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brain-memory-system大脑记忆系统

Agent Skill

brain-memory-system 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。

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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:brain-memory-system(大脑记忆系统)
来源仓库:https://github.com/mwmdeadpool/brain-memory-system
安装命令:
openclaw skills install brain-memory-system
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 brain-memory-system

简介

受大脑结构启发设计的情景-语义-程序记忆架构。

  • 分别管理事实、经验与操作流程三类信息。
  • 支持记忆衰减与关联激活机制模拟。brain-memory-system 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 需定义记忆分类标签与更新频率策略。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 建议设置记忆容量上限防止性能下降。

SKILL.md

name
cognitive-brain
description
Unified cognitive memory system inspired by human brain architecture. Provides episodic memory (hippocampus), semantic facts (neocortex), procedural memory with LLM-driven evolution (cerebellum), attention filtering (thalamus), sleep consolidation, and soul erosion health metrics. Use when storing experiences, recalling memories, managing facts, creating/evolving procedures, filtering incoming information by importance, running memory consolidation, or checking memory health. Replaces separate facts/proc CLIs with a single brain command.

Cognitive Brain

Unified memory system modeled on human brain architecture. One CLI (brain) for all memory operations.

Architecture

SystemBrain RegionWhat it does
EpisodicHippocampusTime-stamped experiences with emotional tags
SemanticNeocortexStructured facts (entity/key/value with FTS5)
ProceduralCerebellumVersioned workflows that evolve from failures
AttentionThalamusScore incoming info → store/summarize/discard
ConsolidationSleep replayBatch-process episodes → extract facts
HealthSoul erosionDetect memory drift, conflicts, flatness

Installation

# 1. Initialize the database
sqlite3 brain.db < scripts/schema.sql

# 2. Link the CLI
ln -sf "$(pwd)/scripts/brain.sh" ~/.local/bin/brain
chmod +x scripts/brain.sh

# 3. (Optional) Migrate existing daily logs
python3 scripts/migrate-daily-logs.py --dir /path/to/memory/ --db brain.db

Environment Variables

VariableDefaultPurpose
BRAIN_DB<skill>/brain.dbPath to brain database
BRAIN_AGENTmargotAgent identity for scoping
BRAIN_FACTS_DBmemory/facts.dbLegacy facts database path
BRAIN_LLM_URLGoogle Gemini endpointOpenAI-compatible chat completions URL
BRAIN_LLM_KEY*(none — must be set)*API key for LLM provider (required for proc evolve)
BRAIN_LLM_MODELgemini-2.5-flashModel name for evolution reasoning

Credentials & Scope

Required for brain proc evolve only:

  • BRAIN_LLM_KEY — Your API key for the LLM provider. Set via env var or brain config set key <value>.
  • No credentials are auto-discovered or read from platform stores.
  • Without a key, proc evolve falls back to local pattern-based evolution (no LLM needed).

Data scope:

  • All data stays in your brain.db file (local SQLite).
  • brain facts reads/writes BRAIN_FACTS_DB (default: facts.db in skill directory).
  • brain wm reads/writes SESSION_STATE (default: SESSION-STATE.md in workspace root).
  • No data is sent externally except LLM API calls during proc evolve.

Quick Reference

Store & Recall

brain store "Fixed the deploy pipeline" --title "Deploy Fix" --emotion relieved --importance 8
brain ingest "Docker OOM at 3 AM" --title "OOM Event" --source mqtt  # attention-gated
brain recall "deploy pipeline" --type all --limit 5
brain episodes 2026-03-15
brain emotions 7
brain important 8 14

Facts (Semantic Memory)

brain facts get Darian favorite_movie
brain facts set Mae birthday "September 12" --category date --permanent
brain facts search "SSH" --limit 5
brain facts list --entity Darian --limit 10
brain facts stats

Procedures (Cerebellum)

brain proc create deploy-api --title "Deploy API" --steps '["Pull latest","Run tests","Deploy"]'
brain proc success deploy-api
brain proc fail deploy-api --step 2 --error "Tests timed out" --fix "Increased timeout to 60s"
brain proc evolve deploy-api           # LLM rewrites steps from failure patterns
brain proc evolve deploy-api --dry-run # preview without applying
brain proc history deploy-api          # full evolution timeline
brain proc list

Attention Filter

brain filter "GPU temperature 72°C" --source mqtt    # → discard (routine)
brain filter "SSH brute force from new IP" --source security  # → store (novel threat)

Consolidation

brain consolidate --dry-run    # preview what would be processed
brain consolidate              # run sleep replay

Health (Soul Erosion Detection)

brain health           # 7-metric scored report
brain health -v        # verbose with all details
brain health --json    # machine-readable for crons

Configuration

brain config show              # current LLM config
brain config set model gpt-4o  # change model
brain config set url http://localhost:11434/v1/chat/completions  # switch to Ollama

Multi-Agent

brain --agent bud store "Patrol complete" --title "Bud Patrol" --importance 3
brain --agent bud proc list   # sees own + shared procedures
brain who                     # show all agents in the system

Procedure Evolution Flow

The core innovation — procedures that rewrite themselves from failure patterns:

  1. Record failures with step-level granularity: brain proc fail <slug> --step N --error "desc"
  2. At 3+ failures, brain suggests evolution
  3. brain proc evolve <slug> analyzes patterns:

- Repeat offender steps (same step failing multiple times) - Brittle chains (consecutive step failures) - Error keyword clustering (timeout, auth, permission, etc.)

  1. LLM synthesizes and rewrites steps — adds pre-checks, reorders, annotates with [vN: reason]
  2. Local fallback if LLM unavailable — pattern-matching inserts defensive steps
  3. Full version history preserved: brain proc history <slug>

Health Metrics

Seven metrics, each scored 1-10:

MetricWhat it detects
Memory FreshnessTime since last recorded episode
Consolidation DebtBacklog of unprocessed episodes
Importance CalibrationEverything rated 8+? Nothing is important
Emotional DiversityFlatlined to one emotion = loss of range
Fact ConsistencyContradictory facts = identity fragmentation
Procedure HealthSuccess rates dropping on learned behaviors
Recording CadenceSilent days creating memory gaps

Schema

Database: SQLite with WAL mode, FTS5 full-text search, foreign keys.

Tables: episodes, episodes_fts, facts, facts_fts, procedures, procedure_history, working_memory, consolidation_log, brain_meta.

Initialize with: sqlite3 brain.db < scripts/schema.sql

Files

FilePurpose
scripts/brain.shMain CLI dispatcher
scripts/schema.sqlDatabase schema
scripts/attention.pyThalamic attention filter (rule-based scoring)
scripts/consolidate.pySleep replay consolidation pipeline
scripts/erosion.pySoul erosion health metrics
scripts/evolve.pyProcedure evolution engine (LLM + local fallback)
scripts/facts.pySemantic fact storage wrapper
scripts/migrate-daily-logs.pyImport existing daily markdown logs

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。

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按下载量换算1,747

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