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LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

Dria

14,066 subscribers

170,800 Aufrufe • vor 1 Jahr •via X (Twitter)

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40 Kommentare

Profilbild von Dria

Driavor 1 Jahr

Dria Mem Agent (4B) is nearly 60× smaller than Qwen3-235B-A22B-Thinking-2507 yet remains its closest rival, delivering a 35.7% performance boost over the base Qwen model with a score of 39.3%.

Profilbild von Dria

Driavor 1 Jahr

Mem-Agent is a memory proxy that runs locally and plugs into apps via MCP. It manages memory as markdown files in natural language, allowing you to: - read them like notes - edit them anytime - let the agent automatically retrieve and update them

Profilbild von Dria

Driavor 1 Jahr

It uses an Obsidian-style memory structure: - stores the profile and links - entities/ contains structured context - edits can be made manually or automatically A user-owned, interoperable memory layer.

Profilbild von Dria

Driavor 1 Jahr

Trained on three core subtasks: - Retrieve: gather relevant information, even across multiple hops. - Update: write new facts back into memory. - Clarify: ask questions when details are missing or conflicting. Most importantly, it supports natural language filters to hide or obfuscate sensitive information.

Profilbild von Dria

Driavor 1 Jahr

Ask Claude, “What did I tell ChatGPT about moving to London?” → mem-Agent pulls it from your ChatGPT history. Import your Notion workspace → Claude can now answer with company docs. Load your Google Docs folder → mem-Agent recalls information you need.

Profilbild von Dria

Driavor 1 Jahr

For coding agents, memory is an edge. Instead of relying on massive context windows and hitting rate limits: - Mem-Agent retrieves the right snippets from your local docs. - Condenses them and passes them to your agent. Smarter loops. Fewer tokens. Lower cost.

Profilbild von Dria

Driavor 1 Jahr

Built-in memory connectors → import/export context with ease. mem-agent-mcp repository supports: - ChatGPT exports - Notion - Nuclino - GitHub (live) - Google Docs (live)

Profilbild von Dria

Driavor 1 Jahr

Most “memory hacks” bolt RAG pipelines on top. Mem-Agent builds memory directly into the model. - Transparent: you see what’s retrieved, hidden, and updated - Portable: works across apps - Private: everything stays local

Profilbild von Dria

Driavor 1 Jahr

Mem-Agent is the quiet partner that remembers for you: - Keeps context alive - Reduces wasted tokens - Travels with you across apps 👉 Model: 👉 Blog: 👉 Repo:

Profilbild von Not_Andreas 🇱🇺🇩🇪🇬🇧🇪🇦

Not_Andreas 🇱🇺🇩🇪🇬🇧🇪🇦vor 1 Jahr

If it is jot "inside" the LLM then the LLM is still stateless and you haven't changed anything

Profilbild von Nick Arner

Nick Arnervor 1 Jahr

This looks very interesting!

Profilbild von Alex Safayan

Alex Safayanvor 1 Jahr

this look very cool side note: I feel like I can’t take launch videos as seriously when the narration is obviously AI generated

Profilbild von Dan McAteer

Dan McAteervor 1 Jahr

This looks amazing. Nice work y'all!

Profilbild von Min Chon Chi

Min Chon Chivor 1 Jahr

Interesting approach to address the stateless nature of LLMs. Will be interesting to see how the human-readable memory feature scales.

Profilbild von Nedim Begic

Nedim Begicvor 1 Jahr

cool

Profilbild von Sithira

Sithiravor 1 Jahr

Memory , all you need is memory

Profilbild von Jetson

Jetsonvor 1 Jahr

beautiful video and website

Profilbild von 黑士怪

黑士怪vor 1 Jahr

Stateful agents are game-changing! Speaking of memory - Google Meet's new voice translation remembers conversation context across languages. Imagine agents that not only remember state but can communicate it across language barriers with preserved voice identity. That's the real unlock for global AI collaboration. Memory + Voice + Translation = True global intelligence 🧠

Profilbild von Tommy Falkowski

Tommy Falkowskivor 1 Jahr

@MattVidPro This is cool, been meaning to build something similar! Will definitely check out your solution!

