Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add ImpKind/hippocampus-skill
Or install specific skill: npx add-skill https://github.com/ImpKind/hippocampus-skill
# Description
Background memory organ for AI agents. Runs separately from the main agentβencoding, decaying, and reinforcing memories automatically. Just like the real hippocampus in your brain. Based on Stanford Generative Agents (Park et al., 2023).
# SKILL.md
name: hippocampus
description: "Background memory organ for AI agents. Runs separately from the main agentβencoding, decaying, and reinforcing memories automatically. Just like the real hippocampus in your brain. Based on Stanford Generative Agents (Park et al., 2023)."
metadata:
openclaw:
emoji: "π§ "
version: "3.1.0"
author: "Community"
repo: "https://github.com/ImpKind/hippocampus-skill"
requires:
bins: ["python3", "jq"]
install:
- id: "manual"
kind: "manual"
label: "Run install.sh"
instructions: "./install.sh --with-cron"
Hippocampus Skill
"Memory is identity. This skill is how I stay alive."
The hippocampus is the brain region responsible for memory formation. This skill makes memory capture automatic, structured, and persistentβwith importance scoring, decay, and reinforcement.
Quick Start
# Install
./install.sh --with-cron
# Load core memories
./scripts/load-core.sh
# Search with importance weighting
./scripts/recall.sh "query" --reinforce
# Apply decay (runs daily via cron)
./scripts/decay.sh
Core Concept
The LLM is just the engineβraw cognitive capability. The agent is the accumulated memory. Without these files, there's no continuityβjust a generic assistant.
Memory Lifecycle
CAPTURE β SCORE β STORE β DECAY/REINFORCE β RETRIEVE
β β
ββββββββββββββββββββββββββββββββββββββββββββββ
Memory Structure
$WORKSPACE/
βββ memory/
β βββ index.json # Central weighted index
β βββ user/ # Facts about the user
β βββ self/ # Facts about the agent
β βββ relationship/ # Shared context
β βββ world/ # External knowledge
βββ HIPPOCAMPUS_CORE.md # Auto-generated for OpenClaw RAG
Scripts
| Script | Purpose |
|---|---|
decay.sh |
Apply 0.99^days decay to all memories |
reinforce.sh |
Boost importance when memory is used |
recall.sh |
Search with importance weighting |
load-core.sh |
Output high-importance memories |
sync-core.sh |
Generate HIPPOCAMPUS_CORE.md |
preprocess.sh |
Extract signals from transcripts |
All scripts use $WORKSPACE environment variable (default: ~/.openclaw/workspace).
Importance Scoring
Initial Score (0.0-1.0)
| Signal | Score |
|---|---|
| Explicit "remember this" | 0.9 |
| Emotional/vulnerable content | 0.85 |
| Preferences ("I prefer...") | 0.8 |
| Decisions made | 0.75 |
| Facts about people/projects | 0.7 |
| General knowledge | 0.5 |
Decay Formula
Based on Stanford Generative Agents (Park et al., 2023):
new_importance = importance Γ (0.99 ^ days_since_accessed)
- After 7 days: 93% of original
- After 30 days: 74% of original
- After 90 days: 40% of original
Reinforcement Formula
When a memory is accessed and useful:
new_importance = old + (1 - old) Γ 0.15
Each use adds ~15% of remaining headroom toward 1.0.
Thresholds
| Score | Status |
|---|---|
| 0.7+ | Core β high priority |
| 0.4-0.7 | Active β normal retrieval |
| 0.2-0.4 | Background β specific search only |
| <0.2 | Archive candidate |
Memory Index Schema
memory/index.json:
{
"version": 1,
"lastUpdated": "2025-01-20T19:00:00Z",
"decayLastRun": "2025-01-20",
"memories": [
{
"id": "mem_001",
"domain": "user",
"category": "preferences",
"content": "User prefers concise responses",
"importance": 0.85,
"created": "2025-01-15",
"lastAccessed": "2025-01-20",
"timesReinforced": 3,
"keywords": ["preference", "concise", "style"]
}
]
}
Cron Jobs
Set up via OpenClaw cron:
# Daily decay at 3 AM
openclaw cron add --name hippocampus-decay \
--cron "0 3 * * *" \
--session main \
--system-event "π§ Run: WORKSPACE=\$WORKSPACE decay.sh"
# Weekly consolidation
openclaw cron add --name hippocampus-consolidate \
--cron "0 21 * * 6" \
--session main \
--system-event "π§ Weekly consolidation time"
OpenClaw Integration
Add to memorySearch.extraPaths in openclaw.json:
{
"agents": {
"defaults": {
"memorySearch": {
"extraPaths": ["HIPPOCAMPUS_CORE.md"]
}
}
}
}
This bridges hippocampus (index.json) with OpenClaw's RAG (memory_search).
