๐ง Memory Graphs: Visualize How Your AI Remembers
When building AI agents with long-term memory, debugging is a challenge. You know something was remembered โ but: โกWhen was it created? โกWhat replaced it? โกWhy is it being recalled now? โกWhy was it created as a memory in the first place? Memory Graphs is our first attempt to fix that. ๐ See memory evolution Watch facts evolve across conversations: โfavorite color = blueโ โ green โ red โ yellow...
๐ New Provider: Google Gemini Support is Live!
@Mnexium AI Now supports all three major AI providers! โ OpenAI ChatGPT models โ Anthropic Claude Models โ Google Gemini Models โ NEW Why this matters: Your users can now seamlessly switch between providers while keeping their memory and context intact. Learn something with GPT-4 โ Recall it with Gemini โ Continue with Claude. Same user. Same memories. Any model. How it works: Just use the...
๐ง AI apps need memory but building it yourself is brutal
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 โrolling your ownโ really means: Vector DBs + embeddings + tuning Extracting memories from conversations (and resolving conflicts) Designing user...
๐ง ๐๐ ๐๐ฉ๐ฉ๐ฌ ๐๐๐ข๐ฅ ๐๐๐๐๐ฎ๐ฌ๐ ๐ญ๐ก๐ ๐ฆ๐๐ฆ๐จ๐ซ๐ฒ ๐ข๐ฌ ๐๐๐
๐ง ๐๐ ๐๐ฉ๐ฉ๐ฌ ๐๐จ๐งโ๐ญ ๐๐๐ข๐ฅ ๐๐๐๐๐ฎ๐ฌ๐ ๐ญ๐ก๐ ๐ฆ๐จ๐๐๐ฅ ๐ข๐ฌ ๐๐๐. ๐๐ก๐๐ฒ ๐๐๐ข๐ฅ ๐๐๐๐๐ฎ๐ฌ๐ ๐ญ๐ก๐ ๐ฆ๐๐ฆ๐จ๐ซ๐ฒ ๐ข๐ฌ. As more teams ship AI assistants, one quiet problem keeps showing up: โก๏ธ ๐๐จ๐ง๐ฏ๐๐ซ๐ฌ๐๐ญ๐ข๐จ๐ง๐ฌ ๐ ๐๐ญ ๐ฅ๐จ๐ง๐ ๐๐ซ โก๏ธ ๐๐จ๐ง๐ญ๐๐ฑ๐ญ ๐ค๐๐๐ฉ๐ฌ ๐ ๐๐ญ๐ญ๐ข๐ง๐ ๐ซ๐-๐ฌ๐๐ง๐ญ โก๏ธ ๐๐จ๐ฌ๐ญ๐ฌ ๐๐ฑ๐ฉ๐ฅ๐จ๐๐ โ ๐๐ง๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ฉ๐ฌ Above we've together the comparison below to show how the main โmemoryโ approaches stack up โ and when each one actually makes sense. What stood out: ๐น...
Switch between ChatGPT and Claude โ without losing memory or context
We just shipped multi-provider support in @Mnexium AI โ so you can change LLMs without resetting conversations, user context or memories. The problem When teams switch providers, they usually lose everything: conversation history user preferences long-term memory learned context Every conversation starts from zero. Not great for UX โ or retention. What Mnexium does Mnexium now works with both:...
Mnexium โ a memory layer so AI apps donโt forget anything
Most AI products eventually hit the same problem They forget who the user isโฆ and the experience feels generic again. Weโve been working on @Mnexium AI , a simple memory layer for AI apps that: remembers users across sessions recalls relevant context automatically keeps conversation history lean (rolling summaries) creates structured user profiles over time No vector DB setup, no custom...
Feature Update: Rolling Conversation Summaries โ Cut Chat Costs Without Losing Context
We built a feature to solve a problem most AI apps eventually run into: The longer the conversation, the more you keep paying to resend the entire chat history โ over and over. Blog here (https://www.mnexium.com/blogs/chat-summarization) Docs here (https://www.mnexium.com/docs#summarize) That โtoken taxโ adds up fast. In the blog, we walked through a realistic scenario: 40 messages per...
๐ New Feature in Mnexium: Profiles that Build Themselves
Profiles that populate automatically Mnexium can build structured user profiles based on what users say in conversations โ without any separate onboarding forms. If a user says: โIโm Sarah from Acmeโ Mnexium records: name = Sarah company = Acme You define the schema You choose what fields exist: company_name job_title subscription_tier preferred_pharmacy shipping_preference Anything that makes...
๐ Getting-started: Build a ChatGPT-style app with persistent memory
In this new getting-started guide, you will learn how to build a ChatGPT-style application that includes persistent memory, conversation history, and semantic recall โ all using a single API from Mnexium. The guide walks through how Mnexium simplifies AI memory by replacing complex setups such as: โข vector databases โข embedding pipelines โข retrieval logic โข custom chat storage Instead, memory,...
