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What you can do with Rizzo Flow

Rizzo Flow is an open, local-first tool that returns typed, probabilistic decisions (yes/no, choices, rubric scores, numbers) from unstructured state using a local LLM over an HTTP API.

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Rizzo-AI-Academy/rizzo-flow on GitHub

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What do you want to do?

4 use cases

  • Get typed LLM decisions from text on your own machine

    Run a local decision API and get yes/no, choice and score answers as JSON with probabilities.

    View prompt
    In the rizzo-flow repo, start the server with `uv run rizzo serve`, then POST a JSON body with the state from <path-to-text-file> and one yes/no question to http://127.0.0.1:8017/v1/systemone using model rizzo-latest. Return the typed decision and its probabilities. Verify that the response contains a probability for each option.

  • Triage a support ticket from the command line

    Run one decision script on a ticket file without starting a server.

    View prompt
    In the rizzo-flow repo, write a JSON file at <path-to-ticket.json> with the ticket text as state and an is_urgent question, then run `uv run rizzo decide <path-to-ticket.json>`. Return the decision and its probability. Verify that the output is a typed answer and not free text.

  • Run existing TypeSafe Jev API code against localhost

    Change one URL so code written for the TypeSafe API calls a local model instead.

    View prompt
    In the rizzo-flow repo, start the server with `uv run rizzo serve`, then point the existing TypeSafe API client in <path-to-app> at http://127.0.0.1:8017 and run its test suite. Return the failing or passing tests. Verify that each decision response has the same shape the client expects.

  • Run a smaller or quantized model on modest hardware

    Pick a lighter model size or quantization to run decisions on a laptop with limited memory.

    View prompt
    In the rizzo-flow repo, run `uv run rizzo devices`, then start the server with `uv run rizzo serve --size 1.7b` and send the same three questions from <path-to-test-file> that were answered by the default model. Return the answers and probabilities side by side. Verify that accuracy differences are recorded before choosing a model.

How to use Rizzo Flow

  1. Check you have what it needs: Python 3.11 or newer; git; uv (https://docs.astral.sh/uv/).
  2. Open Rizzo-AI-Academy/rizzo-flow on GitHub and read the README and license.
  3. Install Python 3.11 or newer, git and uv.
  4. Run git clone https://github.com/Rizzo-AI-Academy/rizzo-flow && cd rizzo-flow
  5. Run uv sync --locked
  6. Run uv run rizzo download and wait for the model and runtime to finish.

These steps come from our top use case, Get typed LLM decisions from text on your own machine, which also has a copy-paste agent prompt.

Rizzo Flow alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Python 3.11 or newer
  • git
  • uv (https://docs.astral.sh/uv/)
  • About 4.4 GB of free disk space for the default 4B model, plus the llama.cpp runtime (about 11 MB on a Mac, 30 MB with Vulkan, about 570 MB with CUDA)
  • A machine that can run llama.cpp: an Apple, NVIDIA, AMD or Intel GPU, or no GPU at all
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Questions about Rizzo Flow

What is Rizzo Flow?

Rizzo Flow is an open, local-first tool that returns typed, probabilistic decisions (yes/no, choices, rubric scores, numbers) from unstructured state using a local LLM over an HTTP API. It is an open-source project on GitHub: https://github.com/Rizzo-AI-Academy/rizzo-flow.

Who is Rizzo Flow for?

Developers and small teams who want typed LLM decisions from text on their own machine, without sending data to a hosted service.

What can I do with Rizzo Flow?

4 use cases so far, for example: Get typed LLM decisions from text on your own machine; Triage a support ticket from the command line; Run existing TypeSafe Jev API code against localhost. Each has steps and a copy-paste agent prompt.

What do I need to run Rizzo Flow?

Python 3.11 or newer; git; uv (https://docs.astral.sh/uv/); About 4.4 GB of free disk space for the default 4B model, plus the llama.cpp runtime (about 11 MB on a Mac, 30 MB with Vulkan, about 570 MB with CUDA).

What are alternatives to Rizzo Flow?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Rizzo Flow safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.