0bbfb3bf1e
Rename the overview section to focus on why EverOS matters in both English and Chinese READMEs. Simplify the EverMind ecosystem copy around EverOS as the core memory architecture and mirror that positioning in the Chinese README.
764 lines
26 KiB
Markdown
764 lines
26 KiB
Markdown
<div align="center" id="readme-top">
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<p align="center">
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<a href="https://x.com/evermind"><img src="https://img.shields.io/badge/EverMind-000000?labelColor=gray&style=for-the-badge&logo=x&logoColor=white" alt="X"></a>
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<a href="https://huggingface.co/EverMind-AI"><img src="https://img.shields.io/badge/🤗_HuggingFace-EverMind-F5C842?labelColor=gray&style=for-the-badge" alt="HuggingFace"></a>
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<a href="https://discord.gg/gYep5nQRZJ"><img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Fv10%2Finvites%2FgYep5nQRZJ%3Fwith_counts%3Dtrue&query=%24.approximate_presence_count&suffix=%20online&label=Discord&color=404EED&labelColor=gray&style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a>
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<a href="https://github.com/EverMind-AI/EverOS/discussions/67"><img src="https://img.shields.io/badge/WeCom-EverMind_社区-07C160?labelColor=gray&style=for-the-badge&logo=wechat&logoColor=white" alt="WeChat"></a>
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</p>
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[Website](https://evermind.ai) · [Documentation](https://docs.evermind.ai) · [Blog](https://evermind.ai/blogs) · [中文](README.zh-CN.md)
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</div>
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<br>
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<details>
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<summary><kbd>Table of Contents</kbd></summary>
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<br>
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- [EverOS 1.0.0 Highlights](#everos-100-highlights)
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- [Why EverOS](#why-everos)
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- [Quick Start](#quick-start)
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- [Architecture At A Glance](#architecture-at-a-glance)
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- [Storage Layout](#storage-layout)
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- [Features](#features)
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- [Project Structure](#project-structure)
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- [Documentation](#documentation)
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- [Use Cases](#use-cases)
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- [Stay Tuned](#stay-tuned)
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- [EverMind Ecosystems](#evermind-ecosystems)
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- [Contributing](#contributing)
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<br>
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</details>
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## EverOS 1.0.0 Highlights
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> [!IMPORTANT]
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>
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> **EverOS 1.0.0 is a major release for self-evolving memory.** It brings a
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> local-first runtime, Markdown as the source of truth, hybrid retrieval,
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> multimodal ingestion, user and agent memory scopes, and modular algorithms
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> through [EverAlgo](https://github.com/EverMind-AI/EverAlgo).
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>
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> **Watch this repository** for the next wave of memory-system work, including
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> Wiki-style knowledge layers and Dreaming for deeper offline evolution.
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<table>
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<tr>
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<td width="33%" valign="top">
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<strong>Markdown As Source Of Truth</strong><br>
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All memory is persisted as <code>.md</code> files: readable, editable,
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grep-able, Git-versioned, and openable directly in Obsidian.
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</td>
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<td width="33%" valign="top">
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<strong>Local Three-Part Stack</strong><br>
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Markdown + SQLite + LanceDB keep vectors, BM25, and scalar filters
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local. No MongoDB, Elasticsearch, or Redis required.
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</td>
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<td width="33%" valign="top">
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<strong>Dual-Track Memory</strong><br>
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Agent memory (<code>cases</code> / <code>skills</code>) and user memory
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(<code>episodes</code> / <code>profile</code>) are extracted independently.
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</td>
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</tr>
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<tr>
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<td width="33%" valign="top">
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<strong>Multimodal Ingestion</strong><br>
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Text, images, audio, documents, PDFs, HTML, and email are unified into
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searchable memory.
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</td>
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<td width="33%" valign="top">
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<strong>Self-Evolution</strong><br>
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Common skills are extracted from real usage; repeated patterns become
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reusable workflows, no retraining required.
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</td>
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<td width="33%" valign="top">
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<strong>Orthogonal Retrieval</strong><br>
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Search independently by <code>user_id</code>, <code>agent_id</code>,
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<code>app_id</code>, <code>project_id</code>, and <code>session_id</code>.
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</td>
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</tr>
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</table>
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Why EverOS
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EverOS is an open-source Python framework for self-evolving long-term
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memory across agents and platforms. It gives makers one portable memory
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layer for every agent they use - Claude Code, Codex, OpenClaw, Hermes,
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and more - so context, decisions, files, and trajectories can follow the
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work instead of staying trapped in one tool.
