agent-memory-systems▌
davila7/claude-code-templates · updated Apr 8, 2026
Memory architecture for agents: retrieval strategies that determine whether agents remember or forget.
- ›Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns
- ›Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time
- ›Includes anti-patterns like sto
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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★28 reviews- ★★★★★Daniel Nasser· Dec 4, 2024
I recommend agent-memory-systems for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ama Verma· Nov 23, 2024
Keeps context tight: agent-memory-systems is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Harper Choi· Oct 26, 2024
Keeps context tight: agent-memory-systems is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Carlos Dixit· Oct 14, 2024
agent-memory-systems is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Oshnikdeep· Sep 25, 2024
I recommend agent-memory-systems for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★James Perez· Sep 17, 2024
We added agent-memory-systems from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Sep 5, 2024
agent-memory-systems has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Emma Anderson· Sep 5, 2024
agent-memory-systems fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Shikha Mishra· Aug 24, 2024
Solid pick for teams standardizing on skills: agent-memory-systems is focused, and the summary matches what you get after install.
- ★★★★★Li Kapoor· Aug 24, 2024
We added agent-memory-systems from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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