Since early on, the question we’ve gotten most is “when Windows?”

It’s here. The Osaurus for Windows public beta is open today, and it’s free.

In the video, it closes out a month of books on a Windows PC: matches every invoice to the bank statement, catches a double payment and two supplier overcharges, flags an unpaid bill, and writes the reconciliation workbook plus a note for the owner. Qwen3.8 27B, running locally.

A few things about the Windows build:

• Native C#, no Electron
• Same idea as on the Mac: your AI, your files, your machine
• It’s a beta, so expect rough edges

Huge thanks to u/SonofNun, who built the Windows port. This one’s his.

Get it here: osaurus.ai/windows

We’d rather hear what’s broken than what’s great. If something breaks, post it here or in Discord and we’ll get on it.

What hardware are you running it on? That tells us what to tune for first.

Opened up my Osaurus this morning, and find my account has 90.2k credits! Problem is, I never bought any.

What's going on here? I'm not going to use them anyway, I run fully local.

Anytime an agent tries to work with files, the app crashes.

Title:

0.25.15: repeated SIGTRAP crashes during file actions on macOS 27.0.1

Environment:

Osaurus 0.25.15

macOS 27.0.1 (26A434)

Apple Silicon, Mac17,9

Observed:

The app repeatedly terminates while taking actions on files.

Crashes observed at 03:11 and 03:18 on September 30.

Attached report:

EXC_BREAKPOINT (SIGTRAP)

Faulting thread: 28

Queue: com.apple.root.user-initiated-qos.cooperative

ARM exception: brk 1

Top Osaurus image offsets:

62345776

62349568

74264064

74261220

Executable UUID:

c5ca1a1d-4bb8-30e7-acd1-3e565f352377

Please symbolicate the attached report against the matching

0.25.15 build to identify the failing function.

I am trying to import my chat history from ChatGPT. However, the import button seems to have been removed in the newer versions. I performed a factory reset on the application, but the onboarding popups appear right after the import popup, causing the import popup to disappear.

Is there any way to import data after completing the onboarding?

Additionally, I want to move my memory from ChatGPT to Osaurus and use it as a personal agent. What is the best way to do this?

I have an M2 MacBook with 16GB of RAM. I have Osaurus running raptor-v0.5-8b-a1b-jang_6m. It’s basically unusable due to slow performance and swapping.

What is performance like on current MacBooks? I might consider an upgrade if I could shift some of my work to Osaurus from Claude, but I don’t want to spend the money if I can’t get a decent model going at performance levels at least comparable to Claude.

Would love to hear from people what kind of config you would suggest…

Really tired of this damn thing popping up all the damned time. How do I make it go away?

Hi. Great product. Blown away by osaurus.

I run it on my Mac mini which is running headless and always on, but my main device is actually my MacBook.
I would love some richer way of interacting with the agents - A bit like the osaurus interface. I of course have the iMessage integration working, but there are a lot of limitations outside of simple text.

What are the rest of you doing? Is it really each agent in slack?

Of course this would solve the smartphone access which would be next :-)

Any idea how to achieve this?

This one's for the base Airs and minis.

About 40% of you run Macs with 16GB or less, and it's been the most consistent piece of feedback since day one: the good models don't fit, and the ones that fit aren't good at agent work.

Raptor 0.5 is our answer. 8B MoE, only ~1B parameters active per token, 6.3 GB on disk. Fine-tuned on the actual Osaurus tool surface, so it knows the harness natively: zero schema violations across 281 tool calls on our bench. The session cache streams to SSD and survives restart, so a long session picks up where it left off instead of re-prefilling.

Real talk on hardware: 16GB is comfortable. 8GB works but it's tight, and I'd love to hear from 8GB users how it holds up in practice.

What it's for: file and folder work, checking reports against spreadsheets, backups before touching anything, drafting emails, long agent sessions. What it's not: a coder. Route code-writing to a big model, Raptor runs the workflow around it.

Full writeup with three unedited demos: osaurus.ai/models/raptor-05

It's in the model list in the app now. Training corpus is fully open too, per-row provenance and all.

This is the first Raptor. Raptor 1 comes next, trained deeper on the harness and built for longer sessions. Which is why I'm asking: what do you actually want a small resident model to do all day? The answers here genuinely shape the training corpus.

Break it and tell me what you find. You know we prefer the bug reports over the compliments.

This is exactly the problem I face all the time. Never mind the model. Ornith 1, 1.5 and now Raptor. Raptor is the worst. It simply hogs memory.

Osaurus is a great idea but now I am inclined to pull the plug.

A humble suggestion to Team Osaurus:

Instead of new features, please focus on getting the basics right:

-custom agent capabilties auto-load, custom load

-tool calling

-stickiness of workspace folder

-file create, esit, append, overwrite

-connecting to local harnesses like llama. cpp especially if you can't add model options in 8/ 16 GB ram world

EDIT:

Detail - unsloth/Qwen3.5-9B-GGUF:Q4_K_M on my llama.cpp works well. It find Exa MCP tools and finishes the task. Memory pressure on mac mini m4 \ 16 gb stays in green. But Osaurus scans this model but can't use it because of missing json.

