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Your own subscriptions or API keys · Open-source client

Your AI team hands off the work.

They split the task, work in parallel, talk it over and check each other's work. You choose the model for every step, and which connected computer runs it.

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  1. Plan GPTMac · 9.7s
  2. Build DeepSeekLinux · 12.7s
  3. Review GeminiMac · 17.7s
  4. ✓ Review passed

Replayed from a real run: one sentence in, three agents handing off across two computers.

You already juggle several AIs by hand. Nolo makes them one team.

What you do across tabs today becomes one conversation with agents you define.

Juggling AI windows yourself

  • Copy context between tabs by hand
  • Paste output into another model to check it
  • Start over when a subscription hits its limit
  • Conclusions scattered across chat logs

Nolo

  • Agents keep their role, prompt, model and memory
  • An independent agent reviews the work
  • Switch agents mid-conversation; history carries over
  • Conclusions land in pages and docs

Three things that make it a team

A companion that stays

Each agent keeps its name, role, model and memory, and gets to know you better the more you work together. Call it next week and it is still the same one.

Tell it once to answer in English and lead with the conclusion, and it already does so in a brand-new conversation. Switch models and you don't start over.

It hands off by itself

When one finishes, the next one picks it up, and you can have a model from another vendor check the work. Say one sentence, then go do something else.

You pick

Which model takes each step, and where it runs. Models: your subscriptions, your own API keys, or local models. Machines: your Mac, your Linux box, your own server.

More than one way to work together

Split a task across agents, send one question to several models at once, or have them answer on their own and then cross-check, with disagreements kept on the record. No flowchart to draw.

Tareas de código: de la división del trabajo al resultado listo para usar

Un patrón común: un modelo potente planifica, otros agents construyen en paralelo, un agent independiente revisa y el trabajo se itera hasta que cumple los requisitos.

Una pregunta, múltiples perspectivas a la vez

Un agent orquestador envía la misma consulta a varios modelos en paralelo. Obtienes diferentes ángulos de inmediato, sin cambiar de pestaña a mano.

De opiniones paralelas a una conclusión clara

Un agent orquestador reúne opiniones de varios modelos, modera el debate cuando hace falta y te entrega consensos, discrepancias y próximos pasos.

  1. 1Describe el objetivo
  2. 2Construye en paralelo
  3. 3Entrega el resultado

Why now

Three things are already true for many people who use AI every day.

  1. More than one AI, already paid for

    Many people already pay for several AI subscriptions or keys, each good at something different. What's missing is a way to make them work together.

  2. Agents can finish real work

    An agent can now take a whole piece of work from plan to checked result on its own. Handing off between agents is the next step.

  3. Your work lives on several devices

    A laptop, a desktop, a server, a phone. The work should run where your files and tools already are.

AI can research, build, compare, check, and move in parallel.

Why you do it.What good looks like.What you truly want to make.

That stays with you.

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Open-source client · Releases match public source · Your subscriptions or BYOK · View source

Questions people ask

Those add abilities to one agent. Nolo runs a team: who does the work, whose subscription pays for it, which computer it runs on, who picks it up next, and who reviews it.

Yes. Your subscriptions, your own API keys (BYOK) and local models all work.

On a computer or server you have connected. It needs to be switched on and running nolo connect. If an agent uses a subscription you signed into on that machine, the sign-in there has to still be valid.

When agents run on your own computers, files and commands stay on those machines. The client is open source, so you can read exactly what it does and what it sends.

EntradaAsignaciónEnviarAprobadoIterar
Chat
Tablero de tareas
Minimax
Coordinación de tareas
Antigravity
Implementación de interfaz
Codex
Lógica y datos
Codex
Control de calidad
Lanzamiento
Resultado en vivo
Chat
Tablero de tareas
Minimax
Coordinación de tareas
Antigravity
Implementación de interfaz
Codex
Lógica y datos
Codex
Control de calidad
Lanzamiento
Resultado en vivo

¿Merece la pena lanzar el MVP ahora?

Minimax
Orquestador
OpenAI
GPT
Grok
Grok
Gemini
Gemini

¿Merece la pena lanzar el MVP ahora?

Minimax
Orquestador
OpenAI
GPT
Grok
Grok
Gemini
Gemini

¿Deberíamos migrar a microservicios?

OpenAI
GPT
Grok
Grok
Gemini
Gemini
Orquestador
ConsensoDiscrepancias abiertasSiguiente paso

¿Deberíamos migrar a microservicios?

OpenAI
GPT
Grok
Grok
Gemini
Gemini
Orquestador
ConsensoDiscrepancias abiertasSiguiente paso