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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 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install clawdbrunner/hybrid-memory

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

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:

  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:

### 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

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/clawdbrunner/hybrid-memory
Version
—
Commit
6de955c09c53a596a85b75249c82c1264a2c9075
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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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