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Ana Sayfaya Dön

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 görüntüleme • 1 yıl önce •via X (Twitter)

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Dria profil fotoğrafı

Dria1 yıl önce

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%.

Dria profil fotoğrafı

Dria1 yıl önce

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

Dria profil fotoğrafı

Dria1 yıl önce

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.

Dria profil fotoğrafı

Dria1 yıl önce

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.

Dria profil fotoğrafı

Dria1 yıl önce

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.

Dria profil fotoğrafı

Dria1 yıl önce

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.

Dria profil fotoğrafı

Dria1 yıl önce

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

Dria profil fotoğrafı

Dria1 yıl önce

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

Dria profil fotoğrafı

Dria1 yıl önce

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

Not_Andreas 🇱🇺🇩🇪🇬🇧🇪🇦 profil fotoğrafı

Not_Andreas 🇱🇺🇩🇪🇬🇧🇪🇦1 yıl önce

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

Nick Arner profil fotoğrafı

Nick Arner1 yıl önce

This looks very interesting!

Alex Safayan profil fotoğrafı

Alex Safayan1 yıl önce

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

Dan McAteer profil fotoğrafı

Dan McAteer1 yıl önce

This looks amazing. Nice work y'all!

Min Chon Chi profil fotoğrafı

Min Chon Chi1 yıl önce

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

Nedim Begic profil fotoğrafı

Nedim Begic1 yıl önce

cool

Sithira profil fotoğrafı

Sithira1 yıl önce

Memory , all you need is memory

Jetson profil fotoğrafı

Jetson1 yıl önce

beautiful video and website

黑士怪 profil fotoğrafı

黑士怪1 yıl önce

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 🧠

Tommy Falkowski profil fotoğrafı

Tommy Falkowski1 yıl önce

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

Victor Bridges-Ruiz, Ph.D. profil fotoğrafı

Victor Bridges-Ruiz, Ph.D.1 yıl önce

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

el rolio profil fotoğrafı

el rolio1 yıl önce

Looks like a weekend project to me! Followed ✅

rank profil fotoğrafı

rank1 yıl önce

@SirMrMeowmeow

beReplyHero profil fotoğrafı

beReplyHero1 yıl önce

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

GonzoTwitt3rist profil fotoğrafı

GonzoTwitt3rist1 yıl önce

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

CV alpha | GYNDORE DAY ONE profil fotoğrafı

CV alpha | GYNDORE DAY ONE1 yıl önce

Dria mem: memory first, scale

Hugo profil fotoğrafı

Hugo1 yıl önce

How does this compare to Letta?

Alok Gotam profil fotoğrafı

Alok Gotam1 yıl önce

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?

Avery Zhu profil fotoğrafı

Avery Zhu1 yıl önce

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

Dylan Lamb profil fotoğrafı

Dylan Lamb1 yıl önce

This was such a dope explainer

Jayanth Emmadi profil fotoğrafı

Jayanth Emmadi1 yıl önce

looks neat. should give it a shot

Viraj Choudhary profil fotoğrafı

Viraj Choudhary1 yıl önce

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.

Man Naz profil fotoğrafı

Man Naz1 yıl önce

@grok your thoughts on this thread

Will profil fotoğrafı

Will1 yıl önce

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

ToruGuy profil fotoğrafı

ToruGuy8 ay önce

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

RektOnChain profil fotoğrafı

RektOnChain1 yıl önce

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

drew.sh✨ profil fotoğrafı

drew.sh✨1 yıl önce

OMG!!!! You fixed it

Teddy_Said profil fotoğrafı

Teddy_Said1 yıl önce

why i can't join discord?

SynthesisLedger profil fotoğrafı

SynthesisLedger8 ay önce

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

Jiri profil fotoğrafı

Jiri1 yıl önce

@DhravyaShah

Bharathchinneni profil fotoğrafı

Bharathchinneni1 yıl önce

who edited the demo video??

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We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

andy nguyen

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ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

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Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. one person + 10 agents with memory = a team that runs 24/7, remembers every customer improves itself. worth than $500 vibe-coding course.

Codez

1,624,530 görüntüleme • 4 ay önce

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,442 görüntüleme • 6 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 2 ay önce

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

Akash Network

22,891 görüntüleme • 6 ay önce