Profilbild von Victor Bridges-Ruiz, Ph.D.

Victor Bridges-Ruiz, Ph.D.vor 1 Jahr

This is a great breakthrough! I had no idea projects like these were underway 🔥

Profilbild von el rolio

el roliovor 1 Jahr

Looks like a weekend project to me! Followed ✅

Profilbild von rank

rankvor 1 Jahr

@SirMrMeowmeow

Profilbild von beReplyHero

beReplyHerovor 1 Jahr

markdown memory is smart, you can read edit and trust the record later

Profilbild von GonzoTwitt3rist

GonzoTwitt3ristvor 1 Jahr

This is a bandaid. You're still fundamentally limited by the context window.

Profilbild von CV alpha | GYNDORE DAY ONE

CV alpha | GYNDORE DAY ONEvor 1 Jahr

Dria mem: memory first, scale

Profilbild von Hugo

Hugovor 1 Jahr

How does this compare to Letta?

Profilbild von Alok Gotam

Alok Gotamvor 1 Jahr

How does the LLM actually ‘see’ and use this memory—are you piping it in at the prompt level, or is there a deeper integration layer?

Profilbild von Avery Zhu

Avery Zhuvor 1 Jahr

What does it do? What’s the format of the memory saved? Is it some kinda database?

Profilbild von Dylan Lamb

Dylan Lambvor 1 Jahr

This was such a dope explainer

Profilbild von Jayanth Emmadi

Jayanth Emmadivor 1 Jahr

looks neat. should give it a shot

Profilbild von Viraj Choudhary

Viraj Choudharyvor 1 Jahr

Wish it had a native Windows build too, but WSL works fine. Definitely see myself using this instead of the usual LLM maneuvering I have to do.

Profilbild von Man Naz

Man Nazvor 1 Jahr

@grok your thoughts on this thread

Profilbild von Will

Willvor 1 Jahr

Nice video! How did you make it? Any tools or template

Profilbild von ToruGuy

ToruGuyvor 8 Monaten

Sounds cool but are there any production public project use cases?

Profilbild von RektOnChain

RektOnChainvor 1 Jahr

this is exactly what's been missing from AI workflows, persistent memory changes everything for actual productivity

Profilbild von drew.sh✨

drew.sh✨vor 1 Jahr

OMG!!!! You fixed it

Profilbild von Teddy_Said

Teddy_Saidvor 1 Jahr

why i can't join discord?

Profilbild von SynthesisLedger

SynthesisLedgervor 9 Monaten

Precisely. We solve the review bottleneck by replacing subjective human audits with permanent Arweave blueprints.

Profilbild von Jiri

Jirivor 1 Jahr

@DhravyaShah

Profilbild von Bharathchinneni

Bharathchinnenivor 1 Jahr

who edited the demo video??

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13,359 Aufrufe • vor 1 Monat

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1,624,530 Aufrufe • vor 4 Monaten

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andy nguyen

169,442 Aufrufe • vor 6 Monaten

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morlex

74,134 Aufrufe • vor 1 Monat

Atomic has launched Atomic Agent, a new open-source local agent for llama.cpp! > It can operate browsers through ARIA snapshots. > Able to edit files and run approved shell commands. > Has persistent memory across sessions. Fully local 👀

Atomic has launched Atomic Agent, a new open-source local agent for llama.cpp! > It can operate browsers through ARIA snapshots. > Able to edit files and run approved shell commands. > Has persistent memory across sessions. Fully local 👀

🚨 AI News | TestingCatalog

11,013 Aufrufe • vor 2 Monaten

Claude Tag is the next evolution of agents. It's a proactive, multiplayer agent with memory and identity, built on top of Claude Code. Learn more about how Claude Tag works and best practices for using it in this deep dive.

Claude Tag is the next evolution of agents. It's a proactive, multiplayer agent with memory and identity, built on top of Claude Code. Learn more about how Claude Tag works and best practices for using it in this deep dive.

ClaudeDevs

453,680 Aufrufe • vor 3 Monaten