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Agent Skill · 研究检索

rag-memory(RAG 记忆)

用于搭建或维护带检索增强的 RAG 工作流,适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式,避免把未命中的资料或过期内容包装成确定事实。

rag-memory 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 22,753。

总安装 4,468周安装 179GitHub Stars 公开资料未说明下载 1,446核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:rag-memory(RAG 记忆)
来源仓库:https://github.com/mbojer/rag-memory
安装命令:
openclaw skills install rag-memory
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install rag-memory

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

提供基于 Qdrant 的矢量内存搜索与自动同步 .md 文件记录功能。通过 nomic-embed 模型实现 Postgres 数据向量化与相似度匹配。增强 Agent 长期记忆能力,支持历史对话内容的快速回溯引用。首次使用前需初始化向量数据库并建立文件监听同步机制。

安装命令:openclaw skills install rag-memory

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install rag-memory;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

rag-memory 怎么安装?

在终端执行 openclaw skills install rag-memory。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

rag-memory 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

rag-memory 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

rag-memory 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

rag-memory 用的人多吗?

公开数据是累计安装 4,468 次,近一周安装 179 次,下载 1,446 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

rag-memory 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
rag-memory

RAG 记忆 · 本页

4,468—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
rag-memory
version
1.1.1
description
Vector memory search and RAG skill for OpenClaw. Provides vector_search tool backed by Qdrant, auto-syncs memory .md files and Postgres records via nomic-embed-text embeddings. Triggers on "search memory", "vector search", "resync memory", "RAG", "qdrant".
metadata
openclaw
homepage
https://clawhub.ai/mbojer/rag-memory
emoji
🧠
requires
bins
env
primaryEnv
EMBED_API_KEY

RAG Memory Skill

Architecture (as of 2026-04-04)

All files live under ~/.openclaw/skills/rag-memory/ — the single source of truth.

FilePurpose
config.envCredentials and tuning (gitignored)
config.env.exampleTemplate with no secrets (tracked in git)
sync_to_qdrant.pyIngest pipeline: Postgres + .md files → Qdrant
requirements.txtPython deps for sync script
venv/Python virtualenv (gitignored)
plugin/index.jsOpenClaw plugin — registers vector_search tool and before_prompt_build hook
plugin/package.jsonPlugin manifest
plugin/openclaw.plugin.jsonPlugin config schema
systemd/Source-of-truth copies of systemd units (deploy with sudo cp + daemon-reload)
references/config-paths.example.mdFile and credential location template (copy to config-paths.md and fill in)
references/operational.mdWhen/how to use vector_search

Plugin is installed at ~/.openclaw/extensions/rag-memory/ and configured in ~/.openclaw/openclaw.json under plugins.entries.rag-memory.

Qdrant collections: {QDRANT_COLLECTION_PREFIX}_memory (daily logs + facts) and {QDRANT_COLLECTION_PREFIX}_docs (tools + skill docs). 768-dim, nomic-embed-text via OpenAI-compatible embedding endpoint.


When to use vector_search

Always prefer vector_search over reading full memory files:

  • Before reading any daily log or MEMORY.md → scope: memory
  • Before reading TOOLS.md or any SKILL.md → scope: docs
  • Unsure → scope: all

Only fall back to reading the file directly if vector_search returns no results.


Sync management

TaskCommand
Incremental sync (unsynced rows only)~/.openclaw/skills/rag-memory/venv/bin/python ~/.openclaw/skills/rag-memory/sync_to_qdrant.py
Full resync (rebuild all vectors)... sync_to_qdrant.py --full
Single file... sync_to_qdrant.py --file /path/to/file.md

Incremental sync also fires automatically when any .md file changes in ~/.openclaw/workspace/memory/ (via sysclaw-rag-watch.path). Nightly full sync runs at 03:00 (sysclaw-rag-sync.timer).


Updating the plugin

If plugin/index.js is changed:

SKILL=~/.openclaw/skills/rag-memory
openclaw plugins install $SKILL/plugin
openclaw gateway restart

If config.env tuning values change, also update the matching values in plugins.entries.rag-memory.config in openclaw.json (especially score_threshold and top_k).


