=None):
         try:
-            answer = self.llm.call(
-                self.messages,
-                callbacks=self.callbacks,
-            )
+            while not isinstance(formatted_answer, AgentFinish):
+                if not self.request_within_rpm_limit or self.request_within_rpm_limit():
+                    answer = self.llm.call(
+                        self.messages,
+                        callbacks=self.callbacks,
+                    )
+
+                    if answer is None or answer == "":
+                        self._printer.print(
+                            content="Received None or empty response from LLM call.",
+                            color="red",
+                        )
+                        raise ValueError(
+                            "Invalid response from LLM call - None or empty."
+                        )
+
+                    if not self.use_stop_words:
+                        try:
+                            self._format_answer(answer)
+                        except OutputParserException as e:
+                            if (
+                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
+                                in e.error
+                            ):
+                                answer = answer.split("Observation:")[0].strip()
+
+                    self.iterations += 1
+                    formatted_answer = self._format_answer(answer)
+
+                    if isinstance(formatted_answer, AgentAction):
+                        tool_result = self._execute_tool_and_check_finality(
+                            formatted_answer
+                        )
+
+                        # Directly append the result to the messages if the
+                        # tool is "Add image to content" in case of multimodal
+                        # agents
+                        if formatted_answer.tool == self._i18n.tools("add_image")["name"]:
+                            self.messages.append(tool_result.result)
+                            continue
+
+                        else:
+                            if self.step_callback:
+                                self.step_callback(tool_result)
+
+                            formatted_answer.text += f"\nObservation: {tool_result.result}"
+
+                        formatted_answer.result = tool_result.result
+                        if tool_result.result_as_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=tool_result.result,
+                                text=formatted_answer.text,
+                            )
+                        self._show_logs(formatted_answer)
+
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
+
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=self._i18n.errors(
+                                    "force_final_answer_error"
+                                ).format(formatted_answer.text),
+                                text=formatted_answer.text,
+                            )
+                        else:
+                            formatted_answer.text += (
+                                f'\n{self._i18n.errors("force_final_answer")}'
+                            )
+                            self.have_forced_answer = True
+                    self.messages.append(
+                        self._format_msg(formatted_answer.text, role="assistant")
+                    )
+
+        except OutputParserException as e:
+            self.messages.append({"role": "user", "content": e.error})
+            if self.iterations > self.log_error_after:
+                self._printer.print(
+                    content=f"Error parsing LLM output, agent will retry: {e.error}",
+                    color="red",
+                )
+            return self._invoke_loop(formatted_answer)
+
         except Exception as e:
-            self._printer.print(
-                content=f"Error during LLM call: {e}",
-                color="red",
-            )
-            raise e
-
-        if not answer:
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
-
-        return answer
-
-    def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
-        """Process the LLM response and format it into an AgentAction or AgentFinish."""
-        if not self.use_stop_words:
-            try:
-                # Preliminary parsing to check for errors.
-                self._format_answer(answer)
-            except OutputParserException as e:
-                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                    answer = answer.split("Observation:")[0].strip()
-
-        return self._format_answer(answer)
-
-    def _handle_agent_action(
-        self, formatted_answer: AgentAction, tool_result: ToolResult
-    ) -> Union[AgentAction, AgentFinish]:
-        """Handle the AgentAction, execute tools, and process the results."""
-        add_image_tool = self._i18n.tools("add_image")
-        if (
-            isinstance(add_image_tool, dict)
-            and formatted_answer.tool.casefold().strip()
-            == add_image_tool.get("name", "").casefold().strip()
-        ):
-            self.messages.append(tool_result.result)
-            return formatted_answer  # Continue the loop
-
-        if self.step_callback:
-            self.step_callback(tool_result)
-
-        formatted_answer.text += f"\nObservation: {tool_result.result}"
-        formatted_answer.result = tool_result.result
-
-        if tool_result.result_as_answer:
-            return AgentFinish(
-                thought="",
-                output=tool_result.result,
-                text=formatted_answer.text,
-            )
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
+                str(e)
+            ):
+                self._handle_context_length()
+                return self._invoke_loop(formatted_answer)
+            else:
+                raise e
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _invoke_step_callback(self, formatted_answer) -> None:
-        """Invoke the step callback if it exists."""
-        if self.step_callback:
-            self.step_callback(formatted_answer)
-
-    def _append_message(self, text: str, role: str = "assistant") -> None:
-        """Append a message to the message list with the given role."""
-        self.messages.append(self._format_msg(text, role=role))
-
-    def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
-        """Handle OutputParserException by updating messages and formatted_answer."""
-        self.messages.append({"role": "user", "content": e.error})
-
-        formatted_answer = AgentAction(
-            text=e.error,
-            tool="",
-            tool_input="",
-            thought="",
-        )
-
-        if self.iterations > self.log_error_after:
-            self._printer.print(
-                content=f"Error parsing LLM output, agent will retry: {e.error}",
-                color="red",
-            )
-
-        return formatted_answer
-
-    def _is_context_length_exceeded(self, exception: Exception) -> bool:
-        """Check if the exception is due to context length exceeding."""
-        return LLMContextLengthExceededException(
-            str(exception)
-        )._is_context_limit_error(str(exception))
-
     def _show_start_logs(self):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
@@ -309,11 +218,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self._printer.print(
                 content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
             )
-            description = (
-                getattr(self.task, "description") if self.task else "Not Found"
-            )
             self._printer.print(
-                content=f"\033[95m## Task:\033[00m \033[92m{description}\033[00m"
+                content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
             )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
@@ -355,68 +261,40 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 )
 
     def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
-        try:
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageStartedEvent(
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                    ),
-                )
-            tool_usage = ToolUsage(
-                tools_handler=self.tools_handler,
-                tools=self.tools,
-                original_tools=self.original_tools,
-                tools_description=self.tools_description,
-                tools_names=self.tools_names,
-                function_calling_llm=self.function_calling_llm,
-                task=self.task,  # type: ignore[arg-type]
-                agent=self.agent,
-                action=agent_action,
-            )
-            tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+        tool_usage = ToolUsage(
+            tools_handler=self.tools_handler,
+            tools=self.tools,
+            original_tools=self.original_tools,
+            tools_description=self.tools_description,
+            tools_names=self.tools_names,
+            function_calling_llm=self.function_calling_llm,
+            task=self.task,  # type: ignore[arg-type]
+            agent=self.agent,
+            action=agent_action,
+        )
+        tool_calling = tool_usage.parse(agent_action.text)
 
