[opensourcedex]

EverOS is a free, open source ai development platforms project written in Python and released under Apache-2.0. It has 13,758 GitHub stars, 961 forks and 73 open issues, and was last pushed 3 days ago. On this registry it ranks #54 of 155 tracked projects in AI Development Platforms, with 5 head-to-head comparisons available. It gained 445 stars over the last 30 tracked days.

What is EverOS?

EverOS is an Apache-2.0 Python library and local-first memory runtime that gives AI agents and their builders one portable, Markdown-native memory layer spanning coding assistants, apps, devices, and workflows.

What it is

EverOS is a Python library and memory runtime for agents and makers. It stores conversations, files, and agent trajectories as readable Markdown, then syncs local SQLite and LanceDB indexes so retrieval stays fast and memories can be reused and evolved across sessions. It lives in the agentic AI and agent-memory corner of the Python ecosystem, and ships with integrations for DeepSeek Harness, Hermes, OpenClaw, and Dify, alongside Raven, where it comes built in.

The concrete thing it replaces is whatever ad hoc memory each assistant, app, or workflow invents on its own: chat history buried in a proprietary store, vector or graph database state that a person cannot read or diff, or a dashboard that owns the memory rather than the user. EverOS makes the Markdown files the canonical source of truth, so memory can be opened in an editor, committed to Git, and carried between tools instead of being trapped behind a single vendor's API.

Key capabilities

  • Markdown as the canonical source of truth: .md files that are readable, editable, diffable, and Git-versionable.
  • Direct file editing with a cascade watcher, so changes made by hand in the Markdown sync back into the indexes.
  • A local three-part stack of Markdown, SQLite, and LanceDB, with no MongoDB, Elasticsearch, or Redis required.
  • Separate first-class user and agent tracks: user episodes and profiles alongside agent cases and skills.
  • Orthogonal retrieval filtered by user_id, agent_id, app_id, project_id, and session_id.
  • A Knowledge Wiki of editable, source-backed Markdown pages with taxonomy, CRUD APIs, and topic search.
  • Reflection, an offline evolution pass that merges episode clusters and refines profiles and skills between sessions.

Who uses it and how

  • Individual makers running coding assistants locally who want one memory that follows them across apps, devices, and workflows.
  • Teams already on an integrated platform such as Raven, where EverOS is built in and needs no separate deployment.
  • Builders wiring durable memory into DeepSeek Harness, Hermes, OpenClaw, or Dify through the documented integration guides.
  • Users who want to inspect or correct what an agent remembers by editing the underlying .md files directly.
  • Anyone needing long-horizon consolidation, where Reflection merges episode clusters between sessions instead of relying on retrieval alone.

Getting started

Install with uv pip install everos or pip install everos, then run everos demo for a key-free walkthrough, or everos init followed by everos server start to bring up the local server on http://127.0.0.1:8000. Tier 1 needs Python 3.12+ and one OpenRouter API key written into ~/.everos/everos.toml; business endpoints live under /api/v2.

How it compares

The facts provide no list of paid products that EverOS replaces, and they name no directly comparable tool, so it stands alone in this registry as the Markdown-native, local-first agent memory layer. The README contrasts it with other agent memory libraries generically rather than by name: those usually keep memory in API, vector, graph, dashboard, or database state, whereas EverOS keeps .md files canonical and edits cascade back through a watcher. Where those alternatives lean on managed services, vector or graph databases, or server stacks, EverOS runs on Markdown, SQLite, and LanceDB alone.

When to use it — and when not to

A self-hoster must run a local server process and keep the memory root available, and embedding and rerank remain disabled until configured, so the one-key Tier 1 setup starts with LLM-backed writes and keyword search rather than semantic retrieval. Anyone unwilling to maintain a Python 3.12+ process, or who needs a fully managed service with no local component, should look elsewhere. The README also flags that the older /api/v1 prefix is a legacy alias with an uncertain future, so new integrations should target /api/v2 from the start.

project readme (upstream, from github) — read inline

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Website · Documentation · Blog · 中文


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.

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 if you want a different memory root. Pass the same --root 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

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Live2D Character With Memory

Add long-term memory to a real-time Live2D character, powered by TEN Framework.

Code

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Computer-Use With Memory

Run screenshot-based analysis with computer-use and store the results in memory.

Live Demo

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Game Of Thrones Memories

A demonstration of AI memory infrastructure through an interactive Q&A experience with A Game of Thrones.

Code

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Claude Code Plugin

Persistent memory for Claude Code. Automatically saves and recalls context from past coding sessions.

Code

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Memory Graph Visualization

Explore stored entities and relationships in a graph interface. Frontend demo; backend integration is in progress.

Live Demo

Documentation


EverMind Ecosystem

EverMind connects memory research, production-ready products, and practical integrations into one open-source ecosystem.

Products
EverOS A local-first, Markdown-native long-term memory runtime for agents and users.
Raven A memory-first, self-improving agent harness with proactivity, context control, and skill evolution.
EverMe (CLI) A CLI and agent plugin suite for cross-device, cross-agent personal memory.
Research & Evaluation
SkillCorpus Curated, retrieval-ready agent skill corpora with retrieval and evaluation tooling.
EverAlgo Stateless extraction, ranking, parsing, and memory operators that power EverOS.
HyperMem Hypergraph-based hierarchical memory for coarse-to-fine long-term conversation retrieval.
MSA Memory Sparse Attention for scalable latent memory and 100M-token contexts.
EverMemBench Evaluation of factual recall, applied reasoning, and personalized generalization in memory systems.
EvoAgentBench Longitudinal evaluation of agent self-evolution, transfer efficiency, error avoidance, and skill use.
Integrations
OpenClaw OpenClaw plugin for automatic recall, capture, and session-memory lifecycle management.
Hermes Agent Hermes plugin for persistent memory across Hermes sessions.
DeepSeek Harness DSH plugin for memory-aware DeepSeek Harness agents.
Dify Self-hosted and cloud tools for explicit memory search and storage in workflows and agents.

Together, these projects form EverMind's research-to-runtime stack: methods and benchmarks become reusable memory infrastructure, products, and agent integrations.



Contributing

Contributions are welcome across the whole repository: memory methods, benchmark coverage, use-case examples, documentation, and bug fixes. Browse Issues to find a good entry point, then open a PR when you are ready.


[!TIP]

Welcome all kinds of contributions 🎉

Help make EverOS better. Code, documentation, benchmark reports, use-case write-ups, and integration examples are all valuable. Share your projects on social media to inspire others.

Connect with one of the EverOS maintainers @elliotchen100 on 𝕏 or @cyfyifanchen on GitHub for project updates, discussions, and collaboration opportunities.

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Code Contributors

EverOS Contributors

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License

Apache License 2.0 — see NOTICE for third-party attributions.

Citation

If you use EverOS in research, see CITATION.md.


Frequently asked questions

Is EverOS free to use?

EverOS is open source under the Apache-2.0 licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does EverOS do?

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

What is EverOS written in?

EverOS is primarily written in Python. Its source is publicly available at https://github.com/EverMind-AI/EverOS, and it has 13,758 GitHub stars.