05 Memory memory system

Memory System

Krnl-AI implements multiple memory types, each serving a distinct cognitive function. All memory is stored locally via SQLite in community mode.

Memory Types

Type	Purpose	Persistence
Working Memory	Immediate context for the current cycle	Volatile (in-memory)
Episodic Memory	History of past execution cycles	SQLite
Semantic Memory	Factual knowledge and relationships	SQLite (vectors)
Emotional Memory	History of emotional state transitions	SQLite

Working Memory

Stores the current input and intermediate processing state during a cognitive cycle.

from krnlai import CognitiveAgent

agent = CognitiveAgent()
agent.working_memory.store("current context")
context = agent.working_memory.recall()

Episodic Memory

Records each cognitive cycle as an episode with input, output, timestamp, and metadata.

# Episodic memory is automatically populated after each cycle
# You can query recent episodes:
agent.episodic_memory.recent(5)

# Search by episode type:
agent.episodic_memory.search("cycle")

Semantic Memory

Stores factual knowledge that can be retrieved via semantic search.

# Store a fact
agent.semantic_memory.store_fact(
    subject="project",
    predicate="uses",
    object_val="SQLite",
    confidence=0.9,
)

# Search for relevant facts
results = agent.semantic_memory.search("storage backend")

Emotional Memory

Tracks emotional state transitions over time.

# View emotional timeline
agent.emotional_memory.timeline()

# Search emotional history by trigger
agent.emotional_memory.search_by_trigger("error")

# Count recorded states
agent.emotional_memory.count

CLI Memory Commands

krnlai memory search "my query"
krnlai memory snapshot
krnlai memory metrics

Sidecar Memory API

# Search memory via HTTP
curl -X POST http://localhost:5001/memory/search \
  -H "Content-Type: application/json" \
  -d '{"query": "project decision"}'

# Get memory metrics
curl http://localhost:5001/memory/metrics