-            if isinstance(tool_calling, ToolUsageErrorException):
-                tool_result = tool_calling.message
-                return ToolResult(result=tool_result, result_as_answer=False)
-            else:
-                if tool_calling.tool_name.casefold().strip() in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                    name.casefold().strip() for name in self.tool_name_to_tool_map
-                ]:
-                    tool_result = tool_usage.use(tool_calling, agent_action.text)
-                    tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
-                    if tool:
-                        return ToolResult(
-                            result=tool_result, result_as_answer=tool.result_as_answer
-                        )
-                else:
-                    tool_result = self._i18n.errors("wrong_tool_name").format(
-                        tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
+        if isinstance(tool_calling, ToolUsageErrorException):
+            tool_result = tool_calling.message
+            return ToolResult(result=tool_result, result_as_answer=False)
+        else:
+            if tool_calling.tool_name.casefold().strip() in [
+                name.casefold().strip() for name in self.tool_name_to_tool_map
+            ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                name.casefold().strip() for name in self.tool_name_to_tool_map
+            ]:
+                tool_result = tool_usage.use(tool_calling, agent_action.text)
+                tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
+                if tool:
+                    return ToolResult(
+                        result=tool_result, result_as_answer=tool.result_as_answer
                     )
-                return ToolResult(result=tool_result, result_as_answer=False)
-
-        except Exception as e:
-            # TODO: drop
-            if self.agent:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageErrorEvent(  # validation error
-                        agent_key=self.agent.key,
-                        agent_role=self.agent.role,
-                        tool_name=agent_action.tool,
-                        tool_args=agent_action.tool_input,
-                        tool_class=agent_action.tool,
-                        error=str(e),
-                    ),
+            else:
+                tool_result = self._i18n.errors("wrong_tool_name").format(
+                    tool=tool_calling.tool_name,
+                    tools=", ".join([tool.name.casefold() for tool in self.tools]),
                 )
-            raise e
+        return ToolResult(result=tool_result, result_as_answer=False)
 
     def _summarize_messages(self) -> None:
         messages_groups = []
@@ -466,50 +344,58 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
 
     def _handle_crew_training_output(
-        self, result: AgentFinish, human_feedback: Optional[str] = None
+        self, result: AgentFinish, human_feedback: str | None = None
     ) -> None:
-        """Handle the process of saving training data."""
+        """Function to handle the process of the training data."""
         agent_id = str(self.agent.id)  # type: ignore
-        train_iteration = (
-            getattr(self.crew, "_train_iteration", None) if self.crew else None
-        )
-
-        if train_iteration is None or not isinstance(train_iteration, int):
-            self._printer.print(
-                content="Invalid or missing train iteration. Cannot save training data.",
-                color="red",
-            )
-            return
 
+        # Load training data
         training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
-        training_data = training_handler.load() or {}
+        training_data = training_handler.load()
 
-        # Initialize or retrieve agent's training data
-        agent_training_data = training_data.get(agent_id, {})
-
-        if human_feedback is not None:
-            # Save initial output and human feedback
-            agent_training_data[train_iteration] = {
-                "initial_output": result.output,
-                "human_feedback": human_feedback,
-            }
-        else:
-            # Save improved output
-            if train_iteration in agent_training_data:
-                agent_training_data[train_iteration]["improved_output"] = result.output
+        # Check if training data exists, human input is not requested, and self.crew is valid
+        if training_data and not self.ask_for_human_input:
+            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
+                train_iteration = self.crew._train_iteration
+                if agent_id in training_data and isinstance(train_iteration, int):
+                    training_data[agent_id][train_iteration][
+                        "improved_output"
+                    ] = result.output
+                    training_handler.save(training_data)
+                else:
+                    self._printer.print(
+                        content="Invalid train iteration type or agent_id not in training data.",
+                        color="red",
+                    )
             else:
                 self._printer.print(
-                    content=(
-                        f"No existing training data for agent {agent_id} and iteration "
-                        f"{train_iteration}. Cannot save improved output."
-                    ),
+                    content="Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
-                return
 
-        # Update the training data and save
-        training_data[agent_id] = agent_training_data
-        training_handler.save(training_data)
+        if self.ask_for_human_input and human_feedback is not None:
+            training_data = {
+                "initial_output": result.output,
+                "human_feedback": human_feedback,
+                "agent": agent_id,
+                "agent_role": self.agent.role,  # type: ignore
+            }
+            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
+                train_iteration = self.crew._train_iteration
+                if isinstance(train_iteration, int):
+                    CrewTrainingHandler(TRAINING_DATA_FILE).append(
+                        train_iteration, agent_id, training_data
+                    )
+                else:
+                    self._printer.print(
+                        content="Invalid train iteration type. Expected int.",
+                        color="red",
+                    )
+            else:
+                self._printer.print(
+                    content="Crew is None or does not have _train_iteration attribute.",
+                    color="red",
+                )
 
