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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Openclaw记忆插件Knowledge Graph + Memory；Knowledge Graph Context Engine for OpenClaw — extracts structured triples from conversations, compresses context 75%, enables cross-session experience reuse
| Date | Stars |
|---|---|
| 2026-07-31 | 503 |
| 2026-08-02 | 504 |
| 2026-08-06 | 504 |
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<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:[email protected]">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 Recall now runs **two parallel paths** that merge results: - **Precise path**: vector/FTS5 search → community expansion → graph walk → PPR ranking - **Generalized path**: query vector vs community summary embeddings → community members → PPR ranking Community summaries are generated immediately after each community detection cycle (every 7 turns), so the generalized path is available from the first maintenance window. ### Episodic context (conversation traces) The top 3 PPR-ranked nodes now pull their **original user/assistant conversation snippets** into the context. The agent sees not just structured triples, but the actual dialogue that produced them — improving accuracy when reapplying past solutions. ### Universal embedding support The embedding module now uses raw `fetch` instead of the `openai` SDK, making it compatible with **any OpenAI-compatible endpoint** out of the box: - OpenAI, Azure OpenAI - Alibaba DashScope (`text-embedding-v4`) - MiniMax (`embo-01`, 1536d — uses `texts` + `type` body format, not OpenAI `input`) - Ollama, llama.cpp, vLLM (local models) - Any endpoint that implements `POST /embeddings` ### Windows one-click installer v2.0 ships a **Windows installer** (`.exe`). Download from [Releases](https://github.com/adoresever/graph-memory/releases): 1. Download `graph-memory-installer-win-x64.exe` 2. Run the installer — it auto-detects your OpenClaw installation 3. The installer configures `plugins.slots.contextEngine`, adds the plugin entry, and restarts the gateway ## Real-world results <p align="center"> <img src="docs/images/token-comparison.png" alt="Token comparison: 7 rounds" width="85%" /> </p> 7-round conversation installing bilibili-mcp + login + query: | Round | Without graph-memory | With graph-memory | |-------|---------------------|-------------------| | R1 | 14,957 | 14,957 | | R4 | 81,632 | 29,175 | | R7 | **95,187** | **23,977** | **75% compression.** Red = linear growth without graph-memory. Blue = stabilized with graph-memory. <p align="center"> <img src="docs/images/token-sessions.png" alt="Cross-session recall" width="85%" /> </p> ## How it works ### The Knowledge Graph graph-memory builds a typ
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:47d5f361ac4e2f4f, topic:knowledge-graph, topic:memory, desc:knowledge graph