Can Osaurus be pointed to my localhost that can run llama.cpp with Qwen3.5 ?

EDIT OVER

8/16 GB macs is a huge base and hungry for local.

I'm looking for a best way to integrate Osaurus with my personal knowledge base in Obsidian. I didn't find any success story in Internet. Personally I see a few options: either to add Obsidian folders as a source to every request or to add Obsidian via MCP to agents. Didn't try anything yet and asking for recommendations

I am trying to set up Osaurus to connect to the Blender MCP I have set up. I know it works because Claude Desktop works fine and has control like I want, but with Osaurus I get a time-out error. Has anyone done this before and could help me out?

Why is it in pain and what can I do to fix it?

New chats now open on the Orchestrator, a built-in agent with two jobs.

First, it configures Osaurus for you. Ask it what's set up, ask it to change a setting, ask it to create an agent. It plans the change, shows you an approval card with the exact diff, and applies nothing until you confirm.

Second, it delegates. It can spawn your custom agents and allowed models as subagents, run them in parallel, and weave the results back into your conversation. You set the budgets: tokens, turns, tool calls, seconds, parallel spawns.

Some design decisions we want feedback on:

The Orchestrator deliberately has no hands-on tools. No sandbox, no working folder, no browser, no computer use. Those belong to your custom agents. The thing with the power to reconfigure your setup should not also be the thing touching your files.

It's in-app only. The HTTP API rejects it by construction, so only your saved custom agents are reachable over HTTP, plugins, or schedulers.

Local model delegation uses single-residency handoff: RAM-safety preflight, unload the chat model, run the subagent, restore. If the subagent model won't fit, it refuses before evicting anything and tells you the shortfall instead of thrashing your machine. There's an experimental coexistence mode if you have the RAM for both.

In the demo we posted today, one conversation created a marketing agent and scheduled it to draft copy every morning at 6am. No settings menus involved.

Settings live under Management, Orchestrator tab. Rename it whatever you want. Someone will name theirs Jarvis within the hour.

What would you delegate first? And is default-on spawn for new custom agents the right call, or should that be opt-in?

I've been using Osauris for a while now, and it's getting better and better.

But there's an annoying issue when the selected LLM tries to figure out its capabilities.

It keeps trying for a couple of times, It checks if the plugin is there, and it is (see screenshot below). The Telegram channel is also configured, see screenshot.

Is this user error? Or do i have to configure something to make this work? Seems to me this is basic functionality, the model should easily retrieve the capabilities it has. See screenshot.

macOS 26.6.2

M4 MacBook Air 24GB ram

Osaurus 0.23.1

Models tried: Bonsai 27b Ternary JANG, Gemma 4 12b it 4bit, Gemma 4 E4B it 8bit

Goal: Find certain types of info in a plain text file and overwrite it with a different set of info.

Text file: 36KB, 516 lines, 35,680 characters, Osaurus says ~10K tokens when I attach the file to chat.

Problem: Every model I try is soooo slooooow. The models can’t read the whole file at a time. It parses it in chunks. It’s so slow I have yet to actually finish the task. I give up and hit stop.

Are my computer specs simply too insufficient to do anything useful with Osaurus?

A lot of you have asked for this one.

Until now, memory lived with a single agent. If you told your Nutritionist something, your Gym Buddy had no idea. Every new chat meant re-explaining context you'd already given.

Projects fix that. Group related chats into a project and they share three things:

• Instructions: shared context added to every chat in the project
• Knowledge: collections every chat in the project can search, with per-agent access control
• Memory: what any chat learns, every agent in the project recalls

In the demo video, I tell the Nutritionist (running Ornith 1.0 9B) that I benched 50kg. Then I open a chat with the Gym Buddy, running a completely different model (Gemma 4 12B), and ask my current bench max. It answers 50kg. Memory survives across agents and across model switches.

Everything stays local like the rest of Osaurus. You control which agents get access to which knowledge collections.

What would you want projects to do next? Curious how you'd organize yours.

Polishing Reddit post title and content

I wanted Claude Code to confer with my Osaurus agent whenever it creates a plan.

I initially tried using the Osaurus MCP, but with more than 400 tools, it seemed faster and simpler to call only the /agents endpoints I needed. As a bonus, it saves the conversations as Markdown transcripts, which I expose as a Knowledge collection in Osaurus.

I packaged it up as Copa in case it’s useful to anyone else:

https://github.com/gmr/copa

I'm a therapist (of the mental health variety), and there are plenty in my field who are anti-AI due to privacy concerns (and understandably so... keeping client info anonymous is very, very important for ethical and legal reasons). Putting my client's PHI into the cloud is not an option for me, but if I'm going to be as effective as possible as a therapist then clearly I can benefit from incorporating AI into my practice (the opportunities with AI are truly legion). Enter Osaurus: finally an LLM that keeps my client data secure and anonymous (and local!) while still giving me the ability to take advantage of AI tools is incredibly impactful. More people (and especially therapists and others in the medical realm) should be aware this product exists.