     def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
         prompt = prompt.replace("{input}", inputs["input"])
@@ -525,124 +411,79 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return {"role": role, "content": prompt}
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """Handle human feedback with different flows for training vs regular use.
-
-        Args:
-            formatted_answer: The initial AgentFinish result to get feedback on
-
-        Returns:
-            AgentFinish: The final answer after processing feedback
         """
-        human_feedback = self._ask_human_input(formatted_answer.output)
-
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
-
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
-
-    def _is_training_mode(self) -> bool:
-        """Check if crew is in training mode."""
-        return bool(self.crew and self.crew._train)
-
-    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
-    ) -> AgentFinish:
-        """Process feedback for training scenarios with single iteration."""
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        improved_answer = self._invoke_loop()
-        self._handle_crew_training_output(improved_answer)
-        self.ask_for_human_input = False
-        return improved_answer
-
-    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
-    ) -> AgentFinish:
-        """Process feedback for regular use with potential multiple iterations."""
-        feedback = initial_feedback
-        answer = current_answer
-
-        while self.ask_for_human_input:
-            # If the user provides a blank response, assume they are happy with the result
-            if feedback.strip() == "":
-                self.ask_for_human_input = False
-            else:
-                answer = self._process_feedback_iteration(feedback)
-                feedback = self._ask_human_input(answer.output)
-
-        return answer
-
-    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process a single feedback iteration."""
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        return self._invoke_loop()
-
-    def _log_feedback_error(self, retry_count: int, error: Exception) -> None:
-        """Log feedback processing errors."""
-        self._printer.print(
-            content=(
-                f"Error processing feedback: {error}. "
-                f"Retrying... ({retry_count + 1}/{MAX_LLM_RETRY})"
-            ),
-            color="red",
-        )
-
-    def _log_max_retries_exceeded(self) -> None:
-        """Log when max retries for feedback processing are exceeded."""
-        self._printer.print(
-            content=(
-                f"Failed to process feedback after {MAX_LLM_RETRY} attempts. "
-                "Ending feedback loop."
-            ),
-            color="red",
-        )
-
-    def _handle_max_iterations_exceeded(self, formatted_answer):
-        """
-        Handles the case when the maximum number of iterations is exceeded.
-        Performs one more LLM call to get the final answer.
+        Handles the human feedback loop, allowing the user to provide feedback
+        on the agent's output and determining if additional iterations are needed.
 
         Parameters:
-            formatted_answer: The last formatted answer from the agent.
+            formatted_answer (AgentFinish): The initial output from the agent.
 
         Returns:
-            The final formatted answer after exceeding max iterations.
+            AgentFinish: The final output after incorporating human feedback.
         """
-        self._printer.print(
-            content="Maximum iterations reached. Requesting final answer.",
-            color="yellow",
-        )
+        while self.ask_for_human_input:
+            human_feedback = self._ask_human_input(formatted_answer.output)
 
-        if formatted_answer and hasattr(formatted_answer, "text"):
-            assistant_message = (
-                formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
-            )
-        else:
-            assistant_message = self._i18n.errors("force_final_answer")
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer, human_feedback)
 
-        self.messages.append(self._format_msg(assistant_message, role="assistant"))
+            # Make an LLM call to verify if additional changes are requested based on human feedback
+            additional_changes_prompt = self._i18n.slice(
+                "human_feedback_classification"
+            ).format(feedback=human_feedback)
 
-        # Perform one more LLM call to get the final answer
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
+            retry_count = 0
+            llm_call_successful = False
+            additional_changes_response = None
 
-        if answer is None or answer == "":
-            self._printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-            raise ValueError("Invalid response from LLM call - None or empty.")
+            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
+                try:
+                    additional_changes_response = (
+                        self.llm.call(
+                            [
+                                self._format_msg(
+                                    additional_changes_prompt, role="system"
+                                )
+                            ],
+                            callbacks=self.callbacks,
+                        )
+                        .strip()
+                        .lower()
+                    )
+                    llm_call_successful = True
+                except Exception as e:
+                    retry_count += 1
+
+                    self._printer.print(
+                        content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
+                        color="red",
+                    )
+
+            if not llm_call_successful:
+                self._printer.print(
+                    content="Error processing feedback after multiple attempts.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
+                break
+
+            if additional_changes_response == "false":
+                self.ask_for_human_input = False
+            elif additional_changes_response == "true":
+                self.ask_for_human_input = True
+                # Add human feedback to messages
+                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
+                # Invoke the loop again with updated messages
+                formatted_answer = self._invoke_loop()
+
+                if self.crew and self.crew._train:
+                    self._handle_crew_training_output(formatted_answer)
+            else:
+                # Unexpected response
+                self._printer.print(
+                    content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
 
-        formatted_answer = self._format_answer(answer)
-        # Return the formatted answer, regardless of its type
         return formatted_answer

src/crewai/agents/parser.py

@@ -94,13 +94,6 @@ class CrewAgentParser:
 
         elif includes_answer:
             final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
-            # Check whether the final answer ends with triple backticks.
-            if final_answer.endswith("```"):
-                # Count occurrences of triple backticks in the final answer.
-                count = final_answer.count("```")
-                # If count is odd then it's an unmatched trailing set; remove it.
-                if count % 2 != 0:
-                    final_answer = final_answer[:-3].rstrip()
             return AgentFinish(thought, final_answer, text)
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
@@ -124,15 +117,11 @@ class CrewAgentParser:
             )
 
     def _extract_thought(self, text: str) -> str:
-        thought_index = text.find("\n\nAction")
-        if thought_index == -1:
-            thought_index = text.find("\n\nFinal Answer")
-        if thought_index == -1:
-            return ""
-        thought = text[:thought_index].strip()
-        # Remove any triple backticks from the thought string
-        thought = thought.replace("```", "").strip()
-        return thought
+        regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
+        thought_match = re.search(regex, text, re.DOTALL)
+        if thought_match:
+            return thought_match.group(1).strip()
+        return ""
 
     def _clean_action(self, text: str) -> str:
         """Clean action string by removing non-essential formatting characters."""

