🗺️ Project Roadmap

NodeLLM is evolving to support more complex AI-native Node.js applications.

🚀 Near-Term Priorities

💾 Managed State & Persistence

Seamless Long-Term Memory.

We are exploring “Persistence Adapters” for standard Node.js ORMs (Prisma, Drizzle). Instead of complex class inheritance, we provide modular adapters that automatically sync conversion state to your database.

// Concept: Persistence Adapters
import { PrismaAdapter } from "@node-llm/prisma";

// Bind the LLM directly to a database record
const chat = NodeLLM.chat("gpt-4o", {
  persistence: new PrismaAdapter(prisma.chat, { id: "chat_123" })
});

// Automatically fetches history -> Calls API -> Saves response
await chat.ask("Continue where we left off");
  • Schema-Driven: Define your chat schema in Prisma/Drizzle, we handle the sync.
  • Zero-State: Your Node.js process stays stateless; history lives in Postgres/MySQL.
  • Goal: Drop-in persistence for any Node.js backend.

📂 Expanded Example Library

We learn by doing. We will double down on high-quality, full-stack reference implementations covering:

  • RAG Knowledge Base: A verified pattern for “Chat with your Docs”.
  • Voice Interface: Real-time audio-in/audio-out.
  • Local-First Agent: Zero-latency offline agents using Ollama + Llama 3.

🛡️ Ongoing

  • Security First: Continued investment in Context Isolation, PII hooks, and adversarial defense.
  • Zero-Dependency Core: Keeping the core library lightweight while moving heavy integrations to separate packages (e.g. @node-llm/tools).