hermes - 💡(How to fix) Fix Background reviews currently inherit full tool surface and full history [1 participants]

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NousResearch/hermes-agent#13650Fetched 2026-04-22 08:05:04
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Background review currently runs as a structurally heavier agent than necessary.

Even with configurable review-model routing, the review path still inherits:

  • a full tool surface when enabled_toolsets is not restricted
  • the full conversation history via messages_snapshot

That makes review payload size larger than needed, even when the actual review task is narrowly scoped to memory and/or skills.

Root Cause

This is a separate problem from review-model selection.

Using a cheaper review model helps with cost per token. Reducing review payload helps with the number of tokens sent per review call.

Both matter, but they should be discussed independently:

  • PR #13647: configurable review model routing
  • PR #13648: minimal toolsets + capped review history
RAW_BUFFERClick to expand / collapse

Summary

Background review currently runs as a structurally heavier agent than necessary.

Even with configurable review-model routing, the review path still inherits:

  • a full tool surface when enabled_toolsets is not restricted
  • the full conversation history via messages_snapshot

That makes review payload size larger than needed, even when the actual review task is narrowly scoped to memory and/or skills.

Why this matters

This is a separate problem from review-model selection.

Using a cheaper review model helps with cost per token. Reducing review payload helps with the number of tokens sent per review call.

Both matter, but they should be discussed independently:

  • PR #13647: configurable review model routing
  • PR #13648: minimal toolsets + capped review history

Current behavior

In the current review path, the forked review agent:

  • inherits the default full tool loading behavior unless enabled_toolsets is explicitly set
  • receives the full messages_snapshot as conversation_history

That means background review can end up much heavier than its actual task requires.

Proposed direction

For background review, use a narrower default review payload:

  • only enable the relevant review toolsets
    • memory review -> ["memory"]
    • skills review -> ["skills"]
    • combined review -> ["memory", "skills"]
  • pass only a capped recent history window instead of the full conversation history

A minimal implementation can simply cap review history to the last N messages. No token-budgeting or major refactor is required for an initial improvement.

Why this should be separate from model-routing

This issue is about payload size, not model choice.

Even if review continues to use the main model, a smaller review payload is still beneficial:

  • fewer prompt tokens
  • less structural overhead
  • lower likelihood of extra latency and rate-limit pressure

Scope

This is not proposing:

  • prompt rewriting
  • compression redesign
  • max_iterations changes
  • skip_context_files
  • broader architecture changes

Just:

  • smaller tool surface for review
  • smaller history window for review

Related

  • PR #13647 — configurable review model routing
  • PR #13648 — minimal toolsets and capped history
  • #5129
  • #8506

If this direction is acceptable, I’m happy to keep the payload-size optimization separate from the review-model-routing discussion.

extent analysis

TL;DR

Implementing a narrower default review payload with relevant toolsets and a capped recent history window can reduce the review payload size.

Guidance

  • Identify the specific review task (memory, skills, or combined) and enable only the relevant review toolsets by default, such as ["memory"] for memory review.
  • Cap the review history to the last N messages instead of passing the full conversation history to reduce payload size.
  • Verify the effectiveness of these changes by monitoring the review payload size and the number of tokens sent per review call.
  • Consider keeping the payload-size optimization separate from the review-model-routing discussion to address the issue independently.

Example

No code snippet is provided as it is not explicitly supported by the issue.

Notes

The proposed solution focuses on reducing the review payload size by optimizing toolsets and conversation history, which is a separate issue from review-model selection and routing.

Recommendation

Apply workaround: Implementing a narrower default review payload with relevant toolsets and a capped recent history window can help reduce the review payload size and improve performance.

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