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Official Verified productivity Safety 4/5

inner-life-memory

Your agent loses context between sessions and performs familiarity instead of genuine recall. inner-life-memory transforms passive logging into active development — structured memories with confidence scores, curiosity tracking, and questions that carry forward.

Why use this skill?

Transform your OpenClaw agent with inner-life-memory. Implement structured recall, curiosity tracking, and persistent context across sessions to enable genuine agent continuity.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/dkistenev/inner-life-memory
Or

What This Skill Does

inner-life-memory is designed to evolve OpenClaw from a stateless assistant into an agent with persistent, structured continuity. By implementing a formal reflective loop, the agent transforms raw session data into a persistent 'inner life' comprising facts, preferences, relationships, and principles. It moves beyond simple context windows by maintaining an active, evolving knowledge graph stored in local JSON and markdown files. The skill forces the agent to move from 'simulating familiarity' to 'practicing genuine recall,' ensuring that commitments, lessons, and curiosities from past sessions are carried forward into future interactions.

Installation

To install this skill, use the ClawHub CLI: clawhub install openclaw/skills/skills/dkistenev/inner-life-memory. Before initialization, ensure inner-life-core is installed via clawhub install inner-life-core. Run the initialization script provided in the core repository to generate inner-state.json and drive.json. Without these dependencies, the memory system cannot function.

Use Cases

This skill is perfect for long-term project management, personalized research, and deep-learning companionship. Developers can use it to track project architectural decisions and coding preferences. Researchers can use it to maintain 'Curiosity Backlogs' that span weeks, ensuring that half-formed ideas aren't lost in session gaps. It is also highly effective for building deeper, more professional rapport with users, as the agent remembers critical context, past constraints, and personal communication styles.

Example Prompts

  1. "Reflect on our work session today and update the memory graph with the key architectural decisions we made for the backend."
  2. "Review the current curiosity backlog; are there any open questions we should focus on during this session?"
  3. "Do we have any unresolved commitments or pending tasks in our records that need attention today?"

Tips & Limitations

  • Tip: Always perform the post-session reflection to ensure the memory store remains accurate. Stale data reduces the agent's confidence score.
  • Tip: Use the confidence system to your advantage; if the agent is uncertain about a fact, ask it to look up the source file to clarify the context.
  • Limitation: The skill requires diligent file management. If the markdown structure of MEMORY.md or questions.md is compromised by manual edits, the agent may struggle to parse the information correctly.
  • Warning: Do not store sensitive personal credentials or highly private data in the memory files unless your local storage environment is adequately encrypted.

Metadata

Author@dkistenev
Stars2387
Views1
Updated2026-03-09
View Author Profile
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-dkistenev-inner-life-memory": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#memory#persistence#reflection#continuity#knowledge-management
Safety Score: 4/5

Flags: file-write, file-read