据 AlphaMoat 统计,截至 2026-09,Hybrid Memory 累计下载 3,444 次、获 1 个星标,在全部 5,078 个 AI 技能中排名第 3,227 位,在「AI 智能体」分类(969 个)中排名第 654 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 113 | 3,349 | 1 | - |
| SkillHub ↗ | 131 | 3,444 | 1 | — |
各平台的计数口径不同,统计时间不一致。
该技能帮助区分文档与整理笔记的搜索,以及时间事实、对话和实体关系的 Graphiti 查询,并提供记录重要事实的流程。持久记忆可能保存个人上下文;使用前应明确可记录内容、避免秘密和敏感个人信息,并确认数据如何查看和删除。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,444 | — | 1 |
| 2026-08 | 3,256 | +2.7% | 1 |
| 2026-07 | 3,170 | +7.0% | 1 |
| 2026-06 | 2,964 | +4.4% | 1 |
| 2026-05 | 2,838 | — | 1 |
| 2026-03 | 2,305 | — | 1 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
name: hybrid-memory
description: Hybrid memory strategy combining OpenClaw's built-in vector memory with Graphiti temporal knowledge graph. Use when you need to recall past context, answer temporal questions ("when did X happen?"), or search memory files. Provides decision framework for when to use memory_search vs Graphiti.
---
# Hybrid Memory System
Two memory systems, each with different strengths. Use both.
## When to Use Which
| Question Type | Tool | Example |
|--------------|------|---------|
| Document content | `memory_search` | "What's in GOALS.md?" |
| Curated notes | `memory_search` | "What are our project guidelines?" |
| Temporal facts | Graphiti | "When did we set up Slack?" |
| Conversations | Graphiti | "What did the user say last Tuesday?" |
| Entity tracking | Graphiti | "What projects involve Alice?" |
## Quick Reference
### memory_search (Built-in)
Semantic search over markdown files (`MEMORY.md`, `memory/**/*.md`).
```
memory_search query="your question"
```
Then use `memory_get` to read specific lines if needed.
### Graphiti (Temporal)
Search for facts with time awareness:
```bash
graphiti-search.sh "your question" GROUP_ID 10
```
Log important facts:
```bash
graphiti-log.sh GROUP_ID user "Name" "Fact to remember"
```
Common group IDs:
- `main-agent` — Primary agent
- `user-personal` — User's personal context
## Recall Pattern
When answering questions about past context:
1. **Temporal questions** → Check Graphiti first
2. **Document questions** → Use `memory_search`
3. **Uncertain** → Try both, combine results
4. **Low confidence** → Say you checked but aren't sure
## AGENTS.md Template
Add to your AGENTS.md:
```markdown
### Memory Recall (Hybrid)
**Temporal questions** ("when?", "what changed?", "last Tuesday"):
```bash
graphiti-search.sh "query" main-agent 10
```
**Document questions** ("what's in X?", "find notes about Y"):
```
memory_search query="your query"
```
When answering past context: check Graphiti for temporal, memory_search for docs.
```
## Setup
Full setup guide: https://github.com/clawdbrunner/openclaw-graphiti-memory
**Part 1: OpenClaw Memory** — Configure embedding provider (Gemini recommended)
**Part 2: Graphiti** — Deploy Docker stack, install sync daemons
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。