LobeHub vs Loma

LobeHub 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.

LobeHub

LobeHub lets you define a goal and have the system assemble an agent team, dispatch parallel workers across tasks, and surface results without you approving every step. The agent marketplace and skill library — reportedly over 332,000 skills and 64,000 MCP server connections — mean you're not building from scratch each time. Memory is white-box and editable, so agents don't silently drift from your preferences. Where it gets difficult: the self-hosted path requires you to manage your own infrastructure, and the complexity of multi-agent coordination means debugging a failed task chain is non-trivial. Teams running production workloads tend to add observability tooling — the Langfuse integration listed on the page suggests this is an expected pattern, not an edge case.

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	LobeHub	Loma
Pricing	Paid	Free
Price	$9.9/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Web, macOS, Windows, iOS, Android, Docker, Vercel	Self-hosted, Slack, web dashboard
Released	2021	—
Pros	Auto team formation assembles the right agents for a task without manual wiring, so you avoid maintaining a canvas diagram that breaks every time requirements change. Parallel agent execution across a shared context means a 500-issue sweep that would take hours sequentially finishes while you're offline — the vendor's own example, not a marketing abstraction. Provider-agnostic model routing across Google, AWS Bedrock, DeepSeek, and others means swapping the underlying model when costs spike or quality drops is a configuration change, not a rebuild. White-box, editable memory means when an agent starts behaving off-model, you inspect and correct the memory directly instead of re-tuning prompts and hoping the behavior changes. Self-hosted deployment is supported, so teams with data sovereignty requirements or air-gapped environments are not forced onto a cloud-only architecture.	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	When a multi-agent chain fails mid-task, the platform's autonomous model gives you limited native visibility into which step broke and why — teams running production workloads add Langfuse or equivalent external tracing, meaning they maintain a second system from the start. Self-hosting the infrastructure moves the operational burden entirely onto your team: model hosting, uptime, updates, and scaling are your problem, not LobeHub's. Teams without DevOps capacity to manage this consistently end up back on the cloud tier or move to a fully managed platform. The autonomous dispatch model is a poor fit when workflows require a human to review and approve before each next step runs — there is no explicit approval gate in the described architecture. Teams that need audit trails with sign-off at every decision point abandon this for tools built around explicit human-in-the-review-loop workflows.	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

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

Frequently asked questions

What is the difference between LobeHub and Loma?

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

Is LobeHub 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.

LobeHub vs Loma: which should I pick?

Pick LobeHub 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.