src/crewai/cli/cli.py

@@ -1,13 +1,11 @@
-import os
 from importlib.metadata import version as get_version
-from typing import Optional, Tuple
+from typing import Optional
 
 import click
 
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
-from crewai.cli.crew_chat import run_chat
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -203,6 +201,7 @@ def install(context):
 @crewai.command()
 def run():
     """Run the Crew."""
+    click.echo("Running the Crew")
     run_crew()
 
 
@@ -343,18 +342,5 @@ def flow_add_crew(crew_name):
     add_crew_to_flow(crew_name)
 
 
-@crewai.command()
-def chat():
-    """
-    Start a conversation with the Crew, collecting user-supplied inputs,
-    and using the Chat LLM to generate responses.
-    """
-    click.secho(
-        "\nStarting a conversation with the Crew\n" "Type 'exit' or Ctrl+C to quit.\n",
-    )
-
-    run_chat()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/constants.py

@@ -17,12 +17,6 @@ ENV_VARS = {
             "key_name": "GEMINI_API_KEY",
         }
     ],
-    "nvidia_nim": [
-        {
-            "prompt": "Enter your NVIDIA API key (press Enter to skip)",
-            "key_name": "NVIDIA_NIM_API_KEY",
-        }
-    ],
     "groq": [
         {
             "prompt": "Enter your GROQ API key (press Enter to skip)",
@@ -91,12 +85,6 @@ ENV_VARS = {
             "key_name": "CEREBRAS_API_KEY",
         },
     ],
-    "sambanova": [
-        {
-            "prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
-            "key_name": "SAMBANOVA_API_KEY",
-        }
-    ],
 }
 
 
@@ -104,14 +92,12 @@ PROVIDERS = [
     "openai",
     "anthropic",
     "gemini",
-    "nvidia_nim",
     "groq",
     "ollama",
     "watson",
     "bedrock",
     "azure",
     "cerebras",
-    "sambanova",
 ]
 
 MODELS = {
@@ -128,75 +114,6 @@ MODELS = {
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
     ],
-    "nvidia_nim": [
-        "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
-        "nvidia_nim/nvidia/nemotron-4-mini-hindi-4b-instruct",
-        "nvidia_nim/nvidia/llama-3.1-nemotron-70b-instruct",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-8b",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-70b",
-        "nvidia_nim/nvidia/vila",
-        "nvidia_nim/nvidia/neva-22",
-        "nvidia_nim/nvidia/nemotron-mini-4b-instruct",
-        "nvidia_nim/nvidia/usdcode-llama3-70b-instruct",
-        "nvidia_nim/nvidia/nemotron-4-340b-instruct",
-        "nvidia_nim/meta/codellama-70b",
-        "nvidia_nim/meta/llama2-70b",
-        "nvidia_nim/meta/llama3-8b-instruct",
-        "nvidia_nim/meta/llama3-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-8b-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-405b-instruct",
-        "nvidia_nim/meta/llama-3.2-1b-instruct",
-        "nvidia_nim/meta/llama-3.2-3b-instruct",
-        "nvidia_nim/meta/llama-3.2-11b-vision-instruct",
-        "nvidia_nim/meta/llama-3.2-90b-vision-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/google/gemma-7b",
-        "nvidia_nim/google/gemma-2b",
-        "nvidia_nim/google/codegemma-7b",
-        "nvidia_nim/google/codegemma-1.1-7b",
-        "nvidia_nim/google/recurrentgemma-2b",
-        "nvidia_nim/google/gemma-2-9b-it",
-        "nvidia_nim/google/gemma-2-27b-it",
-        "nvidia_nim/google/gemma-2-2b-it",
-        "nvidia_nim/google/deplot",
-        "nvidia_nim/google/paligemma",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.2",
-        "nvidia_nim/mistralai/mixtral-8x7b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-large",
-        "nvidia_nim/mistralai/mixtral-8x22b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.3",
-        "nvidia_nim/nv-mistralai/mistral-nemo-12b-instruct",
-        "nvidia_nim/mistralai/mamba-codestral-7b-v0.1",
-        "nvidia_nim/microsoft/phi-3-mini-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-mini-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-8k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-mini-instruct",
-        "nvidia_nim/microsoft/phi-3.5-moe-instruct",
-        "nvidia_nim/microsoft/kosmos-2",
-        "nvidia_nim/microsoft/phi-3-vision-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-vision-instruct",
-        "nvidia_nim/databricks/dbrx-instruct",
-        "nvidia_nim/snowflake/arctic",
-        "nvidia_nim/aisingapore/sea-lion-7b-instruct",
-        "nvidia_nim/ibm/granite-8b-code-instruct",
-        "nvidia_nim/ibm/granite-34b-code-instruct",
-        "nvidia_nim/ibm/granite-3.0-8b-instruct",
-        "nvidia_nim/ibm/granite-3.0-3b-a800m-instruct",
-        "nvidia_nim/mediatek/breeze-7b-instruct",
-        "nvidia_nim/upstage/solar-10.7b-instruct",
-        "nvidia_nim/writer/palmyra-med-70b-32k",
-        "nvidia_nim/writer/palmyra-med-70b",
-        "nvidia_nim/writer/palmyra-fin-70b-32k",
-        "nvidia_nim/01-ai/yi-large",
-        "nvidia_nim/deepseek-ai/deepseek-coder-6.7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-chat",
-        "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
-    ],
     "groq": [
         "groq/llama-3.1-8b-instant",
         "groq/llama-3.1-70b-versatile",
@@ -216,43 +133,10 @@ MODELS = {
         "watsonx/ibm/granite-3-8b-instruct",
     ],
     "bedrock": [
-        "bedrock/us.amazon.nova-pro-v1:0",
-        "bedrock/us.amazon.nova-micro-v1:0",
-        "bedrock/us.amazon.nova-lite-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        "bedrock/us.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-opus-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/us.meta.llama3-2-11b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-90b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-8b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-3-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-405b-instruct-v1:0",
-        "bedrock/eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/eu.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/eu.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/eu.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/eu.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/apac.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/apac.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/amazon.nova-pro-v1:0",
-        "bedrock/amazon.nova-micro-v1:0",
-        "bedrock/amazon.nova-lite-v1:0",
         "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0",
         "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
         "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
         "bedrock/anthropic.claude-v2:1",
         "bedrock/anthropic.claude-v2",
         "bedrock/anthropic.claude-instant-v1",
@@ -267,26 +151,13 @@ MODELS = {
         "bedrock/ai21.j2-mid-v1",
         "bedrock/ai21.j2-ultra-v1",
         "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
         "bedrock/mistral.mistral-7b-instruct-v0:2",
         "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
     ],
-    "sambanova": [
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
-        "sambanova/QwQ-32B-Preview",
-        "sambanova/Qwen2.5-72B-Instruct",
-        "sambanova/Qwen2.5-Coder-32B-Instruct",
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
-        "sambanova/Meta-Llama-3.1-70B-Instruct",
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-    ],
 }
 
