agent-memory-enterpriseEnterprise-grade 5-layer agent memory system with routing, scoring, and multi-backend storage. Use when building production AI agents that need persistent me...
Install via ClawdBot CLI:
clawdbot install laojun509/agent-memory-enterpriseGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated May 6, 2026
A customer support agent that remembers past interactions, preferences, and issue histories across multiple sessions. It uses the 5-layer memory to provide context-aware responses, improving resolution time and customer satisfaction.
An AI assistant that guides users through complex workflows like software deployment or project management. It tracks task progress and state, remembers intermediate results, and can resume interrupted processes seamlessly.
A tutoring agent that adapts to a student's knowledge level, learning pace, and preferred teaching style. It remembers past lessons, quiz results, and mistakes to tailor future explanations and exercises.
An agent that assists patients with managing chronic conditions by remembering symptoms, medication schedules, and doctor recommendations. It uses long-term memory to track health trends and provide timely alerts.
A financial advisor agent that maintains user profiles with risk tolerance, investment history, and preferences. It retrieves relevant market knowledge and past recommendations to offer personalized portfolio advice.
Offer the memory system as a cloud service with tiered pricing based on memory capacity and number of users. Recurring revenue from monthly or annual subscriptions.
License the memory system to enterprises that want to embed it into their own AI products. Charge a one-time fee plus ongoing support and updates.
Provide API access to individual memory layers (e.g., context, knowledge) with usage-based billing. Suitable for developers building custom agents.
💬 Integration Tip
Start by configuring the three backends (Redis, PostgreSQL, ChromaDB) via environment variables or config file, then test each memory layer independently before enabling the intelligent router for combined retrieval.
Scored May 6, 2026
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
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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
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.
Billions decentralized identity for agents. Link agents to human identities using Billions ERC-8004 and Attestation Registries. Verify and generate authentic...