742 lines
25 KiB
Markdown
742 lines
25 KiB
Markdown
<div align="center" id="readme-top">
|
||
|
||
|
||
|
||
<p align="center">
|
||
<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>
|
||
<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>
|
||
<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>
|
||
<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>
|
||
</p>
|
||
|
||
[Website](https://evermind.ai) · [Documentation](https://docs.evermind.ai) · [Blog](https://evermind.ai/blogs) · [中文](README.zh-CN.md)
|
||
|
||
</div>
|
||
|
||
|
||
<br>
|
||
|
||
<details>
|
||
<summary><kbd>Table of Contents</kbd></summary>
|
||
|
||
<br>
|
||
|
||
- [Why Ever OS](#why-ever-os)
|
||
- [Quick Start](#quick-start)
|
||
- [Use Cases](#use-cases)
|
||
- [Documentation](#documentation)
|
||
- [EverMind Ecosystems](#evermind-ecosystems)
|
||
- [Contributing](#contributing)
|
||
|
||
<br>
|
||
|
||
</details>
|
||
|
||
|
||
## Why Ever OS
|
||
|
||
EverOS is a Python library and local-first memory runtime for agents and
|
||
makers. It gives one portable memory layer across coding assistants, apps,
|
||
devices, and workflows from day one. It stores conversations, files, and agent
|
||
trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes
|
||
for fast retrieval and self-evolving reuse.
|
||
|
||
<table>
|
||
<tr>
|
||
<th width="28%">Title</th>
|
||
<th width="36%">EverOS</th>
|
||
<th width="36%">Other Agent Memory Libraries</th>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Markdown source of truth</strong></td>
|
||
<td>✅ Canonical <code>.md</code> files that are readable, editable, diffable, and Git-versioned</td>
|
||
<td>❌ Usually API, vector, graph, dashboard, or database state</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Direct file editing</strong></td>
|
||
<td>✅ Edit <code>.md</code> files; cascade watcher syncs</td>
|
||
<td>❌ Usually SDK, API, dashboard, or backend update paths</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Local three-part stack</strong></td>
|
||
<td>✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required</td>
|
||
<td>❌ Often depends on managed services, vector DBs, graph DBs, or server stacks</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>User + agent tracks</strong></td>
|
||
<td>✅ User <code>episodes/profile</code> and agent <code>cases/skills</code> are separate first-class surfaces</td>
|
||
<td>❌ Usually centered on chat history, profiles, entities, facts, or retrieval records</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Orthogonal retrieval</strong></td>
|
||
<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>
|
||
<td>❌ Usually app, namespace, tenant, thread, or graph scoped</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Knowledge Wiki</strong></td>
|
||
<td>✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search</td>
|
||
<td>❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>Reflection</strong></td>
|
||
<td>✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions</td>
|
||
<td>❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement</td>
|
||
</tr>
|
||
</table>
|
||
|
||
<br>
|
||
|
||
## Quick Start
|
||
|
||
> One OpenRouter API key is enough to start EverOS, write durable memories,
|
||
> and retrieve them with keyword search.
|
||
|
||
### Prerequisites
|
||
|
||
- Python 3.12+
|
||
- One [OpenRouter API key](https://openrouter.ai/keys)
|
||
|
||
### 1. Install
|
||
|
||
```bash
|
||
uv pip install everos
|
||
# or: pip install everos
|
||
```
|
||
|
||
### 2. Try the standalone demo — no key required
|
||
|
||
Before configuring a provider or starting the server, run:
|
||
|
||
```bash
|
||
everos demo
|
||
```
|
||
|
||
The command asks for one memory and one recall question, then opens a
|
||
full-screen terminal visualizer. It is hardcoded and local to the CLI: it does
|
||
not need an API key, start or call the EverOS server, or change anything in the
|
||
real memory workflow below.
|
||
|
||
<p align="center">
|
||
<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">
|
||
</p>
|
||
|
||
Press `r` to replay and `q` to quit. For a non-interactive preview, use
|
||
`everos demo --plain`; for the looping showroom view, use
|
||
`everos demo --cinematic`. See [docs/everos-demo.md](docs/everos-demo.md) for
|
||
the visualizer's scope.
