Michael Neale

One of the desires of goose (well for some of us) was to avoid the constant asking for permissions, delegating all the decisions to end users in an attempt to keep agent execution of tools safe. Sometimes that gets pretty noisy and annoying and ends up being less secure when you get tired of reading and approving.

You can of course adjust settings as you see fit, but it is nice to consider how things could be made safe without assuming that you can interrupt the user constantly for permission, especially around things they may not currently have the context for (in their head!)

In goose there are layers of things you can enable, but we wanted to also think about general solutions when we observed agents (of all kinds) being really helpful, and as a side effect, being accidentally harmful. This birthed "adversary mode" where the idea is: why not use another agent to fight fire with fire. Agents want to be helpful, and they can be oriented to help the user, but another one can be oriented to protect against the agent "helping" the user, to keep things in policy, and safe.

You can use your subscriptions for codex, claude and gemini now with goose, thanks to ACP! (Agent Client Protocol). Codex is also special in that you can login directly to chatgpt - nothing else needs to be installed.

Gemini natively supports ACP, so it now works with a gemini acp provider in goose. At the time of writing, claude requires just one utility installed just once.

In 2025 we did a fairly cutting edge take on whole device automation using Android (code name was gosling) which was an on-device agent that would take over your device (mic even used it to do some shopping - which he realized after some things arrived at his door that it had automatically purchased as the result of an email - hence the PoC/experimental label!)

Recently we consolidated the apps for goose mobile.

The goose-ios client is more production ready, and in the app store (still early days). We hope to have a port of that to Android, which will be strictly a client (and won't take over your device!) to your remote agent. The aim of the client (vs an on device agent) is for you to take your work on the go with you.

Really great for long running tasks, checking on things, or just shooting off an idea but still keeping things local to your personal agent (where all your stuff is) securely.

We're excited to announce two new ways to interact with goose: a native iOS app for mobile access and native terminal integration. Both give you more flexibility in how and where you use your AI agent.

Data visualization just got a major upgrade in Goose. With the new MCP-UI auto visualiser feature, you no longer need to manually request charts, graphs, or visual representations of your data. Goose now automatically detects when data would benefit from visualization and renders interactive visual components directly in your conversation.

You shouldn’t need a credit card to vibe code with goose. While goose is completely free to use, the reality is that most performant LLMs aren't. You want to experience goose in action without breaking the bank or jumping through hoops. We've been thinking about how to make that first step easier for newcomers to goose.

That's why we're thrilled about our newest provider integration: Tetrate's Agent Router Service. New goose users can get $10 in credits to use goose with any model on the Tetrate platform.

Goose recently released support for MCP-UI which allows MCP servers to suggest and contribute user interface elements back to the agent.

MCP-UI sits on top of the protocol, but instead of text/markdown being the result, servers can return content that the client can render richly (including interactive GUI content).

Over ten years ago, Docker came onto the scene and introduced developers en masse to the concept and practice of containers. These containers helped solve deployment and build-time problems, and in some cases, issues with development environments. They quickly became mainstream. The technology underlying containers included copy-on-write filesystems and lightweight, virtual-machine-like environments that helped isolate processes and simplify cleanup.

Dagger, the project and company founded by Docker’s creator Solomon Hykes, has furthered the reach of containers for developers.

One project that emerged from this work is Container Use, an MCP server that gives agents an interface for working in isolated containers and git branches. It supports clear lifecycles, easy rollbacks, and safer experimentation, without sacrificing the ergonomics developers expect from local agents.

Container Use brings containerized, git-branch-isolated development directly into your Goose workflow. While still early in its development, it's evolving quickly and already offers helpful tools for lightweight, branch-specific isolation when you need it.

Not every task needs a genius. And not every step should cost a fortune.

That's something we've learned while scaling Goose, our open source AI agent. The same model that's great at unpacking a planning request might totally fumble a basic shell command, or worse - it might burn through your token budget doing it.

So we asked ourselves: what if we could mix and match models in a single session?

Not just switching based on user commands, but building Goose with an actual system for routing tasks between different models, each playing to their strengths.

This is the gap the lead/worker model is designed to fill.

A couple of weeks back, Qwen 3 launched with a raft of capabilities and sizes. This model showed promise and even in very compact form, such as 8B parameters and 4bit quantization, was able to do tool calling successfully with goose. Even multi turn tool calling.

I haven't seen this work at such a scaled down model so far, so this is really impressive and bodes well for both this model, but also future open weight models both large and small. I would expect the Qwen3 larger models work quite well on various tasks but even this small one I found useful.