OpenClaw

hybrid-memory

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.

clawdbrunner0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install clawdbrunner/hybrid-memory

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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

来源信息

Source: github
Repository: openclaw/skills
Path: skills/clawdbrunner/hybrid-memory
Version: —
Commit: 6de955c09c53a596a85b75249c82c1264a2c9075
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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

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