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769 lines
25 KiB
Markdown
769 lines
25 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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- [Why Ever OS](#why-ever-os)
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- [Quick Start](#quick-start)
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- [Use Cases](#use-cases)
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- [Documentation](#documentation)
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- [Star Us](#star-us)
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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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## Why Ever OS
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EverOS is a Python library and local-first memory runtime for agents and
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makers. It gives one portable memory layer across coding assistants, apps,
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devices, and workflows from day one. It stores conversations, files, and agent
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trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes
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for fast retrieval and self-evolving reuse.
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<table>
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<tr>
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<th width="28%">Title</th>
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<th width="36%">EverOS</th>
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<th width="36%">Other Agent Memory Libraries</th>
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</tr>
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<tr>
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<td><strong>Markdown source of truth</strong></td>
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<td>✅ Canonical <code>.md</code> files that are readable, editable, diffable, and Git-versioned</td>
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<td>❌ Usually API, vector, graph, dashboard, or database state</td>
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</tr>
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<tr>
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<td><strong>Direct file editing</strong></td>
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<td>✅ Edit <code>.md</code> files; cascade watcher syncs</td>
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<td>❌ Usually SDK, API, dashboard, or backend update paths</td>
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</tr>
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<tr>
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<td><strong>Local three-part stack</strong></td>
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<td>✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required</td>
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<td>❌ Often depends on managed services, vector DBs, graph DBs, or server stacks</td>
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</tr>
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<tr>
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<td><strong>User + agent tracks</strong></td>
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<td>✅ User <code>episodes/profile</code> and agent <code>cases/skills</code> are separate first-class surfaces</td>
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<td>❌ Usually centered on chat history, profiles, entities, facts, or retrieval records</td>
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</tr>
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<tr>
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<td><strong>Orthogonal retrieval</strong></td>
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<td>✅ Search by <code>user_id</code>, <code>agent_id</code>, <code>app_id</code>, <code>project_id</code>, and <code>session_id</code></td>
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<td>❌ Usually app, namespace, tenant, thread, or graph scoped</td>
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</tr>
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<tr>
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<td><strong>Knowledge Wiki</strong></td>
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<td>✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search</td>
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<td>❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files</td>
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</tr>
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<tr>
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<td><strong>Reflection</strong></td>
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<td>✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions</td>
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<td>❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement</td>
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</tr>
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</table>
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<br>
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## Quick Start
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> Goal: play with the memory visualizer first, then start EverOS, write one
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> real memory, and search it back.
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### 0. Prerequisites
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- Python 3.12+
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- No API keys are needed for `everos demo`.
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- To run the real server-backed memory flow, create two provider keys before
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`everos init`:
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| Capability | Provider | Used for | Fill these `.env` slots |
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| --- | --- | --- | --- |
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| Chat + multimodal | [OpenRouter](https://openrouter.ai/) | `LLM` / `MULTIMODAL` | `EVEROS_LLM__API_KEY`, `EVEROS_MULTIMODAL__API_KEY` |
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| Embedding + rerank | [DeepInfra](https://deepinfra.com/) | `EMBEDDING` / `RERANK` | `EVEROS_EMBEDDING__API_KEY`, `EVEROS_RERANK__API_KEY` |
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You can use other OpenAI-compatible providers by changing the matching
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`*__BASE_URL` fields in `.env`.
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### 1. Install
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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. Play With The Demo
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Run this before configuring API keys or starting the server:
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```bash
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everos demo
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```
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The command asks for one memory and one recall question, then opens a
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full-screen terminal UI. This is an educational visualizer: it is hardcoded,
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local to the CLI, and does not connect to the EverOS server. Its job is to make
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the memory lifecycle visible: conversation -> memory sphere -> recall -> source
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proof -> confetti. See [docs/everos-demo.md](docs/everos-demo.md) for the demo
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scope and TUI source layout.
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The sphere moves through ingest, extraction, indexing, recall, source reveal,
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and a confetti burst after the first memory lands. Press `r` to replay and `q`
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to quit.
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<p align="center">
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<img src="https://gist.githubusercontent.com/cyfyifanchen/afa2cf40bf138a3ec96d917e8f2791a2/raw/d4ce82a6ddd7b3ebaf221e4825af993aeca5a7ce/everos-demo-tui-animation.svg" alt="Animated EverOS demo preview showing the memory sphere moving through recall and confetti states" width="720">
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</p>
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For the looping showroom view used in README media, run:
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```bash
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everos demo --cinematic
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```
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If your shell is not interactive, or you want a copyable preview, use:
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```bash
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everos demo --plain
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```
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### 3. Configure
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Generate a starter `.env` file, then fill the four API key slots shown in the
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generated comments. With the default setup, paste your OpenRouter key into the
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`LLM` / `MULTIMODAL` slots and your DeepInfra key into the `EMBEDDING` /
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`RERANK` slots.
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```bash
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everos init
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# or, from a source checkout:
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cp .env.example .env
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```
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`everos init` writes `./.env` by default. Use `everos init --xdg` to
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write `${XDG_CONFIG_HOME:-~/.config}/everos/.env` instead.
