Memory infrastructure for your AI agents

Mnexium is a memory management platform for LLMs. Store, score, and retrieve long‑term context with a simple API. Built for ChatGPT, Claude, and custom agents.

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Built for ChatGPT, Claude, and custom agents

Long‑term memory in LLM apps is hard

Teams re‑implement vector stores, custom schemas, scoring logic, and retrieval for every app. Mnexium gives you a centralized, API‑first memory layer so you can focus on your product.

  • Centralized memory store for all your agents
  • Simple REST API and SDKs
  • Built‑in scoring: importance, recency, visibility
  • Governance: who can see what, and when

What you get

API‑first memory layer

Store & retrieve memories with clean REST/JSON endpoints.

Access tokens

Create revocable tokens for tools to pull user memories.

Scoring & ranking

Importance & recency scoring, filtering, and search.

Chat governance

Attach memories, enforce visibility, and keep context safe.

How it works

Ingest

Send user or app events to Mnexium as memories via REST.

Score

Mnexium scores, tags, and stores memories (importance, timestamps, visibility).

Retrieve

Your agent calls a single endpoint per token to fetch relevant memories.

POST /api/memories
fetch('/api/memories', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    text: 'User prefers dark mode',
    kind: 'preference',
    visibility: 'shared',
    importance: 80,
    tags: ['ui', 'theme']
  })
})
GET /api/memories/[token]
// Token-gated read for tools/agents
fetch('/api/memories/atk_xxx?limit=50')
  .then(r => r.json())
  .then(({ data }) => console.log(data))

Use cases

Personal AI assistants

Remember preferences, routines, and history across sessions.

Sales & support copilots

Recall tickets, CRM notes, outcomes — fast.

Developer tools & IDE agents

Persist codebase knowledge and user habits.

Productivity apps

Calendar context, notes, tasks, and recurring patterns.

Built for developers

No infra to manage. Bring your own LLM — OpenAI, Anthropic, or self‑hosted.

Node/TypeScript
// Simple: fetch memories for an access token
const res = await fetch('https://mnexium.com/api/memories/atk_xxx?limit=25');
const { data } = await res.json();
// feed into your LLM prompt ...

Roadmap

  • Per‑org policies and chat governance
  • Advanced ranking and RAG integrations
  • Multi‑tenant memory and audit logs
  • SDKs for JS/TS, Python, and more

Pricing

Early access

Start free while in beta. Generous limits for early adopters.

  • • Fair usage limits
  • • Access tokens & memory APIs
  • • Upgrade paths for teams
Get started

FAQ

How do I use Mnexium with ChatGPT?

Create an access token, then call the token-gated /api/memories/[token] endpoint from your tool or GPT action to fetch context.

What is a memory?

A small JSON record describing a fact, preference, event, or note. Mnexium stores and scores memories for relevance.

How is Mnexium different from a vector DB?

Mnexium is an opinionated memory layer that includes scoring, visibility, and governance — not just embeddings.

Can I revoke an access token?

Yes. You can revoke tokens at any time; requests using revoked tokens will be denied.

Is Mnexium suitable for production apps?

Yes. It is designed for multi-agent, multi-user workloads and will offer enterprise controls as we progress.

What languages do you support?

Start with REST/JSON from any language. SDKs for JS/TS and Python are on the roadmap.