OmniRouter

OmniRouter is Lemonade's approach to multimodal agentic workflows. Instead of building a proprietary agent runtime into Lemonade, we expose each modality as an OpenAI-compatible tool that any existing LLM agent (Continue, OpenHands, Claude Code, your own app) can call against Lemonade's endpoints.

You bring the LLM loop. Lemonade brings the tools.

How it works

1. You describe the tools to your LLM in OpenAI tool-calling format.
2. The LLM decides which tool to call and with what arguments.
3. Your client code executes each tool_call against the corresponding Lemonade endpoint (/v1/images/generations, /v1/audio/speech, etc.) and feeds the result back as a tool message.
4. The LLM continues, calling more tools or producing a final response.

This is the standard OpenAI tool-calling loop. The tool schemas OmniRouter provides are plain JSON (no Lemonade-specific client library required), the endpoints they target are OpenAI-compatible, and the server returns standard response shapes.

Collections

A Collection is a preconfigured bundle of models sized for a hardware tier. Selecting a collection in the Lemonade desktop app loads one LLM + one image model + one ASR + one TTS — all the pieces OmniRouter's tools need in a single click.

Collection	LLM	Image	ASR	TTS
Ultra Collection	Qwen3.5-35B-A3B-GGUF	Flux-2-Klein-9B-GGUF (gen + edit)	Whisper-Large-v3-Turbo	kokoro-v1
Lite Collection	Qwen3.5-4B-GGUF	SD-Turbo (gen only)	Whisper-Tiny	kokoro-v1

Collections are hidden from the default /v1/models listing so OpenAI-compatible clients don't see "Ultra Collection" as if it were a real model. They surface with ?show_all=true and appear in the desktop app's model list.

Use a Collection. Every part of this doc assumes one is loaded — the desktop app, examples/lemonade_tools.py, and the tools themselves were all validated against the Ultra and Lite Collections above.

If you're the developer wiring OmniRouter into your own agent and you want to substitute models, you can, but you take on the compatibility work: any LLM you swap in must carry the tool-calling label, and each tool you want to call needs one downloaded model whose labels include the row's "Needs a model with label" entry from the tools table below. That's a developer-path discovery step, not a user configuration; the simple answer for everyone else is "install a Collection."

Available tools

The canonical definitions live in src/app/src/renderer/utils/toolDefinitions.json — a single source of truth used by the desktop app and this documentation.

Tool	Endpoint	Needs a model with label
generate_image	POST /v1/images/generations	image
edit_image	POST /v1/images/edits	edit
text_to_speech	POST /v1/audio/speech	tts or speech
transcribe_audio	POST /v1/audio/transcriptions	audio or transcription
analyze_image	POST /v1/chat/completions	LLM with vision

Endpoint request/response shapes are documented in the Endpoints Spec.

Quick start

pip install openai
python examples/lemonade_tools.py "Generate an image of a sunset"
python examples/lemonade_tools.py "Say hello world out loud"

examples/lemonade_tools.py shows the full agentic loop — tool definitions, LLM call with tools=[...], executing each tool_call, and feeding the result back. Fewer than 150 lines of Python.

Using your own agent

Integrate OmniRouter into an existing agent by following the pattern in examples/lemonade_tools.py:

1. Point your OpenAI-compatible client at http://localhost:13305/v1.
2. Copy the tool entries from toolDefinitions.json into your agent's tool list (or load the JSON directly).
3. When your agent receives a tool_call for one of these tools, POST to the corresponding endpoint from the table above and feed the response back to the LLM as a tool message.
4. If you want to pick models programmatically rather than rely on a Collection being loaded, query GET /v1/models?show_all=true and match the labels array against the "Needs a model with label" column above.

The example script implements all four steps end-to-end against the generate_image and text_to_speech tools.