openclaw-memory-system

Manage OpenClaw memory retrieval, context injection, and post-task memorization.

Updated Feb 18, 2026
One-click install
npx skills add https://github.com/ollieb89/openrepo --skill openclaw-memory-system
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: openclaw-memory-system
Source: https://github.com/ollieb89/openrepo/tree/main/skills/openclaw-memory-system
Command: npx skills add https://github.com/ollieb89/openrepo --skill openclaw-memory-system

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the complexities of managing AI memory within the OpenClaw orchestration framework, ensuring that agent interactions and task executions are properly contextualized and that learnings are stored effectively.

Core Features & Use Cases

  • Memory Retrieval & Injection: Understand how and when memory (memU) is retrieved and injected into agent prompts (SOUL/MEMORY context).
  • Post-Task Memorization: Trace how task outcomes are used to update and store agent learnings.
  • Use Case: Debugging why an AI agent isn't recalling previous task details or ensuring that successful code implementations are correctly stored for future reference.

Quick Start

Use the openclaw-memory-system skill to trace the memory injection path for a task.

Frequently Asked Questions about openclaw-memory-system

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does AI memory injection work in an LLM orchestration framework?

AI memory injection retrieves stored context (memU) and inserts it into agent prompts via SOUL/MEMORY paths. This ensures the LLM runtime has the necessary historical task context before generating a response.

Why is my AI agent not recalling previous task details during orchestration?

AI agent recall failures often stem from broken memory injection paths or skipped post-task memorization. You can trace the memU retrieval and SOUL/MEMORY context injection to validate where the memory pipeline fails.

How do I debug memory context injection for different agent types?

Debug memory context injection by scoping memory operations to specific agent types like L2_PM, L3_CODE, and L3_TEST. Validate the LLM runtime dispatch and memory injection paths within the OpenClaw framework.

When do I need to validate post-task memorization for AI agents?

Validate post-task memorization when task outcomes must be stored for future reference. This ensures successful code implementations and agent learnings are captured and available for subsequent memU retrieval.

Does OpenClaw memory management support context injection for code and test agents?

OpenClaw memory management scopes context injection for L2_PM, L3_CODE, and L3_TEST agent types. It validates memorization processes and LLM runtime dispatch to ensure correct memory behavior across different agent roles.

What is the best way to trace AI task memory behavior in an orchestration runtime?

Trace AI task memory behavior by analyzing memU retrieval, SOUL/MEMORY context injection, and post-task memorization. Validate the LLM runtime dispatch to ensure memory operations are correctly scoped and executed.