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feat(memory): Add status metadata and session-scoped retrieval for action-oriented memories

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
25/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Ferma
Stack tecnologico
python
Ambito
ai, backend-api-design

Direzione di ricerca

Start by reading the agent_memory branch plugin and the existing memory retrieval API, especially how memories are represented and scored. The proposed changes span status metadata, session filtering, and relevance decay, so first get maintainer alignment on the approach; done would mean an agreed scope and behavior for all three parts.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

methods

Problem

When EverMemOS is used with long-running agents (e.g., OpenClaw 24/7 agents), completed action items from past sessions are retrieved alongside current context. The agent then re-executes already-finished tasks because it cannot distinguish resolved actions from active ones.

Root cause
  1. No status metadata on memories — completed and pending action items look identical in retrieval results.
  2. No session boundary awareness — retrieval treats all memories equally regardless of which session produced them.
  3. No relevance decay for action-oriented memories — a "deploy X" instruction from 3 days ago has the same retrieval weight as one from 5 minutes ago.
Real-world impact

In production OpenClaw deployments, we observe:

  • Agents re-running deployment scripts that already succeeded
  • Duplicate messages sent because a "send update to user" memory resurfaces
  • Manual workaround required: prefixing resolved items with [RESOLVED] markers in memory text

Proposed Solution

1. Status metadata on memories

Add an optional status field to memory entries:

{
  "content": "Deploy hotfix to production",
  "type": "episodic",
  "status": "resolved",
  "resolved_at": "2026-03-22T10:30:00Z"
}

Possible values: active (default) | resolved | superseded

2. Session-scoped retrieval option

Add a session_id field to memories and a retrieval filter:

GET /api/v1/memories?session_id=abc123&include_resolved=false

This allows agents to scope retrieval to the current session while optionally including cross-session context.

3. Relevance decay for action memories

For memories tagged as action-oriented (trait type or custom tag), apply a time-based decay factor to retrieval scoring. Informational memories (facts, preferences) would remain unaffected.

Alternatives Considered

  • Client-side markers ([RESOLVED] prefix in text): Currently used as a workaround. Fragile — depends on LLM parsing text markers correctly.
  • TTL-based expiry (related to #95): Too blunt — expires all memories, not just completed actions.
  • Manual deletion: Loses valuable historical context that may be useful for retrospectives.

Additional Context

  • We run EverMemOS with OpenClaw agents across Telegram, WeChat, and Discord channels with shared groupId.
  • The agent_memory branch plugin is the integration point.
  • Happy to contribute implementation if the team aligns on the approach.
Lingua principale
Python
Stelle
13.3k
Fork
926
Merge medio
17h 8m
PR unite (30g)
32

Preparare l'ambiente

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Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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