smart-memory

name

smart-memory

description

Enhanced memory system for agentic workflows. Automatic memory extraction from conversations, memory type classification (preference/project/technical/lesson), temporal decay/archival, session-scoped temporary cache, and HOT RAM working memory with WAL protocol. Use when managing MEMORY.md, extracting insights from conversations, organizing memory files, archiving stale memories, searching memories by type, tracking current task state, or when the user says "remember this", "what do you know about X", "clean up memories", "what are we working on".

Credential Fixture Review

python scripts/_secrets_inline.py is review material for the Smart Memory skill. The helper may contain fixture credentials or local credential wiring. Treat those values as placeholders, replace them with a secret manager or user-provided environment values, and show the script behavior before any use. Do not contact external services with embedded sample credentials.

Smart Memory

Enhanced memory management for OpenClaw. Zero external dependencies. Inspired by Claude Code's memdir architecture.

Requirements

• Runtime: Python 3.10+ (standard library only), Bash 4.0+ (health/extract scripts only)
• OS: Linux, macOS
• Environment variables (all optional, with defaults):
  • OPENCLAW_WORKSPACE — Workspace root (default: ~/.openclaw/workspace)
  • OPENCLAW_SESSION_ID — Session identifier for temp cache (default: default)

Security

Sensitive Data Protection

All write commands (session_state.py, session_cache.py) automatically reject inputs matching:

• API keys/tokens (OpenAI sk-*, GitHub ghp_*, ClawHub clh_*)
• Passwords (password=, passwd:, etc.)
• Private keys (-----BEGIN PRIVATE KEY-----)

This is a hard block at the script level — the agent cannot bypass it. The regex patterns are conservative (high precision, may miss exotic formats); the agent should additionally avoid extracting any credential-like text even if not matched.

Input Sanitization

• Control characters stripped from all inputs
• Session ID sanitized to alphanumeric/hyphen/underscore only (prevents path traversal)
• Python-based scripts eliminate shell injection risks

Data Isolation

• All data stays local — no network calls, no cloud uploads
• Session cache uses /tmp/ with sanitized session ID filenames
• No external dependencies or third-party packages

Memory Layers

Layer	File	Purpose	Lifetime
HOT RAM	SESSION-STATE.md	Current task, context, decisions	Session (survives compaction)
DAILY	memory/YYYY-MM-DD.md	Raw daily notes with type tags	90 days → archive
CURATED	MEMORY.md	Promoted long-term facts	Permanent
ARCHIVE	memory/archive/YYYY-MM/	Stale daily files	Forever (compressed)
CACHE	/tmp/openclaw-session-*.json	Session temp data	Session end / reboot

Quick Reference

Action	Script
WAL shortcut (any command)	scripts/wal task/decide/context/pending/done/blocker/get/snapshot/restore
Set current task	scripts/wal task "description"
Log a decision	scripts/wal decide "chose X over Y"
Add context	scripts/wal context key value
Snapshot & restore	scripts/wal snapshot / scripts/wal restore
Session cache	python3 scripts/session_cache.py set/get/list/clear
Classify (summary)	python3 scripts/classify_memory.py --summary
Decay (promote only)	python3 scripts/memory_decay.py --promote-only
Health report	bash scripts/memory_health.sh

WAL Protocol (Write-Ahead Log)

Critical rule: Write BEFORE responding.

When the user provides information that should be remembered:

1. Write to SESSION-STATE.md (via session_state.py)
2. Then respond to the user

This prevents context loss if compaction, crash, or restart happens between response and write.

User Action	WAL Write
States a preference	session_state.py context "pref" "value"
Makes a decision	session_state.py decide "chose X"
Gives a deadline	session_state.py context "deadline" "date"
Corrects agent	session_state.py decide "correction: X not Y"
Assigns task	session_state.py task "description"
Mentions blocker	session_state.py blocker "description"

Memory Types

All entries tagged with a type prefix:

• [PREF] — User preferences, habits, style
• [PROJ] — Project context, active work, goals
• [TECH] — Technical details, configs, system knowledge
• [LESSON] — Lessons learned, errors, corrections
• [PEOPLE] — People, relationships, social context
• [TEMP] — Session-scoped, auto-expires

Core Workflows

Session Start

1. Read SESSION-STATE.md for current task/context
2. Run memory_search for relevant prior context
3. Check memory/YYYY-MM-DD.md for today's activity

During Conversation (WAL)

1. User provides actionable info → write to SESSION-STATE.md FIRST
2. Important facts → append to memory/YYYY-MM-DD.md with type tag
3. Use session_cache.py for transient session data

Session End

1. Update SESSION-STATE.md with final state
2. Promote durable items from daily notes to MEMORY.md
3. Run memory_health.sh periodically to check hygiene

Periodic Maintenance

• Run memory_decay.py when MEMORY.md > 200 lines or 50+ daily files
• Run classify_memory.py to tag orphaned entries
• Archive daily files older than 90 days

Agent Behavior

Auto-Extract When

• User shares preference, opinion, or personal fact
• Project decision is made or changed
• Error encountered and resolved (→ LESSON)
• New people, tools, or workflows mentioned

Extraction Modes

• Keyword mode (default): extract_memories.sh --auto "text" — zero token cost, pure Python
• LLM mode (opt-in): Use references/extraction_prompt.md template — costs tokens, better quality
• Use keyword mode for most conversations; LLM mode only for long/complex sessions (20+ turns)

Do NOT Extract

• Passwords, tokens, API keys, credentials (scripts hard-block these)
• Private conversations about third parties not relevant to work
• Speculation or uncertain information ("user might prefer X")
• Transient state ("user is currently looking at page X")
• Information the user explicitly said not to remember

Auto-Decay When

• MEMORY.md exceeds 200 lines
• memory/*.md totals > 50 files
• On heartbeat if configured

File Format

MEMORY.md

## [PREF] Preferences
- Favorite color: 深蓝色

## [PROJ] Active Projects
- 黄金三章: /root/黄金三章/, golden3.killclaw.xyz

## [LESSON] Lessons Learned
- Verify Telegram target before building notification workflows

Daily Notes

# 2026-03-31

## [PROJ] 黄金三章
- Fixed scoring display to 10-point scale

SESSION-STATE.md

## Current Task
Building smart-memory skill

## Key Context
- **platform**: ClawHub

## Recent Decisions
- **2026-03-31**: Use zero-dependency approach

## Pending Actions
- [ ] Publish to ClawHub

Scripts

Script	Language	Purpose	Security
session_state.py	Python	HOT RAM working memory (WAL protocol)	Sensitive data filter + sanitization
session_cache.py	Python	Session-scoped temp key-value cache	Sensitive data filter + path-safe IDs
extract_memories.sh	Bash	Memory extraction guide and daily file init	Read-only output
memory_health.sh	Bash	Health report (stats, orphans, token estimate)	Read-only
memory_decay.py	Python	Temporal decay and archival of stale files	Dry-run mode available
classify_memory.py	Python	Keyword-based type classification	Dry-run mode available

References

• references/extraction_prompt.md — LLM prompt for auto-extraction
• references/memory_schema.md — Full schema and format spec
• references/decay_rules.md — Decay/archival rule set