mentedb

import { MenteDB, MemoryType, EdgeType, CognitionStream, TrajectoryTracker } from 'mentedb'; // Initialize your embedding model (example placeholder) const embeddingFromYourModel = Array(1536).fill(0.123); const queryEmbedding = Array(1536).fill(0.456); async function runMenteDB() { // Open or create a database const db = new MenteDB('./my-agent-memory-data'); console.log('MenteDB initialized.'); // Store a memory const id = db.store({ content: 'The deploy key rotates every 90 days', memoryType: MemoryType.Semantic, embedding: embeddingFromYourModel, tags: ['infra', 'security'], }); console.log(`Stored memory with ID: ${id}`); // Vector similarity search const hits = db.search(queryEmbedding, 2); console.log('Search hits:', hits); // MQL recall with token budget const ctx = db.recall('RECALL similar("deploy key rotation") LIMIT 10'); console.log('Recall context:', ctx.text, `(${ctx.totalTokens} tokens)`); // Relate memories (assuming 'otherId' exists, e.g., from another store call) // const otherId = db.store({ content: 'Another related fact', memoryType: MemoryType.Semantic, embedding: embeddingFromYourModel }); // db.relate(id, otherId, EdgeType.Supersedes); // console.log(`Related ${id} to ${otherId}`); // Simulate an LLM token stream for cognition const stream = new CognitionStream(); const llmTokens = ['Hello', ' ', 'world', '!']; for (const token of llmTokens) { stream.feedToken(token); } console.log('Drained CognitionStream buffer:', stream.drainBuffer()); // Track reasoning trajectory const tracker = new TrajectoryTracker(); tracker.recordTurn('Discuss JWT auth', 'investigating', ['Which algorithm?', 'Token lifetime?']); tracker.recordTurn('Token lifetime', 'decided:15 minutes'); console.log('Resume context:', tracker.getResumeContext()); console.log('Predicted next topics:', tracker.predictNextTopics()); // Forget a memory db.forget(id); console.log(`Forgot memory with ID: ${id}`); // Close the database db.close(); console.log('MenteDB closed.'); } runMenteDB().catch(console.error);