Issue with Main Agent Overriding Correct Responses from Tool Agents in LangFlow

[vij-06]

Hi everyone,

In our LangFlow setup, we have multiple agents, each handling specific tasks. These individual agents are added as tool components to a main agent. The main agent is responsible for routing queries to the appropriate agent based on the user input.

The issue we're facing is that the responses generated by the individual agents are correct and accurate. However, when these responses are passed back to the main agent, the final output is often incorrect, inconsistent, or changes when the same question is asked multiple times.

We’re looking for suggestions or best practices on how to ensure the main agent doesn’t alter or degrade the correct responses from the tool agents. Has anyone encountered this behavior or found a workaround to preserve the original response?

Any help or guidance would be greatly appreciated!

Thanks in advance.

[dosubot[bot]]

Hello @vij-06! I'm Dosu, a bot here to help you solve bugs, answer questions, and become a contributor. I'm ready to assist you while we wait for a human maintainer. How can I help you today?

To ensure that the main agent in LangFlow does not alter or degrade the correct responses from the tool agents, you can use the PassMessageComponent as a workaround. This component is designed to forward the input message unchanged, preserving the original response. The pass_message method in the PassMessageComponent simply returns the input message as the output, ensuring that the message remains unaltered.

Here is a relevant code snippet from the PassMessageComponent:

class PassMessageComponent(Component):
    display_name = "Pass"
    description = "Forwards the input message, unchanged."
    name = "Pass"
    icon = "arrow-right"

    inputs = [
        MessageInput(
            name="input_message",
            display_name="Input Message",
            info="The message to be passed forward.",
            required=True,
        ),
        MessageInput(
            name="ignored_message",
            display_name="Ignored Message",
            info="A second message to be ignored. Used as a workaround for continuity.",
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Output Message", name="output_message", method="pass_message"),
    ]

    def pass_message(self) -> Message:
        self.status = self.input_message
        return self.input_message

This component can be used to maintain the integrity of the responses from the tool agents by passing them through without modification. Additionally, the ignored_message input can be used as a workaround for continuity, ensuring that only the intended message is forwarded ^[1][2].

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[vij-06]

@dosu The agent used here is OpenAI Agent.

[vij-06]

@dosu The agent we are using is OpenAI Agent. So, how to ensure the main agent doesn’t alter or degrade the correct responses from the tool agents.

[dosubot[bot]]

To ensure that the main agent using the OpenAI Agent in LangFlow does not alter or degrade the correct responses from the tool agents, you can consider the following approaches:

1. Configuration Settings: Adjust the OpenAI Agent's configuration settings to minimize alterations. For instance, set the temperature to a lower value to make the output more deterministic and reduce randomness. Ensure that the max_tokens setting is sufficient to capture the entire response without truncation ^[1].

2. Tool Mode: Use the Tool Mode feature to ensure that components are correctly recognized as tools. This allows the main agent to route queries appropriately and maintain the integrity of the responses from the tool agents ^[2].

3. Agent Instructions: Clearly define the agent's instructions and system prompts to ensure that the main agent understands its role in preserving the responses from the tool agents. This can help in maintaining the context and ensuring that the main agent does not override the responses ^[3].

4. Error Handling and Logging: Utilize LangFlow's error handling and logging mechanisms to track any issues related to response degradation. This can help in identifying and debugging any inconsistencies in the responses ^[4].

By carefully configuring these aspects, you can help ensure that the main agent preserves the original responses from the tool agents.

To continue talking to Dosu, mention @dosu.

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