Hearth vs OpenLegion

Hearth and OpenLegion 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.

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.

OpenLegion

Each agent gets its own isolated container, spend cap, and vault-proxied credentials — so a rogue agent can't drain your API budget or leak credentials to the next task in the queue. The platform deploys a coordinated fleet from a plain-English description of the function you need: a sales pipeline, a content studio, a research desk. Credential handling and per-agent budgets are locked down by default, which means you're not retrofitting security after something goes wrong. The ceiling appears when your workflow needs branching logic that the template model can't express — at that point you're describing edge cases in natural language and hoping the agent interprets them correctly. Teams with deterministic multi-step requirements often add a separate orchestration layer to compensate.

Attribute	Hearth	OpenLegion
Pricing	Free	Paid
Price	—	$19/mo
Free trial	No	7 days
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	Windows (primary); macOS/Linux from source	Web, Self-hosted (Docker)
Released	—	2026-02
Pros	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.	Per-agent spend caps enforce budget ceilings at the container level, so a misconfigured agent or a prompt injection that triggers excessive tool calls cannot consume your entire LLM budget before you notice. Vault-proxied credential handling means raw API keys and account credentials are never passed between agents in plaintext, which removes a common attack surface in multi-agent setups where credentials flow through shared memory. Support for over 100 LLM providers with no markup on usage, so switching the model backing a specific agent — say, moving a high-volume scraping agent from a premium model to a cheaper one — is a configuration change, not a rebuild. Container isolation per agent means a failure or security event in one agent's environment does not propagate to the rest of the fleet, so a single broken workflow doesn't take down concurrent production tasks. Native trigger integrations with Slack, Discord, Telegram, WhatsApp, and webhooks mean agents can be kicked off from tools your team already uses, so you avoid building a separate scheduling or event layer to connect the platform to your existing stack.
Cons	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.	Workflows that depend on precise conditional branching — route this lead differently based on company size, or skip invoice processing if the vendor field is blank — have to be described in natural language rather than defined in code. At production volume, the agent's interpretation drifts, and teams running exception-heavy operations report adding a rules layer outside the platform to catch the cases that fall through. There is no free tier. Evaluation requires a paid commitment with a money-back window. Teams that need to run a live proof-of-concept against their actual data before budgeting the tool will find the evaluation model friction — and some will default to an open-source alternative like n8n or a code-first framework they can run locally at zero cost. The platform is closed-source, which means teams with strict compliance requirements who need to audit the agent runtime itself — not just the action logs — cannot inspect the execution layer. Organizations in regulated industries that hit this wall during security review switch to a self-hostable, open-source orchestration framework where the full stack is auditable.

Bottom line

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

Frequently asked questions

What is the difference between Hearth and OpenLegion?

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

Is Hearth better than OpenLegion?

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.