Skip to main content
AIDiveForge AIDiveForge

Hermes Agent vs LobeHub

Hermes Agent is one agent that you run in a terminal or message on Telegram, which remembers you and builds up its own skills. LobeHub (the project that grew out of LobeChat) is a web workspace where you set up many agents, put them in groups and schedule their work, either on lobehub.com or on your own server. Pick Hermes for a single personal assistant you host and script; pick LobeHub if you want a browser interface for a team of agents and are fine with a hosted plan or running its Docker stack.

License fine print

Hermes is MIT, with no strings. LobeHub's LICENSE is the "LobeHub Community License": Apache 2.0 plus extra conditions. You can use it commercially, including as a hosted service, without changing the code, but building and distributing a derivative work needs a commercial license from LobeHub LLC. That matters if you plan to fork it into a product; for internal self-hosting it mostly doesn't.

Running it yourself

Hermes installs with a one-line script (Linux, macOS, WSL2) or PowerShell on Windows, and can execute its commands locally, in Docker, over SSH, or in Modal, Daytona, Singularity or Vercel Sandbox. You connect any model provider.

LobeHub self-hosts as a web app: one-click deploy buttons for Vercel, Zeabur, Sealos and others, or a Docker Compose setup. It's a bigger stack to keep running than a single agent process, but you get the full UI: Agent Builder, Agent Groups that share pages, schedules and projects, an IM gateway for chatting with agents, MCP plugins and an editable "white-box" memory.

If you'd rather pay than host

Hermes has no hosted plan; you pay your model provider. LobeHub Cloud has a free tier with 500,000 credits a month, then Starter at $12.9 a month ($118.8 a year, about $9.9 a month), Premium at $24.9 ($238.8 a year) and Ultimate at $49.9 ($478.8 a year), with 5, 15 and 35 million credits. Enterprise is by quote.

Hermes Agent: Pick Hermes Agent if you want one MIT-licensed assistant you host yourself, reach from Telegram, Slack or Signal, and connect to whatever model you choose.

LobeHub: Pick LobeHub if you want a web workspace for building and scheduling several agents, with a free hosted tier or a Docker deploy on your own server.

Facts checked Oct 18, 2026 against 5 sources

Which one should you try?

Hermes Agent

Free

Best for People who want an assistant that remembers context across sessions

LobeHub

Freemium

Best for Teams wanting autonomous multi-agent systems without constant manual oversight

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Hermes Agent

Hermes Agent

Hermes Agent from Nous Research keeps memory across sessions, saves reusable skills and can be reached through messaging apps. We ran it on three tasks with a local 3B model and with Claude Opus 5.5.

LobeHub

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.

AttributeHermes AgentLobeHub
PricingFreeFreemium
PriceFree (MIT, self-hosted)$12.9/mo (Starter, monthly) or $118.8/yr
Pricing checkedJul 1, 2026Oct 19, 2026
Free trialNot statedNot stated
Open sourceYesNo
Has APIYesYes
Self-hosted optionYesYes
PlatformsmacOS, Linux, Windows (native or WSL2), Android (Termux); runs commands locally or in Docker, SSH, Singularity, Modal, Daytona, Vercel SandboxWeb, macOS, Windows, Linux, iOS, Android; self-host via Docker or Vercel
Released2026-022021
Pros
  • Memory and saved skills carry over between sessions.
  • Reach it from a CLI or messaging apps such as Telegram and Discord.
  • You choose where commands run, including Docker, SSH and hosted sandboxes.
  • Passed all 9 of our tasks with Claude Opus 5.5 and contacted only Anthropic's API while doing it.
  • 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.
Cons
  • Passed 0 of 9 tasks with a 3B local model; it needs a large model and a 64K context.
  • Heavy on tokens: an ~11,500-token prompt on every call and about 47,100 tokens per task, six times Goose's.
  • It installs its Anthropic support from PyPI on first use, so offline setups fail until you pre-install it.
  • 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.

Made up your mind?

Hermes Agent

Free

Best for People who want an assistant that remembers context across sessions

LobeHub

Freemium

Best for Teams wanting autonomous multi-agent systems without constant manual oversight

Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

Share this comparison