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arunrlverma
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agent_memory

Store and retrieve AI agent facts, decisions, and preferences in a cloud JSON file.

Updated Feb 12, 2026
Search Tags:#decision making#context#recall#memory#persistence#user preferences
One-click install
npx skills add https://github.com/arunrlverma/openclaw-skills --skill agent-memory-arunrlverma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent_memory
Source: https://github.com/arunrlverma/openclaw-skills/tree/main/skills/memory
Command: npx skills add https://github.com/arunrlverma/openclaw-skills --skill agent-memory-arunrlverma

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill provides a persistent memory system, allowing the agent to recall facts, decisions, and user preferences across conversations, ensuring continuity and personalized interactions.

Core Features & Use Cases

  • Persistent Memory: Stores information like user preferences, past decisions, and important facts.
  • Contextual Recall: Automatically loads memory at the start of conversations and allows querying past information.
  • Use Case: If a user previously stated their preference for morning workouts, the agent can recall this information later to suggest relevant activities.

Quick Start

Load your memory at the start of the conversation.

Frequently Asked Questions about agent_memory

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

FAQPage Schema
How do I store user preferences and facts for AI agent memory persistence?▼

AI agent memory persistence stores user preferences, facts, and past decisions in a JSON file located in cloud storage. This allows the agent to automatically load and recall contextual information at the start of new conversations, ensuring continuity across sessions.

How does contextual recall work for past decisions in an AI conversation?▼

Contextual recall works by automatically loading saved memory at the start of a conversation. The agent queries stored information, such as user preferences and past decisions, from a persistent JSON file to provide personalized and continuous interactions.

How do I load memory at the start of a conversation using a JSON file?▼

To load memory at the start of a conversation, the system retrieves stored facts, decisions, and preferences from a JSON file saved in cloud storage. This operation initializes the agent's contextual awareness for the current session.

Can I search through stored information and facts in an agent's persistent memory?▼

Yes, you can search through stored information in persistent memory. The system supports operations to query and retrieve specific facts, user preferences, and past decisions saved within the JSON storage file.

What are the limitations of using a JSON file for AI agent memory storage?▼

Using a JSON file for memory storage limits complex querying capabilities and may impact performance at scale. It relies on cloud storage availability for persistence and is best suited for storing structured facts, decisions, and preferences rather than unstructured data.

Does agent_memory require cloud storage to save user preferences?▼

Yes, agent_memory requires cloud storage to maintain persistent memory. It utilizes a JSON file stored in the cloud to ensure facts, decisions, and user preferences are reliably saved and retrieved across different conversations.

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