by clawdbrunner · Codex Skill · ★ 70
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OpenClaw Hybrid Memory: QMD + Graphiti + Shared Files A complete three-layer memory system for OpenClaw multi-agent setups. Architecture ┌─────────────────────────────────────────────────────┐ │ Agent Memory │ │ │ │ Layer 1: Private Files (QMD) │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ piper/ │ │ paige/ │ │ dean/ │ ... │ │ │ memory/ │ │ memory/ │ │ memory/ │ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ Per-agent vector search via memorysearch │ │ │ │ Layer 2: Shared Files (shared/) │ │ ┌──────────────────────────────────┐ │ │ │ user-profile.md │ │ │ │ agent-roster.md │ │ │ │ infrastructure.md │ │ │ │ graphiti-memory.md │ │ │ └──────────────────────────────────┘ │ │ Symlinked into each agent workspace as shared/ │ │ Indexed by QMD alongside private files │ │ │ │ Layer 3: Shared Knowledge Graph (Graphiti) │ │ ┌──────────────────────────────────┐ │ │ │ openclaw-clawd (write own) │ │ │ │ openclaw-piper (write own)
| Stars | 70 |
| Forks | 7 |
| Language | Shell |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 70.4668094219043/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-01 |
| Created | 2026-01-31 |
| Platforms | cli |
| Est. Tokens | ~4k |
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openclaw-graphiti-memory is Hybrid memory system for OpenClaw using Graphiti temporal knowledge graph. It is categorized as a Codex Skill with 70 GitHub stars.
openclaw-graphiti-memory is primarily written in Shell. It covers topics such as ai-agents, clawdbot, graphiti.
You can find installation instructions and usage details in the openclaw-graphiti-memory GitHub repository at github.com/clawdbrunner/openclaw-graphiti-memory. The project has 70 stars and 7 forks, indicating an active community.
openclaw-graphiti-memory is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-graphiti-memory on Agent Skills Hub include awesome-openclaw, atlas, openclaw-foundry. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.