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openmemopenmem 控制

Agent Skill

openmem 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

5,536

周安装

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下载量

1,939
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:openmem(openmem 控制)
来源仓库:https://github.com/danavfrost/openmem
安装命令:
openclaw skills install openmem
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 openmem

简介

管理 SQLite 长期记忆存储,延长内存生命周期。

  • 支持压缩归档与选择性恢复机制。openmem 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适用于需要持久化上下文的长对话代理。
  • 可能涉及数据库文件读写与备份操作。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 建议定期清理过期记忆以减少资源占用。

SKILL.md

name
openmem
description
SQLite long-term memory compression system for extended memory life. Adds tools for agents to control their memory functions.
metadata

OpenMem v1.2.5

SQLite-backed long-term memory.

MIT License — free to use, modify, redistribute. No attribution required.

Privacy & Scope

Read this before installing.

  • Full session transcripts are read during compression. Both manual (compress.py read) and automated (auto_compress.py) compression read the complete raw JSONL session files. The full content is passed to the extractor — everything in that session is visible. Only 3–10 selected items are written to the DB; the raw content is never stored permanently. This is intentional but privacy-sensitive: sessions may contain passwords, keys, or personal data entered during a conversation.
  • Automated compression destroys original session files. auto_compress.py replaces session JSONL files older than 24h with a one-line stub after memories are confirmed. This is irreversible. Run with --no-wipe to disable this behaviour, or skip auto-compression entirely.
  • Network calls are made during auto-compression. auto_compress.py calls openclaw capability model run to extract memories using the user's configured model. OpenClaw handles all provider routing and auth — session excerpts are passed as a prompt through the local gateway. The MCP server itself makes no network calls.
  • Cache file is plaintext. After every memory write, top memories are written to openmem-cache.json in the same directory as the DB, unencrypted. Trust level is identical to the DB file.
  • Persistent presence. setup.py registers an MCP server and an hourly cron job with your OpenClaw gateway. OpenMem has ongoing read access to session files and write access to the local DB while enabled. Run uninstall.py to remove all registered components.

Filesystem & Credential Access

What this skill reads and writes — declared up-front.

Reads:

  • ~/.openclaw/agents/main/sessions/*.jsonl — session transcripts, read in full during compression (see Privacy above).

Writes:

  • ~/.openclaw/workspace/memory/openmem.db — SQLite memory database
  • ~/.openclaw/workspace/memory/openmem-cache.json — plaintext top-memory cache, written after every memory write
  • ~/.openclaw/workspace/memory/auto_compress_state.json — last-run state for the daily guard
  • ~/.openclaw/logs/openmem-compress.log — compression log

All paths are overridable via env vars (see Environment Variables).

MCP Tool Calls

When the OpenMem MCP server is registered, use these native tool calls directly:

ToolPurpose
memory_addStore a new memory (content, category, importance, source)
memory_searchFTS search with relevance + importance + recency ranking
memory_updateChange content, category, or importance by ID
memory_deleteRemove a memory by ID
memory_listList memories sorted by importance / recency / access
memory_statsTotal count, breakdown by category, date range

Installation

After openclaw skills install openmem, run setup once:

python3 ~/.openclaw/workspace/skills/openmem/scripts/setup.py

This creates the database, checks requirements, registers the MCP server, registers the auto-compression launchd job (macOS), and prints the next steps (hook enable).

Uninstall

Removes the cron job, MCP server, and bootstrap hook. Your database is not deleted — its path is printed so you can export or remove it yourself.

python3 ~/.openclaw/workspace/skills/openmem/scripts/uninstall.py

CLI Quick Reference

SCRIPTS=~/.openclaw/workspace/skills/openmem/scripts

# Add a memory
python3 $SCRIPTS/mem.py add "User prefers concise responses" --category preference

# Search
python3 $SCRIPTS/mem.py search "response style"

# List top memories
python3 $SCRIPTS/mem.py list --limit 20

# Stats
python3 $SCRIPTS/mem.py stats

# --- Session compression ---
# List uncompressed sessions
python3 $SCRIPTS/compress.py pending

# Read a session (then you summarize it into mem.py add calls)
python3 $SCRIPTS/compress.py read <session-id>

# Mark compressed after adding memories
python3 $SCRIPTS/compress.py mark-done <session-id> --memory-count 5

Categories

fact · insight · preference · correction · event · general

Compression Workflow

Automatic (default)