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EverOS stores conversations, agent trajectories, and files as readable
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Markdown, then syncs local SQLite and LanceDB indexes for fast retrieval.
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Agents can reuse past cases and skills, improve from repeated workflows,
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and become more proactive over time.
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The system is built around three boundaries:
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1. **Memory content stays readable** - Markdown is the durable source of truth.
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2. **Runtime state stays local** - SQLite tracks state and LanceDB handles vector, BM25, and scalar-filter search.
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3. **Algorithms stay modular** - [EverAlgo](https://github.com/EverMind-AI/EverAlgo) owns memory algorithms; EverOS owns runtime, persistence, online flows, and offline evolution.
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Quick Start
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### 1. Install EverOS
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```bash
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uv pip install everos
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# or: pip install everos
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```
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### 2. Initialize Configuration
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Generate a starter `.env` file, then fill the API key fields shown in
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the generated comments.
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```bash
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everos init
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```
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`everos init` writes `./.env` by default. Use `everos init --xdg` to
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write `${XDG_CONFIG_HOME:-~/.config}/everos/.env` instead.
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### 3. Start The Server
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```bash
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everos --help
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everos server start
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```
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`everos server start` searches for `.env` in this order: `--env-file <path>` →
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`./.env` (cwd) → `${XDG_CONFIG_HOME:-~/.config}/everos/.env` → `~/.everos/.env`.
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The endpoint stack is OpenAI-protocol compatible (OpenAI / OpenRouter / vLLM /
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Ollama / DeepInfra) - override `*__BASE_URL` in the generated `.env` to point
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at any of them.
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For a step-by-step walkthrough (add a conversation, flush, search, then
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read the markdown), see [QUICKSTART.md](QUICKSTART.md).
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### Optional: Ingest Multimodal Files
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To ingest non-text content (image / pdf / audio / office documents)
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through `/api/v1/memory/add` `content` items, install the optional
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extra:
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```bash
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uv pip install 'everos[multimodal]' # or: pip install 'everos[multimodal]'
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```
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This pulls in `everalgo-parser` (with the `[svg]` bundle for SVG
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support via cairosvg) and wires up the multimodal LLM client
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(`EVEROS_MULTIMODAL__*` fields in `.env`, defaults to
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`google/gemini-3-flash-preview` via OpenRouter).
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**Office document support requires LibreOffice as a system dependency.**
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The parser shells out to `soffice` (LibreOffice's headless renderer) to
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convert `.doc` / `.docx` / `.ppt` / `.pptx` / `.xls` / `.xlsx` to PDF
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before feeding the result into the multimodal LLM. Without LibreOffice,
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office uploads return HTTP 415 with a clear error message; PDF / image
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/ audio / HTML / email parsing is unaffected.
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Install on the host before serving office documents:
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```bash
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brew install --cask libreoffice # macOS
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sudo apt-get install -y libreoffice # Debian / Ubuntu
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```
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### For Contributors
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```bash
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git clone https://github.com/EverMind-AI/EverOS.git
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cd EverOS
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uv sync # creates ./.venv and installs deps
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source .venv/bin/activate # or prefix commands with `uv run`
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everos init # fill the four API key slots in .env (two distinct keys)
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everos --help
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make test
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```
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Architecture At A Glance
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```
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┌───────────────────────────────────────────────┐
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│ entrypoints/ (CLI + HTTP API) │ presentation
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├───────────────────────────────────────────────┤
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│ service/ (use cases: memorize/retrieve) │ application
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├───────────────────────────────────────────────┤
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│ memory/ (extract + search + cascade) │ domain
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├───────────────────────────────────────────────┤
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│ infra/ (markdown / sqlite / lancedb) │ infrastructure
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└───────────────────────────────────────────────┘
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↑ ↑
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component/ core/
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(LLM/Embedding) (observability/lifespan)
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```
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DDD 5 layers, single-direction dependency. See [docs/architecture.md](docs/architecture.md).
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Storage Layout
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```
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~/.everos/
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├── default_app/ # app_id ("default" → "default_app" on disk)
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│ └── default_project/ # project_id ("default" → "default_project")
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│ ├── users/<user_id>/
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│ │ ├── user.md # profile
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│ │ ├── episodes/ # daily-log episodes (visible)
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│ │ ├── .atomic_facts/ # nested facts (dotfile-hidden)
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│ │ └── .foresights/ # predictive memory (dotfile-hidden)
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│ └── agents/<agent_id>/
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│ ├── agent.md
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│ ├── .cases/ # one task case per entry
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│ └── skills/ # named procedural memories
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├── .index/ # derived indexes (rebuildable from md)
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│ ├── sqlite/system.db # state + queue + audit
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│ └── lancedb/*.lance/ # vector + BM25 + scalar
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└── .tmp/ # transient working files
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```
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Open any `<app>/<project>/users/<user_id>/` folder in Obsidian — your
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agent's brain is just files. The dotfile directories (`.atomic_facts/`,
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`.foresights/`, `.cases/`) stay hidden by default so the visible folder
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is the user-facing memory surface, while extracted derivatives sit
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quietly alongside.
