Hybrid Memory

该 Skill 定义了一套混合记忆策略：文档类问题用 OpenClaw 内置 memory_search 语义检索 markdown 记忆文件，时序类问题（何时发生、上周说了什么、实体关联）用 Graphiti 时序知识图谱检索，并通过 graphiti-log.sh 记录事实；同时给出决策表、召回流程与 AGENTS.md 模板。

clawdbrunner

热度 72.20

最近更新 2026-05-11 15:51:31

分类：知识依赖：代码执行 / 读写文件 / 第三方 API应用场景：笔记整理 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add hybrid-memory

Metadata

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:

graphiti-search.sh "your question" GROUP_ID 10

Log important facts:

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:

Temporal questions → Check Graphiti first
Document questions → Use memory_search
Uncertain → Try both, combine results
Low confidence → Say you checked but aren't sure

AGENTS.md Template

Add to your AGENTS.md:

### 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