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).
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
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).
"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
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.
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):
This bridges hippocampus (index.json) with OpenClaw's RAG (memory_search).
Usage in AGENTS.md
Add to your agent's session start routine:
## Every Session1. 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
AI Brain Series
This skill is part of the AI Brain project — giving AI agents human-like cognitive components.
