EverOS README

The project's README from github.com/EverMind-AI/EverOS. As one markdown file: README.md.


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.

DeepSeek Harness
DeepSeek Harness		 Hermes
Hermes		 OpenClaw
OpenClaw		 Raven
Raven		 Dify
Dify

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 for Smart Glasses

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

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Reunite - Find With EverOS

Parents describe what they remember. Children describe what they recall. Reunite uses semantic memory to surface the connections.

Learn more

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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

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AI Coding Assistants With EverOS

Universal long-term memory layer for AI coding assistants, powered by EverOS.

Code

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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

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Rokid AI Assistant With EverOS

Connect to EverOS within Rokid Glasses enabling long-term memory for all of your smart activities.

Coming soon
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Creative Assistant With Memory

Creative assistant with long-term memory, so your creative context stays available across sessions.

Coming soon

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Earth Online Memory Game

Earth Online is a memory-aware productivity game that turns everyday planning into a living quest log.

Code

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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

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Your Personal Tasting Universe

Record, visualize, and explore your tasting journey through an immersive 3D star map.

Code

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EverOS Open Her

Build AI that feels. Open-source persona engine - personality emerges from neural drives, not prompts. Inspired by Her.

Code

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Browser Agent For Personal Memory

Ruminer brings persistent memory to a browser agent so it can carry personal context across web tasks.

Plugin
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EverMem Sync With EverOS

One command to connect any AI coding CLI to EverOS (formerly called EverMemOS) for long-term memory.

Code

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MCO - Orchestrate AI Coding Agents

MCO equips your primary agent with an agent team that can work together to solve complex tasks.

Code

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Study Buddy With Self-Evolving Memory

Study proactively with an agent that has self-evolving memory.

Code

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Alzheimer's Memory Assistant

Empowering individuals with advanced memory support and daily assistance.

Code

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Memory-Driven Multi-Agent NPC Experience

An iOS sci-fi mystery game where players explore and uncover the truth.

Code

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Mobi Companion

An iOS app where users create, nurture, and live with a personalized AI companion called Mobi.

Code
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AI Wearable With Memory

A context-native AI wearable that listens to everyday life and converts conversations into memory.

Code

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Legacy OpenClaw Agent Memory

Archived pre-1.0.0 plugin reference. New integrations should use the current EverOS API.

[Learn more](docs/migratio

EverOS (EverMind-AI/EverOS)

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EverOS
What is this?EverOS is a local-first, Markdown-native memory layer for AI agents: it stores conversations, files, and agent trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes so any coding assistant, app, or workflow can retrieve and keep building on the same persistent memory.
Is this a library I import, or a server I run?Both: EverOS is distributed as a Python library/runtime, but day-to-day you run it as a local server exposing /api/v2 endpoints that agents call to add and search memory, while the actual data stays on disk as plain Markdown files plus SQLite and LanceDB indexes.
How do I get it running?Install EverOS, try the standalone demo first (no key or server needed) to watch the ingest -> extract -> index -> recall loop, then start the EverOS server and add/flush/search memories through its /api/v2 endpoints.
Do I need an API key to start it?You need Python 3.12+ and a single OpenRouter API key to run the server with Tier 1 features (memory add/flush, Markdown storage, keyword search); the step-2 standalone demo works without any key at all.
What do I lose with just the one-key setup?With only an LLM key you get core memory flow and keyword search; vector/hybrid search, reflection, and skill extraction need an embedding provider, and agentic search plus the Knowledge Wiki additionally require a rerank provider. Requesting a feature you haven't configured returns a clear HTTP 422.
Where does my memory data actually live?Memory is kept locally as canonical, Git-versionable Markdown files (default under ~/.everos) with local SQLite and LanceDB indexes — there's no MongoDB, Elasticsearch, or Redis dependency and no mandatory cloud service.
What makes EverOS different from other agent-memory libraries?Instead of hiding state in a vector DB, graph, or dashboard, EverOS keeps editable Markdown as the single source of truth, tracks separate user and agent memory surfaces, and lets you retrieve by user, agent, app, project, or session independently.
What is 'Reflection' in EverOS?Reflection is an offline memory-evolution process that merges episode clusters and refines agent profiles and skills between sessions, rather than only doing retrieval-time lookups like most memory libraries.
Which agent tools already integrate with EverOS?EverOS ships ready-made integrations for DeepSeek Harness, Hermes, OpenClaw, and Dify, and it comes built into the Raven assistant.
Can EverOS ingest images, PDFs, or Office documents?Yes, with the optional multimodal extra installed; Office files (.doc/.docx/.ppt/.pptx/.xls/.xlsx) additionally need LibreOffice on the host, since the parser shells out to 'soffice' to convert them to PDF before multimodal processing.
What are similar projects?MemOS, memcode, brigade, hindsight, Memori, OpenViking, LongMemory, memvid
Which topics is it in?dify, persistent-memory, llm-memory, clawdbot, memory-system, ai-memory, memory-management, long-term-memory, hermes-agent, python3, vector-database, semantic-search
Creator
EverMind-AI
Size
3.9 MB
Stars
13.5K
Updated
CommitOct 16Releasev1.4.2Oct 14
Age
11mo
Commits · 90 Days