-DEFAULT_LLM_MODEL = "gpt-4o-mini"
-
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 
 

src/crewai/cli/crew_chat.py

@@ -1,536 +0,0 @@
-import json
-import platform
-import re
-import sys
-import threading
-import time
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple
-
-import click
-import tomli
-from packaging import version
-
-from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
-from crewai.crew import Crew
-from crewai.llm import LLM
-from crewai.types.crew_chat import ChatInputField, ChatInputs
-from crewai.utilities.llm_utils import create_llm
-
-MIN_REQUIRED_VERSION = "0.98.0"
-
-
-def check_conversational_crews_version(
-    crewai_version: str, pyproject_data: dict
-) -> bool:
-    """
-    Check if the installed crewAI version supports conversational crews.
-
-    Args:
-        crewai_version: The current version of crewAI.
-        pyproject_data: Dictionary containing pyproject.toml data.
-
-    Returns:
-        bool: True if version check passes, False otherwise.
-    """
-    try:
-        if version.parse(crewai_version) < version.parse(MIN_REQUIRED_VERSION):
-            click.secho(
-                "You are using an older version of crewAI that doesn't support conversational crews. "
-                "Run 'uv upgrade crewai' to get the latest version.",
-                fg="red",
-            )
-            return False
-    except version.InvalidVersion:
-        click.secho("Invalid crewAI version format detected.", fg="red")
-        return False
-    return True
-
-
-def run_chat():
-    """
-    Runs an interactive chat loop using the Crew's chat LLM with function calling.
-    Incorporates crew_name, crew_description, and input fields to build a tool schema.
-    Exits if crew_name or crew_description are missing.
-    """
-    crewai_version = get_crewai_version()
-    pyproject_data = read_toml()
-
-    if not check_conversational_crews_version(crewai_version, pyproject_data):
-        return
-
-    crew, crew_name = load_crew_and_name()
-    chat_llm = initialize_chat_llm(crew)
-    if not chat_llm:
-        return
-
-    # Indicate that the crew is being analyzed
-    click.secho(
-        "\nAnalyzing crew and required inputs - this may take 3 to 30 seconds "
-        "depending on the complexity of your crew.",
-        fg="white",
-    )
-
-    # Start loading indicator
-    loading_complete = threading.Event()
-    loading_thread = threading.Thread(target=show_loading, args=(loading_complete,))
-    loading_thread.start()
-
-    try:
-        crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
-        crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
-        system_message = build_system_message(crew_chat_inputs)
-
-        # Call the LLM to generate the introductory message
-        introductory_message = chat_llm.call(
-            messages=[{"role": "system", "content": system_message}]
-        )
-    finally:
-        # Stop loading indicator
-        loading_complete.set()
-        loading_thread.join()
-
-    # Indicate that the analysis is complete
-    click.secho("\nFinished analyzing crew.\n", fg="white")
-
-    click.secho(f"Assistant: {introductory_message}\n", fg="green")
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "assistant", "content": introductory_message},
-    ]
-
-    available_functions = {
-        crew_chat_inputs.crew_name: create_tool_function(crew, messages),
-    }
-
-    chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
-
-
-def show_loading(event: threading.Event):
-    """Display animated loading dots while processing."""
-    while not event.is_set():
-        print(".", end="", flush=True)
-        time.sleep(1)
-    print()
-
-
-def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
-    """Initializes the chat LLM and handles exceptions."""
-    try:
-        return create_llm(crew.chat_llm)
-    except Exception as e:
-        click.secho(
-            f"Unable to find a Chat LLM. Please make sure you set chat_llm on the crew: {e}",
-            fg="red",
-        )
-        return None
-
-
-def build_system_message(crew_chat_inputs: ChatInputs) -> str:
-    """Builds the initial system message for the chat."""
-    required_fields_str = (
-        ", ".join(
-            f"{field.name} (desc: {field.description or 'n/a'})"
-            for field in crew_chat_inputs.inputs
-        )
-        or "(No required fields detected)"
-    )
-
-    return (
-        "You are a helpful AI assistant for the CrewAI platform. "
-        "Your primary purpose is to assist users with the crew's specific tasks. "
-        "You can answer general questions, but should guide users back to the crew's purpose afterward. "
-        "For example, after answering a general question, remind the user of your main purpose, such as generating a research report, and prompt them to specify a topic or task related to the crew's purpose. "
-        "You have a function (tool) you can call by name if you have all required inputs. "
-        f"Those required inputs are: {required_fields_str}. "
-        "Once you have them, call the function. "
-        "Please keep your responses concise and friendly. "
-        "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
-        "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
-        "If you are ever unsure about a user's request or need clarification, ask the user for more information. "
-        "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
-        "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
-        f"\nCrew Name: {crew_chat_inputs.crew_name}"
-        f"\nCrew Description: {crew_chat_inputs.crew_description}"
-    )
-
-
-def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
-    """Creates a wrapper function for running the crew tool with messages."""
-
-    def run_crew_tool_with_messages(**kwargs):
-        return run_crew_tool(crew, messages, **kwargs)
-
-    return run_crew_tool_with_messages
-
-
-def flush_input():
-    """Flush any pending input from the user."""
-    if platform.system() == "Windows":