After setup, an OpenClaw cron job runs auto_compress.py every hour (via --tools exec, so it can only run the script — no loops possible). The script itself handles all guards:

  1. Checks it hasn't already run today
  2. Checks the agent has been inactive for 2+ hours
  3. Finds all uncompressed sessions (skipping the most recently active one)
  4. Calls Ollama to extract 3–8 memories per session (falls back to heuristics if Ollama is unavailable)
  5. Deduplicates each candidate against existing memories (word-overlap check)
  6. Inserts and confirms each memory is in the DB before proceeding
  7. Wipes sessions older than 24h to a stub after memories are confirmed
  8. Logs everything to ~/.openclaw/logs/openmem-compress.log

To run it manually:

python3 $SCRIPTS/auto_compress.py --force          # skip guards, compress now
python3 $SCRIPTS/auto_compress.py --dry-run        # preview without writing
python3 $SCRIPTS/auto_compress.py --no-wipe        # compress but keep session files

On-demand (agent)

You can also trigger compression by saying:

  • *"compress my sessions"*
  • *"save this to long-term memory"*

Step by step:

  1. python3 $SCRIPTS/compress.py pending — find sessions not yet compressed
  2. python3 $SCRIPTS/compress.py read <id> — read the conversation
  3. Extract 3–10 key facts, corrections, preferences, and insights
  4. Use memory_add tool call (or mem.py add) for each memory
  5. python3 $SCRIPTS/compress.py mark-done <id> --memory-count <N>

What to extract:

  • Facts the user stated about their system, preferences, or projects
  • Mistakes made and the correct approach
  • Decisions reached and why
  • Important events (deploys, incidents, milestones)

What to skip: Raw command output, transient errors, small talk.

Deduplication

memory_add automatically checks for similar existing memories before inserting (word-overlap similarity ≥ 65%). If a near-duplicate is found, it returns the existing memory ID instead of inserting. Pass "check_duplicate": false to force an insert.

Importance Guide

ScoreMeaning
0.9–1.0Critical — always surface (key preferences, major corrections)
0.7–0.8Important — surface often
0.5–0.6Normal (default)
0.3–0.4Low — background context
0.0–0.2Archive only

OpenAuto Integration

If OpenAuto is installed alongside OpenMem, the two work together:

  • OpenAuto defers long-term writes to OpenMem — when memory_add is available, OpenAuto uses it instead of writing directly to MEMORY.md
  • OpenMem replaces MEMORY.md searchesmemory_search handles ranked FTS lookup so OpenAuto doesn't need to grep flat files
  • Session compression feeds OpenMem — OpenAuto's Working Buffer and daily notes are the source material; saying "compress my sessions" extracts durable memories into OpenMem
  • Bootstrap injection bridges both — OpenMem injects top memories as OPENMEM.md at session start, which OpenAuto reads alongside its own workspace files

Both skills work independently. If both are installed and OpenMem's MCP server is registered, the integration activates automatically — no extra setup required.

Bootstrap Hook

The bootstrap hook auto-injects your top memories at session start.

Enable once:

openclaw hooks enable openmem

Memories appear in OPENMEM.md at the start of every session. Control injection count with OPENMEM_BOOTSTRAP_LIMIT (default: 12).

Database Schema

See references/schema.md for full schema details.

Environment Variables

VariableDefaultDescription
OPENMEM_DB~/.openclaw/workspace/memory/openmem.dbDatabase path
OPENMEM_BOOTSTRAP_LIMIT12Memories injected at bootstrap
OPENMEM_SESSIONS_DIR~/.openclaw/agents/main/sessionsSession files location
OPENMEM_COMPRESS_INACTIVITY2Minimum inactivity hours before auto-compress runs

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

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安全审计

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权限和风险

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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