Agent Brain
Any catalog chat model from OpenAI, Anthropic, Google or xAI runs the main loop, at a thinking level you set — off to max. The loop’s supporting slots run on one second provider you choose.
Private Agentic Cloud
Put a fleet of AI agents to work in your own private cloud. Each one takes a job, works your tools and your documents with every frontier model behind it, and reports back when it is done.
Design custom agents
General intelligence answers anyone’s question; it does not know your work. A BusyBot agent is intelligence you shape — the brain it thinks with, the mind it carries, the tools it may reach, the way it grows — fine-tuned to your workloads, not a one-size harness you cannot open.
Or start with a brief Describe the agent you want and the Builder Assistant proposes a role, a skill, tools and knowledge — you accept the proposal part by part.
Any catalog chat model from OpenAI, Anthropic, Google or xAI runs the main loop, at a thinking level you set — off to max. The loop’s supporting slots run on one second provider you choose.
Four instruction fields, written in plain language and edited in place: Role, Skill, Context, Format. Who the agent is, how it works, what it knows about your business, how it answers.
Tools from 452 nodes, each with its own credential, called when the agent decides to. Saved workflows attach as single tools — a whole pipeline in one step. Knowledge bases are searched mid-run, whole documents read and cited.
Runs leave lessons: a per-tool Do / Don’t ledger of real outcomes, Growth Reviewer proposals you apply with a diff, Training against a plan you review first. Growth off means stop changing, never forget.
Workflows
Drag 452 nodes onto a canvas and connect them. If you have built workflows on a canvas before, you already know this one. Independent branches run in parallel; every run executes as an isolated Kubernetes Job on its own node pool. Start one by hand, on a schedule, from a trigger — or let an agent in your fleet call the whole workflow as a single tool.
Knowledge bases
Upload PDFs, Office files, code, text, audio and video. Each file is extracted to markdown, summarised in passes — per document and per knowledge base — and indexed. An agent searches it through a dedicated search subagent that reads whole documents mid-run, follows references, and cites what it used.
Every frontier model
One workspace with every frontier model behind it. Chat with the model you want and switch mid-thread with your context intact, or burst one prompt to all of them at once — the same models your agents choose their brain from.
4 frontier providers · 67 models · one workspace
Switch from GPT to Claude to Gemini to Grok inside one conversation and your context travels with you — each model picks up exactly where the last one left off. Conversations, files and instructions live in your private cloud, and Model Radar probes the providers daily so no listed model is a dead one.
Send one prompt and every model answers together — GPT, Claude, Gemini and Grok streaming side by side in one place. Pick a Lite, Regular, Reasoning or Custom squad, compare the takes as they arrive and keep the one you like best.
Integrations
Each one is a workflow step and an agent tool: wire it after a trigger, schedule it, or hand it to an agent that decides when to call it. Every page lists the real operations, parameters and outputs.
How to connect each service — 295 credential types How workflows and agents fit together From the blog
No subscription — try it on your own terms
$20prepaid, minimum
Full-priced tokens
Monthly, cancel anytime
$20per month
50% off tokens
Your own private cloud, at full speed
$100per month
75% off tokens
Teams, with a dedicated URL
$40per seat / month · 5 seats minimum
90% off tokens
Questions
Yes. An agent’s brain — the model that runs its main loop — is any catalog chat model from OpenAI, Anthropic, Google or xAI, at a thinking level you set from off to max. The loop’s supporting slots (plan, replan, reflect, strategy, extract) run on one second provider you choose, which may differ from the main loop’s. Two frontier providers, one agent.
Model Radar probes all four providers’ model APIs every day, so the catalog never lists a dead model: whatever you can pick in chat, in a Burst squad or as an agent’s brain is a model that answers. The fleet at the foot of this page is that live catalog, by name.
Your workflows can: a workflow runs on a schedule (Trial 2 at ≥ 60 min · Starter 10 at ≥ 15 min · Pro 100 at ≥ 1 min · Enterprise unlimited at ≥ 1 min) or from one of 85 triggers, every run an isolated Kubernetes Job, and an agent can call a saved workflow as a single tool. An agent run starts when you hand it a job; it then works on its own — plans, calls tools in batches, reflects, asks only when it needs you — and streams into the sidebar’s live-run feed, where you can stop it mid-run from anywhere. Every run is metered and credit-gated against your balance.
Yes. Mid-run an agent may ask you a question — with choices or a short form — and pause until you answer, then carry on with your answer in hand. Before each batch of tool calls it shows a preamble of what it is about to do; after each batch it reflects and may write down a finding; it may replan. All of it streams live into the conversation.
Lessons. Each tool keeps a Do / Don’t ledger of outcomes on your account — what worked, what failed and why — written by the agent in flight and by a Notekeeper after the run. A Growth Reviewer proposes new versions of the Skill and Role into a suggestion queue with evidence and a diff; you apply, edit or dismiss each one, and every version is kept — any restorable, any freezable. Training sessions let the agent practise its tools against a plan you review first. Growth off means stop changing, never forget.
Yes. A temporal header gives every iteration of the loop the current date and time in your time zone, so “this week”, “by Friday” and “since the last run” mean what you mean, whenever the run starts.
In your own private workspace on BusyBot’s Kubernetes cluster in the US. Each customer’s data is isolated to its own account (hash-tag isolation); credentials for the 295 credential types are AES-256-GCM encrypted at rest, never logged, and checked against the owning account on every use; every workflow run executes as its own isolated Job on its own node pool, and code nodes run sandboxed. Conversations, files and instructions stay in that workspace as you move between models.
Tokens at your plan’s markup over provider list price, workflow compute by the hour beyond what your plan includes, and storage measured nightly, all settling into one credit balance. Trial is prepaid from $20 with credits good 12 months; subscriptions start at $20/month. Bring your own model keys on Starter, Pro, Enterprise.
Every frontier model
4 frontier providers · 67 models · one workspace