feat: improve memory prioritization — user preferences over procedural knowledge
Inspired by OpenAI Codex's memory prompt improvements (openai/codex#14493) which focus memory writes on user preferences and recurring patterns rather than procedural task details. Key insight: 'Optimize for reducing future user steering — the most valuable memory prevents the user from having to repeat themselves.' Changes: - MEMORY_GUIDANCE (prompt_builder.py): added prioritization hierarchy and the core principle about reducing user steering - MEMORY_SCHEMA (memory_tool.py): reordered WHEN TO SAVE list to put corrections first, added explicit PRIORITY guidance - Memory nudge (run_agent.py): now asks specifically about preferences, corrections, and workflow patterns instead of generic 'anything' - Memory flush (run_agent.py): now instructs to prioritize user preferences and corrections over task-specific details
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@@ -73,9 +73,15 @@ DEFAULT_AGENT_IDENTITY = (
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MEMORY_GUIDANCE = (
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"You have persistent memory across sessions. Save durable facts using the memory "
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"tool: user preferences, environment details, tool quirks, and stable conventions. "
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"Memory is injected into every turn, so keep it compact. Do NOT save task progress, "
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"session outcomes, or completed-work logs to memory; use session_search to recall "
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"those from past transcripts."
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"Memory is injected into every turn, so keep it compact and focused on facts that "
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"will still matter later.\n"
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"Prioritize what reduces future user steering — the most valuable memory is one "
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"that prevents the user from having to correct or remind you again. "
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"User preferences and recurring corrections matter more than procedural task details.\n"
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"Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO "
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"state to memory; use session_search to recall those from past transcripts. "
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"If you've discovered a new way to do something, solved a problem that could be "
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"necessary later, save it as a skill with the skill tool."
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)
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SESSION_SEARCH_GUIDANCE = (
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+5
-3
@@ -3542,7 +3542,8 @@ class AIAgent:
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flush_content = (
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"[System: The session is being compressed. "
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"Please save anything worth remembering to your memories.]"
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"Save anything worth remembering — prioritize user preferences, "
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"corrections, and recurring patterns over task-specific details.]"
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)
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_sentinel = f"__flush_{id(self)}_{time.monotonic()}"
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flush_msg = {"role": "user", "content": flush_content, "_flush_sentinel": _sentinel}
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@@ -4541,8 +4542,9 @@ class AIAgent:
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self._turns_since_memory += 1
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if self._turns_since_memory >= self._memory_nudge_interval:
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user_message += (
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"\n\n[System: You've had several exchanges in this session. "
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"Consider whether there's anything worth saving to your memories.]"
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"\n\n[System: You've had several exchanges. Consider: "
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"has the user shared preferences, corrected you, or revealed "
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"something about their workflow worth remembering for future sessions?]"
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)
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self._turns_since_memory = 0
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@@ -439,11 +439,13 @@ MEMORY_SCHEMA = {
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"Memory is injected into future turns, so keep it compact and focused on facts "
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"that will still matter later.\n\n"
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"WHEN TO SAVE (do this proactively, don't wait to be asked):\n"
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"- User corrects you or says 'remember this' / 'don't do that again'\n"
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"- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n"
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"- You discover something about the environment (OS, installed tools, project structure)\n"
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"- User corrects you or says 'remember this' / 'don't do that again'\n"
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"- You learn a convention, API quirk, or workflow specific to this user's setup\n"
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"- You identify a stable fact that will be useful again in future sessions\n\n"
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"PRIORITY: User preferences and corrections > environment facts > procedural knowledge. "
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"The most valuable memory prevents the user from having to repeat themselves.\n\n"
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"Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO "
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"state to memory; use session_search to recall those from past transcripts.\n"
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"If you've discovered a new way to do something, solved a problem that could be "
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