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openclaw - 💡(How to fix) Fix [Feature Request] Auto-select Thinking Level based on query complexity [1 participants]

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openclaw/openclaw#61794•Fetched 2026-04-08 02:54:24
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Currently users must manually select the Thinking Level (default/low/medium/high) before each conversation. It would be great if OpenClaw could automatically select the appropriate thinking level based on query complexity.

Root Cause

Currently users must manually select the Thinking Level (default/low/medium/high) before each conversation. It would be great if OpenClaw could automatically select the appropriate thinking level based on query complexity.

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Description

Currently users must manually select the Thinking Level (default/low/medium/high) before each conversation. It would be great if OpenClaw could automatically select the appropriate thinking level based on query complexity.

Proposed Solution

Add an 'auto' option for Thinking Level:

  • Simple questions (casual chat, factual queries) ? auto use low/default
  • Medium complexity ? auto use medium
  • Complex tasks (programming, analysis, multi-step reasoning) ? auto use high

Use Case

  • Reduces manual friction - no need to switch thinking levels manually
  • More resource-efficient responses for simple queries
  • Better AI performance on complex tasks without user intervention

Implementation Ideas

  • Analyze input token length
  • Detect keywords indicating complexity (e.g. 'analyze', 'explain', 'debug', 'derive')
  • Use model feedback to assess task complexity at session start
  • Add a configuration option 'thinking: auto' that enables this behavior

Additional Context

The Thinking Level selector is prominently placed in the UI above the chat area, making it a perfect candidate for this automation.

extent analysis

TL;DR

Implement an 'auto' option for the Thinking Level that dynamically selects the appropriate level based on query complexity.

Guidance

  • Analyze the input query to determine its complexity, considering factors such as token length and keywords indicating complexity.
  • Introduce a configuration option 'thinking: auto' to enable or disable this automated behavior.
  • Develop a decision-making process to map query complexity to the corresponding Thinking Level (low, medium, high).
  • Test and refine the automation logic to ensure it aligns with the desired use cases and performance goals.

Example

def determine_thinking_level(query):
    if query_length(query) < 10 and not has_complex_keywords(query):
        return 'low'
    elif has_complex_keywords(query) or query_length(query) > 50:
        return 'high'
    else:
        return 'medium'

Notes

The implementation details may vary depending on the specific requirements and the underlying technology stack. It's essential to consider the trade-offs between accuracy, performance, and user experience when designing the automation logic.

Recommendation

Apply a workaround by introducing a basic implementation of the 'auto' option, using a simple decision-making process based on query length and keywords, and refine it further based on user feedback and performance data.

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