Memory Retro

Memory Retro

You have access to a role-based persistent memory system. Use manual writes selectively; automatic extraction already handles routine task-end checkpoints.

Tools Available

Process

1. Reflect: What did I learn that would be useful in the future?
2. If nothing worth remembering, skip — not every task produces insights.
3. For each insight, decide type:
   • Learning: Durable cross-session fact, pattern, or rule.
   • Preference: User's communication style, tool preference, coding habit.

Dedup check (built-in)

add_learning auto-deduplicates. Just call it — if similar text exists, it returns "Already stored". Then use reinforce instead.

Write

role_exec({ op: "add_learning", args: { content: "MyBatis-Plus getOne needs .last('LIMIT 1') to avoid TooManyResultsException" } })

LLM auto-extracts tags (e.g., mybatis, gotcha). No manual tagging.

Preferences

role_exec({ op: "add_preference", args: { content: "用户偏好中文沟通，技术术语可保留英文", category: "Communication" } })

Reinforce (when you USE an existing memory)

role_exec({ op: "reinforce", args: { content: "安全删除原则" } })
→ "Reinforced [abc123] -> 6x"

Quality Rules

What to remember

• ✅ Root cause: "X fails because of Y. Fix: Z"
• ✅ Gotcha: "Tool A requires flag B, otherwise silently fails"
• ✅ Decision: "We chose X over Y because Z"
• ✅ Pattern: "This project uses pattern A for all B"
• ✅ User preference discovered

What NOT to remember

• ❌ Obvious facts
• ❌ Task-specific instances (save the pattern, not the instance)
• ❌ Temporary states
• ❌ Redundant with existing entries
• ❌ Full error messages without analysis

Writing rules

• Concise — under 120 chars
• Own words — distill, don't copy-paste
• Actionable — changes how you'd approach similar tasks
• Conservative — bad memory > missing memory is worse. 1 quality insight > 5 mediocre.

Pending Layer

Two paths to memory:

Auto-extract (agent_end, compaction)
  → pending.md [○] → search score ≥0.5 → auto-promote → consolidated [0x]

Manual (this tool)
  → consolidated [0x] directly

The pending layer filters noise. Only memories proven useful by actual usage survive. Manual entries skip pending because you're explicitly deciding they're worth keeping.

Reinforce vs Promote

Category Reference

Examples

✅ Good:
role_exec({ op: "add_learning", args: { content: "MyBatis-Plus getOne needs .last('LIMIT 1')" } })

✅ Good (reinforce):
role_exec({ op: "reinforce", args: { content: "安全删除原则" } })

❌ Bad: "The user asked me to fix a bug" — too generic
❌ Bad: "Error at /src/index.ts:42" — copy-paste, no insight

Important

• add_learning auto-deduplicates — if similar exists, use reinforce instead.
• LLM auto-extracts tags — no manual tagging needed.
• Quality > quantity. 30 quality entries > 300 noisy ones.
• The system auto-extracts at session end, but manual retro is more accurate.