EverOS

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![EverOS banner](https://github.com/user-attachments/assets/8e217d39-5d15-4c6c-9b54-3e83add4e0f2)

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[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

> Goal: play with the memory visualizer first, then start EverOS, write one
> real memory, and search it back.

### 0. Prerequisites

- Python 3.12+
- No API keys are needed for `everos demo`.
- To run the real server-backed memory flow, create two provider keys before
  `everos init`:

| Capability | Provider | Used for | Fill these `.env` slots |
| --- | --- | --- | --- |
| Chat + multimodal | [OpenRouter](https://openrouter.ai/) | `LLM` / `MULTIMODAL` | `EVEROS_LLM__API_KEY`, `EVEROS_MULTIMODAL__API_KEY` |
| Embedding + rerank | [DeepInfra](https://deepinfra.com/) | `EMBEDDING` / `RERANK` | `EVEROS_EMBEDDING__API_KEY`, `EVEROS_RERANK__API_KEY` |

You can use other OpenAI-compatible providers by changing the matching
`*__BASE_URL` fields in `.env`.

### 1. Install

```bash
uv pip install everos
# or: pip install everos
```

### 2. Play With The Demo

Run this before configuring API keys or starting the server:

```bash
everos demo
```

The command asks for one memory and one recall question, then opens a
full-screen terminal UI. This is an educational visualizer: it is hardcoded,
local to the CLI, and does not connect to the EverOS server. Its job is to make
the memory lifecycle visible: conversation -> memory sphere -> recall -> source
proof -> confetti. See [docs/everos-demo.md](docs/everos-demo.md) for the demo
scope and TUI source layout.

The sphere moves through ingest, extraction, indexing, recall, source reveal,
and a confetti burst after the first memory lands. Press `r` to replay and `q`
to quit.

<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>

For the looping showroom view used in README media, run:

```bash
everos demo --cinematic
```

If your shell is not interactive, or you want a copyable preview, use:

```bash
everos demo --plain
```

### 3. Configure

Generate a starter `.env` file, then fill the four API key slots shown in the
generated comments. With the default setup, paste your OpenRouter key into the
`LLM` / `MULTIMODAL` slots and your DeepInfra key into the `EMBEDDING` /
`RERANK` slots.

```bash
everos init
# or, from a source checkout:
cp .env.example .env
```

`everos init` writes `./.env` by default. Use `everos init --xdg` to
write `${XDG_CONFIG_HOME:-~/.config}/everos/.env` instead.

### 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
```

Expected response:

```json
{"status":"ok"}
```

`everos server start` searches for `.env` in this order: `--env-file <path>` →
`./.env` (cwd) → `${XDG_CONFIG_HOME:-~/.config}/everos/.env` → `~/.everos/.env`.
The endpoint stack is OpenAI-protocol compatible (OpenAI / OpenRouter / vLLM /
Ollama / DeepInfra) - override `*__BASE_URL` in the generated `.env` to point
at any of them.

Now make the demo real. In the second terminal, run:

```bash
everos demo --live
```

Live demo mode connects to the running server and performs the real
`/health` -> `/api/v1/memory/add` -> `/api/v1/memory/flush` ->
`/api/v1/memory/search` flow before opening the same memory sphere UI. Use
`--server-url <url>` if your server is not on `http://127.0.0.1:8000`.

### 5. Try Your First Memory

Add a tiny conversation:

```bash
TS=$(($(date +%s)*1000))

curl -X POST http://127.0.0.1:8000/api/v1/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.\"}
    ]
  }"
```

Force extraction for the local demo:

```bash
curl -X POST http://127.0.0.1:8000/api/v1/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/v1/memory/search \
  -H 'Content-Type: application/json' \
  -d '{
    "user_id": "alice",
    "app_id": "default",
    "project_id": "default",
    "query": "Where do I like to climb?",
    "top_k": 5
  }'
```

You should see the Yosemite memory in the response. If the result is empty on
the first try, wait a moment and retry; Markdown is written synchronously, while
the local index catches up in the background.

> [!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).

### Optional: Ingest Multimodal Files

To ingest non-text content (image / pdf / audio / office documents)
through `/api/v1/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) and wires up the multimodal LLM client
(`EVEROS_MULTIMODAL__*` fields in `.env`, defaults to
`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/EverMi