Intent Guide

Persistent Memory API for OpenAI Apps

If you are building on OpenAI and need long-term user memory, stable chat history, and structured app context, Mnexium gives you a production-ready memory layer without stitching together multiple systems.

What This Solves

Most OpenAI apps start stateless. Once users return, you need continuity: what the user likes, what they already said, and what the assistant should remember next. Teams typically add a vector DB, custom extraction logic, conversation storage, and glue code. That works, but it increases complexity quickly.

Mnexium gives you one runtime object to control memory learning, memory recall, and chat history in the same request. You keep OpenAI, and add durable context in a few lines.

Quickstart (OpenAI + Mnexium)

Send your normal chat request and append the mnx object.

curl -X POST "https://www.mnexium.com/api/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-openai-key: $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      { "role": "user", "content": "My name is Alex and I prefer concise answers." }
    ],
    "mnx": {
      "subject_id": "user_123",
      "chat_id": "chat_abc",
      "learn": true,
      "recall": true,
      "history": true
    }
  }'

The first request learns memory. Later requests for the same subject_id can recall relevant facts and prior context automatically.

Records + Memory for Production Apps

Mnexium Records gives you schema-backed, transactional app data alongside conversational memory. In chat requests, mnx.records controls record recall before generation and record extraction after generation.

curl -X POST "https://www.mnexium.com/api/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-openai-key: $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{ "role": "user", "content": "What deals do we have with TechCorp?" }],
    "mnx": {
      "subject_id": "user_123",
      "records": {
        "recall": true,
        "learn": "auto",
        "tables": ["account", "deal"]
      }
    }
  }'

• records.learn supports "auto", "force", or false.
• When using "force", records.tables is required.
• Set records.sync: true when writes must complete before response.

When to Use Mnexium

• You need memory that persists across sessions.
• You want OpenAI compatibility without rebuilding your stack.
• You need deterministic records and user profiles alongside chat memory.

When Not to Use Mnexium

• Your app is fully stateless and session continuity does not matter.
• You already built a memory stack and are satisfied with maintenance overhead.

Mnexium vs DIY Memory Stack

Pricing Snapshot

Start free, then move to Pro with included usage and overages. For larger teams, Enterprise offers custom limits and commercial terms. See the full pricing section on the homepage for current limits.

FAQ