Limova vs OpenLabor: Nine Fixed Agents or Custom Employees
Limova ships nine preconfigured agents for €69.90/mo. OpenLabor builds employees around the work you actually do. The difference is architecture, not price.
Published 2026-08-01
The short version
Limova gives you nine ready-made AI agents — phone, WhatsApp, chatbot, marketing, prospecting, SEO, HR, accounting, legal — for €69.90 to €119.90 a month. Onboarding takes an afternoon and the French support is genuinely good.
OpenLabor gives you AI employees that run on an agentic harness: a real filesystem, a shell, the ability to write and run code, and a skill you can define yourself. Setup takes longer. The ceiling is much higher.
If your work fits inside one of Limova's nine agents, buy Limova. If the useful part of your work is the part nobody else does the same way, keep reading.
What Limova actually is
Limova is a French no-code platform, founded in 2024 out of Nice, that packages nine preconfigured assistants. Tom answers the phone. Charly+ runs on WhatsApp. The others cover marketing, LinkedIn prospecting, SEO, recruitment, bookkeeping and legal questions. Pricing is public: €69.90/mo for the Essential plan, €119.90/mo once telephony is included.
That is a well-built product with an honest promise. It is also, architecturally, a set of chat prompts wired to a fixed list of integrations. Each agent has a script. You pick the script that is closest to your problem.
Public flat pricing is the tell. You can only publish a fixed price when you can predict exactly what the software will be asked to do — which means the scope is fixed by design. Sintra sells the same shape in English, with twelve helpers instead of nine.
Where fixed scope stops working
Every business we onboard has a handful of processes that make it money, and they are never the generic ones. A recruiter's real workflow is not "post a job." It is: pull the new applications, cross-check each CV against the last six placements that worked, rewrite the shortlist note the way the client likes to read it, drop it in the shared drive, ping the account manager.
Five steps. Four systems. One of them is a spreadsheet with a layout only that company uses.
A nine-agent catalogue cannot express that. Not because the model is not smart enough — because there is no place to put it. There is a marketing agent and a recruitment agent and neither has a filesystem, a shell, or a way to be taught a procedure that did not ship in the box.
So the work splits: the AI does the generic 30%, and you do the 70% that was the reason you wanted help.
The architectural difference
This is the part that matters, and it is not about which model is better. Assume the models are identical, because they very nearly are: everyone rents from the same three or four labs.
A chat wrapper takes your message, adds a system prompt, calls a language model once, and posts the reply to a fixed integration. It is stateless between runs. It cannot inspect a file, run a script, check its own work, or recover from a step that failed. When something falls outside the script, it produces text about the problem instead of solving it.
An agentic harness — the class of tool Claude Code belongs to, and what OpenLabor employees run on — gives the model a workspace it can read and write, a shell it can execute, and a loop where it takes an action, looks at the result, and takes the next one. That loop is the whole difference. It means an employee can open your CSV, notice the column names changed, adapt, finish, and tell you what it changed.
The reason that matters is arithmetic. A model that gets each individual step right nine times out of ten gets a five-step task right 59% of the time in one pass, and a ten-step task 35% of the time — and it never announces which pass went wrong. A loop that verifies each step before taking the next one is the only thing that stops those odds from multiplying. The long version of that argument is here.
Everything else follows from it:
Limova — OpenLabor
Underlying model — Frontier model, rented — Frontier model, rented
Model calls per task — One — As many as the work takes
Agents — 9 preconfigured — Roles you staff, plus skills you define
Custom procedures — Within each agent's script — Written as a skill, versioned in the workspace
Runs code — No — Yes — shell and filesystem
Memory — Per-conversation — Persistent workspace + org files every employee reads
Multi-step recovery — Fails to text — Sees the error, retries, reports
Audit trail — Conversation history — Every action logged per employee
Pricing — €69.90–€119.90/mo, public — Scoped to the work, quoted
Best at — Answering, drafting, standard flows — Running a process end to end
The same request, two architectures
"Pull last month's numbers out of the export, compare them to the three months before, and write the client update the way we always write it."
In a nine-agent catalogue. The marketing agent can write the update — that part is genuinely good. It cannot open the export, so you open it and paste the numbers. It has not seen the last three months, so you paste those too. It has no memory of the way you always write it, so you fix the tone. You did the work. It did the typing.
On a harness. The employee opens the export from the shared drive, notices that finance renamed a column last month, adjusts, computes the comparison, reads the last three updates you sent so the format matches, drafts it, and flags the one number that moved enough to deserve a sentence of explanation.
Same model in both cases. The difference is that one of them was allowed to look.
"Custom" is not a euphemism for slow
The objection to bespoke is always setup time. Fair. So here is how it actually goes.
An employee arrives already able to do the generic work — write, research, draft, schedule, connect to your tools. That part is live the same day. What takes a week or two is the specific part: we sit with the process you described above, write it down as a skill the employee reads before it acts, run it, watch where it goes wrong, and fix the instruction rather than the output.
After that it runs unattended, and the thing that makes it valuable is exactly the thing a catalogue cannot ship: it is yours. SOUL.md, USER.md and AGENTS.md is how that gets written down.
Be fair: pick Limova if
- Your priority is a phone line that gets answered and a WhatsApp assistant that replies fast.
- You want a published price and a card payment today, with no scoping call.
- You are a French SMB and French-language support with data in France is a hard requirement.
- Your processes are standard, and you would honestly rather they stay standard.
That is a real segment, and Limova serves it well. Their Trustpilot score is not an accident.
Pick OpenLabor if
- The work you want automated is specific to your company and does not resemble anyone else's.
- You need an employee that can touch files, run code, and finish a multi-system process without a human in the middle.
- You want the procedure written down and versioned, so it survives the person who set it up.
- You would rather pay for the work that gets done than for a seat in a catalogue.
The honest summary
Limova sells nine good answers. OpenLabor builds the answer to your question.
Both are legitimate products. The mistake is buying a fixed catalogue for a problem that is not fixed, then concluding AI does not work when the 70% that mattered is still on your plate. If you are still mapping the field, the honest 2026 comparison places everyone else on it.
If you want to know which one you are, describe your most annoying weekly process out loud. If it fits in one sentence with no company-specific nouns in it, buy the catalogue. If it does not, book a call and we will scope it.
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