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.

533 stars

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

$curl -o ~/.claude/skills/hybrid-memory/SKILL.md --create-dirs "https://raw.githubusercontent.com/sundial-org/awesome-openclaw-skills/main/skills/hybrid-memory/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/hybrid-memory/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How hybrid-memory Compares

Feature / Agenthybrid-memoryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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 daemons

Related Skills

memory-setup

533
from sundial-org/awesome-openclaw-skills

Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.

memory-manager

533
from sundial-org/awesome-openclaw-skills

Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.

memory-hygiene

533
from sundial-org/awesome-openclaw-skills

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

lancedb-memory

533
from sundial-org/awesome-openclaw-skills

No description provided.

git-notes-memory

533
from sundial-org/awesome-openclaw-skills

Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.

portfolio-watcher

533
from sundial-org/awesome-openclaw-skills

Monitor stock/crypto holdings, get price alerts, track portfolio performance

portainer

533
from sundial-org/awesome-openclaw-skills

Control Docker containers and stacks via Portainer API. List containers, start/stop/restart, view logs, and redeploy stacks from git.

portable-tools

533
from sundial-org/awesome-openclaw-skills

Build cross-device tools without hardcoding paths or account names

polymarket

533
from sundial-org/awesome-openclaw-skills

Trade prediction markets on Polymarket. Analyze odds, place bets, track positions, automate alerts, and maximize returns from event outcomes. Covers sports, politics, entertainment, and more.

polymarket-traiding-bot

533
from sundial-org/awesome-openclaw-skills

No description provided.

polymarket-analysis

533
from sundial-org/awesome-openclaw-skills

Analyze Polymarket prediction markets for trading edges. Pair Cost arbitrage, whale tracking, sentiment analysis, momentum signals, user profile tracking. No execution.

polymarket-agent

533
from sundial-org/awesome-openclaw-skills

Autonomous prediction market agent - analyzes markets, researches news, and identifies trading opportunities