Elvex vs Hearth

Elvex 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.

Elvex

The platform lets teams build agents with guided tooling, share them across departments via a shared agent library, and swap underlying models — Gemini, Claude, GPT, Llama, or custom — without rebuilding the agent. Governance is a first-class feature: admins apply guardrails, set permissions, and get full usage visibility before anything ships. Agents run up to 40 tool interactions per loop with conditional logic and triggers, which covers most document review, ticket routing, and research workflows. The ceiling appears when workflows require branching logic complex enough that the guided builder can't express it — at that point, teams either simplify the agent or wait for support to intervene. Elvex is cloud-only, so organizations with data residency requirements or air-gapped environments hit a hard stop before they start.

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	Elvex	Hearth
Pricing	Paid	Free
Price	$30/user/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	No	Yes
Platforms	Cloud-based SaaS (web application via elvex.com, mobile-optimized interface)	Windows (primary); macOS/Linux from source
Released	2023	—
Pros	Model-agnostic routing across Gemini, Claude, GPT, Llama, and custom models, so swapping providers when cost or quality demands shift is a configuration change — not a rebuild that strands your existing agents. Guided agent builder designed for non-technical employees, which means AI adoption reaches operations, HR, and legal teams without every agent becoming an IT backlog item. Shared agent library with cross-team visibility, so a well-configured contract review agent built by one team is available to the whole department rather than duplicated six times with six different prompts. Usage-based pricing instead of per-seat licensing, so teams running agents sporadically don't subsidize teams running high-volume workflows — which makes incremental rollout and ROI measurement feasible without committing to a headcount-priced contract. Admin-controlled guardrails, permissions, and usage analytics built into the platform, so compliance and cost controls are in place before agents reach end users rather than bolted on after an audit request.	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	The guided builder hits a ceiling on conditional branching: agents that need to take meaningfully different paths based on what a prior step returned — across more than two or three decision branches — exceed what a non-technical user can configure without developer help. Teams with that complexity either simplify the workflow or add a developer, at which point the 'no code required' premise no longer holds. There is no self-hosted or private-cloud deployment option documented by the vendor. Organizations with strict data residency rules, air-gapped environments, or legal constraints on sending document content to a third-party cloud are blocked entirely — and those teams move to self-hostable alternatives rather than waiting for a deployment option that isn't on the documented roadmap. The platform's agent logic is opaque to end users by design — non-technical employees run agents but don't inspect or debug them. When an agent produces a wrong output at scale (a mis-routed ticket, an incorrect contract flag), diagnosing the cause requires either admin-level access or vendor support involvement, which adds latency to fixes that technical teams on code-based platforms would resolve themselves.	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

Elvex is paid while Hearth is free; Hearth is open source; only Elvex exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Elvex and Hearth?

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

Is Elvex 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.

Elvex vs Hearth: which should I pick?

Pick Elvex 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.