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Features
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- **Hybrid retrieval**: BM25 + vector (HNSW/IVF-PQ) + scalar filter, single-query in LanceDB
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- **Cascade index sync**: edit a `.md` → file watcher → entry-level diff → LanceDB sync, sub-second
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- **Multi-source extraction**: conversations / agent trajectories / file knowledge
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- **Dual-track memory**: user-track (Episodes / Profiles) + agent-track (Cases / Skills)
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- **Async-first**: full asyncio, single event loop
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- **Multi-modal**: text + small image / audio inline; large media via S3/OSS reference
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Project Structure
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```
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everos/ # repo root
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├── src/everos/ # main package (src layout)
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│ ├── entrypoints/ # cli + api
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│ ├── service/ # use case orchestration
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│ ├── memory/ # domain: extract + search + cascade + prompt_slots
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│ ├── infra/ # storage: markdown + lancedb + sqlite
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│ ├── component/ # cross-cutting: llm / embedding / config / utils
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│ ├── core/ # runtime: observability / lifespan / context
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│ └── config/ # configuration data + Settings schema
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├── tests/ # unit / integration / golden / fixtures
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├── docs/ # design docs
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└── .claude/ # team-shared rules + skills (auto-loaded by Claude Code)
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```
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Documentation
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- [docs/overview.md](docs/overview.md) — Project overview & vision
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- [docs/architecture.md](docs/architecture.md) — DDD layered architecture & dependency rules
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- [docs/engineering.md](docs/engineering.md) — Engineering & dev-efficiency infrastructure (CI / tooling / Claude Code)
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- [docs/use-cases.md](docs/use-cases.md) — Full use-case gallery and integration examples
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- [docs/migration-to-1.0.0.md](docs/migration-to-1.0.0.md) — Legacy API and infrastructure migration notes
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- [CHANGELOG.md](CHANGELOG.md) — Release notes
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- [CONTRIBUTING.md](CONTRIBUTING.md) — How to contribute
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- [.claude/rules/](.claude/rules/) — Detailed coding conventions (auto-loaded by Claude Code)
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<br>
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<div align="right">
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[](#readme-top)
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</div>
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## Use Cases
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Use cases show what persistent memory makes possible in real products and
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workflows. Some examples are packaged in this repository; others point to
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external demos or integrations you can study and adapt.
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<table>
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<tr>
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<td width="50%" valign="top">
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[](https://evermind.ai/usecase_reunite)
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#### Reunite - Find With EverOS
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Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.
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[Learn more](https://evermind.ai/usecase_reunite)
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</td>
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<td width="50%" valign="top">
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[](https://github.com/tt-a1i/hive)
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#### Hive Orchestrator
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Browser-native hive-mind for CLI coding agents - Claude Code, Codex, Gemini, and OpenCode collaborate as real PTY processes via a team protocol.
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[Code](https://github.com/tt-a1i/hive)
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</td>
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</tr>
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<tr>
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<td width="50%" valign="top">
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[](https://github.com/tt-a1i/evermemos-mcp)
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#### AI Coding Assistants With EverOS
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Universal long-term memory layer for AI coding assistants, powered by EverOS.
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[Code](https://github.com/tt-a1i/evermemos-mcp)
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</td>
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<td width="50%" valign="top">
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[](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
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#### AI Data Technician
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An agentic AI system that learns from scientist interaction to inspect, analyze, and classify high-dimensional time series data - with persistent memory that improves across sessions.
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[Code](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
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</td>
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</tr>
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<tr>
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<td width="50%" valign="top">
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#### Rokid AI Assistant With EverOS
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Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities.
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Coming soon
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</td>
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<td width="50%" valign="top">
|
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|
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#### Creative Assistant With Memory
|
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Creative assistant with long-term memory, so your creative context stays available across sessions.
