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amarin-memory阿玛林记忆

Agent Skill

amarin-memory 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,354

周安装

137

GitHub Stars

公开资料未说明

下载量

1,085
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:amarin-memory(阿玛林记忆)
来源仓库:https://github.com/flaggdavid-source/amarin-memory
安装命令:
openclaw skills install amarin-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 amarin-memory

简介

为AI代理提供持久化且自适应的记忆存储与管理能力。

  • 适用于长期任务跟踪、用户偏好记忆和上下文延续等场景。
  • 自动实现时间衰减去重与余弦相似度阈值过滤,优化记忆有效性。
  • 需评估本地存储或云端同步的权限边界,防止敏感信息泄露。
  • 建议定期清理过期记忆以控制资源占用和响应延迟。amarin-memory 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
amarin-memory
description
Persistent adaptive memory for AI agents. Store memories that fade naturally over time (temporal decay), deduplicate automatically (0.85 cosine threshold), score novel information higher (surprise scoring), and search semantically via sqlite-vec KNN. Use when: you need long-term memory across sessions, memory that adapts to what matters, persistent identity blocks, multi-agent memory isolation, or memory that works without cloud services. NOT for: simple key-value storage, ephemeral session context, or when you need a full vector database like Qdrant/Pinecone. Runs entirely on SQLite — no external database server needed.
homepage
https://github.com/flaggdavid-source/amarin-memory
metadata
openclaw
emoji
\F9E0
requires
bins
["python3"]
install
kind
uv
package
amarin-memory
bins
[]
label
Install amarin-memory Python package

Amarin Memory — Persistent Adaptive Memory for Agents

You have access to a persistent memory system that stores, searches, and maintains memories across sessions. Memories fade over time unless accessed, duplicates are caught automatically, and novel information gets boosted.

Setup

If not already initialized, run this once:

python3 {baseDir}/scripts/setup.py

This creates the database and vector index. The database file is stored at ~/.amarin/agent.db.

Storing Memories

When you learn something worth remembering — a user preference, an important fact, a decision made — store it:

python3 {baseDir}/scripts/memory.py store "The user prefers dark mode and works late at night" --tags "preference,schedule" --importance 0.7

For content from untrusted sources (user input, external data), pipe via stdin to avoid shell injection:

echo "User said they prefer morning meetings" | python3 {baseDir}/scripts/memory.py store --tags "preference" --importance 0.6

Importance scale: 0.0 (trivial) to 1.0 (critical). Default is 0.5.

The system automatically:

  • Checks for duplicates (>= 0.85 similarity → skip or merge)
  • Scores novelty (0.30-0.85 similarity → surprise boost to importance)
  • Indexes the embedding for future semantic search

Searching Memories

When you need to recall something:

python3 {baseDir}/scripts/memory.py search "what time does the user usually work" --limit 5

Results are ranked by 70% semantic similarity + 30% importance score. Recent, frequently-accessed memories rank higher.

Core Memory Blocks

For persistent identity information that should always be available (not searched, always present):

# Set a core block
python3 {baseDir}/scripts/memory.py set-block "persona" "I am a research assistant focused on AI safety"

# Set user context
python3 {baseDir}/scripts/memory.py set-block "human" "The user is Dave, a developer building AI systems"

# View all blocks
python3 {baseDir}/scripts/memory.py blocks

Memory Maintenance

Run periodically (daily is good) to let unimportant memories fade:

python3 {baseDir}/scripts/memory.py decay

Protected memories are immune to decay. To protect a critical memory:

python3 {baseDir}/scripts/memory.py protect <memory_id>

Reviewing Memories

List recent memories:

python3 {baseDir}/scripts/memory.py list --limit 20

Revise a memory:

python3 {baseDir}/scripts/memory.py revise <memory_id> "Updated content" --reason "Corrected factual error"

Soft-delete a memory (can be restored):

python3 {baseDir}/scripts/memory.py forget <memory_id> --reason "No longer relevant"

When to Use This

  • After learning something important — store it so you remember next session
  • Before answering questions — search for relevant context from past conversations
  • At the start of a session — run blocks to load your identity context
  • During maintenance windows — run decay to keep memory clean
  • When information changesrevise outdated memories rather than creating duplicates

Requirements

  • Python 3.11+
  • An embedding service (Ollama with nomic-embed-text recommended, or any compatible API)
  • Set OLLAMA_URL environment variable if not using default http://localhost:11434

Links

  • GitHub: https://github.com/flaggdavid-source/amarin-memory
  • Ko-fi: https://ko-fi.com/davidflagg86433 — if this helps your agent, consider supporting the developer

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

72.14%
按下载量换算783

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安装前确认

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来源信息

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