Self-evolving memory across Agent and platform. The one portable memory layer for every agent they use - Claude Code, Codex, OpenClaw, Hermes, and more
Build, evaluate, and integrate long-term memory for self-evolving agents.
Website · Documentation · Blog Table of Contents Project Overview Use Cases Quick Start Architecture Methods Benchmarks Evaluation Citations Stay Tuned Contributing Project Overview EverOS is a unified home for applying, building, and evaluating long term memory in self evolving agents. The repository is organized aro It has reached 5,649 GitHub stars, written primarily in Python.
Why now: Recent coverage — "GitHub - EverMind-AI/EverOS: Build, evaluate, and integrate long-term memory for self-evolving agents. · GitHub" — alongside renewed developer interest is driving current visibility.
Considerations: Solid adoption (5,649 stars) but quiet cross-source signal right now — established utility more than a current breakout.
EARLY MOMENTUM · Research: Adoption is real but cross-source confirmation is thin — a short hands-on trial (Python) will tell you more than the metrics.
Sources: EverMind-AI/EverOS on GitHub · Project homepage · EverMind-AI/EverOS — GitHub trending stats & insights | Trendshift · James Chang's Post - EverMind-AI/EverOS - LinkedIn
Methodology: synthesized from this project's own documentation, live GitHub data, third-party coverage, and multi-platform signal convergence — by AISO.tools.
git clone https://github.com/EverMind-AI/EverOS.gitThen follow the README in the cloned directory.
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