agentdb-persistent-memory-patterns

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name: agentdb-persistent-memory-patterns description: AgentDB Persistent Memory Patterns operates on 3 fundamental principles: allowed-tools: Read, Write, Edit, Task, TodoWrite, Glob, Grep

AgentDB Persistent Memory Patterns

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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Overview

Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants.

SOP Framework: 5-Phase Memory Implementation

Phase 1: Design Memory Architecture (1-2 hours)

• Define memory schemas (episodic, semantic, procedural)
• Plan storage layers (short-term, working, long-term)
• Design retrieval mechanisms
• Configure persistence strategies

Phase 2: Implement Storage Layer (2-3 hours)

• Create memory stores in AgentDB
• Implement session management
• Build long-term memory persistence
• Setup memory indexing

Phase 3: Test Memory Operations (1-2 hours)

• Validate store/retrieve operations
• Test memory consolidation
• Verify pattern recognition
• Benchmark performance

Phase 4: Optimize Performance (1-2 hours)

• Implement caching layers
• Optimize retrieval queries
• Add memory compression
• Performance tuning

Phase 5: Document Patterns (1 hour)

• Create usage documentation
• Document memory patterns
• Write integration examples
• Generate API documentation

Quick Start

import { AgentDB, MemoryManager } from 'agentdb-memory';

// Initialize memory system
const memoryDB = new AgentDB({
  name: 'agent-memory',
  dimensions: 768,
  memory: {
    sessionTTL: 3600,
    consolidationInterval: 300,
    maxSessionSize: 1000
  }
});

const memoryManager = new MemoryManager({
  database: memoryDB,
  layers: ['episodic', 'semantic', 'procedural']
});

// Store memory
await memoryManager.store({
  type: 'episodic',
  content: 'User preferred dark theme',
  context: { userId: '123', timestamp: Date.now() }
});

// Retrieve memory
const memories = await memoryManager.retrieve({
  query: 'user preferences',
  type: 'episodic',
  limit: 10
});

Memory Patterns

Session Memory

const session = await memoryManager.createSession('user-123');
await session.store('conversation', messageHistory);
await session.store('preferences', userPrefs);
const context = await session.getContext();

Long-Term Storage

await memoryManager.consolidate({
  from: 'working-memory',
  to: 'long-term-memory',
  strategy: 'importance-based'
});

Pattern Learning

const patterns = await memoryManager.learnPatterns({
  memory: 'episodic',
  algorithm: 'clustering',
  minSupport: 0.1
});

Success Metrics

• Memory persists across agent restarts
• Retrieval latency < 50ms (p95)
• Pattern recognition accuracy > 85%
• Context maintained with 95% accuracy
• Memory consolidation working

MCP Requirements

This skill operates using AgentDB's npm package and API only. No additional MCP servers required.

All AgentDB memory operations are performed through:

• npm CLI: npx agentdb@latest
• TypeScript/JavaScript API: import { AgentDB, MemoryManager } from 'agentdb-memory'

Additional Resources

• Full documentation: SKILL.md
• Process guide: PROCESS.md
• AgentDB Memory Docs: https://agentdb.dev/docs/memory

Core Principles

AgentDB Persistent Memory Patterns operates on 3 fundamental principles:

Principle 1: Memory Layering - Separate Short-Term, Working, and Long-Term Storage

Memory systems mirror human cognition by organizing information across distinct temporal layers. Short-term memory handles immediate context (current conversation), working memory maintains active task state, and long-term memory consolidates important patterns for future retrieval.

In practice:

• Store conversation context in session memory with TTL expiration (1-hour default)
• Use working memory for active agent tasks and intermediate computation results
• Consolidate proven patterns and user preferences to long-term storage using importance-based criteria

Principle 2: Pattern Learning - Extract Reusable Knowledge from Episodic Memory

Raw episodic memories (specific events) are valuable but incomplete. True intelligence emerges when systems detect patterns across episodes - recurring user preferences, common error scenarios, effective solution strategies - and encode them as semantic knowledge.

In practice:

• Run clustering algorithms on episodic memory to identify recurring patterns (min support threshold: 10%)
• Convert pattern clusters into semantic memory entries with confidence scores
• Use procedural memory to store proven solution workflows that can be replayed in similar contexts

Principle 3: Performance-First Retrieval - Sub-50ms Latency with HNSW Indexing

Memory systems fail if retrieval is slower than computation. Production AI agents require sub-50ms memory access to maintain real-time responsiveness, necessitating HNSW indexing, quantization, and aggressive caching strategies.

In practice:

• Build HNSW indexes on all memory stores during initialization (M=16, efConstruction=200)
• Apply product quantization for 4x memory reduction without accuracy loss
• Implement LRU caching with 70%+ hit rate for frequently accessed memories

Common Anti-Patterns

Anti-Pattern	Problem	Solution
Memory Hoarder - Store Everything Forever	Unbounded storage growth leads to slow retrieval, high costs, and context pollution. Agents retrieve irrelevant memories from 6 months ago.	Implement aggressive TTL policies (1-hour sessions, 30-day working memory, importance-based long-term retention). Use consolidation strategies to compress episodic memories into semantic patterns.
Flat Memory - Single Storage Layer	All memories treated equally creates retrieval chaos. No distinction between current conversation context and learned patterns from last year.	Use 3-layer architecture: session (ephemeral), working (task-scoped), long-term (consolidated). Apply different retrieval strategies per layer (recency for session, relevance for semantic).
Retrieval Thrashing - Query Every Memory Store on Every Request	Exhaustive searches across all memory layers cause latency spikes (200ms+ retrieval). Agents spend more time remembering than acting.	Use cascading retrieval: session first (fastest), semantic second (indexed), episodic last (cold storage). Implement query routing based on memory type and recency. Cache hot paths.

Conclusion

AgentDB Persistent Memory Patterns transforms stateless AI agents into intelligent systems with genuine memory. By implementing layered storage (session, working, long-term), pattern learning algorithms, and performance-optimized retrieval, you enable agents to accumulate knowledge across interactions rather than starting from zero on every request. The 5-phase SOP ensures systematic implementation from architecture design through performance tuning, with success validated through sub-50ms retrieval latency and 95%+ context accuracy.

This skill is essential when building chat systems requiring conversation history, intelligent assistants that learn user preferences over time, or multi-agent systems coordinating through shared memory. The pattern learning capabilities distinguish AgentDB from basic vector databases - instead of merely storing embeddings, it actively extracts reusable knowledge from experience. When agents can remember what worked before, recall user preferences without re-asking, and apply proven patterns to new problems, they transition from tools to true collaborators.

The performance requirements are non-negotiable for production systems. Users abandon agents that "think" for 500ms between responses. By combining HNSW indexing, quantization, and caching strategies, you achieve both intelligent memory and real-time responsiveness - the foundation for AI systems that feel genuinely aware.