|
||
|
||
### 3. Initialize and add your OpenRouter key
|
||
|
||
```bash
|
||
everos init
|
||
```
|
||
|
||
This creates `~/.everos/everos.toml` and `~/.everos/ome.toml`. Open
|
||
`~/.everos/everos.toml`; the generated model and OpenRouter URL are already
|
||
correct, so replace only the empty `api_key`:
|
||
|
||
```toml
|
||
[llm]
|
||
model = "openai/gpt-4.1-mini"
|
||
api_key = "<OPENROUTER_API_KEY>"
|
||
base_url = "https://openrouter.ai/api/v1"
|
||
```
|
||
|
||
This is the smallest Tier 1 setup: memory add, flush, Markdown persistence,
|
||
cascade indexing, and keyword search.
|
||
|
||
Use `everos init --root <path>` if you want a different memory root. Pass the
|
||
same `--root <path>` to subsequent commands.
|
||
|
||
### 4. Start EverOS
|
||
|
||
```bash
|
||
everos server start
|
||
```
|
||
|
||
Keep the server running, then open a second terminal and check it:
|
||
|
||
```bash
|
||
curl http://127.0.0.1:8000/health
|
||
```
|
||
|
||
Look for `"status":"ok"`. With this one-key setup, `capabilities.llm` is
|
||
`true`; embedding and rerank remain `false` until you configure them.
|
||
|
||
### 5. Add and retrieve your first memory
|
||
|
||
> [!NOTE]
|
||
> Business endpoints live under `/api/v2`. The older `/api/v1` prefix still
|
||
> resolves to the same handlers so existing integrations keep working, but it
|
||
> is a legacy alias that may be removed in a future major release — write new
|
||
> code against `/api/v2`.
|
||
|
||
Add a tiny conversation:
|
||
|
||
```bash
|
||
TS=$(($(date +%s)*1000))
|
||
|
||
curl -X POST http://127.0.0.1:8000/api/v2/memory/add \
|
||
-H 'Content-Type: application/json' \
|
||
-d "{
|
||
\"session_id\": \"demo-001\",
|
||
\"app_id\": \"default\",
|
||
\"project_id\": \"default\",
|
||
\"messages\": [
|
||
{\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $TS, \"content\": \"I love climbing in Yosemite every spring.\"},
|
||
{\"sender_id\": \"alice\", \"role\": \"user\", \"timestamp\": $((TS+10000)), \"content\": \"My favorite coffee shop is Blue Bottle in SOMA.\"}
|
||
]
|
||
}"
|
||
```
|
||
|
||
Flush the memory at the end of the session:
|
||
|
||
```bash
|
||
curl -X POST http://127.0.0.1:8000/api/v2/memory/flush \
|
||
-H 'Content-Type: application/json' \
|
||
-d '{"session_id":"demo-001","app_id":"default","project_id":"default"}'
|
||
```
|
||
|
||
Search it back:
|
||
|
||
```bash
|
||
curl -X POST http://127.0.0.1:8000/api/v2/memory/search \
|
||
-H 'Content-Type: application/json' \
|
||
-d '{
|
||
"user_id": "alice",
|
||
"app_id": "default",
|
||
"project_id": "default",
|
||
"query": "Where do I like to climb?",
|
||
"method": "keyword",
|
||
"top_k": 5
|
||
}'
|
||
```
|
||
|
||
You should see the Yosemite memory in the response. Keep
|
||
`"method": "keyword"` in this one-key setup because the API defaults to hybrid
|
||
search, which requires an embedding provider.
|
||
|
||
> [!TIP]
|
||
> **First memory unlocked.**
|
||
> You just gave EverOS a fact, flushed it into durable Markdown-backed memory,
|
||
> and searched it back through the local index. That is the core loop.
|
||
> Want to see the source of truth? Open `~/.everos` and inspect the generated
|
||
> Markdown files.
|
||
|
||
For annotated responses and the Markdown files EverOS creates, see
|
||
[QUICKSTART.md](QUICKSTART.md).
|
||
|
||
### What works with one key?
|
||
|
||
The OpenRouter one-key setup is EverOS Tier 1. It supports server startup,
|
||
memory add and flush, durable Markdown storage, cascade indexing, and keyword
|
||
search. Add optional providers only when you need the features below:
|
||
|
||
| Configuration | Adds |
|
||
| --- | --- |
|
||
| `[llm]` only | Core memory flow and keyword search |
|
||
| Add `[embedding]` | Vector/user hybrid search, reflection, and skill extraction |
|
||
| Add `[rerank]` too | Agentic search, default agent hybrid search, and Knowledge Wiki |
|
||
| Add `[multimodal]` and parser extra | Image, PDF, audio, and office-file ingestion |
|
||
|
||
Missing optional capabilities are reported by `/health` and return a clear
|
||
HTTP 422 if you request a feature that needs them.
