Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Hippocampus
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).
skill openclawclawhub Free
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).
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- ARCHITECTURE.md, CHANGELOG.md, CONFIG-UPGRADE.md, README.md, SKILL.md, agentdir/AGENTS.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 3.9.0
Provenance
- Publisher
- ImpKind
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
ScriptPurposepreprocess.shExtract signals from conversation transcriptsencode-pipeline.shScore signals, prepare for LLM summarizationdecay.shApply 0.99^days decay to all memoriesrecall.shSearch with importance weightingload-core.shOutput high-importance memories for session startsync-core.shGenerate HIPPOCAMPUS_CORE.mdconsolidate.shWeekly review helperreflect.shSelf-reflection promptsgenerate-dashboard.shGenerate 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).
Initial Score (0.0-1.0)
SignalScoreExplicit "remember this"0.9Emotional/vulnerable content0.85Preferences ("I prefer...")0.8Decisions made0.75Facts about people/projects0.7General knowledge0.5
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
ScoreStatus0.7+Core — loaded at session start0.4-0.7Active — normal retrieval0.2-0.4Background — specific search only<0.2Archive 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" \`\`\`
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. PartFunctionStatushippocampusMemory formation, decay, reinforcement✅ Liveamygdala-memoryEmotional processing✅ Livevta-memoryReward and motivation✅ Livebasal-ganglia-memoryHabit formation🚧 Developmentanterior-cingulate-memoryConflict detection🚧 Developmentinsula-memoryInternal 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- agentdir/AGENTS.md
- ARCHITECTURE.md
- CHANGELOG.md
- CONFIG-UPGRADE.md
- README.md