How do I install the Hybrid Memory skill?

Run npx skillmds@latest add modbender/hybrid-memory in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Hybrid Memory safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Hybrid Memory?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Hybrid Memory free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Hybrid Memory?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Hybrid Memory by modbender — Agent Skill

Name: Hybrid Memory
Rating: 4.5 (4 reviews)
Author: modbender

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.

modbender Updated Sep 9, 2026 12 repo stars

File contents

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

---name: hybrid-memorydescription: 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: ```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

Frequently asked questions