|
||
|
||
> [!NOTE]
|
||
> `everos demo --live` is different from the standalone demo in step 2: it
|
||
> connects to a running server and uses the real add/flush/search flow. It uses
|
||
> hybrid search, so add an embedding provider before you run it.
|
||
|
||
### Optional: Ingest Multimodal Files
|
||
|
||
To ingest non-text content (image / pdf / audio / office documents)
|
||
through `/api/v2/memory/add` `content` items, install the optional
|
||
extra:
|
||
|
||
```bash
|
||
uv pip install 'everos[multimodal]' # or: pip install 'everos[multimodal]'
|
||
```
|
||
|
||
This pulls in `everalgo-parser` (with the `[svg]` bundle for SVG support via
|
||
cairosvg). Configure the `[multimodal]` section in `everos.toml`; its default
|
||
model is `google/gemini-3-flash-preview` via OpenRouter.
|
||
|
||
**Office document support requires LibreOffice as a system dependency.**
|
||
The parser shells out to `soffice` (LibreOffice's headless renderer) to
|
||
convert `.doc` / `.docx` / `.ppt` / `.pptx` / `.xls` / `.xlsx` to PDF
|
||
before feeding the result into the multimodal LLM. Without LibreOffice,
|
||
office uploads return HTTP 415 with a clear error message; PDF / image
|
||
/ audio / HTML / email parsing is unaffected.
|
||
|
||
Install on the host before serving office documents:
|
||
|
||
```bash
|
||
brew install --cask libreoffice # macOS
|
||
sudo apt-get install -y libreoffice # Debian / Ubuntu
|
||
```
|
||
|
||
### For Contributors
|
||
|
||
```bash
|
||
git clone https://github.com/EverMind-AI/EverOS.git
|
||
cd EverOS
|
||
uv sync # creates ./.venv and installs deps
|
||
source .venv/bin/activate # or prefix commands with `uv run`
|
||
everos demo --plain # try the local educational demo; no API keys needed
|
||
everos init # add one OpenRouter key to ~/.everos/everos.toml
|
||
|
||
everos --help
|
||
make test
|
||
```
|
||
|
||
<br>
|
||
<div align="right">
|
||
|
||
[](#readme-top)
|
||
|
||
</div>
|
||
|
||
## Use Cases
|
||
|
||
Now that you have had your first successful EverOS moment, explore what people
|
||
are building with persistent memory across agents, apps, and community
|
||
integrations.
|
||
|
||
Use cases show what persistent memory makes possible in real products and
|
||
workflows. Some examples are packaged in this repository; others point to
|
||
external demos or integrations you can study and adapt.
|
||
|
||
<table>
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://evermind.ai/usecase_reunite)
|
||
|
||
#### Reunite - Find With EverOS
|
||
|
||
Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.
|
||
|
||
[Learn more](https://evermind.ai/usecase_reunite)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/tt-a1i/hive)
|
||
|
||
#### Hive Orchestrator
|
||
|
||
Browser-native hive-mind for CLI coding agents - Claude Code, Codex, Gemini, and OpenCode collaborate as real PTY processes via a team protocol.
|
||
|
||
[Code](https://github.com/tt-a1i/hive)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/tt-a1i/evermemos-mcp)
|
||
|
||
#### AI Coding Assistants With EverOS
|
||
|
||
Universal long-term memory layer for AI coding assistants, powered by EverOS.
|
||
|
||
[Code](https://github.com/tt-a1i/evermemos-mcp)
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
[](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
|
||
|
||
#### AI Data Technician
|
||
|
||
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.
|
||
|
||
[Code](https://github.com/yuansui123/AI-Data-Technician-EverMemOS)
|
||
|
||
</td>
|
||
</tr>
|
||
|
||
<tr>
|
||
<td width="50%" valign="top">
|
||
|
||
|
||
|
||
#### Rokid AI Assistant With EverOS
|
||
|
||
Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities.
|
||
|
||
Coming soon
|
||
|
||
</td>
|
||
<td width="50%" valign="top">
|
||
|
||
|
||
|
||
#### Creative Assistant With Memory
|
||
|
||
Creative assistant with long-term memory, so your creative context stays available across sessions.
|
||
|
||
Coming soon
|
||
|
||
</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/xunyud/Earth-Online)
|
||
|
||
#### Earth Online Memory Game
|
||
|
||
Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log.
|
||
|
||
[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)
|
||
|
||
#### 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)
|
||
|
||
#### 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>
|
||
|
||
## 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>
|