-        # Windows platform
-        import msvcrt
-
-        while msvcrt.kbhit():
-            msvcrt.getch()
-    else:
-        # Unix-like platforms (Linux, macOS)
-        import termios
-
-        termios.tcflush(sys.stdin, termios.TCIFLUSH)
-
-
-def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
-    """Main chat loop for interacting with the user."""
-    while True:
-        try:
-            # Flush any pending input before accepting new input
-            flush_input()
-
-            user_input = get_user_input()
-            handle_user_input(
-                user_input, chat_llm, messages, crew_tool_schema, available_functions
-            )
-
-        except KeyboardInterrupt:
-            click.echo("\nExiting chat. Goodbye!")
-            break
-        except Exception as e:
-            click.secho(f"An error occurred: {e}", fg="red")
-            break
-
-
-def get_user_input() -> str:
-    """Collect multi-line user input with exit handling."""
-    click.secho(
-        "\nYou (type your message below. Press 'Enter' twice when you're done):",
-        fg="blue",
-    )
-    user_input_lines = []
-    while True:
-        line = input()
-        if line.strip().lower() == "exit":
-            return "exit"
-        if line == "":
-            break
-        user_input_lines.append(line)
-    return "\n".join(user_input_lines)
-
-
-def handle_user_input(
-    user_input: str,
-    chat_llm: LLM,
-    messages: List[Dict[str, str]],
-    crew_tool_schema: Dict[str, Any],
-    available_functions: Dict[str, Any],
-) -> None:
-    if user_input.strip().lower() == "exit":
-        click.echo("Exiting chat. Goodbye!")
-        return
-
-    if not user_input.strip():
-        click.echo("Empty message. Please provide input or type 'exit' to quit.")
-        return
-
-    messages.append({"role": "user", "content": user_input})
-
-    # Indicate that assistant is processing
-    click.echo()
-    click.secho("Assistant is processing your input. Please wait...", fg="green")
-
-    # Process assistant's response
-    final_response = chat_llm.call(
-        messages=messages,
-        tools=[crew_tool_schema],
-        available_functions=available_functions,
-    )
-
-    messages.append({"role": "assistant", "content": final_response})
-    click.secho(f"\nAssistant: {final_response}\n", fg="green")
-
-
-def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
-    """
-    Dynamically build a Littellm 'function' schema for the given crew.
-
-    crew_name: The name of the crew (used for the function 'name').
-    crew_inputs: A ChatInputs object containing crew_description
-                 and a list of input fields (each with a name & description).
-    """
-    properties = {}
-    for field in crew_inputs.inputs:
-        properties[field.name] = {
-            "type": "string",
-            "description": field.description or "No description provided",
-        }
-
-    required_fields = [field.name for field in crew_inputs.inputs]
-
-    return {
-        "type": "function",
-        "function": {
-            "name": crew_inputs.crew_name,
-            "description": crew_inputs.crew_description or "No crew description",
-            "parameters": {
-                "type": "object",
-                "properties": properties,
-                "required": required_fields,
-            },
-        },
-    }
-
-
-def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
-    """
-    Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
-
-    Args:
-        crew (Crew): The crew instance to run.
-        messages (List[Dict[str, str]]): The chat messages up to this point.
-        **kwargs: The inputs collected from the user.
-
-    Returns:
-        str: The output from the crew's execution.
-
-    Raises:
-        SystemExit: Exits the chat if an error occurs during crew execution.
-    """
-    try:
-        # Serialize 'messages' to JSON string before adding to kwargs
-        kwargs["crew_chat_messages"] = json.dumps(messages)
-
-        # Run the crew with the provided inputs
-        crew_output = crew.kickoff(inputs=kwargs)
-
-        # Convert CrewOutput to a string to send back to the user
-        result = str(crew_output)
-
-        return result
-    except Exception as e:
-        # Exit the chat and show the error message
-        click.secho("An error occurred while running the crew:", fg="red")
-        click.secho(str(e), fg="red")
-        sys.exit(1)
-
-
-def load_crew_and_name() -> Tuple[Crew, str]:
-    """
-    Loads the crew by importing the crew class from the user's project.
-
-    Returns:
-        Tuple[Crew, str]: A tuple containing the Crew instance and the name of the crew.
-    """
-    # Get the current working directory
-    cwd = Path.cwd()
-
-    # Path to the pyproject.toml file
-    pyproject_path = cwd / "pyproject.toml"
-    if not pyproject_path.exists():
-        raise FileNotFoundError("pyproject.toml not found in the current directory.")
-
-    # Load the pyproject.toml file using 'tomli'
-    with pyproject_path.open("rb") as f:
-        pyproject_data = tomli.load(f)
-
-    # Get the project name from the 'project' section
-    project_name = pyproject_data["project"]["name"]
-    folder_name = project_name
-
-    # Derive the crew class name from the project name
-    # E.g., if project_name is 'my_project', crew_class_name is 'MyProject'
-    crew_class_name = project_name.replace("_", " ").title().replace(" ", "")
-
-    # Add the 'src' directory to sys.path
-    src_path = cwd / "src"
-    if str(src_path) not in sys.path:
-        sys.path.insert(0, str(src_path))
-
-    # Import the crew module
-    crew_module_name = f"{folder_name}.crew"
-    try:
-        crew_module = __import__(crew_module_name, fromlist=[crew_class_name])
-    except ImportError as e:
-        raise ImportError(f"Failed to import crew module {crew_module_name}: {e}")
-
-    # Get the crew class from the module
-    try:
-        crew_class = getattr(crew_module, crew_class_name)
-    except AttributeError:
