Claude Skill
adoresever/graph-memory
Graph Memory is an OpenClaw plugin that extracts structured triples from conversations, compresses context by 75%, and enables cross-session experience reuse via a knowledge graph engine.
Overview
Repository
Install this Skill
git clone https://github.com/adoresever/graph-memory.gitRegistry
git clone https://github.com/adoresever/graph-memory.gitnpm installnpx vitest run # verify 80 tests passnpx vitest # watch mode
Summary
Graph Memory is an OpenClaw memory plugin that extracts structured triples from conversations, compresses context by 75%, and enables cross-session experience reuse via a knowledge graph engine.
Openclaw记忆插件：知识图谱+记忆；OpenClaw知识图谱上下文引擎——从对话中提取结构化三元组，压缩75%上下文，支持跨会话经验复用
Key features
- Extracts structured triples from conversations
- Compresses context by 75% for efficiency
- Enables cross-session experience reuse
- Built on SQLite for lightweight storage
- Integrates with OpenClaw plugin ecosystem
Use cases
- Long-running AI agent conversations
- Knowledge retention across chat sessions
- Context compression for token-limited models
- Building persistent memory for coding assistants
README excerpt
<p align="center"> <img src="docs/images/banner.jpg" alt="graph-memory" width="100%" /> </p> <h1 align="center">graph-memory</h1> <p align="center"> <strong>Knowledge Graph Context Engine for OpenClaw</strong><br> By <a href="mailto:Wywelljob@gmail.com">adoresever</a> · MIT License </p> <p align="center"> <a href="#installation">Installation</a> · <a href="#how-it-works">How it works</a> · <a href="#configuration">Configuration</a> · <a href="README_CN.md">中文文档</a> </p> --- <p align="center"> <img src="docs/images/hero.png" alt="graph-memory overview" width="90%" /> </p> ## What it does When conversations grow long, agents lose track of what happened. graph-memory solves three problems at once: 1. **Context explosion** — 174 messages eat 95K tokens. graph-memory compresses to ~24K by replacing raw history with structured knowledge graph nodes 2. **Cross-session amnesia** — Yesterday's bugs, solved problems, all gone in a new session. graph-memory recalls relevant knowledge automatically via FTS5/vector search + graph traversal 3. **Skill islands** — Self-improving agents record learnings as isolated markdown. graph-memory connects them: "installed libgl1" and "ImportError: libGL.so.1" are linked by a `SOLVED_BY` edge **It feels like talking to an agent that learns from experience. Because it does.** <p align="center"> <img src="docs/images/graph-ui.png" alt="graph-memory knowledge graph visualization with community detection" width="95%" /> </p> > *58 nodes, 40 edges, 3 communities — automatically extracted from conversations. Right panel shows the knowledge graph with community clusters (GitHub ops, B站 MCP, session management). Left panel shows agent using `gm_stats` and `gm_search` tools.* ## What's new in v2.0 ### Community-aware recall