gemini-cli - ✅(Solved) Fix Investigate IDE companion memory pipeline from worker execution to recall injection [1 pull requests, 1 participants]

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google-gemini/gemini-cli#25549Fetched 2026-04-17 08:55:21
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The Newgate-style memory path around the IDE companion currently has enough pieces to look plausible in code, but the live execution path is not proven end to end.

This issue tracks an execution-first investigation of the following path:

GH Copilot CLI gpt-5-mini worker -> automatic fleet log -> KI queue -> promoted knowledge -> indexed memory -> recall injection

Root Cause

The Newgate-style memory path around the IDE companion currently has enough pieces to look plausible in code, but the live execution path is not proven end to end.

This issue tracks an execution-first investigation of the following path:

GH Copilot CLI gpt-5-mini worker -> automatic fleet log -> KI queue -> promoted knowledge -> indexed memory -> recall injection

Fix Action

Fixed

PR fix notes

PR #25550: docs: add Newgate memory pipeline investigation design

Description (problem / solution / changelog)

Summary

  • add a design note for investigating the IDE companion memory pipeline from worker execution to recall injection
  • link the design from IDE integration docs
  • document the verified gaps that must be proven in runtime order

Resolves #25549

Changed files

  • docs/ide-integration/index.md (modified, +4/-0)
  • docs/ide-integration/newgate-memory-pipeline-investigation.md (added, +190/-0)
RAW_BUFFERClick to expand / collapse

Summary

The Newgate-style memory path around the IDE companion currently has enough pieces to look plausible in code, but the live execution path is not proven end to end.

This issue tracks an execution-first investigation of the following path:

GH Copilot CLI gpt-5-mini worker -> automatic fleet log -> KI queue -> promoted knowledge -> indexed memory -> recall injection

Verified current state

  • The bridge can be running while the GH Copilot CLI gpt-5-mini worker is not actually active.
  • fleet_log is only written when it is called explicitly.
  • KI promotion currently stops at file creation under knowledge/.
  • Recall exists as a memory/tool surface, but it is not injected into the worker prompt path.
  • The current Pipeline① packetizer is JavaScript-specific, so it cannot directly audit the Python/shell hook path.

Investigation goals

  1. Prove worker boot with a real gpt-5-mini run.
  2. Prove worker completion automatically emits structured fleet logs.
  3. Prove success auto-generates a KI queue candidate.
  4. Prove promotion auto-indexes promoted knowledge.
  5. Prove recall is injected back into the next worker prompt.
  6. Prove operator-visible status surfaces show the active stage and failures.

Proposed artifact

A design note for this investigation has been prepared and will be linked from the PR that addresses this issue.

extent analysis

TL;DR

Investigate and prove the end-to-end execution path of the Newgate-style memory path around the IDE companion by addressing the six investigation goals.

Guidance

  • Start by proving worker boot with a real gpt-5-mini run to establish a baseline for further investigation.
  • Verify that worker completion automatically emits structured fleet logs by checking the log output after a worker run.
  • Focus on auto-generating a KI queue candidate and promoting it to test the knowledge indexing and recall injection.
  • Use the proposed design note as a reference to ensure all components are correctly integrated and functioning as expected.
  • Test operator-visible status surfaces to ensure they accurately reflect the active stage and any failures.

Example

No code snippet is provided as the issue does not contain specific code references.

Notes

The investigation goals provide a clear roadmap for proving the end-to-end execution path, but the issue lacks specific technical details about the components and their interactions.

Recommendation

Apply workaround: Address each investigation goal systematically to prove the execution path and identify any issues or bottlenecks. This approach will help to ensure that all components are working correctly and provide a clear understanding of the system's behavior.

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