What is the downside of assing more tools to one agent_executor?

[mraguth]

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Example Code

agent_executor = AgentExecutor(
    agent=agent, 
    tools=tools, 
    max_iterations=40, 
    early_stopping_method="force",    ##### Changed from generate due to Generate error.
    allow_dangerous_code=True,
    allow_dangerous_requests=True,
    verbose=False,
) | (lambda x: x["output"])

Description

For my use case I am getting better token usage and performance by adding more tools to agent_executor shown above. As of now tools are like below. How python agent executor work? is there any issue? Do I need to divest to different agents? Please advise.

repl = PythonAstREPLTool(      ##### This is the function brining df in to the agent_executor
    locals={"df": df},
    name="python_repl",
    description="Runs Python code and returns the output of the final line",
    args_schema=PythonInputs,
    prompt = prompt,
)

from langchain_core.tools import tool

@tool
def add(first_int: int, second_int: int) -> int:
    "Add two integers."
    return first_int + second_int

@tool
def subtract(first_int: int, second_int: int) -> int:
    "Subtract second integer from first integer."
    return first_int - second_int

@tool
def multiply(first_int: int, second_int: int) -> int:
    "Multiply two integers."
    return first_int * second_int

@tool
def divide(first_int: int, second_int: int) -> float:
    "Divide first integer by second integer."
    if second_int == 0:
        raise ValueError("Cannot divide by zero")
    return first_int / second_int

@tool
def modulus(first_int: int, second_int: int) -> int:
    "Find the modulus of first integer by second integer."
    if second_int == 0:
        raise ValueError("Cannot find modulus by zero")
    return first_int % second_int

@tool
def exponentiate(base: int, exponent: int) -> int:
    "Exponentiate base to the power of exponent."
    return base ** exponent

@tool
def integer_divide(first_int: int, second_int: int) -> int:
    "Perform integer division of first integer by second integer."
    if second_int == 0:
        raise ValueError("Cannot perform integer division by zero")
    return first_int // second_int

tavily = TavilySearchResults(max_results=5)

tools = [repl, retriever_tool, add, subtract, multiply, divide, modulus, exponentiate, integer_divide,tavily]

System Info

Name: langchain
Version: 0.3.2

[dosubot[bot]]

Adding more tools to an AgentExecutor can lead to increased complexity and potential compatibility issues. The AgentExecutor is designed to work with a specific set of tools, and it validates that the tools provided are compatible with the agent. If the tools differ from what the agent is designed to handle, it can raise a ValueError due to a mismatch. Additionally, having more tools can increase the decision-making complexity for the agent, potentially leading to longer execution times or more iterations to reach a decision.

LangChain suggests considering more advanced agents, such as those built with LangGraph, which provide greater flexibility and control. LangGraph replaces the traditional agent executor with a graph that manages the agent's cycles and tracks the state, offering a more robust framework for handling complex interactions and tool integrations. This might be a better approach if your tasks or tools are distinct enough to warrant separate handling ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu can you provide some langchain agentic examples?

[dosubot[bot]]

Examples of LangChain agents include:

• ZeroShotAgent
• ChatAgent
• OpenAIFunctionsAgent
• XMLAgent
• OpenAIMultiFunctionsAgent
• ConversationalAgent
• ConversationalChatAgent
• StructuredChatAgent
• ReActTextWorldAgent

LangChain also offers various agent toolkits that integrate with external resources and services, such as:

• AINetworkToolkit
• AmadeusToolkit
• AzureCognitiveServicesToolkit
• FileManagementToolkit
• GmailToolkit
• JiraToolkit
• JsonToolkit
• MultionToolkit
• NasaToolkit
• NLAToolkit
• O365Toolkit
• OpenAPIToolkit
• PlayWrightBrowserToolkit
• PowerBIToolkit
• SlackToolkit
• SteamToolkit
• SQLDatabaseToolkit
• SparkSQLToolkit
• ZapierToolkit

Additionally, LangChain includes experimental agents like:

• create_csv_agent
• create_pandas_dataframe_agent
• create_spark_dataframe_agent
• create_xorbits_agent

Note that some agents, such as ZeroShotAgent, ChatAgent, OpenAIFunctionsAgent, OpenAIMultiFunctionsAgent, ConversationalAgent, ConversationalChatAgent, StructuredChatAgent, and XMLAgent, have been deprecated in favor of using the LCEL builder over a class ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu since I am starting a Agentic frame work, do you prefer LangGraph than LangChain? thoughts?

[dosubot[bot]]

You should prefer using LangGraph over LangChain when starting an agentic framework. LangGraph offers performant orchestrations of application components while maintaining concise and readable code. It includes built-in persistence, support for cycles, and prioritizes controllability, which are advantageous for developing complex agentic frameworks. Additionally, LangGraph supports built-in persistence and streaming of chain steps, offering greater control and customizability ^[1].

To continue talking to Dosu, mention @dosu.