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 stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
Key insight: Memory isn't just storage - it's retrieval.
A million stored facts mean nothing if you can't find the right one.
Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets.
The field is fragm
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skill mdSKILL.md2,317 B
docsSUMMARY.md528 B
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SKILL.md
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
