ClawHub

Memory Stack Core

Agents
CoreCompactionPersistence

Core memory resilience layer: WAL (Write-Ahead Log), Working Buffer, and three-layer memory integration. Prevents context loss during compaction and ensures critical state survives session restarts. Works with any OpenClaw agent.

Install

openclaw skills install @neroagent/memory-stack-core

Memory Stack Core

Transforms your agent's memory from fragile to antifragile. Implements proven patterns from the Claude Code leak and ClawHub's compaction-survival + session-persistence.

The Problem

LLM context windows fill up. When compaction happens, older messages get summarized. Summaries lose precision:

  • Exact file paths → "some file"
  • Specific numbers → "approximately 42"
  • Decisions → "we decided to do something"
  • Preferences → forgotten

Your agent wakes up after compaction dumber. Every session restarts from scratch.

The Solution: Three-Layer Memory Stack

┌────────────────────────────────────────┐
│   Long-term (MEMORY.md)                │  ← Curated wisdom, never edit manually
├────────────────────────────────────────┤
│   Daily Logs (memory/YYYY-MM-DD.md)    │  ← Conversation summaries
├────────────────────────────────────────┤
│   Working Buffer (memory/working-buffer.md)  │  ← Danger zone captures (60%+ context)
├────────────────────────────────────────┤
│   WAL (memory/wal.jsonl)               │  ← Write-Ahead Log: specifics as they appear
└────────────────────────────────────────┘

WAL (Write-Ahead Log)

When: Immediately upon receiving a human message that contains any:

  • Corrections ("Actually it's X not Y")
  • Proper nouns (names, places, products)
  • Preferences ("I prefer...")
  • Decisions ("Let's do X")
  • Draft changes (edits to active work)
  • Specific values (numbers, dates, IDs, URLs, paths)

What: Write a structured JSON line to memory/wal.jsonl with:

{
  "timestamp": "2026-04-01T16:20:00Z",
  "category": "decision|preference|path|value|correction|draft",
  "content": "the specific detail",
  "context": "surrounding message snippet"
}

Why: WAL entries are tiny, numerous, and survive forever. They're the source of truth for specifics.

Working Buffer

When: Token utilization reaches 60% (tracked via session_status).

What: Append every human + assistant exchange (full text) to memory/working-buffer.md:

## 2026-04-01 16:25:00 (turn 47)

**User:**
<message>

**Assistant:**
<response>

Why: The buffer is a file, so it doesn't count against context. It's your safety net for the danger zone. When compaction inevitably happens, you can recover from the buffer.

Daily Logs & Long-term

Already in OpenClaw. We integrate by:

  • At 80% context, suggest /wrap_up to flush to daily log
  • Periodic (weekly) review to promote daily log entries to MEMORY.md

Usage

Automatic Mode (recommended)

The skill hooks into your agent's message processing:

  1. Install skill
  2. Enable WAL and buffer in agent config (or use defaults)
  3. Nothing else — the skill automatically:
    • Scans human messages for specifics → WAL
    • Monitors token usage → activates buffer at 60%
    • Provides /memory_health command to view status

Manual Commands

  • tool("memory-stack-core", "wal_write", {...}) — manually add WAL entry
  • tool("memory-stack-core", "wal_read", {"limit": 50}) — view recent WAL
  • tool("memory-stack-core", "buffer_read", {"tail": 1000}) — view buffer tail
  • tool("memory-stack-core", "memory_health", {}) — get health report

Recovery Protocol

When context is lost (e.g., after compaction or new session):

  1. Read memory/working-buffer.md last entries
  2. Read recent WAL entries (last 50)
  3. Read yesterday's + today's memory/YYYY-MM-DD.md
  4. Reconstruct missing specifics

The skill provides a recover() helper (used automatically by agent if configured).

Configuration

Create memory-stack-config.json in workspace root (optional):

{
  "wal": {
    "enabled": true,
    "auto_capture": true,
    "max_entries": 10000
  },
  "buffer": {
    "enabled": true,
    "threshold_token_percent": 60,
    "max_size_mb": 10
  },
  "integration": {
    "auto_wrap_up_at_token_percent": 80,
    "include_buffer_in_wrap_up": true
  }
}

Performance

  • WAL write: <1ms (append to file)
  • Buffer append: <1ms
  • Memory overhead: ~100B per WAL entry; ~1KB per buffer turn
  • Disk: WAL grows ~1-2KB per conversation; buffer ~5-10KB per session

Negligible impact.

Compatibility

  • Works with any OpenClaw agent (uses standard tool interface)
  • No external dependencies
  • Compatible with compaction-survival patterns (this is an implementation)
  • Enhances session-persistence by providing WAL + buffer layers

FAQ

Q: Do I need to change my agent?
A: Only to optionally call memory_health or recover if you want explicit control. Otherwise install and go.

Q: What if I already use session-persistence?
A: This skill implements the WAL + buffer layers that session-persistence mentions. They're complementary.

Q: Will WAL fill my disk?
A: WAL is capped at max_entries (default 10k). Old entries can be archived to memory/wal-archive.jsonl monthly.

Q: Can I use without ToolRegistry?
A: Yes, the skill provides standalone scripts too (scripts/wal.py, scripts/buffer.py).

License

Commercial. One-time purchase includes lifetime updates. Team licenses allow unlimited agents.

Built with insights from the Claude Code leak and ClawHub community.

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