hippocampus-memoryPersistent 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 via ClawdBot CLI:
clawdbot install ImpKind/hippocampus-memory"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.
# 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.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 jobsThe 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.
PREPROCESS → SCORE → SEMANTIC CHECK → REINFORCE or CREATE → DECAYKey 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.
$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| 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 |
Visual dashboard showing all installed brain skills:
./scripts/generate-dashboard.sh
open ~/.openclaw/workspace/brain-dashboard.htmlIDENTITY.mdWhen 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.htmlAll scripts use $WORKSPACE environment variable (default: ~/.openclaw/workspace).
| 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 |
Based on Stanford Generative Agents (Park et al., 2023):
new_importance = importance × (0.99 ^ days_since_accessed)During encoding, the LLM compares new signals to existing memories:
This happens automatically—no manual reinforcement needed.
| 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.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"]
}
]
}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"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).
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"
\`\`\`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 |
Memory is identity. Text > Brain. If you don't write it down, you lose it.
Generated Mar 1, 2026
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💬 Integration Tip
Ensure the workspace environment variable is correctly set and test cron jobs in isolation before full deployment to avoid disruptions in memory processing.
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