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.
Best use case
hybrid-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using hybrid-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hybrid-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hybrid-memory Compares
| Feature / Agent | hybrid-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 daemonsRelated Skills
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