Agent Memory System v4

生产级 Agent 记忆系统 — 6维坐标编码 + 语义检索 + 智能压缩

What to consider before installing/activating: - This package is a full local memory system: it will read/write a SQLite DB and vector cache under ~/.openclaw/workspace/agent-memory/ (or whatever path the platform uses). Expect persistent storage of conversation contents — review whether that is acceptable for your data sensitivity. - The skill includes runnable Python modules (CLI + many libraries). Although I saw no network endpoints or unexpected credential requests in the reviewed files, 23 files were omitted from the transcript; review all included files (especially obsidian_sync, any modules that might call external APIs or open sockets) before trusting it. - Compression uses an LLM call (llm_fn). Verify how the agent provides that function and whether using it will send memory text to an external LLM provider — if so, you may be leaking private content to that provider. - Optional dependencies like chromadb / sentence-transformers may download models or start background services; audit dependency behavior and disk/network effects. - Recommended steps: run the code in a sandboxed environment first, inspect obsidian_sync and any network/HTTP calls, check where files will be created, consider enabling encryption or restricting filesystem permissions for the memory directory, and decide whether to allow autonomous writes (agent invoking the CLI during conversation). Confidence note: I reviewed many core modules included in the manifest and SKILL.md, and found them coherent with the declared purpose. Because several files were truncated/omitted in the provided bundle, my confidence is medium; a full-line-by-line review of the remaining files would raise confidence to high.

Type: OpenClaw Skill Name: agent-memory-v4 Version: 4.0.0 The agent-memory skill bundle is a comprehensive and well-architected memory management system for AI agents. It implements a 6-dimensional coordinate system (time, person, topic, nature, tool, knowledge) to structure agent experiences using SQLite for relational data and ChromaDB for semantic search. The code across all modules (e.g., memory_system.py, store.py, recall.py) is modular, well-documented, and strictly aligned with the stated purpose of providing long-term memory, deduplication, and self-healing capabilities. No indicators of malicious behavior, such as data exfiltration, unauthorized network calls, or harmful prompt injections, were detected.