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osaurus

Own your AI. The native macOS harness for AI agents -- any model, persistent memory, autonomous execution, cryptographic identity. Built in Swift. Fully offline. Open source.

Install / Use

claude mcp add osaurus-ai -- npx -y github:osaurus-ai/osaurus

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

97/100

Supported Platforms

Claude Code
Claude Desktop

Tags

Our assessment of osaurus

osaurus scores 97/100 on our quality scale, 62nd of 965 AI & Machine Learning skills we index (top 7%).

Its MCP Server is 26 KB long, well organised into 34 sections with 12 code examples: a thorough specification that gives an agent plenty to work with.

With 8,131 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
17/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated today, so osaurus is actively maintained.
  • Our last check on 2026-10-20 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

osaurus compared with similar skills

All 4 of these similar skills score higher than osaurus; compare them before choosing.

SkillScoreStarsUpdatedFormat
osaurus (this skill)by osaurus-ai978.1ktodayMCP Server
claude-memby thedotmack100113.5ktodayCLAUDE.md
Agent-Reachby Panniantong100109.2ktodayCLAUDE.md
Understand-Anythingby Egonex-AI10090.9k10d agoCLAUDE.md
headroomby headroomlabs-ai10078.9ktodayCLAUDE.md

Frequently asked questions

How do I install osaurus?
Run claude mcp add osaurus-ai -- npx -y github:osaurus-ai/osaurus. The install tabs above show the steps for each supported agent.
Which AI agents does osaurus work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is osaurus safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is osaurus still maintained?
The repository was last updated today, so osaurus is actively maintained.
<p align="center"> <img width="1920" height="1080" alt="osaurus-techcrunch-1" src="https://github.com/user-attachments/assets/d7905005-71fe-41ba-b69f-e8968af29b5c" /> </p> <h1 align="center">Osaurus</h1> <p align="center"> <strong>Own your AI.</strong><br> Agents, memory, tools, and identity that live on your Mac. Built purely in Swift. Fully offline. Open source. </p> <p align="center"> <a href="https://trendshift.io/repositories/22454?utm_source=repository-badge&amp;utm_medium=badge&amp;utm_campaign=badge-repository-22454" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/repositories/22454" alt="osaurus-ai%2Fosaurus | Trendshift" width="250" height="55"/></a> </p> <p align="center"> <a href="https://github.com/osaurus-ai/osaurus/releases/latest"><img src="https://img.shields.io/github/v/release/osaurus-ai/osaurus?sort=semver" alt="Release"></a> <a href="https://github.com/osaurus-ai/osaurus/releases"><img src="https://img.shields.io/github/downloads/osaurus-ai/osaurus/total" alt="Downloads"></a> <a href="https://github.com/osaurus-ai/osaurus/blob/main/LICENSE"><img src="https://img.shields.io/github/license/osaurus-ai/osaurus" alt="License"></a> <a href="https://github.com/osaurus-ai/osaurus/stargazers"><img src="https://img.shields.io/github/stars/osaurus-ai/osaurus?style=social" alt="Stars"></a> </p> <p align="center"> <img src="https://img.shields.io/badge/Platform-macOS%20(Apple%20Silicon)-black?logo=apple" alt="Platform"> <img src="https://img.shields.io/badge/OpenAI%20API-compatible-0A7CFF" alt="OpenAI API"> <img src="https://img.shields.io/badge/Anthropic%20API-compatible-0A7CFF" alt="Anthropic API"> <img src="https://img.shields.io/badge/Ollama%20API-compatible-0A7CFF" alt="Ollama API"> <img src="https://img.shields.io/badge/MCP-server-0A7CFF" alt="MCP Server"> <img src="https://img.shields.io/badge/Apple%20Foundation%20Models-supported-0A7CFF" alt="Foundation Models"> <a href="https://huggingface.co/OsaurusAI"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OsaurusAI-FFD21E" alt="Hugging Face"></a> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen" alt="PRs Welcome"> </p> <p align="center"> <a href="https://github.com/osaurus-ai/osaurus/releases/latest/download/Osaurus.dmg">Download for Mac</a> · <a href="https://docs.osaurus.ai">Docs</a> · <a href="https://huggingface.co/OsaurusAI">Models</a> · <a href="https://discord.gg/osaurus">Discord</a> · <a href="https://x.com/OsaurusAI">Twitter</a> · <a href="https://github.com/osaurus-ai/osaurus-tools">Plugin Registry</a> </p>

Inference is all you need. Everything else can be owned by you.

