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Hippocampus

by @impkind

Persistent memory system for AI agents. Automatic encoding, decay, and semantic reinforcement — just like the hippocampus in your brain. Based on Stanford Generative Agents (Park et al., 2023).

Versionv3.9.0
Downloads4,316
Stars4
TERMINAL
clawhub install hippocampus

📖 About This Skill


name: hippocampus-memory title: "Hippocampus - Memory System" description: "Persistent memory system for AI agents. Automatic encoding, decay, and semantic reinforcement — just like the hippocampus in your brain. Based on Stanford Generative Agents (Park et al., 2023)." metadata: openclaw: emoji: "🧠" version: "3.9.0" 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 - Memory System

> "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 persistent—with importance scoring, decay, and semantic reinforcement.

Quick Start

# Install (defaults to last 100 signals)
./install.sh --with-cron

Load core memories at session start

./scripts/load-core.sh

Search with importance weighting

./scripts/recall.sh "query"

Run encoding manually (usually via cron)

./scripts/encode-pipeline.sh

Apply decay (runs daily via cron)

./scripts/decay.sh

Install Options

./install.sh                    # Basic, last 100 signals
./install.sh --signals 50       # Custom signal limit
./install.sh --whole            # Process entire conversation history
./install.sh --with-cron        # Also set up cron jobs

Core Concept

The LLM is just the engine—raw cognitive capability. The agent is the accumulated memory. Without these files, there's no continuity—just a generic assistant.

Memory Lifecycle

PREPROCESS → SCORE → SEMANTIC CHECK → REINFORCE or CREATE → DECAY

Key insight: Reinforcement happens automatically during encoding. When a topic comes up again, the LLM recognizes it's about an existing memory and reinforces instead of creating duplicates.

Memory Structure

$WORKSPACE/
├── memory/
│   ├── index.json           # Central weighted index
│   ├── signals.jsonl        # Raw signals (temp)
│   ├── pending-memories.json # Awaiting summarization (temp)
│   ├── user/                # Facts about the user
│   ├── self/                # Facts about the agent
│   ├── relationship/        # Shared context
│   └── world/               # External knowledge
└── HIPPOCAMPUS_CORE.md      # Auto-generated for OpenClaw RAG

Scripts

| Script | Purpose | |--------|---------| | preprocess.sh | Extract signals from conversation transcripts | | encode-pipeline.sh | Score signals, prepare for LLM summarization | | decay.sh | Apply 0.99^days decay to all memories | | recall.sh | Search with importance weighting | | load-core.sh | Output high-importance memories for session start | | sync-core.sh | Generate HIPPOCAMPUS_CORE.md | | consolidate.sh | Weekly review helper | | reflect.sh | Self-reflection prompts | | generate-dashboard.sh | Generate unified brain dashboard HTML |

Brain Dashboard

Visual dashboard showing all installed brain skills:

./scripts/generate-dashboard.sh
open ~/.openclaw/workspace/brain-dashboard.html

  • Auto-detects other brain skills (amygdala, VTA)
  • Shows install prompts for missing skills
  • Regenerates on install and cron runs
  • Reads avatar/name from IDENTITY.md
  • After Installation (for agents)

    When you install this skill, notify the user about the dashboard:

    > 🧠 Hippocampus installed! > > View your agent's memories in the Brain Dashboard: > ~/.openclaw/workspace/brain-dashboard.html

    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 × (0.99 ^ days_since_accessed)
    

  • After 7 days: 93% of original
  • After 30 days: 74% of original
  • After 90 days: 40% of original
  • Semantic Reinforcement

    During encoding, the LLM compares new signals to existing memories:

  • Same topic? → Reinforce (bump importance ~10%, update lastAccessed)
  • Truly new? → Create concise summary
  • This happens automatically—no manual reinforcement needed.

    Thresholds

    | Score | Status | |-------|--------| | 0.7+ | Core — loaded at session start | | 0.4-0.7 | Active — normal retrieval | | 0.2-0.4 | Background — 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",
      "lastProcessedMessageId": "abc123",
      "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

    The encoding cron is the heart of the system:

    # Encoding every 3 hours (with semantic reinforcement)
    openclaw cron add --name hippocampus-encoding \
      --cron "0 0,3,6,9,12,15,18,21 * * *" \
      --session isolated \
      --agent-turn "Run hippocampus encoding with semantic reinforcement..."

    Daily decay at 3 AM

    openclaw cron add --name hippocampus-decay \ --cron "0 3 * * *" \ --session isolated \ --agent-turn "Run decay.sh and report any memories below 0.2"

    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" \\\

    Capture Guidelines

    What Gets Captured

  • User facts: Preferences, patterns, context
  • Self facts: Identity, growth, opinions
  • Relationship: Trust moments, shared history
  • World: Projects, people, tools
  • Trigger Phrases (auto-scored higher)

  • "Remember that..."
  • "I prefer...", "I always..."
  • Emotional content (struggles AND wins)
  • Decisions made
  • Event Logging

    Track hippocampus activity over time for analytics and debugging:

    # Log an encoding run
    ./scripts/log-event.sh encoding new=3 reinforced=2 total=157

    Log decay

    ./scripts/log-event.sh decay decayed=154 low_importance=5

    Log recall

    ./scripts/log-event.sh recall query="user preferences" results=3

    Events append to ~/.openclaw/workspace/memory/brain-events.jsonl:

    {"ts":"2026-02-11T10:00:00Z","type":"hippocampus","event":"encoding","new":3,"reinforced":2,"total":157}
    

    Use this for:

  • Trend analysis (memory growth over time)
  • Debugging encoding issues
  • Building dashboards
  • AI Brain Series

    This skill is part of the AI Brain project — giving AI agents human-like cognitive components.

    | Part | Function | Status | |------|----------|--------| | hippocampus | Memory formation, decay, reinforcement | ✅ Live | | amygdala-memory | Emotional processing | ✅ Live | | vta-memory | Reward and motivation | ✅ Live | | basal-ganglia-memory | Habit formation | 🚧 Development | | anterior-cingulate-memory | Conflict detection | 🚧 Development | | insula-memory | Internal state awareness | 🚧 Development |

    References

  • Stanford Generative Agents Paper
  • GitHub: joonspk-research/generative_agents

  • *Memory is identity. Text > Brain. If you don't write it down, you lose it.*

    💡 Examples

    # Install (defaults to last 100 signals)
    ./install.sh --with-cron

    Load core memories at session start

    ./scripts/load-core.sh

    Search with importance weighting

    ./scripts/recall.sh "query"

    Run encoding manually (usually via cron)

    ./scripts/encode-pipeline.sh

    Apply decay (runs daily via cron)

    ./scripts/decay.sh