EverOS

Table of Contents
- [Why EverOS](#why-ever-os) - [Ecosystem Integrations](#ecosystem-integrations) - [Quick Start](#quick-start) - [Use Cases](#use-cases) - [Documentation](#documentation) - [EverMind Ecosystem](#evermind-ecosystem) - [Contributing](#contributing)
## Why EverOS 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.

Title	EverOS	Other Agent Memory Libraries
Markdown source of truth	✅ Canonical .md files that are readable, editable, diffable, and Git-versioned	❌ Usually API, vector, graph, dashboard, or database state
Direct file editing	✅ Edit .md files; cascade watcher syncs	❌ Usually SDK, API, dashboard, or backend update paths
Local three-part stack	✅ Markdown + SQLite + LanceDB; no MongoDB, Elasticsearch, or Redis required	❌ Often depends on managed services, vector DBs, graph DBs, or server stacks
User + agent tracks	✅ User episodes/profile and agent cases/skills are separate first-class surfaces	❌ Usually centered on chat history, profiles, entities, facts, or retrieval records
Orthogonal retrieval	✅ Search by user_id, agent_id, app_id, project_id, and session_id	❌ Usually app, namespace, tenant, thread, or graph scoped
Knowledge Wiki	✅ Editable, source-backed Markdown knowledge pages with taxonomy, CRUD APIs, and topic search	❌ Usually separate from memory, trapped in a dashboard, or not tied back to source files
Reflection	✅ Offline memory evolution that merges episode clusters and refines profiles and skills between sessions	❌ Usually retrieval-only memory with little background consolidation or long-horizon improvement

## Ecosystem Integrations EverOS adds durable memory to the agent and workflow platforms below—and comes built into Raven. Choose an integration to open its setup guide.

DeepSeek Harness

Hermes

OpenClaw

Raven

Dify

## 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 No API key or server setup required—run one command to quickly experience how EverOS stores and recalls memory: ```bash # If you installed EverOS as a package: everos demo # If you cloned or forked this repository and have not activated .venv: uv run everos demo ``` Enter something EverOS should remember, then ask a related question to watch the memory move through ingest -> extract -> index -> recall. ### 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 = "" 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 ` if you want a different memory root. Pass the same `--root ` 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-3.8-flash` 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 uv run everos demo --plain # try the local educational demo; no API keys needed uv run everos init # add one OpenRouter key to ~/.everos/everos.toml uv run everos --help make test ```
## 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.

#### AIUI Sports Agents Sports agents for smart glasses, covering running, cycling, and indoor rowing. AISmartRun includes an optional memory-backend contract for post-run summaries; connecting it to EverOS requires a separately configured backend. [Code](https://github.com/EasonZhu1997/AIUI-Sports-Agents)	#### 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)
#### 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)	#### 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)
#### 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)	#### Rokid AI Assistant With EverOS Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities. Coming soon
#### Creative Assistant With Memory Creative assistant with long-term memory, so your creative context stays available across sessions. Coming soon	#### Earth Online Memory Game Earth Online is a memory-aware productivity game that turns everyday planning into a living