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What you can do with Awesome Decision Models

A curated, community-maintained list of decision models (models that return option probabilities instead of generated text), with the hosted APIs, open-weight models, runtimes, SDKs, benchmarks, and papers around them.

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AnotiaWang/awesome-decision-models on GitHub

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What do you want to do?

4 use cases

  • Route support tickets with a hosted decision API

    Send a ticket and your label set to a hosted decision model and get a probability for each label.

    View prompt
    Using the Hosted APIs section of github.com/AnotiaWang/awesome-decision-models, pick one hosted decision model and call its /v1/systemone endpoint with the ticket text in <ticket-file> and these Choice options: billing, bug, account, other. Expected output: one probability per option that sums to about 1. Verify that the response contains a probability for every option you sent.

  • Run a small open decision model locally

    Pull a small open-weight decision model into Ollama and score your own options on your own machine.

    View prompt
    Using the Open Models section of github.com/AnotiaWang/awesome-decision-models, pick one model that the README lists as available in Ollama and run it locally on the sample state in <sample-file> with the options in <options-file>. Expected output: one probability per option. Verify the license of the chosen model in its model card before you use the output in a product.

  • Check agent tool calls before they run

    Score a proposed tool call against your own allowed options before the agent executes it.

    View prompt
    Using github.com/strands-labs/strands-decider linked from github.com/AnotiaWang/awesome-decision-models, set up the Strands Decider 2B model and score the pending tool call in <tool-call-file> against the allowed actions in <allowed-actions-file>. Expected output: a probability per allowed action and a block or allow decision. Verify that a call outside the allowed list gets a low probability on every option.

  • Flag risky behavior in AI traces with Span-01

    Label each AI trace for behaviors you define, such as prompt injection or agent loops.

    View prompt
    Using the Span-01 entry in github.com/AnotiaWang/awesome-decision-models, classify the traces in <traces-folder> for prompt injection and agent loops through the OpenRouter route the README links. Expected output: one present, absent, or not observable value per behavior per trace. Verify by reading three traces marked present and confirming the behavior is actually there.

How to use Awesome Decision Models

  1. Check you have what it needs: A web browser to open the repo's README and the linked provider docs; An account and API key for the hosted provider you choose (for example TypeSafe console, OpenRouter, or Microsoft Foundry); For local runs: Ollama installed, or a Python environment with the package named in the model's own page.
  2. Open AnotiaWang/awesome-decision-models on GitHub and read the README and license.
  3. Open the Hosted APIs section of the repo and pick a model; Jev and Microsoft-Decision-1 both describe a Choice question over text.
  4. Create an API key in the provider console (for Jev, the TypeSafe console linked from the repo).
  5. Write your queue names as the Choice options and the ticket text as the state.
  6. Send one request to the provider's /v1/systemone endpoint and read back the probability for each option.

These steps come from our top use case, Route support tickets with a hosted decision API, which also has a copy-paste agent prompt.

Awesome Decision Models alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • A web browser to open the repo's README and the linked provider docs
  • An account and API key for the hosted provider you choose (for example TypeSafe console, OpenRouter, or Microsoft Foundry)
  • For local runs: Ollama installed, or a Python environment with the package named in the model's own page
  • A list of your own options (labels, tool names, or rubric levels) to score
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Questions about Awesome Decision Models

What is Awesome Decision Models?

A curated, community-maintained list of decision models (models that return option probabilities instead of generated text), with the hosted APIs, open-weight models, runtimes, SDKs, benchmarks, and papers around them. It is an open-source project on GitHub: https://github.com/AnotiaWang/awesome-decision-models.

Who is Awesome Decision Models for?

Developers and AI agent builders who need to classify, route, or gate inputs and want to compare decision models before picking one.

What can I do with Awesome Decision Models?

4 use cases so far, for example: Route support tickets with a hosted decision API; Run a small open decision model locally; Check agent tool calls before they run. Each has steps and a copy-paste agent prompt.

What do I need to run Awesome Decision Models?

A web browser to open the repo's README and the linked provider docs; An account and API key for the hosted provider you choose (for example TypeSafe console, OpenRouter, or Microsoft Foundry); For local runs: Ollama installed, or a Python environment with the package named in the model's own page; A list of your own options (labels, tool names, or rubric levels) to score.

What are alternatives to Awesome Decision Models?

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

Is Awesome Decision Models 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.