-        raise AttributeError(
-            f"Crew class {crew_class_name} not found in module {crew_module_name}"
-        )
-
-    # Instantiate the crew
-    crew_instance = crew_class().crew()
-    return crew_instance, crew_class_name
-
-
-def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInputs:
-    """
-    Generates the ChatInputs required for the crew by analyzing the tasks and agents.
-
-    Args:
-        crew (Crew): The crew object containing tasks and agents.
-        crew_name (str): The name of the crew.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        ChatInputs: An object containing the crew's name, description, and input fields.
-    """
-    # Extract placeholders from tasks and agents
-    required_inputs = fetch_required_inputs(crew)
-
-    # Generate descriptions for each input using AI
-    input_fields = []
-    for input_name in required_inputs:
-        description = generate_input_description_with_ai(input_name, crew, chat_llm)
-        input_fields.append(ChatInputField(name=input_name, description=description))
-
-    # Generate crew description using AI
-    crew_description = generate_crew_description_with_ai(crew, chat_llm)
-
-    return ChatInputs(
-        crew_name=crew_name, crew_description=crew_description, inputs=input_fields
-    )
-
-
-def fetch_required_inputs(crew: Crew) -> Set[str]:
-    """
-    Extracts placeholders from the crew's tasks and agents.
-
-    Args:
-        crew (Crew): The crew object.
-
-    Returns:
-        Set[str]: A set of placeholder names.
-    """
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-    required_inputs: Set[str] = set()
-
-    # Scan tasks
-    for task in crew.tasks:
-        text = f"{task.description or ''} {task.expected_output or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    # Scan agents
-    for agent in crew.agents:
-        text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
-        required_inputs.update(placeholder_pattern.findall(text))
-
-    return required_inputs
-
-
-def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) -> str:
-    """
-    Generates an input description using AI based on the context of the crew.
-
-    Args:
-        input_name (str): The name of the input placeholder.
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the input.
-    """
-    # Gather context from tasks and agents where the input is used
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        if (
-            f"{{{input_name}}}" in task.description
-            or f"{{{input_name}}}" in task.expected_output
-        ):
-            # Replace placeholders with input names
-            task_description = placeholder_pattern.sub(
-                lambda m: m.group(1), task.description or ""
-            )
-            expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output or ""
-            )
-            context_texts.append(f"Task Description: {task_description}")
-            context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        if (
-            f"{{{input_name}}}" in agent.role
-            or f"{{{input_name}}}" in agent.goal
-            or f"{{{input_name}}}" in agent.backstory
-        ):
-            # Replace placeholders with input names
-            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
-            agent_backstory = placeholder_pattern.sub(
-                lambda m: m.group(1), agent.backstory or ""
-            )
-            context_texts.append(f"Agent Role: {agent_role}")
-            context_texts.append(f"Agent Goal: {agent_goal}")
-            context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        # If no context is found for the input, raise an exception as per instruction
-        raise ValueError(f"No context found for input '{input_name}'.")
-
-    prompt = (
-        f"Based on the following context, write a concise description (15 words or less) of the input '{input_name}'.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    description = response.strip()
-
-    return description
-
-
-def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
-    """
-    Generates a brief description of the crew using AI.
-
-    Args:
-        crew (Crew): The crew object.
-        chat_llm: The chat language model to use for AI calls.
-
-    Returns:
-        str: A concise description of the crew's purpose (15 words or less).
-    """
-    # Gather context from tasks and agents
-    context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)\}")
-
-    for task in crew.tasks:
-        # Replace placeholders with input names
-        task_description = placeholder_pattern.sub(
-            lambda m: m.group(1), task.description or ""
-        )
-        expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output or ""
-        )
-        context_texts.append(f"Task Description: {task_description}")
-        context_texts.append(f"Expected Output: {expected_output}")
-    for agent in crew.agents:
-        # Replace placeholders with input names
-        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
-        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
-        agent_backstory = placeholder_pattern.sub(
-            lambda m: m.group(1), agent.backstory or ""
-        )
-        context_texts.append(f"Agent Role: {agent_role}")
-        context_texts.append(f"Agent Goal: {agent_goal}")
-        context_texts.append(f"Agent Backstory: {agent_backstory}")
-
-    context = "\n".join(context_texts)
-    if not context:
-        raise ValueError("No context found for generating crew description.")
-
-    prompt = (
-        "Based on the following context, write a concise, action-oriented description (15 words or less) of the crew's purpose.\n"
-        "Provide only the description, without any extra text or labels. Do not include placeholders like '{topic}' in the description.\n"
-        "Context:\n"
-        f"{context}"
-    )
-    response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    crew_description = response.strip()
-
-    return crew_description

