Ichiro-Mind is a sophisticated unified memory architecture that provides AI agents with persistent, hierarchical recall through a 4-layer system of hot, warm, cold, and archive storage.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ichiro-mind
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ichiro-mind using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Ichiro-Mind?
Ichiro-Mind is a comprehensive memory framework designed to bridge the gap between temporary context and long-term intelligence for AI agents. By utilizing a unique 4-layer architecture—consisting of the Hot Layer (Working RAM), Warm Layer (Neural Net), Cold Layer (Vectors), and Archive Layer (Long-term)—this tool ensures that information is always stored where it is most accessible. It is a standout among Openclaw Skills for its ability to unify diverse memory approaches like semantic search and associative graphs into a single, cohesive engine.
The system is built on the philosophy that memory is intelligence, not just storage. It incorporates a Neural Graph with spreading activation, allowing agents to find conceptually related memories rather than just relying on simple keyword matches. With its built-in Experience Learning engine, Ichiro-Mind tracks decisions and outcomes, enabling agents to learn from past errors and improve their performance over time through continuous evolution.
Ichiro-Mind Use Cases
- Managing long-running development projects where maintaining context across multiple sessions is critical.
- Tracking complex decision-making trees and their results to improve agentic reasoning.
- Building and maintaining detailed user profiles, preferences, and interaction histories.
- Preventing recurring errors by referencing a dedicated library of learned lessons and past failures.
- Accumulating specialized domain expertise through structured knowledge capture and archival.
How Ichiro-Mind Works
- Capture: Real-time interactions are captured and processed through an auto-extraction pipeline.
- Importance Scoring: The system assigns importance scores to determine the lifecycle and placement of the memory.
- Intelligent Routing: Information is automatically routed to the Hot layer for immediate use, Cold for semantic search, or Archive for persistence.
- Associative Linking: The Warm layer creates neural connections between related concepts using 20 different synapse types.
- Smart Recall: Retrieval queries are dynamically routed to the most efficient layer, balancing speed and depth.
- Hygiene & Optimization: A background engine performs periodic cleanup, deduplication, and token optimization to maintain system health.
Ichiro-Mind Setup
To integrate this memory system into your agentic workflow and expand your Openclaw Skills, use the following commands:
# Install the skill via the hub
clawhub install ichiro-mind
# Initialize the memory architecture
ichiro-mind init
# Configure the Model Context Protocol (MCP) interface
ichiro-mind setup-mcp
You must also provide an OPENAI_API_KEY in your environment variables to support vector embeddings and neural graph processing.
Ichiro-Mind Data Schema & Taxonomy
Ichiro-Mind organizes its data across specialized storage formats to optimize for different retrieval latencies:
| Layer | Storage Type | Primary Document |
|---|---|---|
| HOT | Markdown WAL | SESSION-STATE.md |
| WARM | Graph DB | Associative Neural Graph |
| COLD | LanceDB | Vector Store |
| ARCHIVE | File System | MEMORY.md & /memory/ directory |
The persistent Archive layer utilizes a structured taxonomy including sub-directories for daily logs, structured decisions, evolving entities, and learned lessons.
Ichiro-Mind Advanced Features
- Neural Graph with Spreading Activation that traverses conceptual links rather than just text matches.
- Write-Ahead Log (WAL) protocol for the Hot layer to ensure state survival during compaction.
- Automated Memory Hygiene engine for junk cleanup, deduplication, and token usage optimization.
- Experience Replay capabilities that allow agents to query past decisions before acting.
- Entity Evolution tracking that monitors how concepts and project details change over time.
SKILL.md
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