Table of Contents
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.
Quick Start
One OpenRouter API key is enough to start EverOS, write durable memories,
and retrieve them with keyword search.
Prerequisites
1. Install
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:
# 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.
https://github.com/user-attachments/assets/98cb8e1e-2ca8-4504-b0a6-0b9a040a0a5c
3. Initialize and add your OpenRouter key
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:
[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
everos server start
Keep the server running, then open a second terminal and check it:
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:
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:
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:
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.
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:
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:
brew install --cask libreoffice # macOS
sudo apt-get install -y libreoffice # Debian / Ubuntu
For Contributors
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
|
Reunite - Find With EverOS
Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.
Learn more
|
|
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
|
AI Coding Assistants With EverOS
Universal long-term memory layer for AI coding assistants, powered by EverOS.
Code
|
|
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
|
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 quest log.
Code
|
|
Golutra presents a multi-agent workforce for engineering teams, extending the IDE model from a single assistant to coordinated agents.
Code
|
Your Personal Tasting Universe
Record, visualize, and explore your tasting journey through an immersive 3D star map.
Code
|
|
EverOS Open Her
Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her.
Code
|
Browser Agent For Personal Memory
Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks.
Plugin
|
|
|
|
EverMem Sync With EverOS
One command to connect any AI coding CLI to EverOS (formerly called EverMemOS) for long-term memory.
Code
|
MCO - Orchestrate AI Coding Agents
MCO equips your primary agent with an agent team that can work together to solve complex tasks.
Code
|
|
Study Buddy With Self-Evolving Memory
Study proactively with an agent that has self-evolving memory.
Code
|
Alzheimer's Memory Assistant
Empowering individuals with advanced memory support and daily assistance.
Code
|
|
Memory-Driven Multi-Agent NPC Experience
An iOS sci-fi mystery game where players explore and uncover the truth.
Code
|
Mobi Companion
An iOS app where users create, nurture, and live with a personalized AI companion called Mobi.
Code
|
|
|
|
AI Wearable With Memory
A context-native AI wearable that listens to everyday life and converts conversations into memory.
Code
|
Legacy OpenClaw Agent Memory
Archived pre-1.0.0 plugin reference. New integrations should use the current EverOS API.
[Learn more](docs/migratio
|
EverOS 是一个本地优先、以 Markdown 为原生格式的 AI 智能体记忆层：它将对话、文件和智能体轨迹存储为可读的 Markdown，再同步到本地 SQLite 和 LanceDB 索引中，这样任何编程助手、应用或工作流都可以检索并基于同一份持久记忆继续积累。