Agent Memory Systems
SkillDocs & knowledgeMemory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector s...
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Agent Memory Systems skill
What this skill tells your AI
The instructions your AI receives, as published by benjaminastera/antigravity-awesome-skills in skills/agent-memory-systems/SKILL.md and read by ahel’s review.
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
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Signals
- GitHub stars
- 265
- Forks
- 26
- Last commit
- Sep 2026
Others that do the same job
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agent-memory-systems-benjaminastera- Source
- github.com/benjaminastera/antigravity-awesome-skills