Hearth vs Loma

Hearth and Loma 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.

Loma

Loma sits across your tools — Slack, docs, CRM signals — running agents that handle pre-meeting briefs, RFP responses, bug triage, and onboarding health checks without waiting to be asked. The differentiating claim is the context layer: every resolved ticket, closed deal, and fixed bug is stored as a pattern or skill that future agents draw on, so day 100 is meaningfully faster than day 1. Self-hosted under Apache-2.0, it supports Claude, GPT, and Gemini with swap-anytime routing. The vendor states agents complete RFP questionnaires at ~95% coverage, flagging the remainder for human review. Where it strains is in the gaps the scraped content leaves open — enterprise auth, SLA guarantees, and mature operational tooling are not documented.

Attribute	Hearth	Loma
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Windows (primary); macOS/Linux from source	Self-hosted, Slack, web dashboard
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.	Shared context layer that persists learned patterns across every agent run, which means the fifth RFP your agent completes draws on answers from the previous four rather than starting cold. Provider-agnostic LLM routing across Claude, GPT, and Gemini, so when API costs spike or a model underperforms on a task type, you swap the model without rebuilding the agent. Self-hosted under Apache-2.0, which means deal playbooks, customer health signals, and diagnostic patterns never leave infrastructure you control — critical for teams whose security review would otherwise block a SaaS AI layer. Slack-native task delegation — agents accept @mention assignments and post proactive briefs without requiring a separate interface — so adoption doesn't depend on getting your team to open another tool. Agents flag what they cannot answer rather than hallucinating completions — the RFP workflow surfaces unanswered questions for human review, so you review exceptions rather than auditing every output.
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.	Compliance-gated procurement breaks here: the public documentation carries no mention of SOC 2, HIPAA readiness, or signed SLAs, so any team whose security review requires those artifacts before a tool touches customer data will stall at the vendor assessment stage — at which point they evaluate managed alternatives that ship compliance docs. The context layer's value depends entirely on volume and quality of team activity flowing through Loma — a team of three running occasional tasks builds sparse patterns, and sparse patterns mean agents are not meaningfully better than a cold prompt for months; smaller teams report this lag as the tool failing to deliver on its compounding premise. Enterprise access controls — role-based permissions, audit logs, SSO — are not described anywhere in the vendor's public documentation; teams operating in regulated industries or with strict data governance requirements are left to build these controls themselves or accept the risk, and several will choose a commercial platform instead.

Bottom line

Hearth and Loma are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Hearth and Loma?

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

Is Hearth better than Loma?

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.

Hearth vs Loma: which should I pick?

Pick Hearth if its pricing model, openness, or platform fit matches your constraints; pick Loma 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.