Agent skill · product strategy · aiskillstore
agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Why this skill is useful
Adds executable scripts for initializing and managing memory patterns in AI agents that aren't available in public documentation.
What it needs
Requires node installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AgentDB Memory Patterns What This Skill Does Provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions. Performance: 150x-12,500x faster than traditional solutions with 100% backward compatibility. Prerequisites Node.js 18+ AgentDB v1.0.7+ (via agentic-flow or standalone) Understanding of agent architectures Quick Start with CLI Initialize AgentDB Start MCP Server for Claude Code Create Learning Plugin Quick Start with API Memory Patterns 1. Session Memory 2. Long-Term Memory 3. Pattern Learning Advanced Patterns Hierarchical Memory Memory Consolidation CLI Operations Query Database Import/Export Data Performance Benchmarks Integration with ReasoningBank Learning Plugins Available Algorithms (9 Total) 1. Decision Transformer - Sequence modeling RL (recommended) 2. Q-Learning - Value-based learning 3. SARSA - On-policy TD learning 4. Actor-Critic - Policy gradient with baseline 5. Active Learning - Query selection 6. Adversarial Training - Robustness 7. Curriculum Learning - Progressive difficulty 8. Federated Learning - Distributed learning 9. Multi-task Learning - Transfer learning List and Manage Plugins Reasoning Agents (4 Modules) 1. PatternMatcher - Find similar patterns with HNSW indexing 2. ContextSynthesizer - Generate rich context from multiple sources 3. MemoryOptimizer - Consolidate similar patterns, prune low-quality 4. ExperienceCurator - Quality-based experience filtering Best Practices 1. Enable quantization: Use scalar/binary for 4-32x memory reduction 2. Use caching: 1000 pattern cache for <1ms retrieval 3. Batch operations: 500x faster than individual inserts 4. Train regularly: Update learning models with new experiences 5. Enable reasoning: Automatic context synthesis and optimization 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agentdb-memory-patterns