Here are the primary distinctions:

1. Purpose of Tools for Agents vs. Tasks

   • Agents: Tools given to agents are meant to empower the agent throughout its operational lifespan. They are part of the agent’s capabilities and are accessible during any task execution. These tools reflect the agent’s skill set, enhancing the agent’s overall functionality and efficiency.
   • Tasks: Tools assigned to tasks are utilized specifically within the context of that task. The tools here are employed to achieve a specific goal tied to the task, regardless of the agent performing it. The task defines which tools are necessary to complete its assignment and can override the default tools of an agent.
2. Scope and Context of Tool Usage

   • Agents: The tools assigned to agents are available across all tasks the agent undertakes. For example, if an agent has a web scraping tool, it can use this tool for any task it is assigned to as long as the task requires or permits it.
   • Tasks: Tools assigned to a task are only usable within the context of that specific task. This setup allows for a high degree of control, ensuring the task uses the most appropriate tools. It also enables different agents to use the same task without necessarily needing to have those tools themselves.
3. Control Over Execution

   • Agents: When an agent has tools, it controls when and how those tools are used. This setup is suitable when you want an agent to be autonomous and make decisions on when to leverage its abilities. It’s ideal for agents with expertise or unique skills that require specific tools frequently.
   • Tasks: Assigning tools to tasks grants control over tool usage to the task definition itself. This method is beneficial when a task requires specific actions, such as querying a database or making an API call, that need precise tool usage. This setup allows different agents to perform the task while adhering to the tool requirements set by the task.
4. Tool Management and Overlap

   • Agents: If multiple agents have the same tool, each will manage its instance of that tool, potentially leading to redundant tool usage if not carefully planned. This situation requires mindful management to avoid inefficiencies.
   • Tasks: When tools are task-specific, the tools are only instantiated when the task is executed. This can lead to more streamlined usage and fewer conflicts, as tools are utilized only in the context where they are explicitly needed.

• ✦ Tools at the Task Level (Deterministic Usage):
  • When a tool is assigned directly to a task, it is guaranteed to be used whenever that task is executed, assuming the task’s logic calls for it. This deterministic nature means that the task’s execution path explicitly involves the tool, ensuring consistent and predictable behavior every time the task runs.
  • For example, if a task is designed to query a database, and it has a database connector tool assigned to it, the task will always invoke this tool as part of its execution flow. The tool’s usage is pre-defined and non-negotiable, ensuring the task’s requirements are consistently met.
• ✦ Tools at the Agent Level (Probabilistic Usage):
  • Tools assigned to agents, on the other hand, operate more probabilistically. This means that even though an agent possesses a tool, it may choose to use it or not based on its internal logic, task requirements, or decision-making process during task execution.
  • An agent with a data analysis tool, for example, might not always use it for every task it handles. The agent’s behavior is influenced by factors like task descriptions, agent backstory, goals, or even prior outcomes, making tool usage more dynamic and less predictable.

CrewAI's ability to support not only its native tools but also third-party tools from LangChain and LlamaIndex offers significant advantages.

• ✦ This flexibility allows users to leverage a broader range of functionalities and integrations, enhancing the overall versatility and capability of the platform.

• ✦ Developers are not confined to the tools provided by CrewAI alone; they can seamlessly integrate and utilize the best tools available in the market, tailored to their specific needs.

  
  Screenshot of a collection of LlamaIndex tools (integrations)
  
  In our walkthrough notebook, we have tried a more advanced example that uses toolkits (a suit of tools) from LangChain to create a tool that can manipulate and analyze tabular data by actually running Python code.

• ✦ This tool uses the pandas library to manipulate the data and the ChatOpenAI agent to run the code.

• ✦ While the example is a bit more complex, but we think it's worth to include it because the simplier examples (using a single tool from LangChain) are already well documented in CrewAI's documentation.

• ✦ The toolkits are usually much more powerful and can be used to achieve more complex tasks, but we have yet to come across a comprehensive documentation on how to incorporate them into CrewAI's agents.

  

from langchain.agents import Tool
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent

from langchain_openai import ChatOpenAI
import pandas as pd
  

df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

  

pandas_tool_agent = create_pandas_dataframe_agent(
	llm=ChatOpenAI(temperature=0, model='gpt-4o-mini'),
	df=df,
	agent_type=AgentType.OPENAI_FUNCTIONS,
	allow_dangerous_code=True # <-- This is an "acknowledgement" that this can run potentially dangerous code
)

  

# Create the tool
pandas_tool = Tool(
	name="Manipulate and Analyze tabular data with Code",
	func=pandas_tool_agent.invoke, # <-- This is the function that will be called when the tool is run. Note that there is no `()` at the end
	description="Useful for search-based queries",
)
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for more info about Tools, such as the list of tools or how to create your own tool, see https://docs.crewai.com/core-concepts/Tools/#introduction