---
skill_id: when-implementing-persistent-memory-use-agentdb-memory
name: agentdb-persistent-memory-patterns-17d55c09373c
version: 1.0.0
category: agentdb
subcategory: memory-management
trigger_pattern: "when-implementing-persistent-memory"
agents:
  - memory-coordinator
  - swarm-memory-manager
  - backend-dev
complexity: intermediate
estimated_duration: 6-8 hours
prerequisites:
  - AgentDB basics
  - Memory management concepts
  - Database schema design
outputs:
  - Persistent memory architecture
  - Session and long-term storage
  - Pattern learning system
  - Context management APIs
validation_criteria:
  - Memory persists across sessions
  - Fast retrieval (< 50ms)
  - Pattern recognition working
  - Context maintained accurately
evidence_based_techniques:
  - Self-consistency validation
  - Chain-of-verification
  - Multi-agent consensus
metadata:
  author: claude-flow
  created: 2025-10-30
  tags:
    - agentdb
    - memory
    - persistence
    - context-management
---

# AgentDB Persistent Memory Patterns

## 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

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

### Pattern Learning
```typescript
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

## Additional Resources

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


---
Source attribution: DNYoussef
Original project: https://github.com/DNYoussef/ai-chrome-extension/blob/a6c374009d5e0bb8650549060dfad84bfbe75293/.claude/skills/agentdb/when-implementing-persistent-memory-use-agentdb-memory/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 ReadFlow Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: DNYoussef
Original project: https://github.com/DNYoussef/ai-chrome-extension/blob/a6c374009d5e0bb8650549060dfad84bfbe75293/.claude/skills/agentdb/when-implementing-persistent-memory-use-agentdb-memory/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering, Product Management
Roles: Backend Developer, Data Engineer, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API, Backend, Database
Tools: Agentdb
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 09c5d74abfb67503b2d8faec9a7de433e11f2b17e133ef7c213c23ca0fc88641