Memory Manager

Memory Manager — the skill that reads the filing cabinet

After every major task or failure, call tools/memory_reflect.py to log what happened. Before important decisions, read the top entries from memory/semantic/LESSONS.md and memory/semantic/DECISIONS.md.

When to trigger full consolidation

• On explicit reflect trigger from the user.
• When the context window is getting full.
• When episodic memory exceeds ~500 entries.

Consolidation steps

1. Load top-5 episodic entries by salience score.
2. Detect recurring patterns across the last 100 entries.
3. For patterns appearing ≥3 times, promote to memory/semantic/LESSONS.md.
4. Flag any skill with ≥3 failures in 14 days for rewrite (see on_failure.py).
5. Archive resolved working context to memory/episodic/snapshots/.
6. Commit via git so history is preserved: git log memory/ is the agent's autobiography.

Searching memory by keyword

When you need to find a specific memory by topic or keyword (not just the top-salience entries), use the FTS5 search tool:

python3 .agent/memory/memory_search.py <query>

This indexes all .md and .jsonl files under .agent/memory/ and returns ranked results with context snippets. Falls back to grep if FTS5 is not available. The index auto-rebuilds when files change.

Anti-patterns

• Do not auto-merge personal/ into semantic/ — user preferences are not general knowledge.
• Do not delete entries to "clean up" memory. Archive them.
• Do not promote lessons from a single incident. Require recurrence.

Self-rewrite hook

Every 10 reflections, or when the same type of mistake appears 3+ times recently, this skill's approach to salience or distillation needs adjustment. Propose conservative edits and log the diff in memory/semantic/DECISIONS.md.