Today we re open-sourcing the core memory engine behind @Mnexium AI: CORE-MNX.

GItHub

Why We Built It

Most automation workflows can call a model, but still need substantial glue code for memory, personalization, and structured data. The Mnexium connector makes those capabilities native in n8n.

npm install n8n-nodes-mnexium

As out platform continues to grow and captures more of an AI workload. There will always be new features & improvements we can make. This is one of those, we've always had and seen a need in the platform to direct and instruct our memory generation layer. This is what memory polices offers - the ability to guide Mnexium's memory layer.

Why Memory Policies?

Not every app wants to memorize everything. Some teams need strict extraction rules for compliance, quality, or cost. Others need per-workflow behavior, like high-signal extraction in support chats and minimal extraction in casual chats.

Why Records?

Mnexium memories are great for capturing facts, preferences, and context from conversations. But many AI applications also need to manage structured business data events on a calendar, deals in a pipeline, contacts in a CRM, tasks on a board, inventory items, support tickets.

Until now, you had two choices: build a separate database and API layer for your structured data, or try to shoehorn everything into unstructured memories. Neither is ideal.

The Mnexium SDKs give you a complete memory infrastructure as a service. Install the package, pass your LLM provider key, and your AI remembers.

Node (https://www.npmjs.com/package/@m...)

Python (https://pypi.org/project/mnexium/)

Quick update we just launched a free tier that requires zero signup.

You can now use Mnexium without creating an account.

Just make an API call with your own OpenAI or Anthropic key, and we auto-provision a trial key for you on the spot.

We just shipped multi-provider support in @Mnexium AI so you can change LLMs without resetting conversations, user context or memories.

The problem

When teams switch providers, they usually lose everything:

This short demo shows how Mnexium works as a memory and context layer for AI apps.

Mnexium sits between your app and the LLM to provide:

• Persistent memory across sessions

• Inspectable & resumable chat history

• Structured user profiles and long-term context

• Automatic recall and injection no prompt juggling

The goal is simple: AI apps that remember users, stay consistent, and feel stateful by default.

Hi all - I've built @Mnexium AI and I thought the fastest way to get folks to try was it to build a chat plug-in for websites. I am providing free keys (however much usage it may be) to anyone who is willing to try it.

The plug-in can be found on NPM https://www.npmjs.com/package/@m...

npm install @mnexium/chat

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