Mnexium AI

Persistent, structured memory for AI Agents

110 followers

Persistent, structured memory for AI Agents

110 followers

•

•

🧠 𝐌𝐧𝐞𝐱𝐢𝐮𝐦 = persistent memory for LLM apps. Add one 𝐦𝐧𝐱 object and get chat history, semantic recall, and user profiles that follow users across sessions and providers. 🔄 Works with 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 and 𝐂𝐥𝐚𝐮𝐝𝐞 — same memories, any model. Switch mid-conversation without losing context. ⚙️ No vector DBs or pipelines. A/B test, fail over, and route by cost — your memory layer stays consistent.

Have a question about Mnexium AI? Ask it here and get a real answer.

Anima Playground — AI with an Eye for Design

AI with an Eye for Design

Promoted

Do you use Mnexium AI?

Forum Threads

p/mnexium-ai

•

7h ago

Introducing the Mnexium n8n Connector

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

p/mnexium-ai

•

1d ago

Introducing Memory Policies

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.

p/mnexium-ai

•

3d ago

Records: Structured Data for AI Applications

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.

View all

Maker Comment

Mnexium AI

Maker

📌

Hey Product Hunters - While building AI apps, I kept running into the same problem: the model was great — but it couldn’t remember anything. I had to wire vector DBs, embeddings, retrieval pipelines, chat storage mechanisms. It felt like rebuilding the same thing every time I started a new project. Mnexium fixes that. With one API call, your app gets conversation history, long-term memory, and semantic recall — automatically. I also published a “Getting Started” guide and a working chatGPT clone example. There was something that fundamentally changed when ChatGPT released memories - I want to make that possible for every AI app & agent. I’d love any feedback, especially from folks building agents, copilots, or AI-powered products. What would you like Mnexium to support next? Appreciate it.

Report

2mo ago

See discussion

Forum Threads

p/mnexium-ai

•

7h ago

Introducing the Mnexium n8n Connector

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

p/mnexium-ai

•

1d ago

Introducing Memory Policies

Why Memory Policies?

p/mnexium-ai

•

3d ago

Records: Structured Data for AI Applications

Why Records?

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.

View all

Maker Comment

Mnexium AI

Maker

📌

Report

2mo ago

See discussion