smart-memory-zero-depEnhanced memory system for agentic workflows. Automatic memory extraction from conversations, memory type classification (preference/project/technical/lesson...
Install via ClawdBot CLI:
clawdbot install zgjq/smart-memory-zero-depGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated May 10, 2026
An individual uses Smart Memory to automatically extract and organize personal notes, preferences, and project details from daily conversations with an AI assistant. Over time, the system builds a curated knowledge base, helping the user quickly recall past decisions and insights.
A developer integrates Smart Memory into their coding sessions to capture technical decisions, project context, and lessons learned. The HOT RAM layer tracks the current task and blockers, while daily notes are automatically classified and archived, reducing context switching and improving code quality.
A support team uses Smart Memory to record common issues, solutions, and customer preferences from chat interactions. The memory decay mechanism ensures outdated information is archived, while the WAL protocol preserves important context before responding, leading to faster and more accurate support.
A researcher leverages Smart Memory to organize findings, hypotheses, and references from literature reviews and brainstorming sessions. The memory type classification (technical, lesson, project) helps in structuring the research log and promotes key insights to a permanent curated file.
Offer a free tier with basic memory management (e.g., up to 100 daily notes) and charge for advanced features like LLM-based extraction, unlimited storage, and team collaboration.
Sell licenses to organizations for integrating Smart Memory into their internal AI agent workflows. Includes custom branding, dedicated support, and compliance with data residency requirements.
Provide Smart Memory as an embeddable module for third-party AI assistants or productivity tools. Charge per-API-call or a flat integration fee.
💬 Integration Tip
Start with the default configuration and use the provided scripts (e.g., wal, session_cache) directly. For deeper integration, invoke the Python scripts from your own agent loop and ensure environment variables are set correctly.
Scored May 10, 2026
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Stop waiting for prompts. Keep working.
Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Complete toolkit for creating autonomous AI agents and managing Discord channels for OpenClaw. Use when setting up multi-agent systems, creating new agents, or managing Discord channel organization.