Raven 🐦⬛ just crossed 2,000 GitHub stars and shipped v0.1.5.
The useful signal is not the number. It’s the direction:
agents need memory, but they also need better operating loops.
This release adds deep research for multi-source, cited async reports, and in-tree tracing so
Most agent observability stops at the model layer.
EverOS lets you observe the memory layer in @langfuse too:
- what got stored
- what got recalled
- recall confidence
- token cost across extraction, search, and reflection
The example is open in our repo and runnable in
Raven🐦⬛ now supports deep research via @miromind_ai MiroThinker.
Some questions should not be answered from memory or the first search result.
Raven can now delegate open-ended research, search broadly, cross-check multiple sources, and return a cited report you can inspect
We’re excited to bring @MiniMax_AI to Raven 🐦⬛ as a first-class model option.
MiniMax powers the model. Raven makes that intelligence compound across runs.
Built on EverOS, Raven is a memory-first, self-improving agent harness. It gives models durable user and agent memory,
Great to see participants learning, building, and having fun at the AI for Education Hackathon at Stanford, and getting hands-on with Raven along the way.
Thank you to everyone who joined us. We’re already looking forward to the next one.
Returning after an unforgettable Al for Education Hackathon at @Stanford
Lovely weather, great people, cool new place 🚀
Thanks to @UofBeta, @evermind, @butterbase_ai, @nebiusai for hosting this event!
Update: the EvoAgentBench paper is now on arXiv.
This is the paper behind the benchmark we introduced here: measuring whether agents actually improve through experience, ability transfer, and reuse across tasks.
Paper: arxiv.org/abs/2607.05202
Everyone is talking about self-improving agents. The harder question is how to measure whether an agent is actually getting better.
That is why we built EvoAgentBench: a benchmark for agent self-evolution.
It tests whether agents can learn from past trajectories, extract
EverMe × Kimi Code @Kimi_Moonshot@KimiDevs
Kimi Code now has shared memory.
Connect Kimi Code to EverMe Memory with one prompt, carry context across sessions, agents, machines, and operating systems, and turn what it learns into reusable Skills.
Less cold start. More
In just one week, Raven🐦⬛ crossed 1,000 GitHub stars.
This is worth celebrating, but it still feels like the beginning.
A lot of people have asked what makes Raven different as an agent harness, so here is the simplest answer:
Raven is built around a Proactive Engine
Raven🐦⬛ is two days in and already close to 500 GitHub stars.
A strong start, real momentum, and an incredible community.
Thank you all.
github.com/EverMind-AI/ra…
EverOS just crossed 10,000 GitHub Stars. ⭐
Big milestone, big achievement. Thanks to everyone who's been part of the journey，the best is still ahead.
Stay foolish. Stay tuned.
github.com/EverMind-AI/Ev…
EverOS just crossed 9,000 GitHub stars.🎉
The response to our 1.0.0 -> 1.1.0 release means a lot, especially the interest in Knowledge Wiki and Reflections.
Thank you to everyone sharing the repo and supporting us.
We will keep building agent memory and agentic infra.
Thank
