4.5k★by clawdbrunner
hybrid-memory – OpenClaw Skill
hybrid-memory is an OpenClaw Skills integration for writing workflows. 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.
Skill Snapshot
| 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. OpenClaw Skills integration. |
| owner | clawdbrunner |
| repository | clawdbrunner/hybrid-memory |
| language | Markdown |
| license | MIT |
| topics | |
| security | L1 |
| install | openclaw add @clawdbrunner/hybrid-memory |
| last updated | Feb 7, 2026 |
Maintainer

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 agentuser-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
No README available.
Permissions & Security
Security level L1: Low-risk skills with minimal permissions. Review inputs and outputs before running in production.
Requirements
- OpenClaw CLI installed and configured.
- Language: Markdown
- License: MIT
- Topics:
FAQ
How do I install hybrid-memory?
Run openclaw add @clawdbrunner/hybrid-memory in your terminal. This installs hybrid-memory into your OpenClaw Skills catalog.
Does this skill run locally or in the cloud?
OpenClaw Skills execute locally by default. Review the SKILL.md and permissions before running any skill.
Where can I verify the source code?
The source repository is available at https://github.com/openclaw/skills/tree/main/skills/clawdbrunner/hybrid-memory. Review commits and README documentation before installing.
