🧠 AI apps need memory but building it yourself is brutal

Most AI apps eventually hit the same wall. They forget users unless you build a ton of infrastructure first. This means every AI dev eventually will end up building this infra to provide the best user experience needs for their agent and app.

What “rolling your own” really means:

• Vector DBs + embeddings + tuning

• Extracting memories from conversations (and resolving conflicts)

• Designing user profile schemas and keeping them in sync

• Managing long chat history + summarization pipelines

• Juggling different formats across OpenAI, Claude, etc.

• Hosting, scaling, backups, monitoring…

Timeline: 2–4 weeks (plus ongoing maintenance)
Cost: engineering time + infra + vector DB fees

🚀 With @Mnexium AI , memory is built-in

const client = new OpenAI({
  baseURL: "https://mnexium.com/api/v1",
  defaultHeaders: { "x-openai-key": OPENAI_KEY }
});

await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello" }],
  mnx: {
    subject_id: "user_123",
    history: true,        // Chat history
    learn: true,          // Memory extraction
    recall: true,         // Memory retrieval
    profile: true,        // Structured user profiles
    summarize: "balanced" // Typically ~75% token reduction
  }
});

Timeline: ~5 minutes.

💡 What you get out of the box

✔️ Automatic memory extraction & recall
✔️ User profiles as structured data
✔️ Chat history with smart summarization
✔️ Works across OpenAI and Claude (same memory, any provider)
✔️ Drop-in replacement for the OpenAI SDK

👌 When should you use Mnexium?

Use it when you want memory working today and you’d rather ship features than maintain infrastructure.

(If you do have a full infra team who loves building pipelines… building your own may still make sense.)