Memory System
Context-Aware Semantic Storage (CASS)
The Memory System (formerly CASS — Context-Aware Session Store) provides a 3-layer memory model: Episodic (permanent session history), Working (per-agent with TTL), and Procedural (learned patterns). Uses native vector search for semantic retrieval.
Memory System
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Past decisions and task history stored as high-dimensional vectors in the memories table.
Active project context and semantic knowledge clustered for fast retrieval.
Learned code patterns and styles—agents query by meaning, not keywords.
Memory Types
Episodic
Session-indexed permanent history — task outcomes, errors, solutions
Working
Per-agent current context — auto-expires after configurable TTL
Procedural
Pattern-based learned approaches — reusable techniques (deferred)
agent_learning
Specific learnings stored with importance scores and embeddings
How Semantic Search Works
- 1.Agent queries the Memory System with natural language
- 2.Query converted to vector embedding
- 3.Nearest neighbors found via cosine similarity
- 4.Relevant context returned to agent
Key Operations
- store()Add knowledge with embeddings
- search()Semantic search for relevant context
- update()Refresh stale knowledge
- prune()Remove outdated entries
Storage
The Memory System stores memories with vector embeddings in the DataLayer:
DataLayer → .cass/memory.db (vector-enabled)