ImpKind

hippocampus

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# Install this skill:
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 agentβ€”encoding, 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 agentβ€”encoding, 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: "🧠"
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 persistentβ€”with 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 engineβ€”raw cognitive capability. The agent is the accumulated memory. Without these files, there's no continuityβ€”just a generic assistant.

Memory Lifecycle

CAPTURE β†’ SCORE β†’ STORE β†’ DECAY/REINFORCE β†’ RETRIEVE
   ↑                                            β”‚
   β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Memory Structure

$WORKSPACE/
β”œβ”€β”€ memory/
β”‚   β”œβ”€β”€ index.json           # Central weighted index
β”‚   β”œβ”€β”€ user/                # Facts about the user
β”‚   β”œβ”€β”€ self/                # Facts about the agent
β”‚   β”œβ”€β”€ relationship/        # Shared context
β”‚   └── world/               # External knowledge
└── 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 "🧠 Run: WORKSPACE=\$WORKSPACE decay.sh"

# Weekly consolidation
openclaw cron add --name hippocampus-consolidate \
  --cron "0 21 * * 6" \
  --session main \
  --system-event "🧠 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

🧠 Hippocampus

GitHub
ClawdHub

A living memory system for OpenClaw agents with importance scoring, time-based decay, and reinforcementβ€”just 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 conversationsβ€”it can't constantly stop to decide what to remember. That's what the hippocampus does. It operates as a separate process:

  1. Background encoding: A cron job or separate agent watches conversations and encodes important signals into memory
  2. Automatic decay: Unused memories fade over time (daily cron)
  3. Reinforcement on recall: When memories are accessed, they strengthen automatically

The main agent doesn't "think about" memoryβ€”it 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

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Capture   │────▢│   Score &   │────▢│   Store in  β”‚
β”‚  (encoding) β”‚     β”‚   Classify  β”‚     β”‚  index.json β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜
                                               β”‚
                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                    β”‚
                    β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚    Decay    │◀───▢│   Retrieve  │────▢│  Reinforce  β”‚
β”‚ (0.99^days) β”‚     β”‚  (recall.sh)β”‚     β”‚   on use    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

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 βœ… Live
amygdala-memory Emotional processing βœ… Live
basal-ganglia-memory Habit formation 🚧 Coming
anterior-cingulate-memory Conflict detection 🚧 Coming
insula-memory Internal state awareness 🚧 Coming
vta-memory Reward and motivation 🚧 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.