# Octomind vs Goose: Which Open-Source Goose Alternative Runs in the Cloud? > Compare Octomind and Goose on open source, models, machines, persistence, integrations, pricing, and scheduled agent work. Goose and Octomind start from the same belief: an agent shouldn't be trapped inside one model vendor's subscription. Both are open source under Apache 2.0, both build on open protocols, and both let an agent do real work beyond a chat box. So the decision isn't “open or closed?” It's where you want the agent to live. [Goose](https://goose-docs.ai/) is a strong local, general-purpose agent: polished desktop app, CLI, recipes, subagents, and a large MCP extension catalog. Octomind is an open-source agent runtime with an optional managed cloud — each cloud agent gets its own Linux machine, keeps its files and setup, and can be reached from chat apps or scheduled to run later. If you want a capable agent on the computer already in front of you, Goose is easy to recommend. If you want the agent's computer to outlive your laptop, Octomind is the more direct fit. ## Quick verdict | Question | Goose | Octomind | | ----------------------- | ------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------- | | Runs where | CLI, desktop app, embeddable API | CLI, pipes/CI, daemon, WebSocket, ACP, and managed cloud | | License / OSS | Apache 2.0; open source | Apache 2.0 core; open source | | Pricing model | Free software; pay your model provider or reuse supported subscriptions | Free: every model, pay as you go from credits; cloud plans from $20 first month add machines and a monthly allowance | | Model support | 15+ providers, including Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, and Bedrock | 36 hosted models across Claude, GPT, Gemini, DeepSeek, Qwen, Kimi, GLM, and others; `/model` switches mid-session | | Agent sandbox / machine | Runs locally; no first-party hosted sandbox | Each cloud agent gets its own Linux machine and filesystem; Docker and custom images are supported | | Persistence | Your local repo and files persist; recipes are reusable | Durable machine files and installs, shared storage, and resumable sessions with full replay | | Channels / integrations | MCP with 70+ extensions, GitHub, databases, browsers, Google Drive | Telegram, Slack, WhatsApp, GitHub, web panel, REST API, MCP, WebSocket, and ACP | The short version: Goose is the better local desktop agent. Octomind is the better Goose alternative when “cloud” must mean a durable computer, not just a remote model call. ## Choose Octomind if / choose Goose if ### Choose Octomind if - You want to close your laptop while an agent continues on its own machine. - Files, installed tools, repository state, and session history need to be there tomorrow. - You want hosted model access without juggling provider keys, plus the option to switch models with `/model` mid-session. - Telegram, Slack, WhatsApp, or GitHub should be a real door into the same agent, not just a notification destination. - You need scheduled Routines that wake a suspended machine, run, record an outcome, and deliver the result. - You want an open-source core but don't want to run the infrastructure yourself. ### Choose Goose if - Local execution is the point: your code should stay on your computer and use the tools already installed there. - You want a native desktop experience rather than a cloud control panel. - You need broad provider freedom, including local models through Ollama. - Your agent work extends well beyond code into research, writing, browser work, and personal automation. - You already have model subscriptions or API keys and prefer paying providers directly. - YAML recipes and Goose's established MCP extension catalog match how you automate work. Neither choice is a retreat from open source. It's a choice between running the local runtime yourself and adding a managed, persistent execution layer around an open one. ## What Goose does better Goose's biggest strength is that it's genuinely general-purpose. Research, writing, database work, browser automation, and coding aren't separate products — they're tasks for the same agent. That makes Goose a good daily companion for people whose work crosses boundaries constantly. Its governance is reassuring too. Goose started at Block and now sits under the Agentic AI Foundation at the Linux Foundation. If you worry about an open project becoming one company's funnel, vendor-neutral foundation stewardship matters. Then there's the desktop app. Terminal agents are powerful, but a native desktop surface lowers the entry cost for people who don't want every interaction to start in a shell. MCP Apps can put interactive interfaces inside that experience, and the CLI is still there if you prefer it. Goose also has real provider breadth. Its official docs list more than 15 providers, including local execution through Ollama, and support reusing some existing Claude, ChatGPT, and Gemini subscriptions through ACP. Octomind's hosted catalog is broad, but Goose gives the local operator more direct control over where inference comes from. Finally, Goose's [recipes](https://goose-docs.ai/) are a simple, shareable automation primitive. A YAML workflow is easy to keep beside a project, review, and run again. If the work happens