Usage in AGENTS.md
Add to your agent's session start routine:
## Every Session
1. Run `~/.openclaw/workspace/skills/hippocampus/scripts/load-core.sh`
## When answering context questions
Use hippocampus recall:
\`\`\`bash
./scripts/recall.sh "query" --reinforce
\`\`\`
Capture Guidelines
What to Capture
- User facts: Preferences, patterns, context
- Self facts: Identity, growth, opinions
- Relationship: Trust moments, shared history
- World: Projects, people, tools
Trigger Phrases
Auto-capture when you hear:
- "Remember that..."
- "I prefer...", "I always..."
- Emotional content (struggles AND wins)
- Decisions made
References
Memory is identity. Text > Brain. If you don't write it down, you lose it.
# README.md
π§ Hippocampus
A living memory system for OpenClaw agents with importance scoring, time-based decay, and reinforcementβjust like a real brain.
The Concept
The hippocampus runs in the background, just like the real organ in your brain.
Your main agent is busy having conversationsβit can't constantly stop to decide what to remember. That's what the hippocampus does. It operates as a separate process:
- Background encoding: A cron job or separate agent watches conversations and encodes important signals into memory
- Automatic decay: Unused memories fade over time (daily cron)
- Reinforcement on recall: When memories are accessed, they strengthen automatically
The main agent doesn't "think about" memoryβit just recalls what it needs, and the hippocampus handles the rest. Like a real brain.
Features
- Importance Scoring: Memories rated 0.0-1.0 based on signal type
- Time-Based Decay: Unused memories fade (0.99^days)
- Reinforcement: Used memories strengthen (+15% headroom)
- Background Processing: Encoding runs via cron, not in main agent's context
- OpenClaw Integration: Bridges with memory_search via HIPPOCAMPUS_CORE.md
Installation
cd ~/.openclaw/workspace/skills/hippocampus
./install.sh --with-cron
Or via ClawdHub:
clawdhub install hippocampus
Quick Usage
# Load core memories at session start
./scripts/load-core.sh
# Search with importance weighting
./scripts/recall.sh "project deadline" --reinforce
# Manually boost a memory
./scripts/reinforce.sh mem_001 --boost
# Apply decay (usually via cron)
./scripts/decay.sh
How It Works
βββββββββββββββ βββββββββββββββ βββββββββββββββ
β Capture ββββββΆβ Score & ββββββΆβ Store in β
β (encoding) β β Classify β β index.json β
βββββββββββββββ βββββββββββββββ ββββββββ¬βββββββ
β
ββββββββββββββββββββββββββββ
β
βΌ
βββββββββββββββ βββββββββββββββ βββββββββββββββ
β Decay ββββββΆβ Retrieve ββββββΆβ Reinforce β
β (0.99^days) β β (recall.sh)β β on use β
βββββββββββββββ βββββββββββββββ βββββββββββββββ
Memory Domains
| Domain | Contents |
|---|---|
user/ |
Facts about the human |
self/ |
Agent identity & growth |
relationship/ |
Shared context & trust |
world/ |
External knowledge |
Decay Timeline
| Days Unused | Retention |
|---|---|
| 7 | 93% |
| 30 | 74% |
| 90 | 40% |
Requirements
- Python 3
- jq
- OpenClaw
AI Brain Series
Building cognitive architecture for AI agents:
| Part | Function | Status |
|---|---|---|
| hippocampus | Memory formation, decay, reinforcement | β Live |
| amygdala-memory | Emotional processing | β Live |
| basal-ganglia-memory | Habit formation | π§ Coming |
| anterior-cingulate-memory | Conflict detection | π§ Coming |
| insula-memory | Internal state awareness | π§ Coming |
| vta-memory | Reward and motivation | π§ Coming |
Based On
Stanford Generative Agents: "Interactive Simulacra of Human Behavior" (Park et al., 2023)
License
MIT
Memory is identity. Text > Brain. Part of the AI Brain series.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.