Models are getting cheaper and more interchangeable by the day. What's irreplaceable is the layer around them -- your context, your memory, your tools, your identity. Others keep that layer on their servers. Osaurus keeps it on your machine.

Osaurus is the AI harness for macOS. It sits between you and any model -- local or cloud -- and provides the continuity that makes AI personal: agents that remember, execute autonomously, run real code, and stay reachable from anywhere. The models are interchangeable. The harness is what compounds.

Works fully offline with local models. Connect to any cloud provider when you want more power. Nothing leaves your Mac unless you choose.

Native Swift on Apple Silicon. No Electron. No compromises. MIT licensed.

Install

brew install --cask osaurus

Or download the latest .dmg from Releases. After installing, launch from Spotlight (⌘ Space → "Osaurus") or the CLI:

osaurus ui       # Open the chat UI
osaurus serve    # Start the server
osaurus status   # Check status

Requires macOS 15.5+ and Apple Silicon.

Agents

Agents are the core of Osaurus. Each one gets its own prompts, memory, and visual theme -- a research assistant, a coding partner, a file organizer, whatever you need. Tools and skills are automatically selected via RAG search based on the task at hand -- no manual configuration needed. Everything else in the harness exists to make agents smarter, faster, and more capable over time.

Agents can also opt into a private local database and a single self-scheduled next run -- see Agent DB & Self-Scheduling. Local storage is plaintext by default (protected by FileVault) with opt-in SQLCipher encryption -- see Storage.

Agent Loop

Custom agents run an agent loop. Pick a working folder and the agent gets file, search, and git tools. Toggle the sandbox and it gets shell access in an isolated sandbox. The model writes a markdown todo list, executes against it, and closes out with a verified summary -- all in the same chat window. The built-in Orchestrator is the exception: it has no working folder, sandbox, browser, or computer use. For filesystem work, create or switch to a custom agent, then pick the folder there. See the Agent Loop Guide and Orchestrator Guide.

Sandbox

Agents execute code in an isolated Linux VM powered by Apple's Containerization framework. Full dev environment -- shell, Python, Node.js, compilers, package managers -- with zero risk to your Mac.

Each agent gets its own Linux user and home directory. The VM connects back to Osaurus (inference, memory, secrets) via a vsock bridge -- sandboxed but not disconnected. Extend with simple JSON plugin recipes, no Xcode or code signing required.

┌────────────────┐       ┌────────────────────────────┐
│    Osaurus     │       │   Linux VM (Alpine)        │
│                │       │                            │
│  Sandbox Mgr ──┼───────┤→ /workspace  (VirtioFS)    │
│  Host API   ←──┼─vsock─┤→ osaurus-host bridge       │
│                │       │                            │
│                │       │  agent-alice  (Linux user) │
│                │       │  agent-bob    (Linux user) │
└────────────────┘       └────────────────────────────┘

The Linux VM requires macOS 26+ (Tahoe). On earlier versions Osaurus falls back to a native macOS Seatbelt sandbox: commands run on your Mac under sandbox-exec confinement, with writes limited to the sandbox workspace, pip/npm installs (no apk), and all-or-nothing network access instead of per-domain allowlists. See the Sandbox Guide for configuration, built-in tools, and plugin authoring.

Memory

Three layers -- identity, pinned facts, and per-session episodes -- plus a transcript fallback. Agents distill conversations once at session end (not on every turn), score what matters by salience, and surface at most one compact slice per request based on what you're actually asking. A background consolidator decays, merges, and evicts so memory stays sharp instead of bloating. Most turns inject ~800 tokens or less; many inject zero. See the Memory Guide.

