Introduction

openclaw-amem is an OpenClaw plugin that integrates the A-MEM (Agentic Memory) system — featuring dynamic memory networks, automatic link generation, memory evolution, and in-process consolidation, backed by Qdrant + local Transformers.js + LLM.

No Python required.

This project is a production-ready OpenClaw plugin integration of the A-MEM system. For the original research implementation, see agiresearch/A-MEM.

---

What is A-MEM?

A-MEM is an advanced memory architecture for LLM agents inspired by the Zettelkasten method. Unlike traditional flat vector databases, A-MEM maintains memory as a living, self-evolving semantic graph.

The 5-step lifecycle

1. Note Construction — On write, LLM extracts keywords, tags, a context summary, and categorizes the note (Technical, Business, Personal, Project, Research, System, General).

2. Link Generation — Retrieves top-6 candidates; LLM judges whether to link bidirectionally (similarity > 0.3).

3. Memory Evolution & Strengthening — Up to 3 linked memories have their attributes evolved based on the new context, potentially triggering additional links.

4. Hybrid Retrieval — Fuses vector search (local ONNX multilingual-e5-small, 384-dim) and BM25 using Reciprocal Rank Fusion (RRF), boosted by retrieval frequency (heat).

5. 2-hop BFS Graph Expansion — After RRF top-K selection, BFS traverses the link graph up to 2 hops, appending up to 8 contextually linked notes. Each candidate passes an embedding relevance gate (cos-sim ≥ 0.25) before admission.

---

Architecture

OpenClaw Agent
     │
     ├── memory_search(query)  ──►  openclaw-amem plugin (TypeScript, in-process)
     └── memory_add(text)      ──►       │
                                         ▼
                          ┌──────────────┼──────────────┐
                          ▼              ▼               ▼
                       Qdrant     Transformers.js    LLM (Anthropic)
                    (vector store)  (ONNX embed)   (CRUD decision
                      :6333        384-dim local    + link judgment
                   agent_id ISO   + Jieba BM25     + evolution)

---

Academic Background

Based on the paper: A-MEM: Agentic Memory for LLM Agents — arXiv:2502.12110 (NeurIPS 2025)

@inproceedings{xu2025amem,
  title={A-Mem: Agentic Memory for LLM Agents},
  author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao and Zhang, Yongfeng},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025}
}