From one prompt to three supply variants, in under ten minutes.
Sympheny's REST API and Python SDK are fully documented, so an AI assistant or coding agent can build a project, set up scenario variants and start an optimisation run. What comes out the other side is unchanged: the same deterministic MILP engine solves the model, whether a person or an agent assembled it.
An assistant sets the model up. Sympheny solves it.
One prompt, three supply variants — gas boiler, heat pump with rooftop solar, and district heating — set up through the documented API. Silent clip; every step is captioned on screen.
Get the details and the full setup guide in our documentation →Five steps, from a written brief to three scenarios ready to optimise.
The clip runs through a real setup: a brief written in plain language, the script the assistant produces from it, the assumptions it flags back before running, and the three scenarios that come out the other side.
“Add a new step after step 1 that lists every project…” — and the wizard has one.
AI helps you build the interface. Sympheny optimises deterministically, as always.
A light app is a small, focused interface built on top of Sympheny — a wizard shaped like the study your team actually runs, rather than the whole platform. You build one, and change it, in the Light App Editor.
The clip shows one being changed. A written instruction adds a step to a deployed four-step wizard, the steps below it renumber, and the new screen is live two seconds later. The added step is not a mock: it calls GET /projects against the Sympheny backend and renders what comes back — owner, status, last update — for every project in the account.
A deployed four-step wizard gains a fifth from one written instruction, deployed in two seconds and loading live from the Sympheny backend. Silent clip; every step is captioned on screen. It shows an existing app being extended, not one built from empty.
Talk to us about using AI for your own light app →Everything an agent needs is already documented.
Two of the three are live today: the REST API and the Python SDK are fully documented and drivable now. The MCP server, which lets an assistant connect with no wiring on your side, arrives in the coming weeks.
REST API
Every endpoint is publicly documented, with JWT authentication, request and response schemas and a browser-based API Explorer. Projects, analyses, scenarios, stages, hubs, technologies and solver jobs are all reachable.
REST API reference →Python SDK
sympheny-toolbox, the official client, needs Python 3.11 or newer and installs with pip. It exposes every documented endpoint as a typed method with Pydantic validation, offers matching sync and async clients, and handles login, token caching and refresh for you. Worked guides cover running a solver job and downloading results.
Python SDK guide →MCP server
A remote Model Context Protocol server, so an assistant can call Sympheny over a standard connection instead of hand-written HTTP. Client setup for Claude Code, Claude Desktop, ChatGPT and Gemini CLI is documented ahead of launch, and the endpoint starts answering when the server ships in the coming weeks. Until then the REST API and the SDK do the same job — that is what the walkthrough above uses.
MCP setup, documented ahead of launch →The documentation is written to be read by machines as well as people.
docs.sympheny.com publishes llms.txt, a compact index of the documentation in navigation order with a one-line description per page, and llms-full.txt, the whole set concatenated in reading order. Both follow the llms.txt convention.
The practical effect is that you can hand an assistant a single URL and it has the platform in context — endpoints, schemas, model structure and worked examples — instead of crawling the site or guessing at the shape of an API.
The parts of the work worth automating.
Working with AI assistants and agents.
Can I use Sympheny with an AI assistant or agent?
Yes. Sympheny publishes a REST API and a Python SDK, both fully documented, so an assistant or coding agent can create scenarios, start optimisation runs and read results back. An MCP server arrives in the coming weeks. Setup is documented at docs.sympheny.com. Nothing is connected by default — you decide whether to use it.
Can I edit a light app with an AI assistant?
Yes. The Light App Editor takes a plain-language instruction and changes the app — the clip above adds a wizard step listing every project in the account, backed by a real API call and deployed in two seconds. What it shows is a change to an app that already exists; building one from empty is a longer job. Either way the optimisation behind it is unchanged: the same deterministic MILP engine solves the model.
If I connect an assistant, where does my data go?
To whichever provider you connect. If you point an assistant at Sympheny, the scenario data it sends and receives passes through that provider under your agreement with them, not ours. Sympheny does not choose the provider for you and embeds no third-party model in its own computation pipeline.
Does connecting an assistant change my results?
No. An assistant can build or modify a scenario, but the optimisation always runs on the same deterministic MILP engine. A scenario assembled by an assistant and the identical scenario assembled by hand produce the same results.
Note: An assistant can set the model up. It never solves it.
Do I need to be a developer to use this?
For the API and the SDK, yes — or you need someone on the team who is. Having the assistant write the script lowers the bar considerably, but somebody still has to run it and read what it did. The MCP server is what removes that step, and it arrives in the coming weeks. Until then this is a workflow for teams with some scripting capability; everyone else works in the web app, where the same optimisation runs.
Is under ten minutes realistic?
For the setup shown, yes: a three-variant project with demand drawn from the database, with the API specification available to the assistant and someone who knows what they want to model. A first attempt takes longer, and the optimisation run is on top of it. It is a claim about scenario setup, not about producing a finished energy concept.
When does the MCP server launch?
In the coming weeks. Client setup for Claude Code, Claude Desktop, ChatGPT and Gemini CLI is already documented, and the endpoint starts answering when the server ships. Everything the walkthrough above shows works today without it, through the REST API and the SDK. If you want to be told when MCP lands, mention it on a demo call or write to us.
Want to try it on a project of your own?
Start a free trial and drive it through the API, or book a demo and we will walk through the setup on a project that mirrors yours.