memory-for-openclaw

by szwangw

Install this Skill

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Download memory-for-openclaw.zip

SKILL.md About

---
name: long-term-memory
description: Persistent, cross-session memory for OpenClaw agents. Automatically captures key facts, decisions, user preferences, and project context. Injects relevant memory at session start. Search past conversations with natural language queries. Use when context continuity matters between sessions, when agent needs to remember user/project history, or when answering "do you remember..." questions.
version: 1.0.0
---

# Long-Term Memory

## Overview

This skill gives OpenClaw agents **long-term memory** that persists across sessions.
It replaces the manual MEMORY.md approach with automated capture + structured storage.

**Everything runs locally** — no cloud, no data leaves your machine.

## Core Workflow

```
Session Start → 1. inject_context() → get relevant history
Session Run  → 2. remember() / auto_capture() → save important info
Session End  → 3. summarize() → compress session into memory
```

## Scripts

### `scripts/memory_engine.py` — Core engine

```bash
# Save a memory
python3 scripts/memory_engine.py remember "决定: 使用FastAPI框架" --tags decision,tech --importance 8 --project saas

# Search memories
python3 scripts/memory_engine.py search "技术方案" --tags tech --min-imp 5

# Get context for prompt injection
python3 scripts/memory_engine.py inject "当前任务描述..."

# Auto-capture from text (scans for decisions, facts, preferences)
python3 scripts/memory_engine.py auto "我们决定采用SQLite作为数据库，技术栈为FastAPI..."

# Session management
python3 scripts/memory_engine.py session-start    # returns session_id + context
python3 scripts/memory_engine.py session-end <session_id> --summary "..."

# Stats
python3 scripts/memory_engine.py stats
```

### `scripts/setup.py` — One-time workspace setup

```bash
python3 scripts/setup.py
```

## Memory Structure

- **Storage**: SQLite + FTS5 full-text search
- **Fields**: content, tags[], importance(1-10), source, session, project, timestamps
- **Tags**: Tag memories for filtering (e.g., `decision`, `tech`, `user`, `project:X`)
- **Importance**: 1-10 scale. 8+ = key fact, 6-7 = useful context, 1-5 = normal

## Auto-Capture

The engine automatically detects important content from text:

| Trigger Keywords | Tag | Default Importance |
|:---|---|:---:|
| 决定, 选择, 采用, 改为, 升级, 弃用 | `decision` | 7 |
| 项目名, 产品名, 公司, 版本, 价格 | `fact` | 6 |
| 喜欢, 偏好, 习惯, 不要, 推荐 | `preference` | 6 |
| 技术栈, 框架, 语言, 数据库, API, 部署 | `tech` | 5 |
| 问题, bug, 报错, 异常, 失败 | `problem` | 5 |

## AGENTS.md Integration

Add to your `AGENTS.md` (or the relevant agent's config):

```markdown
## Long-Term Memory Rules

1. On session start: Run `python3 scripts/memory_engine.py inject "current task"` and use the output as context
2. When user shares important info: Use `remember()` to save it
3. Track decisions: Save key decisions with `--importance 8` and tag `decision`
4. Before answering "remember" or "previous" questions: Search memory first
5. On session end: Summarize key outcomes for next session
```

## Data Storage

```
~/.openclaw/workspace/long-term-memory/
├── memory.db          # SQLite database
├── config.json        # Configuration
└── current_context.md # Last built context (for debugging)
```

## Tips

- **Be selective**: Not everything needs remembering. Save decisions, preferences, problems.
- **Use tags**: `project:X` tags make cross-project memory searchable.
- **Importance matters**: 8+ for permanent facts, 5-7 for useful context, 3-4 for temporary.
- **Search before answering**: If user asks "do you remember X?", search memory first.

_meta.json Metadata

{
  "ownerId": "kn70s65fxc5y4p8fvk77ez8a0h833aty",
  "publishedAt": 1778215109682,
  "slug": "memory-for-openclaw",
  "version": "0.2.0"
}