EverMind-AI/EverOSPublic

One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.

AI summary: A portable, local-first memory layer that enables AI agents to retain long-term context across apps.

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PythonApache-2.0Created Oct 28, 2025Last push 1d agoLatest release v1.2.2+243 stars this week+243 this month

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11.8K stars as of Aug 6, 2026, tracked back to Mar 29, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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11,848 stars
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What EverOS does

EverOS provides a unified and portable memory system for AI agents, functioning as a long-term storage layer. It captures and organizes agent context using a local-first, Markdown-native approach, ensuring users own their data. The system allows memory to self-evolve, persisting across different applications, tools, and workflows. This prevents agents from starting from scratch in every session, enabling continuous learning and personalization. By abstracting memory management, it allows developers to focus on agent logic while relying on EverOS for context retention.

AI developers and researchers building agentic systems that require persistent, cross-platform memory. It requires knowledge of Python and building LLM-based applications.

Feature: Implements a local-first architecture ensuring user data privacy and ownership.
Feature: Uses Markdown-native storage for human-readable and easily accessible memory files.
Feature: Provides long-term, self-evolving memory that persists across different agent sessions.
Feature: Integrates seamlessly across various AI applications, tools, and workflows.
Feature: Abstracts complex memory management and RAG processes away from agent logic.

Where teams use it

Persistent Agent Companions

Enables personal AI assistants to remember user preferences and past interactions indefinitely.

Cross-App Context Sharing

Allows an agent in a coding IDE to access context generated by an agent in a research tool.

Workflow Continuity

Ensures multi-step agent workflows can pause and resume without losing their state.

Local RAG Systems

Provides a robust, local backend for retrieval-augmented generation without relying on cloud vectors.

Getting started: pip install everos

README

main branch

Website · Documentation · Blog · 中文

Table of Contents

Why Ever OS
Quick Start
Use Cases
Documentation
EverMind Ecosystems
Contributing

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.

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

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	`LLM` / `MULTIMODAL`	`EVEROS_LLM__API_KEY`, `EVEROS_MULTIMODAL__API_KEY`
Embedding + rerank	DeepInfra	`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

uv pip install everos
# or: pip install everos

2. Play With The Demo

Run this before configuring API keys or starting the server:

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 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.

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

everos demo --cinematic

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

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.

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

everos server start

Keep the server running, then open a second terminal and check it:

curl http://127.0.0.1:8000/health

Expected response:

{"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:

everos demo --live

Live demo mode connects to the running server and performs the real /health -> /api/v2/memory/add -> /api/v2/memory/flush -> /api/v2/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

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.\"}
    ]
  }"

Force extraction for the local demo:

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?",
    "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.

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) 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:

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
source .venv/bin/activate            # or prefix commands with `uv run`
everos demo --plain                  # try the local educational demo; no API keys needed
everos init                          # paste OpenRouter + DeepInfra keys into .env

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.

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.

EverMind Open-Source Ecosystem
Memory Runtime	EverOS - the local memory operating system and research-backed runtime for agent and user memory.
Self-Improving Agent Harness	Raven - the self-improving agent harness that brings memory, proactivity, context control, and skill evolution into terminal-native agents.
Algorithm Engine	EverAlgo - stateless extraction, ranking, parsing, and memory operators that power EverOS.
Hypergraph Memory	HyperMem - hypergraph memory for long-term conversations, with its own benchmark-backed topic -> episode -> fact retrieval method.
Benchmarks	EverMemBench · EvoAgentBench - evaluation suites for conversational memory and agent self-evolution.
Long-Context Research	MSA - Memory Sparse Attention for scalable latent memory and 100M-token contexts.
Personal Memory Layer	EverMe - CLI and agent plugin suite for cross-device, cross-agent personal memory.
Developer Integrations	evermem-claude-code · everos-plugins - plugins, skills, and migration tooling for AI coding agents.

Star history

Contribution activity

Signals and awards

What EverOS does

Where teams use it

Persistent Agent Companions

Cross-App Context Sharing

Workflow Continuity

Local RAG Systems

README

Why Ever OS

Quick Start

0. Prerequisites

1. Install

2. Play With The Demo

3. Configure

4. Start EverOS

5. Try Your First Memory

Optional: Ingest Multimodal Files

For Contributors

Use Cases

Reunite - Find With EverOS

Hive Orchestrator

AI Coding Assistants With EverOS

AI Data Technician

Rokid AI Assistant With EverOS

Creative Assistant With Memory

Earth Online Memory Game

Multi-Agent Orchestration Platform

Your Personal Tasting Universe

EverOS Open Her

Browser Agent For Personal Memory

EverMem Sync With EverOS

MCO - Orchestrate AI Coding Agents

Study Buddy With Self-Evolving Memory

Alzheimer's Memory Assistant

Memory-Driven Multi-Agent NPC Experience

Mobi Companion

AI Wearable With Memory

Legacy OpenClaw Agent Memory

Live2D Character With Memory

Computer-Use With Memory

Game Of Thrones Memories

Claude Code Plugin

Memory Graph Visualization

Documentation

EverMind Ecosystems

Contributing

Code Contributors

License

Citation

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