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[FEATURE]: Support multi-LLM parallel review for /reviews command #3811

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opened 2026-02-16 17:41:34 -05:00 by yindo · 1 comment
yindo commented 2026-02-16 17:41:34 -05:00
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Originally created by @wkissw on GitHub (Dec 23, 2025).

Originally assigned to: @thdxr on GitHub.

Feature hasn't been suggested before.

  • I have verified this feature I'm about to request hasn't been suggested before.

Describe the enhancement you want to request

Problem / Motivation : Currently, the /reviews command likely uses a single LLM to provide feedback. However, different models (e.g., GPT-5, Claude 4.5 Sonnet, DeepSeek-V3) have different strengths, such as code logic analysis, security vulnerability detection, or style consistency. Relying on a single model may lead to biased or incomplete reviews.

Proposed Solution : I suggest enabling the /reviews command to trigger multiple LLMs simultaneously. The system should:
Allow users to specify multiple models in the command or configuration.
Execute the review tasks in parallel.
Consolidate the findings from all models into a single, comprehensive summary report.

Benefits :
Comprehensive Insights: Capture a wider range of potential issues.
Cross-Verification: Reduce false positives by comparing results across models.
Flexibility: Users can choose the best model combination for their specific tech stack.

Originally created by @wkissw on GitHub (Dec 23, 2025). Originally assigned to: @thdxr on GitHub. ### Feature hasn't been suggested before. - [x] I have verified this feature I'm about to request hasn't been suggested before. ### Describe the enhancement you want to request Problem / Motivation : Currently, the /reviews command likely uses a single LLM to provide feedback. However, different models (e.g., GPT-5, Claude 4.5 Sonnet, DeepSeek-V3) have different strengths, such as code logic analysis, security vulnerability detection, or style consistency. Relying on a single model may lead to biased or incomplete reviews. Proposed Solution : I suggest enabling the /reviews command to trigger multiple LLMs simultaneously. The system should: Allow users to specify multiple models in the command or configuration. Execute the review tasks in parallel. Consolidate the findings from all models into a single, comprehensive summary report. Benefits : Comprehensive Insights: Capture a wider range of potential issues. Cross-Verification: Reduce false positives by comparing results across models. Flexibility: Users can choose the best model combination for their specific tech stack.
yindo added the discussion label 2026-02-16 17:41:34 -05:00
yindo commented 2026-02-16 17:41:34 -05:00
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@github-actions[bot] commented on GitHub (Dec 23, 2025):

This issue might be a duplicate of existing issues. Please check:

  • #4789: [FEATURE]: LLM Council support - Directly addresses multiple LLM selection in a 'council' mode
  • #4790: [FEATURE]: Multiple AI models working concurrently with settings adjustable directly in TUI - Requests multiple models working simultaneously

Feel free to ignore if none of these address your specific case.

@github-actions[bot] commented on GitHub (Dec 23, 2025): This issue might be a duplicate of existing issues. Please check: - #4789: [FEATURE]: LLM Council support - Directly addresses multiple LLM selection in a 'council' mode - #4790: [FEATURE]: Multiple AI models working concurrently with settings adjustable directly in TUI - Requests multiple models working simultaneously Feel free to ignore if none of these address your specific case.
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Reference: anomalyco/opencode#3811
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