src/crewai/cli/reset_memories_command.py

@@ -2,7 +2,11 @@ import subprocess
 
 import click
 
-from crewai.cli.utils import get_crew
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
 
 def reset_memories_command(
@@ -26,35 +30,30 @@ def reset_memories_command(
     """
 
     try:
-        crew = get_crew()
-        if not crew:
-            raise ValueError("No crew found.")
         if all:
-            crew.reset_memories(command_type="all")
+            ShortTermMemory().reset()
+            EntityMemory().reset()
+            LongTermMemory().reset()
+            TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
-            return
+        else:
+            if long:
+                LongTermMemory().reset()
+                click.echo("Long term memory has been reset.")
 
-        if not any([long, short, entity, kickoff_outputs, knowledge]):
-            click.echo(
-                "No memory type specified. Please specify at least one type to reset."
-            )
-            return
-
-        if long:
-            crew.reset_memories(command_type="long")
-            click.echo("Long term memory has been reset.")
-        if short:
-            crew.reset_memories(command_type="short")
-            click.echo("Short term memory has been reset.")
-        if entity:
-            crew.reset_memories(command_type="entity")
-            click.echo("Entity memory has been reset.")
-        if kickoff_outputs:
-            crew.reset_memories(command_type="kickoff_outputs")
-            click.echo("Latest Kickoff outputs stored has been reset.")
-        if knowledge:
-            crew.reset_memories(command_type="knowledge")
-            click.echo("Knowledge has been reset.")
+            if short:
+                ShortTermMemory().reset()
+                click.echo("Short term memory has been reset.")
+            if entity:
+                EntityMemory().reset()
+                click.echo("Entity memory has been reset.")
+            if kickoff_outputs:
+                TaskOutputStorageHandler().reset()
+                click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("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/run_crew.py

@@ -1,6 +1,4 @@
 import subprocess
-from enum import Enum
-from typing import List, Optional
 
 import click
 from packaging import version
@@ -9,24 +7,16 @@ from crewai.cli.utils import read_toml
 from crewai.cli.version import get_crewai_version
 
 
-class CrewType(Enum):
-    STANDARD = "standard"
-    FLOW = "flow"
-
-
 def run_crew() -> None:
     """
-    Run the crew or flow by running a command in the UV environment.
-
-    Starting from version 0.103.0, this command can be used to run both
-    standard crews and flows. For flows, it detects the type from pyproject.toml
-    and automatically runs the appropriate command.
+    Run the crew by running a command in the UV environment.
     """
+    command = ["uv", "run", "run_crew"]
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"
+
     pyproject_data = read_toml()
 
-    # Check for legacy poetry configuration
     if pyproject_data.get("tool", {}).get("poetry") and (
         version.parse(crewai_version) < version.parse(min_required_version)
     ):
@@ -36,54 +26,18 @@ def run_crew() -> None:
             fg="red",
         )
 
-    # Determine crew type
-    is_flow = pyproject_data.get("tool", {}).get("crewai", {}).get("type") == "flow"
-    crew_type = CrewType.FLOW if is_flow else CrewType.STANDARD
-
-    # Display appropriate message
-    click.echo(f"Running the {'Flow' if is_flow else 'Crew'}")
-
-    # Execute the appropriate command
-    execute_command(crew_type)
-
-
-def execute_command(crew_type: CrewType) -> None:
-    """
-    Execute the appropriate command based on crew type.
-
-    Args:
-        crew_type: The type of crew to run
-    """
-    command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
-
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)
 
     except subprocess.CalledProcessError as e:
-        handle_error(e, crew_type)
+        click.echo(f"An error occurred while running the crew: {e}", err=True)
+        click.echo(e.output, err=True, nl=True)
+
+        if pyproject_data.get("tool", {}).get("poetry"):
+            click.secho(
+                "It's possible that you are using an old version of crewAI that uses poetry, please run `crewai update` to update your pyproject.toml to use uv.",
+                fg="yellow",
+            )
 
     except Exception as e:
         click.echo(f"An unexpected error occurred: {e}", err=True)
-
-
-def handle_error(error: subprocess.CalledProcessError, crew_type: CrewType) -> None:
-    """
-    Handle subprocess errors with appropriate messaging.
-
-    Args:
-        error: The subprocess error that occurred
-        crew_type: The type of crew that was being run
-    """
-    entity_type = "flow" if crew_type == CrewType.FLOW else "crew"
-    click.echo(f"An error occurred while running the {entity_type}: {error}", err=True)
-
-    if error.output:
-        click.echo(error.output, err=True, nl=True)
-
-    pyproject_data = read_toml()
-    if pyproject_data.get("tool", {}).get("poetry"):
-        click.secho(
-            "It's possible that you are using an old version of crewAI that uses poetry, "
-            "please run `crewai update` to update your pyproject.toml to use uv.",
-            fg="yellow",
-        )

src/crewai/cli/templates/crew/.gitignore

@@ -1,3 +1,2 @@
 .env
 __pycache__/
-.DS_Store

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

@@ -2,7 +2,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is {current_year}.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher

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

@@ -1,62 +1,62 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
-# If you want to run a snippet of code before or after the crew starts,
+# If you want to run a snippet of code before or after the crew starts, 
 # you can use the @before_kickoff and @after_kickoff decorators
 # https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
 
 @CrewBase
 class {{crew_name}}():
-    """{{crew_name}} crew"""
+	"""{{crew_name}} crew"""
 
-    # Learn more about YAML configuration files here:
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml'
-    tasks_config = 'config/tasks.yaml'
+	# Learn more about YAML configuration files here:
+	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
 
-    # If you would like to add tools to your agents, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
-    @agent
-    def researcher(self) -> Agent:
-        return Agent(
-            config=self.agents_config['researcher'],
-            verbose=True
-        )
+	# If you would like to add tools to your agents, you can learn more about it here:
+	# https://docs.crewai.com/concepts/agents#agent-tools
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True
+		)
 
-    @agent
-    def reporting_analyst(self) -> Agent:
-        return Agent(
-            config=self.agents_config['reporting_analyst'],
-            verbose=True
-        )
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
-    @task
-    def research_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['research_task'],
-        )
+	# To learn more about structured task outputs, 
+	# task dependencies, and task callbacks, check out the documentation:
+	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
 
-    @task
-    def reporting_task(self) -> Task:
-        return Task(
-            config=self.tasks_config['reporting_task'],
-            output_file='report.md'
-        )
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_file='report.md'
+		)
 
-    @crew
-    def crew(self) -> Crew:
-        """Creates the {{crew_name}} crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the {{crew_name}} crew"""
+		# To learn how to add knowledge sources to your crew, check out the documentation:
+		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
 
-        return Crew(
-            agents=self.agents, # Automatically created by the @agent decorator
-            tasks=self.tasks, # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-            # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
-        )
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
+		)