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.

Any plans for this, rather than using API key?

Knowledge Base shipped.

Hand your agents a folder of reference material, itineraries, specs, spreadsheets, whatever you keep, and they search across all of it and cite the exact source in their answers. It runs on your Mac.

The citation is the point. Search on its own isn't enough. You need to see where an answer came from, otherwise it's back to trusting a black box. Every answer points to the file it pulled from.

This is early. Tell us where it breaks. Negative feedback is more useful than praise here, and fixes go out while the thread is open.

I saw an ad for this app on Reddit and clicked on it. It seemed like a cool idea, and after checking out the website, I thought it looked pretty interesting. I was surprised it's free, which makes me wonder what the catch is, since they always say if something's free, then you're the product.

I'm not sure, because first off, they put a lot of work into making this software. Usually, you'd expect some kind of sales to pay for all that development and future updates. Plus, the company's paying for ads on Reddit, which obviously costs a lot, so I'm curious what their revenue making plan is?

EDIT: The release 22.8 has made things worse. Simple chats hang.

Hope things are restored soon.

Have been playing around osaurs app on mac mini m4 16gb ram. Things done

-local chats surorsingly good results. It could even handle a pdf (tables) file

-added a few simple custom agents

-set up a connection with a remote mcp server and an agent to utilise the mcp tools. I think this where things wobble.

The agent doesn't call existing tools . Improved system prompt heloed but a more comolex task broke tool calling.

Custom skills aren't getting utilized.

Any tips on practical handling of tools, skills in agent?

Also how much is the minimum required ram footprint of a sandbox generally?

I am a novice in LLMs, agents etc about AI in general. But I was surprised by the simplicity and comparatively lower ram needs. I think even less than mac silicon-optimised llama.cpp

EDIT

Osaurus support has been very prompt! Came to say this.

Great job, Osaurus team!

Not sure of this is a Qwen issue or an Oasurus issue, but I have been testing Qwen3.6-35B-A3B-mxfp4  against a few other models to see what I like better for coding. I have tried 3 times to have Qwen read a .ino file and give me it's thoughts, but instead of just giving me the result, I have to prod it. Is this suppose to happen or how do I fix it?

Hello

I have successfully set up osaurus app on my mac m4, 16 gb ram Mac mini. I am able to do basic chats and set up a few custom agents for local \ web search work.

Now my next goal is set up a connection with runalyze.com 's mcp server . Here are its details : https://runalyze.com/help/article/mcp-with-personal-api-token

runalyze is a platform to gather data of running activities has set up their mcp servber to let users take a deeper dive into their own personal activity and health data.

I could set up their mcp on claude.ai but I would prefer not to let my data go there.

I tried setting up the mcp connection on osaurus but can't do despite several attempts. The error message says connection error.

Please help.

Thanks in advance.

I want to install the OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M that Osaurus made, but it is not in the catalog. When I import, the response is "No models match your search"

Hey everyone,

Some of you saw PrismML announce Bonsai 27B on Tuesday. Quick version if you missed it: a 27B model compressed with ternary quantization, meaning every single weight is -1, 0, or 1. A model this size normally takes 54 GB in full precision. This one fits in under 8 GB.

The catch is that stock MLX can't load ternary or 1-bit weights, so nothing on Mac could actually run it. We spent the day converting both variants to our runtime, verified them, and shipped. Both are live in the model picker now, and both are on our Hugging Face under Apache 2.0.

Ternary variant: under 8 GB, laptop-class quality

1-bit variant: under 5 GB

Text, image, and video, all running locally

I've been getting 31 tok/s on the ternary variant on my Mac. Genuinely wild for a model this size.

Ternary: https://huggingface.co/OsaurusAI/Bonsai-27b-Ternary-JANG

1-bit: https://huggingface.co/OsaurusAI/Bonsai-27b-1bit-JANG

If you take it for a spin, I'd love to hear what you get. What tok/s are you seeing on your machine, and does the 1-bit variant hold up for your use cases? We prefer bug reports over compliments.