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||
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Coming soon
|
||
|
||
</td>
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</tr>
|
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|
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<tr>
|
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<td colspan="2" align="right">
|
||
<a href="#readme-top"><img src="https://img.shields.io/badge/-Back_to_top-gray?style=flat-square" alt="Back to top"></a>
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/xunyud/Earth-Online)
|
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|
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#### Earth Online Memory Game
|
||
|
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Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log.
|
||
|
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[Code](https://github.com/xunyud/Earth-Online)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/golutra/golutra)
|
||
|
||
#### Multi-Agent Orchestration Platform
|
||
|
||
Golutra presents a multi-agent workforce for engineering teams, extending the IDE model from a single assistant to coordinated agents.
|
||
|
||
[Code](https://github.com/golutra/golutra)
|
||
|
||
</td>
|
||
</tr>
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/Yangtze-Seventh/taste-verse)
|
||
|
||
#### Your Personal Tasting Universe
|
||
|
||
Record, visualize, and explore your tasting journey through an immersive 3D star map.
|
||
|
||
[Code](https://github.com/Yangtze-Seventh/taste-verse)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/kellyvv/OpenHer)
|
||
|
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#### EverOS Open Her
|
||
|
||
Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her.
|
||
|
||
[Code](https://github.com/kellyvv/OpenHer)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)
|
||
|
||
#### Browser Agent For Personal Memory
|
||
|
||
Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks.
|
||
|
||
[Plugin](https://chromewebstore.google.com/detail/ruminer-browser-agent/lbccjohfpdpimbhpckljimgolndfmfif)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/nanxingw/EverMem)
|
||
|
||
#### EverMem Sync With EverOS
|
||
|
||
One command to connect any AI coding CLI to EverMemOS long-term memory.
|
||
|
||
[Code](https://github.com/nanxingw/EverMem)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td colspan="2" align="right">
|
||
<a href="#readme-top"><img src="https://img.shields.io/badge/-Back_to_top-gray?style=flat-square" alt="Back to top"></a>
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/mco-org/mco)
|
||
|
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#### MCO - Orchestrate AI Coding Agents
|
||
|
||
MCO equips your primary agent with an agent team that can work together to solve complex tasks.
|
||
|
||
[Code](https://github.com/mco-org/mco)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/onenewborn/StudyBuddy-public)
|
||
|
||
#### Study Buddy With Self-Evolving Memory
|
||
|
||
Study proactively with an agent that has self-evolving memory.
|
||
|
||
[Code](https://github.com/onenewborn/StudyBuddy-public)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/TonyLiangDesign/MemoCare)
|
||
|
||
#### Alzheimer's Memory Assistant
|
||
|
||
Empowering individuals with advanced memory support and daily assistance.
|
||
|
||
[Code](https://github.com/TonyLiangDesign/MemoCare)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/AlexL1024/NeuralConnect)
|
||
|
||
#### Memory-Driven Multi-Agent NPC Experience
|
||
|
||
An iOS sci-fi mystery game where players explore and uncover the truth.
|
||
|
||
[Code](https://github.com/AlexL1024/NeuralConnect)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/elontusk5219-prog/Mobi)
|
||
|
||
#### Mobi Companion
|
||
|
||
An iOS app where users create, nurture, and live with a personalized AI companion called Mobi.
|
||
|
||
[Code](https://github.com/elontusk5219-prog/Mobi)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)
|
||
|
||
#### AI Wearable With Memory
|
||
|
||
A context-native AI wearable that listens to everyday life and converts conversations into memory.
|
||
|
||
[Code](https://github.com/JaMesLiMers/EvermemCompetition-Spiro)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td colspan="2" align="right">
|
||
<a href="#readme-top"><img src="https://img.shields.io/badge/-Back_to_top-gray?style=flat-square" alt="Back to top"></a>
|
||
</td>
|
||
</tr>
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](docs/migration-to-1.0.0.md)
|
||
|
||
#### Legacy OpenClaw Agent Memory
|
||
|
||
Archived pre-1.0.0 plugin reference. New integrations should use the EverOS 1.0.0 API.
|
||
|
||
[Learn more](docs/migration-to-1.0.0.md)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)
|
||
|
||
#### Live2D Character With Memory
|
||
|
||
Add long-term memory to a real-time Live2D character, powered by [TEN Framework](https://github.com/TEN-framework/ten-framework).
|
||
|
||
[Code](https://github.com/TEN-framework/ten-framework/tree/04cb80601374fa9e35b4e544b2dbd23286ca7763/ai_agents/agents/examples/voice-assistant-with-EverMemOS)
|
||
|
||
</td>
|
||
</tr>
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://screenshot-analysis-vercel.vercel.app/)
|
||
|
||
#### Computer-Use With Memory
|
||
|
||
Run screenshot-based analysis with computer-use and store the results in memory.
