Overview
- Skill Key
- clawdbrunner/hybrid-memory
- Author
- clawdbrunner
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/clawdbrunner/hybrid-memory
- Latest Commit SHA
- 6de955c09c53a596a85b75249c82c1264a2c9075
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.
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直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 hybrid-memory 技能。 若已安装,则直接安装 hybrid-memory 技能。
# 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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