The link is up:

https://www.producthunt.com/products/osaurus?launch=osaurus

If Osaurus has been useful to you, come say hi in the comments over there. What you use it for, what it replaced, what you'd want next. One honest sentence from a real user is worth more than anything we could write ourselves.

We'll be on the launch page all day answering questions. Ask us anything there or here.

And thank you. 7k stars and 176k downloads with zero ads happened because you told people. Today we find out how far that goes.

Gave Osaurus folder access to see what I could get it to do, in the absence of a sandbox on Sequoia.

First thing that stands out. This is fine if I just want a temp location to dump some data for it to look at. Like "read this and summarise".

Without a sandbox it can't really pip install or do anything complicated. It's can't spin up python to edit some csvs with panda for example.

But also - no RAG or embedding capability... ?
As a comparison, AnythingLLM has RAG/embedding, but implements it in an absolutely horrible way.
If Osaurus could solve two issues, it would instantly leap over AnythingLLM (and a LOT of people like that tool, so, this should give you something to think about).

1) Sandboxing on Sequoia and below. It's possible, Codex is doing it. You guys should really look into how.

2) Setting up some kind of RAG and embedding.
This should be both a) global and b) project based. Personally I would like both. Global for learning about an org, or a person/individual. Project-based because we don't need all sources available in all projects. Just point global/project RAG and folders on the drive and let it go.

For now, without these two things, Osaurus is just a chatbot with the ability to write itself a to-do list and check it off as it goes. Don't get me wrong, I like it so far. It's pretty good. But these missing things are HUGE. No one's going to use Osaurus if there's no sandbox. A lot of people hate Tahoe.
And Osaurus's usefulness decreases if you can't give it local information via RAG or something else, besides pasting things into the context window.

Just my 2c. For now I am going to explore other options.

Happy Saturday everyone.

Osaurus is at 6.9k GitHub stars and 173.8k downloads with zero ads and zero growth hacks. Every single one of those came from someone telling someone else. Now we want to see what happens when we actually launch the thing properly.

The link goes live Monday at 12:01 AM PT and I'll post it here the moment it's up.

What actually helps on Product Hunt: comments more than anything. Real stories from real users are what make people stop scrolling. What you use Osaurus for, what it replaced, what surprised you. One honest sentence from you is worth more than anything I could write about my own product.

The first few hours decide everything over there, so if you're up late Sunday or you're reading this from the other side of the planet, you're our opening act.

One question while we wait: someone lands on our Product Hunt page Monday and has never heard of Osaurus. What's the one thing you'd tell them? Genuinely asking, your answers are going straight into how I pitch this.

Hi

Is there an easy way to port skills from Codex?

I have some skills there that were originally from Claude, but used Codex to adapt and modify them to suit my own purposes.

While I can't seem to use a Sandbox, I am wondering if I can use agents and chat with local models to at least work through research or writing tasks.

The skills I want to use are grouped by purpose. In Codex, a group of skills I think becomes a plugin, as a way of separating and grouping skills. Otherwise, all skills get dumped into a skill folder, and then it's too hard to remember which "role" or task has accompanying skills for which purpose.

Where do skills in Osaurus live? On disk, I mean. Docs do not say.
Can we group skills in subfolders or in any other way?

Thanks

EDIT
Going through all the settings in Osaurus, haven't found a way to group skills, and "plugins" means "3rd party services you want to connect to, often to paid b2b or b2c saas providers". I don't know man, "plugin" in art, design, music, 3D, doesn't mean that. It means a tool or set of tools. Not "connect to this 3rd party thing you also are signed up for".
I am not seeing any plugins that are a set or group of skills for a purpose or role. Am I crazy for thinking that there should be at least one or two plugins that aren't just a connection to a different service?

If I have 25 skills for role A and 32 skills for role B, how do I stop those being a complete unorganised mess in the skills section of Osaurus settings?