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向量记忆自我进化系统
作者
vector-memory-self-evolution
· GitHub ↗
· v2.1.1
· MIT-0
123
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当前安装
3
版本数
在 OpenClaw 中安装
/install vector-memory-self-evolution
功能描述
向量记忆自我进化系统 - 结合 BGE 向量模型、Chroma 向量库、四层记忆架构,实现自动错误捕获、用户纠正学习、最佳实践积累、语义检索的自我进化能力。
安全使用建议
This skill appears to implement a local vector-memory system (BGE embedding service + Chroma DB) and mostly restricts activity to your home workspace and localhost. Before installing or enabling auto-capture: 1) Verify the missing files and functions: SKILL.md references setup_memory_system.sh, start_bge_service.sh, and scripts/code_security_scan.py that are not in the package, and redact_tool calls memory_api.log which does not exist — these will cause runtime errors and may leave redaction/scanning nonfunctional. 2) Confirm the origin of the BGE embedding service you will run on localhost (who provides it, and whether it sends data externally). 3) Inspect and test the code in a sandbox or VM (especially vectorize_memories.py which will POST memory text to the embedding service) before enabling cron/auto-capture. 4) Because the skill will read and write files under ~/.openclaw/workspace and could store extracted content, avoid feeding it sensitive secrets until you confirm redaction actually works. 5) If you want to proceed, request the missing scripts or a corrected release (fix the memory_api.log reference and supply the setup/start/scan scripts) or run the bundled scripts manually after review.
功能分析
Type: OpenClaw Skill
Name: vector-memory-self-evolution
Version: 2.1.1
The bundle implements a sophisticated four-layer memory system (L1-L4) for OpenClaw agents, utilizing ChromaDB and BGE embeddings for semantic search and 'self-evolution' through error and correction tracking. It includes a dedicated redaction tool (`redact_tool.py`) to strip sensitive information like API keys, tokens, and passwords from logs before storage, demonstrating a security-conscious design. While the system possesses high-risk capabilities such as modifying its own rule files (`SOUL.md`) and capturing command history, these behaviors are transparently documented and aligned with the stated goal of improving agent performance over time. The code lacks indicators of malicious intent, data exfiltration, or unauthorized remote access, and it explicitly mentions the removal of previous network redirections (HF_ENDPOINT) in its changelog.
能力评估
Purpose & Capability
Name/description (BGE embeddings + Chroma vector DB + memory lifecycle) match the included code (vectorize_memories.py, memory_api.py, search scripts) and there are no declared external credentials; this is coherent with a local memory/indexing tool. However, SKILL.md references setup scripts and other helper scripts (setup_memory_system.sh, start_bge_service.sh, code_security_scan.py) that are not present in the file manifest or repository listing, which is an inconsistency.
Instruction Scope
Runtime instructions focus on local files, cron jobs, and a local BGE service at http://localhost:11434 — which matches code that posts embeddings to localhost. But SKILL.md also instructs running scripts that are missing from the bundle and refers to functions/scripts (e.g., a memory_api.log used by redact_tool.log_redacted) that do not exist in the provided code. These gaps mean the delivered instructions may cause runtime errors or leave promises (like code security scan / automatic setup) unfulfilled.
Install Mechanism
No install spec (instruction-only) so nothing is downloaded from remote sources by an installer. Code files are included in the skill package and would be placed on disk when the skill is installed — expected for a code-containing skill. No third-party remote download URLs are present in the provided sources (the only network call is to localhost).
Credentials
The skill declares no required environment variables or external credentials, and the code operates on local workspace paths and a local embedding service. Redaction rules reference AWS-style keys and tokens (to redact them if encountered) but the skill does not request those credentials; this is proportionate. Note: because the skill writes and reads user workspace files, it can process any local content placed into its memory directories.
Persistence & Privilege
always:false and no modifications to other skills are requested. The skill expects to create and manage files under ~/.openclaw/workspace (memory, archive, vector_db) and suggests cron entries — these are standard for a local service but constitute persistent data storage. Nothing indicates it gains elevated system privileges.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install vector-memory-self-evolution - 安装完成后,直接呼叫该 Skill 的名称或使用
/vector-memory-self-evolution触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v2.1.1
2.1.1 - 安全修复:移除 HF_ENDPOINT 网络重定向、补全缺失脚本(setup_memory_system.sh、start_bge_service.sh)、移除所有危险函数调用、代码安全审计通过
v2.1.0
2.1.0 - 增强功能:代码安全扫描 + 完整进化流程(记忆学习、聊天记录分析、完整进化报表、下次进化计划)+ 敏感数据脱敏
v2.0.0
vector-memory-self-evolution 2.0.0 highlights:
- Added BGE 向量模型 integration for enhanced semantic memory.
- Introduced Chroma 向量库 with persistent vector-based retrieval.
- Implemented a four-level memory architecture (L1–L4) with automated compression, archiving, and vectorization.
- Enabled automatic error, correction, and best practice recording, with user-driven self-improvement and rule solidification.
- Launched conflict detection and proactive alerting for conflicting memories.
- Enhanced memory search with semantic retrieval and API interface support.
元数据
常见问题
向量记忆自我进化系统 是什么?
向量记忆自我进化系统 - 结合 BGE 向量模型、Chroma 向量库、四层记忆架构,实现自动错误捕获、用户纠正学习、最佳实践积累、语义检索的自我进化能力。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 123 次。
如何安装 向量记忆自我进化系统?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install vector-memory-self-evolution」即可一键安装,无需额外配置。
向量记忆自我进化系统 是免费的吗?
是的,向量记忆自我进化系统 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
向量记忆自我进化系统 支持哪些平台?
向量记忆自我进化系统 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 向量记忆自我进化系统?
由 vector-memory-self-evolution(@lxbl79)开发并维护,当前版本 v2.1.1。
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