Deploying systemd unit changes

Edit files in systemd/ (source of truth), then deploy using the included script — it substitutes the correct username automatically:

sudo ~/.openclaw/skills/rag-memory/systemd/deploy.sh

Or manually, replacing youruser with the local OpenClaw user:

sudo sed "s/User=openclaw/User=youruser/g" ~/.openclaw/skills/rag-memory/systemd/*.service \
  | sudo tee /etc/systemd/system/sysclaw-rag-{sync,watch}.service > /dev/null
sudo cp ~/.openclaw/skills/rag-memory/systemd/sysclaw-rag-sync.timer /etc/systemd/system/
sudo cp ~/.openclaw/skills/rag-memory/systemd/sysclaw-rag-watch.path /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl restart sysclaw-rag-sync.timer sysclaw-rag-watch.path

Key config values

KeyExample / Default
QDRANT_HOSThttps://qdrant.example.com
EMBED_BASE_URLhttps://litellm.example.com
EMBED_MODELnomic-embed-text
QDRANT_COLLECTION_PREFIXany UUID or unique prefix string
MEMORY_DIR~/.openclaw/workspace/memory
score_threshold0.70 (in both config.env and openclaw.json plugin config)
top_k5

If Qdrant is unreachable

  1. Try once — do not retry in a loop
  2. Fall back to reading the relevant file directly
  3. Note in your response that vector search was unavailable
  4. Check: curl $QDRANT_HOST/readyz
  5. Do not restart Qdrant automatically — alert the user

Not for

Do not activate this skill for:

  • General file search or code search unrelated to memory/RAG
  • Editing files (use read/write tools directly)
  • Tasks that don't involve looking up past context, facts, or tool docs
  • Syncing or resyncing when the user has not asked to update memory
  • Any request where the user explicitly says "don't search memory"

External endpoints

This skill sends data to three external services configured via config.env:

ServiceVariableWhat is sent
Embedding endpoint (OpenAI-compatible)EMBED_BASE_URLText chunks from memory files and Postgres records, for vector embedding
QdrantQDRANT_HOSTEmbedded vectors + metadata for storage and search queries
PostgresPOSTGRES_DSNRead-only queries against memory_entries, daily_logs, tools tables

No data is sent to any Anthropic or third-party endpoint by this skill.


Privacy statement

Memory content (daily logs, facts, tool descriptions) leaves the local machine in two ways:

  1. Text is sent to your configured embedding endpoint (EMBED_BASE_URL) to generate vectors
  2. Vectors + metadata are stored in and queried from your Qdrant instance (QDRANT_HOST)

Both services are self-hosted and configured by the user. No data is sent to any external party by this skill.


Security manifest

Environment variables accessed:

  • QDRANT_HOST, QDRANT_API_KEY, QDRANT_COLLECTION_PREFIX
  • EMBED_BASE_URL, EMBED_API_KEY, EMBED_MODEL
  • POSTGRES_DSN
  • MEMORY_DIR, CHUNK_SIZE, CHUNK_OVERLAP, TOP_K_DEFAULT, SYNC_BATCH_SIZE, EMBED_BATCH_DELAY_MS

Files read:

  • All .md files under MEMORY_DIR
  • config.env (loaded at sync script startup)

Files written:

  • None (no local file writes)

Database writes (Postgres):

  • memory_entries.qdrant_synced_at — updated after successful vector upsert
  • daily_logs.qdrant_synced_at — same
  • tools.qdrant_synced_at — same
  • qdrant_sync_log — one row inserted per sync run (records_synced, duration_ms)

Network:

  • Outbound HTTPS to EMBED_BASE_URL (embedding)
  • Outbound HTTPS to QDRANT_HOST (vector storage/search)
  • Outbound TCP to Postgres host in POSTGRES_DSN

Trust statement

By installing this skill, text from your memory files and database will be sent to the embedding endpoint configured in EMBED_BASE_URL. Vectors are stored in the Qdrant instance at QDRANT_HOST. Both are self-hosted services you configure. Only install if you trust those services with your memory contents.

When auto_inject is enabled (default: true), Qdrant search results are prepended to every prompt context. This data originates from your own self-hosted Qdrant instance — no external party supplies it. The content is sanitized of session boilerplate before storage but is otherwise injected as-is; only enable auto_inject if you trust your Qdrant data as prompt input.


License

MIT-0 — no attribution required.

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

平台分布

OpenClaw

87.33%
按下载量换算1,263

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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