LobeHub vs tutti

LobeHub and tutti 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.

tutti

The core idea: instead of agents exchanging summaries, they share a live project state. Codex sees exactly what Claude changed, what's running, and what's pending — no copy-paste required. The `@` reference system lets any agent or teammate pull from any file or conversation in the workspace without re-uploading. A GUI control center surfaces every pending approval and running task in one view, so you sign off without opening a terminal. The ceiling appears when your workflow involves agents outside Tutti's supported roster or when you need fine-grained infra control — the platform is built for GUI-driven coordination, not headless pipeline automation.

Attribute	LobeHub	tutti
Pricing	Paid	Free
Price	$9.9/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	No
Platforms	Web, macOS, Windows, iOS, Android, Docker, Vercel	—
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 live project state across agents, so Codex reads exactly what Claude produced without a re-briefing step — eliminating the context decay that compounds across every handoff. The `@` reference system lets any agent pull any file or conversation from the workspace by name, so the copy-paste loop between agent sessions stops entirely. Goal-to-task decomposition with manual assignment review, so you keep control over which agent runs each step without scripting the breakdown yourself. Apps run inside the workspace and are callable by agents using existing subscriptions, so Claude can write a PRD and directly invoke a design tool without switching windows or re-authenticating. GUI control center aggregates every running task and pending approval in one view with one-click sign-off, so you stay in the loop without monitoring separate agent sessions across multiple tabs.
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.	Teams that need headless, programmatic pipeline control — triggering agents via API, chaining steps based on returned values, running without a GUI — hit a structural wall. Tutti is built around a visual workspace; there is no evident scripted orchestration layer for fully automated pipelines. Those teams move to a code-first framework or an API-driven orchestration tool instead. The in-workspace app catalog is community-and-vendor-built and early-stage. When a workflow requires an app or integration that does not exist in the catalog, teams either build a custom app (which requires development time Tutti does not eliminate) or accept that the agent must leave the workspace to use an external tool, breaking the shared-state model. Agent support is bounded by whichever models Tutti explicitly connects — Claude, Codex are named in the vendor content. Teams running workflows on models or providers outside that set cannot bring those agents into the shared workspace, which forces the same copy-paste context problem Tutti exists to solve.

Bottom line

LobeHub is paid while tutti is free; tutti 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 tutti?

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

Is LobeHub better than tutti?

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 tutti: which should I pick?

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