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### 4. Start EverOS
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```bash
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everos server start
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```
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Keep the server running, then open a second terminal and check it:
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```bash
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curl http://127.0.0.1:8000/health
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```
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Expected response:
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```json
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{"status":"ok"}
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```
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`everos server start` searches for `.env` in this order: `--env-file <path>` →
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`./.env` (cwd) → `${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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Now make the demo real. In the second terminal, run:
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```bash
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everos demo --live
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```
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Live demo mode connects to the running server and performs the real
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`/health` -> `/api/v1/memory/add` -> `/api/v1/memory/flush` ->
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`/api/v1/memory/search` flow before opening the same memory sphere UI. Use
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`--server-url <url>` if your server is not on `http://127.0.0.1:8000`.
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### 5. Try Your First Memory
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Add a tiny conversation:
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```bash
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TS=$(($(date +%s)*1000))
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curl -X POST http://127.0.0.1:8000/api/v1/memory/add \
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-H 'Content-Type: application/json' \
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-d "{
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\"session_id\": \"demo-001\",
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\"app_id\": \"default\",
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\"project_id\": \"default\",
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\"messages\": [
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{\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $TS, \"content\": \"I love climbing in Yosemite every spring.\"},
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{\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+10000)), \"content\": \"My favorite coffee shop is Blue Bottle in SOMA.\"}
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]
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}"
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```
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Force extraction for the local demo:
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```bash
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curl -X POST http://127.0.0.1:8000/api/v1/memory/flush \
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-H 'Content-Type: application/json' \
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-d '{"session_id":"demo-001","app_id":"default","project_id":"default"}'
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```
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Search it back:
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```bash
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curl -X POST http://127.0.0.1:8000/api/v1/memory/search \
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-H 'Content-Type: application/json' \
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-d '{
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"user_id": "alice",
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"app_id": "default",
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"project_id": "default",
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"query": "Where do I like to climb?",
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"top_k": 5
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}'
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```
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You should see the Yosemite memory in the response. If the result is empty on
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the first try, wait a moment and retry; Markdown is written synchronously, while
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the local index catches up in the background.
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> [!TIP]
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> **First memory unlocked.**
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> You just gave EverOS a fact, flushed it into durable Markdown-backed memory,
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> and searched it back through the local index. That is the core loop.
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> Want to see the source of truth? Open `~/.everos` and inspect the generated
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> Markdown files.
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For annotated responses and the Markdown files EverOS creates, see
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[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 demo --plain # try the local educational demo; no API keys needed
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everos init # paste OpenRouter + DeepInfra keys into .env
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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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## Use Cases
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Now that you have had your first successful EverOS moment, explore what people
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are building with persistent memory across agents, apps, and community
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integrations.
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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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#### 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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Coming soon
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</td>
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</tr>
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<tr>
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<td colspan="2" align="right">
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<a href="#readme-top"><img src="https://img.shields.io/badge/-Back_to_top-gray?style=flat-square" alt="Back to top"></a>
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||
</td>
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||
</tr>
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||
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||
<tr>
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||
<td width="50%" valign="top">
|
||
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||
[](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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|
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[Code](https://github.com/xunyud/Earth-Online)
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|
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</td>
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<td width="50%" valign="top">
|
||
|
||
[](https://github.com/golutra/golutra)
|
||
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||
#### 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>
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||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/kellyvv/OpenHer)
|
||
|
||
#### 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)
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||
|
||
#### 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)
|
||
|
||
#### 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 current EverOS 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>
|
||
|
||
## Documentation
|
||
|
||
- [docs/everos-demo.md](docs/everos-demo.md) — Demo scope and TUI source layout
|
||
- [docs/how-memory-works.md](docs/how-memory-works.md) — Markdown, SQLite, LanceDB, and recall flow
|
||
- [docs/use-cases.md](docs/use-cases.md) — Full use-case gallery and integration examples
|
||
- [docs/engineering.md](docs/engineering.md) — Contributor engineering reference: build, test, CI, conventions
|
||
- [docs/migration-to-1.0.0.md](docs/migration-to-1.0.0.md) — Legacy API migration notes
|
||
- [CHANGELOG.md](CHANGELOG.md) — Release notes
|
||
- [CONTRIBUTING.md](CONTRIBUTING.md) — How to contribute
|
||
|
||
<br>
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|
||
|
||
## Star Us
|
||
|
||
If EverOS is useful to your agent stack, please star the repo. It helps more
|
||
builders discover the project and gives the memory ecosystem a stronger signal
|
||
to keep improving.
|
||
|
||
### 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, AI-native interfaces, and memory evaluation.
|
||
|
||
<table>
|
||
<tr>
|
||
<th colspan="2">EverMind Open-Source Ecosystem</th>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Memory Runtime</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>Self-Improving Agent Harness</strong></td>
|
||
<td><a href="https://github.com/EverMind-AI/Raven">Raven</a> - the self-improving agent harness that brings memory, proactivity, context control, and skill evolution into terminal-native agents.</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>Hypergraph Memory</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: memory 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>
|