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dify - 💡(How to fix) Fix [Agent Node] Optimize Tool Observation Output to Reduce Token Waste Caused by Data Duplication

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1. Is this request related to a challenge you're experiencing? Tell me about your story.

I am building an Agent using the Agent Node in Dify Workflow, which relies heavily on custom tools (e.g., database queries, API calls). I have noticed a significant inefficiency in how the Agent node handles the observation output from these tools. Currently, the system feeds highly repetitive data back to the LLM context. The Challenge: Instead of returning clean data, the Agent node wraps the same tool response in multiple layers (e.g., a raw string, followed by a JSON string, followed by a "tool response" wrapper). Impact: Token Waste: The same information is repeated 2-3 times, tripling the token cost for that step. Context Pollution: It consumes valuable context window space with redundant noise, potentially causing the model to lose focus or hit context limits faster. Cost: It increases inference costs unnecessarily for every tool call.

2. Additional context or comments

I have analyzed the observation logs generated by the Agent node and identified the specific redundancy pattern. Current Behavior (The Issue): The tool response is serialized multiple times, creating a "nested doll" effect. Example of current observation content: VariableMessage(variable_name='result', variable_value='{"result": "...data..."}') + tool response: {"result": "...data..."} In this example, the actual data payload ("...data...") is repeated at least twice within the same context string. Proposed Solution: Please optimize the output parser for the Agent node tools. Remove redundant wrapper layers (like VariableMessage or duplicate tool response: prefixes) if they contain the same payload. Ensure the LLM receives the tool output in a clean, single-layer JSON format (or plain text) to minimize token usage. This optimization is crucial for maintaining cost-efficiency and context window limits in complex Agent workflows.

3. Can you help us with this feature?

  • I am interested in contributing to this feature.

extent analysis

TL;DR

Optimize the Agent node's output parser to remove redundant wrapper layers and ensure the LLM receives tool output in a clean, single-layer JSON format.

Guidance

  • Analyze the observation logs to identify the specific redundancy pattern and understand how the tool response is being serialized multiple times.
  • Modify the output parser to remove unnecessary wrapper layers, such as VariableMessage or duplicate tool response: prefixes, if they contain the same payload.
  • Ensure the optimized output parser can handle different types of tool responses and edge cases to prevent data loss or corruption.
  • Test the optimized output parser with various tool responses to verify that it produces the expected clean and single-layer JSON format.

Example

# Example of optimized output parser
def optimize_tool_response(tool_response):
    # Remove redundant wrapper layers
    if isinstance(tool_response, dict) and 'variable_value' in tool_response:
        tool_response = tool_response['variable_value']
    # Remove duplicate tool response prefixes
    if isinstance(tool_response, str) and tool_response.startswith('tool response: '):
        tool_response = tool_response.replace('tool response: ', '')
    return tool_response

Notes

The proposed solution assumes that the redundancy pattern is consistent and can be identified through log analysis. However, the actual implementation may require additional error handling and edge case consideration to ensure the optimized output parser works correctly in all scenarios.

Recommendation

Apply workaround: Optimize the Agent node's output parser to remove redundant wrapper layers and ensure the LLM receives tool output in a clean, single-layer JSON format. This will help minimize token usage and prevent context window limits in complex Agent workflows.

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#api#optimization#integration issue#index setup#retrieval issue

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dify - 💡(How to fix) Fix [Agent Node] Optimize Tool Observation Output to Reduce Token Waste Caused by Data Duplication