# Build AgentDB Persistent Memory

AI agents often lose useful context between sessions. This skill gives a structured AgentDB workflow for session memory, long-term storage, and pattern learning.

## Install

```bash
npx skillstore add dnyoussef/agentdb-persistent-memory-patterns
```

## Metadata

- Status: approved
- Slug: dnyoussef-agentdb-persistent-memory-patterns
- Version: 1.0.0
- Author version: 1.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: ee71e984d0f1072e9fc2702303577737c1381d023dbb6218b0ea773f95303204
- Author: DNYoussef
- GitHub username: DNYoussef
- License: MIT
- Repository: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/agentdb/when-implementing-persistent-memory-use-agentdb-memory
- Ref: 72d5025b022c77f7a51bdf5c1637c689c80e89d1
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network
- Quality score: 80
- Quality tier: silver
- Public page: https://skillstore.pages.dev/skills/dnyoussef-agentdb-persistent-memory-patterns
- Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-agentdb-persistent-memory-patterns/manifest

## Capabilities

- Guides a five-phase workflow for AgentDB memory implementation.
- Defines episodic, semantic, procedural, and working memory concepts.
- Shows TypeScript examples for storing and retrieving memories.
- Covers session memory, consolidation, and pattern learning flows.
- Lists success metrics for persistence, latency, recognition, and context accuracy.

## Use Cases

- Design Agent Memory: Plan memory layers, storage policies, and retrieval goals before building a stateful AI agent.
- Add Context to Chat: Use session memory and long-term storage patterns to preserve useful context across conversations.
- Validate Memory Behavior: Create checks for persistence, retrieval latency, pattern recognition, and context accuracy.

## Prompt Templates

### Map Memory Needs

```
Use this skill to identify which memory types my AI agent needs. Include session, long-term, and working memory recommendations.
```

### Design Storage Layers

```
Use this skill to design an AgentDB memory architecture for my chat assistant. Include schemas, retrieval needs, and persistence strategy.
```

### Plan Validation Checks

```
Use this skill to create a validation plan for AgentDB memory. Cover persistence, retrieval speed, consolidation, and context accuracy.
```

### Optimize Memory System

```
Use this skill to review my AgentDB memory design for performance and maintainability. Recommend caching, indexing, and consolidation improvements.
```

## Limitations

- It provides guidance and examples, not a complete production application.
- It assumes basic AgentDB knowledge and memory design experience.
- It does not validate package availability or current API compatibility.
- It does not include privacy controls for stored user memory.

## Best Practices

- Start with clear memory types and retention rules before writing implementation code.
- Measure retrieval latency and context accuracy with repeatable tests.
- Store only necessary user memory and define deletion behavior early.

## Anti Patterns

- Do not store all conversation history without retention limits.
- Do not mix short-term context and durable facts in one memory layer.
- Do not skip validation for persistence after agent restarts.

## Security Audit

- Audited at: 2026-07-06T12:01:11.822\+00:00
- Summary: The external command findings are false positives caused by Markdown code fences around TypeScript examples. The hardcoded URL is a documentation link and does not perform a network request. No prompt injection or malicious intent was found in SKILL.md.

## Stats

- Views: 247
- Downloads: 11
- Favorites: 0
- Popularity score: 0