Mnexium AI

Persistent, structured memory for AI Agents

104 followers

Persistent, structured memory for AI Agents

104 followers

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AI Infrastructure Tools

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LLM Developer Tools

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LLM Memory

Mnexium is persistent memory for LLM apps. Add one mnx object and get chat history, semantic recall, and user profiles that follow users across sessions, apps, and model providers. Use the same memory with ChatGPT, Claude, or any LLM. Switch models mid-conversation, A/B test prompts, fail over, or route by cost without losing context. No vector DBs, sync jobs, or custom memory pipelines required.

This is the 2nd launch from Mnexium AI. View more

Mnexium

Launching today

Persistent memory for LLM apps across every model

Mnexium gives AI apps one shared memory layer across models and agents. Add persistent memory, chat history, user profiles, records, and live context with one API. Built for OpenAI, Anthropic, Gemini, and agent workflows, without managing vector DBs, sync jobs, or custom memory pipelines.

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Launch tags:API•Developer Tools•Artificial Intelligence

Launch Team / Built With

ElevenAgents by ElevenLabs — Scale conversations without scaling your team

Scale conversations without scaling your team

Promoted

Mnexium AI

Maker

📌

Hey Product Hunt! This is my second launch here, and I’m excited to share Mnexium. I built it around a simple problem: LLMs are powerful, but they forget. Most AI apps still have to rebuild memory from scratch: chat history, semantic recall, user profiles, saved records, and the context that should carry across sessions. Mnexium gives developers one API for persistent memory in AI apps. Add an mnx object, connect it to your LLM workflow, and your app can remember users, conversations, preferences, and context over time. I’d love feedback from people building AI assistants, agents, copilots, companions, or any app where memory makes the experience feel more useful and personal.

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22h ago

Previous Mnexium AI Launches

Mnexium AIPersistent, structured memory for AI Agents

Launched on January 3rd, 2026

Forum Threads

p/mnexium-ai

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2mo ago

Integrations: bringing live external data into Mnexium runtime

Mnexium Integrations feel like one of the most important parts of the platform because they solve a different problem than memory. It also outlines the completion of the feature-set for the platform. I don't think any more features will offer any more utility.

Memory helps an assistant remember durable user context over time. Integrations let it work with live operational data from external systems right when a response is being generated.

p/mnexium-ai

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4mo ago

Switch between ChatGPT and Claude — without losing memory or context

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:

p/mnexium-ai

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3mo ago

Introducing Cartly: An iOS Receipt Tracking App Built on Mnexium

We just published a new case study on Cartly, an iOS app that uses Mnexium to power a full receipt-tracking AI workflow. We really wanted to see what it would take to get a demo like this up and running.

In the post, we walk through how Cartly uses:

Memory for user preferences and continuity
Records for structured receipts and receipt_items storage
A single mnx runtime object to control identity, history, recall, and record sync
Request trace packets for auditability and debugging in production

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