agent-memory-systems
Memory 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...
Best use case
agent-memory-systems is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Memory 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...
Teams using agent-memory-systems should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-memory-systems/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-memory-systems Compares
| Feature / Agent | agent-memory-systems | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Memory 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...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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` ## When to Use This skill is applicable to execute the workflow or actions described in the overview.
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