|
||
|
||
[Live Demo](https://screenshot-analysis-vercel.vercel.app/)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](use-cases/game-of-throne-demo)
|
||
|
||
#### Game Of Thrones Memories
|
||
|
||
A demonstration of AI memory infrastructure through an interactive Q&A experience with *A Game of Thrones*.
|
||
|
||
[Code](use-cases/game-of-throne-demo)
|
||
|
||
</td>
|
||
</tr>
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](use-cases/claude-code-plugin)
|
||
|
||
#### Claude Code Plugin
|
||
|
||
Persistent memory for Claude Code. Automatically saves and recalls context from past coding sessions.
|
||
|
||
[Code](use-cases/claude-code-plugin)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)
|
||
|
||
#### Memory Graph Visualization
|
||
|
||
Explore stored entities and relationships in a graph interface. Frontend demo; backend integration is in progress.
|
||
|
||
[Live Demo](https://main.d2j21qxnymu6wl.amplifyapp.com/graph.html)
|
||
|
||
</td>
|
||
</tr>
|
||
</table>
|
||
|
||
<br>
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|
||
|
||
## Stay Tuned
|
||
|
||
Star the repo or join the community links above to follow new architecture methods, benchmark releases, memory-enabled use cases, Wiki-style memory, and Dreaming updates.
|
||
|
||
|
||
|
||
### Star History
|
||
|
||
[](https://www.star-history.com/#EverMind-AI/EverOS&Date)
|
||
|
||
<br>
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|
||
|
||
## EverMind Ecosystems
|
||
|
||
EverMind is an open-source ecosystem for long-term memory, self-evolving agents, and memory evaluation.
|
||
|
||
<table>
|
||
<tr>
|
||
<th colspan="2">EverMind Open-Source Ecosystem</th>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Core Memory Architecture</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/EverOS">EverOS</a> - the local memory operating system and research-backed runtime for agent and user memory.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Algorithm Engine</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/EverAlgo">EverAlgo</a> - stateless extraction, ranking, parsing, and memory operators that power EverOS.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Alternative Architecture</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/HyperMem">HyperMem</a> - hypergraph memory for long-term conversations, with its own benchmark-backed topic -> episode -> fact retrieval method.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Benchmarks</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/EverMemBench">EverMemBench</a> · <a href="https://github.com/EverMind-AI/EvoAgentBench">EvoAgentBench</a> - evaluation suites for conversational memory and agent self-evolution.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Long-Context Research</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/MSA">MSA</a> - Memory Sparse Attention for scalable latent memory and 100M-token contexts.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Personal Memory Layer</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/EverMe">EverMe</a> - CLI and agent plugin suite for cross-device, cross-agent personal memory.</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Developer Integrations</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/evermem-claude-code">evermem-claude-code</a> · <a href="https://github.com/EverMind-AI/everos-plugins">everos-plugins</a> - plugins, skills, and migration tooling for AI coding agents.</td>
|
||
</tr>
|
||
</table>
|
||
|
||
Together, these repositories form EverMind's research-to-runtime stack: new memory methods, reusable algorithms, benchmark evidence, and practical agent integrations.
|
||
|
||
<br>
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|
||
|
||
<br>
|
||
|
||
## Contributing
|
||
|
||
Contributions are welcome across the whole repository: architecture methods, benchmark coverage, use-case examples, documentation, and bug fixes. Browse [Issues](https://github.com/EverMind-AI/EverOS/issues) to find a good entry point, then open a PR when you are ready.
|
||
|
||
<br>
|
||
|
||
> [!TIP]
|
||
>
|
||
> **Welcome all kinds of contributions** 🎉
|
||
>
|
||
> Help make EverOS better. Code, documentation, benchmark reports, use-case write-ups, and integration examples are all valuable. Share your projects on social media to inspire others.
|
||
>
|
||
> Connect with one of the EverOS maintainers [@elliotchen200](https://x.com/elliotchen200) on 𝕏 or [@cyfyifanchen](https://github.com/cyfyifanchen) on GitHub for project updates, discussions, and collaboration opportunities.
|
||
|
||
|
||
|
||
|
||
### Code Contributors
|
||
|
||
[](https://github.com/EverMind-AI/EverOS/graphs/contributors)
|
||
|
||
|
||
|
||
|
||
### License
|
||
|
||
[Apache License 2.0](LICENSE) — see [NOTICE](NOTICE) for third-party attributions.
|
||
|
||
### Citation
|
||
|
||
If you use EverOS in research, see [CITATION.md](CITATION.md).
|
||
|
||
<br>
|
||
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|