Goose vs Hearth

Goose and Hearth are both ai agent apps tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Goose

Goose runs as a desktop app, CLI, or embeddable API — built in Rust, so the performance profile is consistent across macOS, Linux, and Windows without a runtime you have to manage separately. The extension system connects to 70+ tools via the Model Context Protocol, meaning a workflow touching GitHub, Google Drive, and a database isn't stitched together with custom glue code — the standard handles the handoff. Recipes let you capture multi-step workflows as YAML configs and share them across a team or drop them into CI. Where the architecture shows its limits: complex conditional branching inside recipes is not the same as writing that logic in code, and teams building workflows that require dynamic decision trees at depth report dropping into Python extensions to compensate — at which point they are maintaining two systems. Community support is Discord-first; the vendor states no paid tier, so production SLA expectations need to be reset before an org-wide rollout.

Hearth

Hearth runs on your own hardware and handles the tasks that usually demand a SaaS subscription: opening applications, reading and writing files, driving a real browser you can watch, and carrying memory of past sessions — all without a single request leaving your network. The MIT license means you can fork it, extend it, and ship modified versions without legal friction. That said, the GitHub repo shows 9 stars and 297 commits from a single-org project, which signals early-stage software rather than a hardened production runtime. Windows is the primary target; Linux and macOS support is not confirmed by the page. Teams that need cross-platform deployment or enterprise support will hit the ceiling fast.

Attribute	Goose	Hearth
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	macOS, Linux, Windows	Windows (primary); macOS/Linux from source
Released	2025	—
Pros	Runs fully on your machine with no required hosted dependency, so proprietary code and internal data never leave your infrastructure unless you route them to an external LLM — which you control. YAML-defined Recipes capture entire multi-step workflows as portable configs, so a workflow one engineer builds on their laptop can run unchanged in CI or be handed to the rest of the team without re-explanation. Connects to 70+ extensions via the Model Context Protocol open standard, which means swapping in a new database, API, or browser tool doesn't require rewriting the agent's integration layer. Provider-agnostic LLM routing across 15+ providers, so switching from OpenAI to Ollama when API costs spike — or to a local model for sensitive data — is a configuration change, not an architecture change. Subagents handle tasks in parallel, so a workflow that would otherwise queue code review behind research behind file processing can run all three at once without tangling the main session context.	Fully local execution with no telemetry or account requirement, which means sensitive file operations and internal automation never leave the machine — eliminating the data-residency risk that blocks cloud tools in regulated environments. MIT license with a self-hosted architecture, so you can fork, modify, and redistribute without licensing negotiation — the thing that stops most teams from customizing a SaaS automation tool at all. Voice and natural-language input connected directly to OS-level actions, so non-technical users can run repetitive file and app tasks without writing scripts or maintaining a workflow canvas. Reusable, installable 'skills' that the community can share, which means automation one developer builds for cleaning a downloads folder can be packaged and reused by anyone on the same stack — no rebuild from scratch. A visible, watchable browser session rather than headless automation, so you can audit exactly what the agent is doing in real time instead of debugging a black-box scraper after it goes wrong.
Cons	Complex conditional branching inside Recipes — logic that depends on what a previous step returned and routes differently based on that — is not a first-class YAML primitive. Teams building workflows with more than two or three decision branches add a Python extension layer to handle the logic, which means they are now maintaining the agent config and the extension code as separate systems. There is no paid support tier, no SLA, and no vendor escalation path. Production incidents land in Discord. Engineering teams at organizations with uptime commitments who discover this after deployment replace Goose with a managed platform — typically one that offers a hosted agent runtime with contractual support — and keep Goose only for local developer tooling. The desktop UI's MCP app rendering (buttons, forms, visualizations inside extensions) is tied to the Goose Desktop client. Teams embedding Goose via the API for headless or server-side automation get none of that interactive surface, so UI-dependent extensions have to be redesigned or abandoned for non-desktop deployments.	The project targets Windows explicitly; the page does not confirm Linux or macOS support. Teams running mixed-OS environments or deploying to Linux servers cannot use Hearth without forking the codebase and porting the OS-control layer themselves — at which point they are maintaining their own tool, not adopting one. At single-digit GitHub stars and a single-org contributor base, there is no meaningful community to surface bugs, maintain compatibility with OS updates, or keep pace with new local model releases. When a Windows update breaks the file-control layer, the fix timeline depends entirely on one maintainer. There is no multi-user, logging, or audit-trail architecture described anywhere in the repo. Teams that need to demonstrate who ran what automation and when — for compliance, for incident review, or for shared-machine safety — will find nothing here and will move to a tool like Open Interpreter paired with structured logging, or a managed RPA platform, before the first audit request arrives.

Bottom line

Only Goose exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Goose and Hearth?

Goose is Free and open source, while Hearth is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Goose better than Hearth?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

Goose vs Hearth: which should I pick?

Pick Goose if its pricing model, openness, or platform fit matches your constraints; pick Hearth otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.