Agent Memory Architecture
Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/1kalin/afrexai-agent-memoryWhat This Skill Does
The Agent Memory Architecture is a zero-dependency, file-based cognitive system designed to provide AI agents with permanent, structured, and organized recall. Unlike traditional LLM session windows that reset upon closure, this architecture uses a hierarchical folder structure (ACTIVE-CONTEXT.md, MEMORY.md, and daily logs) to ensure your agent maintains continuity across infinite sessions. By treating local files as the agent's 'brain,' this skill allows for deep knowledge retention without the complexity of vector databases or cloud APIs.
Installation
To install this skill, use the OpenClaw CLI in your project root:
clawhub install openclaw/skills/skills/1kalin/afrexai-agent-memory
Once installed, initialize the directory structure by prompting the agent to 'Set up the memory architecture' in your workspace root.
Use Cases
- Long-Running Projects: Maintain technical context for software development spanning weeks or months.
- Research Tracking: Document insights, sources, and literature reviews in topic-specific Markdown files.
- Agent Autonomy: Empower agents to document their own decisions, blockers, and progress, allowing for seamless context handovers.
- Reflective Learning: Use the 'Daily Notes' layer to log daily achievements, failures, and lessons learned for quarterly performance reviews.
Example Prompts
- "Check my ACTIVE-CONTEXT.md and summarize the blocker for the current sprint; let's figure out how to resolve it."
- "I just finished the implementation of the new database schema. Please update MEMORY.md to reflect this architectural change and archive today's notes."
- "Review the notes from last week in the memory folder and extract any recurring technical issues that we need to address in our next planning session."
Tips & Limitations
- Maintenance: The system is only as good as the discipline used to update it. Ensure the agent is prompted to clean up 'ACTIVE-CONTEXT.md' regularly to prevent bloat.
- No Search Engine: Since this uses raw file systems, advanced semantic search is limited to the agent's ability to grep or read files. Keep file titles descriptive to assist the agent in locating relevant information quickly.
- Permissions: Ensure the agent has read/write access to the specific subdirectory to avoid runtime errors during memory management.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-1kalin-afrexai-agent-memory": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-write, file-read