on your machine and you want transparent orchestration without a hosted service, Goose fits that bill well. ## What Octomind does better Octomind separates the agent from the laptop. In [Octomind Cloud](/cloud), every agent works on a real Linux machine with its own container and filesystem. Files, installs, and setup persist across sessions and suspension. The machine can build containers, and paid plans can run a custom Docker image based on Octomind's base. That's a different capability from sending prompts to a hosted model while your tools still run locally. The model layer is built for switching, not just configuration. The current [Hub catalog](/hub) lists 36 models spanning Claude, GPT, Gemini, DeepSeek, Qwen, Kimi, GLM, and other open families. Inside a running session, `/model provider:model` changes the active model and saves that choice. You can move a hard design turn to a frontier model, then hand routine work back to a cheaper one without starting over. The core runtime stays Apache 2.0. You can run it on your own hardware, inspect it, fork it, or use the managed service. The commercial layer sells convenience around hosted models and machines — it doesn't close the runtime itself. Pricing follows that split. The free plan is the hub: every model from your own tools, pay as you go from prepaid credits at the provider's price plus 5%. Machines, Routines, and connectors need a subscription — Pro is $50 a month, $20 the first — and each paid plan includes a monthly usage allowance equal to its price before credits take over. Machine work is billed by usage and suspends when idle. The current limits are on the [pricing page](/pricing), in plain numbers rather than hidden behind a vague “fair use” promise. Octomind also keeps one agent reachable through more surfaces. Telegram, Slack, and WhatsApp can drive machines and sessions; GitHub connects issue and PR conversations; the web panel, REST API, WebSocket, ACP, and CLI all reach the same runtime. A phone message can pick up work on the same machine you used from the panel. Finally, [Routines](/blog/routines-scheduled-agents) turn a standing instruction into scheduled work. A Routine wakes the cloud machine, optionally runs a zero-token check first, starts the agent only when there's something to do, records skipped and failed attempts, and sends the result back. Goose recipes are excellent repeatable local workflows; Octomind Routines are built for work that has to happen while your own computer is asleep. ## Documented pain points of Goose There's no Goose pricing scandal, hard quota controversy, or shutdown risk to point at, and inventing one to sharpen a comparison would be dishonest. The real trade-off is architectural: [Goose runs on your machine](https://goose-docs.ai/) and doesn't provide a first-party hosted sandbox or personal cloud machine. That's an advantage for privacy and local control, but it means your computer, credentials, installed tools, network, and uptime are the execution environment. If the laptop sleeps, the local agent stops — unless you've arranged another host yourself. The same goes for persistence. Goose naturally reuses the files on your computer, and recipes can be checked into a repository, but it doesn't manage a separate durable machine you can enter from another device. Teams that want that layer have to supply it. None of this is a flaw hidden behind marketing — it's the direct consequence of a local-first product. If local-first is what you want, these are reasons to choose Goose. They only become pain points when your requirement is unattended work that can't depend on a personal computer. ## FAQ ### Is Goose open source? Yes. Goose is Apache 2.0, governed under the Agentic AI Foundation at the Linux Foundation. Octomind's core runtime is Apache 2.0 too. The real difference isn't license access — it's the managed cloud and machine model wrapped around Octomind. ### Is Octomind a fork of Goose? No. They're separate runtimes with different architectures. Goose is a local general-purpose agent with a desktop app and recipes. Octomind exposes one runtime through CLI, CI, daemon, WebSocket, ACP, and cloud machines, with persistent sessions and managed connectors. ### Can Goose run in the cloud? You can install open-source software on a server you control, so you can run Goose on a remote host. What Goose doesn't offer is a first-party managed personal machine. Octomind provides that machine as part of the service. ### Which has better model support? For direct provider freedom, Goose has the edge: 15+ providers, including local models and several subscription-auth paths. Octomind's hosted Hub currently lists 36 curated models and makes mid-session switching simple. It's a choice between direct control and managed breadth, depending on how you want to operate. ### Which is better for scheduled agents? Goose recipes are good for repeatable workflows you trigger in a running local environment. Octomind Routines fire externally: they wake a suspended cloud machine, run with persistent files, record the outcome, and notify you. For schedules that have to survive your laptop being off, Octomind has the clearer path. Ready to give an open agent its own machine? [Start with Pro](/pricing) — $20 your first month.