Modèles d’automatisation AI

Books a morning's shipments on a carrier website that has no API, from six numbered steps written in plain English. The Browser Act node follows the steps page by page: it reads what is on the screen, and Jev, TypeSafe's decision model on OpenRouter, picks every next move, including which service each customer's note asks for. No step names a button or a field. Jev never writes text, so its output tokens cost nothing, and below a confidence of 0.55 the robot touches nothing and hands the order to a person. Every booking is proven in code before it counts: a new tracking number, on a page that names that order.

Finds a shop that has no website and builds it one. Google Maps is searched through the scrape.do API, the listings are filtered down to businesses with a phone, enough reviews to prove they have customers, and no real site of their own - a Facebook page or a Wix subdomain counts as no site - and the best one is scored out. Its opening hours, ten best reviews and four public domain photographs are gathered, and a DeepSeek Agent writes a single self-contained page from that brief and nothing else. A real browser then photographs the page, and the screenshot and a CSV row are mailed to you. Google Maps has no email column, so the packet comes to you with the shop's phone number in it, and a person makes the call.

A customer support assistant for a website, served to the page as one MCP tool. A visitor and a signed-in patient are answered by two different Hermes Agents, and the difference is not a prompt or a flag, it is two wires: the visitor's agent is wired to the practice documents and to nothing else, so nothing on that side of the canvas can reach a patient record whatever the model is asked. The signed-in tools take no arguments at all. They read the record number the website established from a session cookie, so the model cannot name a patient, and every turn is written to a dialog log the practice can read in the morning.

A conversational chat assistant that takes a return in whatever order the customer explains it. An LLM Agent asks for whatever is missing, and an ordinary automation wired to its tools port looks the order up mid conversation and hands the answer back.

A guided chat assistant that opens a product return, one question at a time. Uses Chat Assistant widget nodes for the questions, checks the order number while the customer is still in the conversation, and loops back to the question when it does not recognise one instead of throwing an exception.

Ask your own documents a question over HTTP. A Robomotion Knowledge Base is indexed by a workspace agent on your machine and searched locally by the robot; one Query Knowledge Base node returns the passages that answer a question, each with the document and heading it came from. Http In and Http Out turn the flow into a service anything can call, and a Min Score fence means it answers "I could not find that" instead of inventing something. Ships four sample documents and a thirty-line Python client.

Serves an ordinary RPA automation to an AI assistant as MCP tools, so a model can ask a question of a system it could never sign into. One Listen HTTP node makes the flow a stateless MCP server; three Tool In nodes declare the contracts a model reads, with no arguments, one required argument and one optional. Behind them a sign in subflow answers a government portal's tax number, password and one-time code from the vault, and two more subflows read VAT returns, penalties and electronic invoices off the page. The caller is given a bearer token and nothing else, and Tool Out's Is Error flag lets the flow refuse a company it holds no mandate for instead of returning an empty answer the model will fill in for itself.

Retrieval-augmented generation over your own documents, with the retrieval handed to the agent instead of done for it. Documents are read, chunked, embedded and stored in LanceDB - an embedded vector database that is just a directory on disk. A DeepSeek Agent then answers questions through a search_knowledge tool built from ordinary flow nodes, writing its own queries and searching as many times as a question needs, with the source document named against every fact.

An invoice-processing DeepSeek Agent that teaches itself vendors. The first invoice from a supplier is worked out from scratch and the procedure is written down as a learned skill in the agent's memory; every invoice after runs on what it learned. Extracted fields are recorded to a CSV ledger through a Tool In / Tool Out pair, and memory syncs to the workspace so a whole fleet can run what one robot learned.

A personal nutrition tracking assistant powered by Chat Assistant and LLM Agent nodes. Uses a SQLite database to log meals, manage user profiles, and track daily calorie and macro goals through a natural conversational interface with support for photo-based food logging.

A ready-to-use conversational AI template powered by Chat Assistant and LLM Agent nodes. Provides a simple chat interface where users can ask questions and receive answers from a large language model, with support for file attachments and graceful error handling.