CEO @EverMind https://xitter.cf/t.co/s0pvFmiABl
Joined December 2025
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Yafeng Deng retweeted
Agent Memory: An AI Companion That Reaches Out First
You mention your coffee preference once. In a later conversation, she remembers.
OpenHer shows how memory can make an AI companion feel more familiar over time.
In this demo, Iris remembers “black, no sugar” and sends a morning message without waiting for you to start the conversation.
EverOS provides the long-term memory behind those remembered details.
OpenHer combines that history with its own personality and proactive messaging systems, so conversations can develop beyond the first introduction.
OpenHer is one example of what you can build with EverOS. Bring persistent memory to your own application with EverOS Cloud.
EverOS Pro users get 3X usage for 6 months.
everos.evermind.ai
Every AI product will need memory. Here's EverOS.
Next release (internal): LoCoMo 94.74% · AMA 80.89% · BEAM-1M 69.42% · SWE-ContextBench 34.34%
Current release: LongMemEval 94.00%
Built around two ideas:
Self-evolving. Every task becomes experience, experience becomes skills, and skills keep improving with use. Your agent gets better without retraining a model or rewriting a prompt.
Universal. One memory layer for any agent, any model, any app. Each agent gets exactly the context its task needs, in open source or in the cloud, on the same API.
Full write-up 👇
This quoted post is unavailable.
EverOS Cloud treats persistent memory as a first-class infrastructure layer — agents finally compound across sessions instead of starting from scratch. This is the missing primitive for long-term agents that genuinely improve with use.
xitter.cf/evermind/status/211218…
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Yafeng Deng retweeted
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Raven is the #1 paper of the month on @huggingface for September 🐦⬛
Thank you all. The Harness of Harnesses, built for RSI, and fully open source 👇
Everyone got faster with AI. Organizations didn't.
Because adding AI to an old org chart just makes each person faster. The way work moves, waits and gets aligned stays the same.
That's AI-augmented, not AI-native.
An AI-native org works differently:
Every piece of work points to one goal.
Humans and AI work side by side, and AI drives by default.
The system gets better on its own.
A model that knows nothing about your organization is a brilliant stranger.
Every organization deserves its own self-improving AGI.
Raven-built multi-agent forecasting systems took #1 and #2 on the FutureX weekly leaderboard 🐦⬛
Forecasting the future is one of the most valuable things intelligence can do. Human experts do it by pulling many sources, cross-checking facts, weighing evidence, and only then making a call.
We built that workflow on Raven, with Raven-Research at the core:
• Gather: agents search the web for each question
• Analyze: they cross-verify sources and draft a forecast
• Verify: a verifier agent approves it before submission
FutureX-Online, Sep Week 4:
🥇 Onyx, 53.06: all sub-agents run on GPT-5.6-Sol
🥈 Basalt, 52.73: DeepSeek V4-Pro, DeepSeek Flash, GPT-5.6-Sol and Claude Fable 5.1 working together
One framework, two directions: many agents on a single model, or many models in one team. That's the Harness of Harnesses in practice.
🛠️ Code: github.com/EverMind-AI/Raven
🌐 Try Raven: raven.evermind.ai
Onyx (Nevar-B) and Basalt (Nevar-A) are our Raven-based submissions.
📊 Leaderboard: futurex.live
Raven just hit #1 Paper of the Day on @huggingface 🎉
Technical report: huggingface.co/papers/2609.3…
Raven's two core ideas:
• The Harness of Harnesses: compose specialized agents into one team
• Built for RSI: the harness itself can be rewritten and improved by AI
Fully open source. Come build with us 👇
github.com/EverMind-AI/Raven
The response to Raven these past two days has blown us away. Thank you! 🐦⬛
Clearly the ideas resonate:
• One harness can't be best at everything, so Raven turns its own specialist harnesses and the agents you already use (Claude Code, Codex and more) into one team
• RSI goes beyond skills: the whole harness, orchestration layer included, is built to be rewritten and improved by AI
And it's not just an idea. It's backed by research. Our technical report is out:
Raven: The Harness of Harnesses for Composable Agentic Intelligence
📄 arxiv.org/abs/2609.33439
🤗 huggingface.co/papers/2609.3…
An upvote on Hugging Face means a lot 🙏
Yafeng Deng retweeted
The response to Raven these past two days has blown us away. Thank you! 🐦⬛
Clearly the ideas resonate:
• One harness can't be best at everything, so Raven turns its own specialist harnesses and the agents you already use (Claude Code, Codex and more) into one team
• RSI goes beyond skills: the whole harness, orchestration layer included, is built to be rewritten and improved by AI
And it's not just an idea. It's backed by research. Our technical report is out:
Raven: The Harness of Harnesses for Composable Agentic Intelligence
📄 arxiv.org/abs/2609.33439
🤗 huggingface.co/papers/2609.3…
An upvote on Hugging Face means a lot 🙏
Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦⬛
One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team.
And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved.
With Raven you can:
1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark).
2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game.
3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord:discord.gg/VVSm8MESch).
More in the video and slides below. Open source, Apache-2.0.
github.com/EverMind-AI/Raven (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)
The response to Raven these past two days has blown us away. Thank you! 🐦⬛
Clearly the ideas resonate:
• One harness can't be best at everything, so Raven turns its own specialist harnesses and the agents you already use (Claude Code, Codex and more) into one team
• RSI goes beyond skills: the whole harness, orchestration layer included, is built to be rewritten and improved by AI
And it's not just an idea. It's backed by research. Our technical report is out:
Raven: The Harness of Harnesses for Composable Agentic Intelligence
📄 arxiv.org/abs/2609.33439
🤗 huggingface.co/papers/2609.3…
An upvote on Hugging Face means a lot 🙏
Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦⬛
One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team.
And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved.
With Raven you can:
1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark).
2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game.
3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord:discord.gg/VVSm8MESch).
More in the video and slides below. Open source, Apache-2.0.
github.com/EverMind-AI/Raven (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)
Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦⬛
One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team.
And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved.
With Raven you can:
1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark).
2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game.
3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord:discord.gg/VVSm8MESch).
More in the video and slides below. Open source, Apache-2.0.
github.com/EverMind-AI/Raven (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)
