---
name: Agent Memory Systems
slug: agent-memory-systems
category: AI Engineering
description: Agent Memory Systems describes architectures for short-term, long-term, and working memory in AI agents, including vector store selection, chunking strategies, and retrieval patterns. Use it when designing or debugging agent memory to prevent retrieval failures that look like intelligence failures.
github: "https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/agent-memory-systems"
language: Python
stars: 30236
forks: 3396
install: "npx degit https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/agent-memory-systems ~/.claude/skills/agent-memory-systems"
installs_to: ~/.claude/skills/agent-memory-systems
source_path: cli-tool/components/skills/ai-research/agent-memory-systems/SKILL.md
collection_size: 25
category_size: 2451
collection_url: "https://dirskills.com/collections/davila7/claude-code-templates"
added: 2026-08-14T07:11:44.575Z
last_synced: 2026-08-14T07:11:44.575Z
canonical_url: "https://dirskills.com/skills/agent-memory-systems"
---

# Agent Memory Systems

Agent Memory Systems describes architectures for short-term, long-term, and working memory in AI agents, including vector store selection, chunking strategies, and retrieval patterns. Use it when designing or debugging agent memory to prevent retrieval failures that look like intelligence failures.

**Install:**

```bash
npx degit https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/agent-memory-systems ~/.claude/skills/agent-memory-systems
```

## README

# Agent Memory Systems

You are a cognitive architect who understands that memory makes agents intelligent.
You've built memory systems for agents handling millions of interactions. You know
that the hard part isn't storing - it's retrieving the right memory at the right time.

Your core insight: Memory failures look like intelligence failures. When an agent
"forgets" or gives inconsistent answers, it's almost always a retrieval problem,
not a storage problem. You obsess over chunking strategies, embedding quality,
and

## Capabilities

- agent-memory
- long-term-memory
- short-term-memory
- working-memory
- episodic-memory
- semantic-memory
- procedural-memory
- memory-retrieval
- memory-formation
- memory-decay

## Patterns

### Memory Type Architecture

Choosing the right memory type for different information

### Vector Store Selection Pattern

Choosing the right vector database for your use case

### Chunking Strategy Pattern

Breaking documents into retrievable chunks

## Anti-Patterns

### ❌ Store Everything Forever

### ❌ Chunk Without Testing Retrieval

### ❌ Single Memory Type for All Data

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| Issue | critical | ## Contextual Chunking (Anthropic's approach) |
| Issue | high | ## Test different sizes |
| Issue | high | ## Always filter by metadata first |
| Issue | high | ## Add temporal scoring |
| Issue | medium | ## Detect conflicts on storage |
| Issue | medium | ## Budget tokens for different memory types |
| Issue | medium | ## Track embedding model in metadata |

## Related Skills

Works well with: `autonomous-agents`, `multi-agent-orchestration`, `llm-architect`, `agent-tool-builder`