Edited to add: Pretty damn pleased it runs as well as it does on 24 GB of RAM.

You can now import your conversation history from other AI apps into Osaurus and keep going with any model.

How it works: export your chats from ChatGPT, Claude, Grok, Gemini, or Open WebUI, then open the import dialog in the History sidebar and point it at the file. Zip or JSON both work, and you can select multiple export files at once. Imported chats show up in your history with an Imported filter and a tag showing where each one came from.

The part we care about most: your chat history stops being locked to whichever app you started it in. Months of context from a cloud subscription can come with you, and you can continue those threads with whatever model you want, cloud or local.

Grab the latest version and try it with your own exports. Which app's export was the most painful to get out of? We want to smooth over the rough edges on each format, so tell us what breaks.

Agent Channels are now working end to end with Slack. You add the agent to a workspace, mention it in a channel, and it picks up the thread, runs the tools it needs, and posts the reply back in the same thread.

The model doing the reasoning was Ornith 1.0 9B in MXFP8, running locally on Apple Silicon. Slack is the surface it answers on, so this is not an offline setup, but the inference stays on the machine.

The permission model is the part worth reading. Channels are provider neutral, so the same agent actions work across Slack, Discord, and Telegram plus custom JSON connections. Reads and writes are allowlisted separately per channel. Inbound messages are gated per sender and default to deny. Sends require an explicit confirmation, and every publish goes through an intent ledger so a retry cannot double post.

What would you want your agent doing in a channel? We are trying to figure out which of these is actually useful day to day versus which just sounds good in a demo.

Greeetings!

I had installed Osaurus many weeks ago but never really got around to giving it a look. Today I installed 0.22.10 from the DMG file. As I was coming from a quite old release, I went to Settings > General > Factory Reset to effectively "start fresh".

The "Resetting Osaurus" spinner appears and spins indefinitely... I gave it a couple minutes. I had to force-quit the app and re-launch. It did seen to "factory reset" everything though. This happens every time I do a factory reset -> app must be force-quit.

Thought I'd mention it here as I'm not sure if this bug is specific to me and my Mac or if factory resetting hangs for everybody. For what its worth, Its a M3 MBP running Tahoe 26.5.2.

...now to resume playing around with this app - its very slick!

We just shipped Browser Use, and the demo that sold us on it was small: a 9B model (Ornith 1.0 9B MXFP8) checking an Amazon shopping cart. It opened a real browser, signed in, navigated to the cart, read the page, and reported back the items and prices. The whole run took about 90 seconds, locally on a Mac.

How it works:

Your agent gets one tool: browser_use. You give it a goal in plain language, and a nested subagent runs a navigate, act, verify loop against a real persistent browser. Each agent gets its own isolated browser profile, so cookies and sign-ins survive across chats and restarts, and are never shared between agents or with your regular browser.

The safety model:

Off by default. You enable it per custom agent, and the built-in Default agent never gets browser access. Every action runs through the same approval gate as Computer Use: reads and navigation are automatic, but typing, form submits, purchases, and anything consequential asks you first. Sign-ins happen in a visible window that you complete yourself. The model never types credentials and never sees them.

If you were using the browser plugin:

Browser Use replaces it. Your existing sign-ins carry over automatically, one profile per agent, no action needed. The plugin card stays in Settings with a banner pointing to the new home.

Try it, break it, and tell us what fails. We prefer negative feedback over positive. If you hit a bug, post it here and we'll ship the fix while the thread is live.

As the screenshot shows osaurus agent couldn't retrieve activity although the mcp server connection is established, mcp server's tools are available in the agent's tool panel. A specific skill was invoked using / command.

I find that the thinking and tool calls dominate any of my chats. It'd be nice if in addition to rolling them up as individual items, they were non-intrusively rolled up across all the entries. One thing that could expand to show all of this instead of pushing the conversation out of view.

I just found out about this, and seems awesome. I have a few specific use-case questions that I was hoping someone could answer.

(I’m REALLY new to this world so please excuse me if my questions are dumb)

I’m a teacher, and the idea of using an AI like this is appealing—it helps keep student and other info off of the net. Our district does have use of CoPilot as well.

1) Can co-pilot be integrated for the could-based tasks? I see Claude, GPT, and Gemini were listed. Would you need the special/premier account with copilot to access these sorts of connections?

2) Has anyone run or have knowledge of using Osarus for things like marking papers using a rubric, or other text heavy tasks that might be used in a school setting?

3) Has anyone used this in a school or education setting and have any tips or creative uses for this in that particular field? I’m so new to this, I’m excited to explore possibilities in areas that I never considered AI use before.

Thanks for anyone that might have insight.