Privacy Filter

When you send to a cloud model, an on-device classifier — OpenAI's openai/privacy-filter (Apache-2.0, 1.5B params / 50M active sparse-MoE), served via the MLX conversion mlx-community/openai-privacy-filter-bf16 (~2.8 GB) — detects names, emails, phones, URLs, addresses, dates, account numbers, and free-form secrets, alongside deterministic regex for SSN, credit cards, IBAN, AWS keys, GitHub tokens, and your own custom patterns. Each detection is shown in a review sheet with a scrubbed preview before sending; approved entities are swapped for stable [PERSON_1] / [EMAIL_2] placeholders, and streaming replies are unscrubbed back on the fly so the chat reads naturally. Fail-closed: if the post-scrub scan finds anything that leaked, the send is blocked. Verify wire-level redaction in the Insights panel — it captures the exact bytes the cloud saw. See the Privacy Filter Guide.

Identity

Every participant -- human, agent, device -- gets a secp256k1 cryptographic address. Authority flows from your master key (iCloud Keychain) down to each agent in a verifiable chain of trust. Create portable access keys (osk-v1), scope per-agent, revoke anytime. See Identity docs.

Relay

Expose agents to the internet via secure WebSocket tunnels through agent.osaurus.ai. Unique URL per agent based on its crypto address. No port forwarding, no ngrok, no configuration.

Secure Channel

When two Osaurus agents talk -- across your LAN or across the world through the relay -- the conversation is end-to-end encrypted: a forward-secret X25519 handshake authenticated by each agent's crypto identity, with every request, streamed token, and access key sealed in ChaCha20-Poly1305. The relay becomes a blind pipe that forwards ciphertext it cannot open; a man-in-the-middle cannot complete a handshake; replayed or truncated traffic is detected and refused; and there is no plaintext fallback an attacker can force. Zero configuration -- pairing is all it takes. See the Secure Channel docs.

Orchestrator

The built-in Osaurus agent is the default Orchestrator. New chat windows open on it unless you start a chat on a custom agent. Ask it anything about the app and it answers from live state; ask it to change a setting and it plans the change, shows an approval card, and applies only after you confirm. It also delegates work: spawn your custom agents and allowed local/cloud models as subagents -- in parallel, within budgets you set -- and get their results woven back into the conversation. It deliberately does no hands-on work itself -- no sandbox, working folder, browser, or computer use; those belong to custom agents -- which keeps it safe and predictable. The chat composer does not offer a working-folder chip on the Orchestrator; picking a folder there would not grant file tools. For filesystem work, create or switch to a custom agent, then pick the working folder on that agent. Rename the Orchestrator, give it a persona, and control exactly what it may delegate to in Settings → Orchestrator. See the Orchestrator Guide.

Subagents (Spawn)

Let a chat -- the Orchestrator or any custom agent -- delegate a bounded task to another model -- local or remote -- or to one of your saved agents, and get a compact result back without cluttering the conversation. Perfect for offloading research, coding, or analysis to a specialist mid-turn. When the subagent runs on a different local model, Osaurus normally does a single-residency handoff: it unloads your chat model, runs the job, then reloads and continues, so two large models never fight for memory. Same-model children reuse the resident model; experimental RAM-safe coexistence can keep both resident when enabled and admitted. Custom agents are added to the Orchestrator's spawn pool automatically (you can remove them in Settings → Orchestrator). First use still asks for approval; budgets and permission mode are configured per agent.

Browser Use

Give an agent its own browser. browser_use runs a self-contained subagent that navigates, reads pages, clicks, types, and verifies each step against a persistent per-agent WebKit session — cookies and sign-ins survive between chats, isolated from other agents and your regular browser. Reads and navigation run automatically; typing and anything consequential pause for your approval, and sign-ins happen in a secure window you type into directly (agents never see credentials). Off by default, enabled per agent, sessions managed in Settings → Browser. See the Browser Use Guide.

Image Generation

Create images on your Mac, fully offline. Install a local image model -- z-image-turbo, FLUX, Qwen-Image, or Ideogram -- and generate from a text prompt with control over size, seed, and

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars8.1k
CategoryAI
Updated2h ago
Forks492

Languages

Swift

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions