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// Skill profile

Hybrid Memory System

name: hybrid-memory

by clawdbrunner · published 2026-03-22

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Last updated
2026-03
// Install command
$ claw add gh:clawdbrunner/clawdbrunner-hybrid-memory
View on GitHub
// Full documentation

---

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 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"):

    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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