Memory Usage

Overview

Use this skill to enforce the Atlas memory pipeline: MemoryManager + LearningPolicy + MemoryConsolidator. This makes memory storage/retrieval consistent, emits learning signals, and keeps the index healthy via consolidation + pruning.

Workflow (Always-on)

1) Initialize policy + memory system

import { MemoryManager } from '../system/memory/manager';
import { LearningPolicy } from '../system/learning/ml-policy';

const policy = new LearningPolicy({});
await policy.initialize();

const memory = new MemoryManager({}, policy);
await memory.initialize();

2) Add memory (always with metadata)

await memory.add({
  type: 'fact',
  content: 'The capital of France is Paris',
  metadata: {
    source: 'session',
    sessionId: 'current',
    author: 'Atlas',
    provenance: { origin: 'user', confidence: 0.9 },
    tags: ['geo']
  }
});

3) Retrieve memory (signals are emitted)

const results = await memory.retrieve({ query: 'France capital', limit: 3 });
// memory_retrieved signal is recorded automatically

4) Consolidate + prune (daily/weekly)

await memory.consolidate({ window: 'last_24_hours' });
await memory.prune({
  age: 'older_than_90_days',
  threshold: 0.3,
  minRetrievalCount: 1
});

Required Behaviors

• Always initialize LearningPolicy before MemoryManager.
• Always use MemoryManager for add/retrieve (no bypassing the index).
• Do not store memory without metadata (source/sessionId/provenance/tags).
• Consolidate regularly and prune low-value entries.

Quick sanity checks

• Vector index size increases after add.
• Retrieval returns results with

  score

  and

  metadata.similarity

  .

• signals.json

  contains

  memory_retrieved

  events after retrieval.