La Mémoire Agentique
pour IA Autonome

-- Agentic Memory: Vector + Graph unified
SELECT * FROM memories
WHERE vector NEAR $embedding
  AND MATCH (a)-[:KNOWS]->(b)
LIMIT 10;

-- GraphRAG: MATCH + NEAR in one query
SELECT doc.*, similarity()
FROM documents doc
WHERE vector NEAR $query
  AND MATCH (doc)-[:CITES]->(ref)
ORDER BY similarity() DESC;

const results = await invoke('plugin:velesdb|search', {
  collection: 'memories',
  vector: embedding,
  topK: 10
});

import { VectorStore } from 'velesdb-wasm';

const store = new VectorStore(768, 'cosine');
const results = store.search(query_vector, 10);

let db = VelesDatabase.open("./agent_memory")
let results = collection.search(embedding, topK: 10)

// <100µs latency for real-time decisions
let context = memory.search(sensor_embedding, 5);
let world_model = graph.traverse(current_node);

./velesdb-server --data-dir /secure/vectors --bind 127.0.0.1:8080

// CRDT sync for collaborative agent memory (Premium)
await sync.connect_peer("agent_b_address");
agent_a.memory.sync();