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What you can do with Agent Jev

AgentJev-0.6B is a Python model server that scores a state, such as a diff, log, or trace, against questions you supply and returns calibrated probabilities in one forward pass without generating text.

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malevrigns/agent-jev on GitHub

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What do you want to do?

4 use cases

  • Gate agent merges on test output probability

    Ask AgentJev whether a test run passed and block the merge when the probability is too low.

    View prompt
    In the agent-jev repository at <repo-path>, start the coding-gates server on port 8149, send the test output in <log-file> with the boolean question 'Did the tests pass?', and return the probability of true. Verify the response by sending a log from a known failing run and confirming the probability is low.

  • Pick the next tool call from a fixed option list

    Give AgentJev the current state and a list of allowed next actions, then route to the top option.

    View prompt
    Using the agent-jev repository at <repo-path>, send the state in <state-file> with a choice question listing the allowed next actions in <action-list>. Return the top option, top_probability, and margin. Verify that the returned option is one of the options you supplied.

  • Score tickets or traces on your own rubric

    Place a ticket or agent trace on a 2 to 10 level rubric and get the level and expected score.

    View prompt
    In the agent-jev repository at <repo-path>, score each item in <items-folder> with a score question built from the rubric in <rubric-file>. Return level, score, and the full distribution for each item. Verify that every level is within the rubric range and that score matches the weighted sum of the distribution.

  • Benchmark AgentJev serving latency on your load

    Run the repository's serving benchmark to see case latency and throughput on your own GPU.

    View prompt
    In the agent-jev repository at <repo-path>, run `python -m jev_service.benchmark_serving` on this machine and report P50 case latency, P90 case latency, and throughput. Verify that the run used the same checkpoint you plan to deploy.

How to use Agent Jev

  1. Check you have what it needs: Python with PyTorch and Transformers installed (the README benchmarks used PyTorch 2.9.1+cu128 and Transformers 4.57.6); A GPU; the README timings were measured on one RTX 5060 with 8 GB; Git access to clone malevrigns/agent-jev and download weights from the linked Hugging Face model repository.
  2. Open malevrigns/agent-jev on GitHub and read the README and license.
  3. Clone the repository with git clone https://github.com/malevrigns/agent-jev and enter the folder. (inferred, check the README)
  4. Fetch the coding-gates candidate weights with python fetch_v1_weights.py --variant coding-gates.
  5. Start the server with python -m jev_service.server --checkpoint models/agentjev-coding-gates/model.safetensors --model-path models/agentjev-coding-gates --temperatures models/agentjev-coding-gates/temperatures.json --port 8149. (inferred, check the README)
  6. Send the captured test output as the state and the boolean question 'Did the tests pass?' to http://localhost:8149 using the request format in the repository's HTTP section. (inferred, check the README)

These steps come from our top use case, Gate agent merges on test output probability, which also has a copy-paste agent prompt.

Agent Jev alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forBuilt for developers and agent builders who need a fast gate, router, or scorer inside an agent loop rather than a text-generating model.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/malevrigns/agent-jev · Back to Hot repos

Requirements

  • Python with PyTorch and Transformers installed (the README benchmarks used PyTorch 2.9.1+cu128 and Transformers 4.57.6)
  • A GPU; the README timings were measured on one RTX 5060 with 8 GB
  • Git access to clone malevrigns/agent-jev and download weights from the linked Hugging Face model repository
  • A state to evaluate, such as a test runner summary, plus questions you have already phrased as boolean, choice, or score
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Questions about Agent Jev

What is Agent Jev?

AgentJev-0.6B is a Python model server that scores a state, such as a diff, log, or trace, against questions you supply and returns calibrated probabilities in one forward pass without generating text. It is an open-source project on GitHub: https://github.com/malevrigns/agent-jev.

Who is Agent Jev for?

Built for developers and agent builders who need a fast gate, router, or scorer inside an agent loop rather than a text-generating model.

What can I do with Agent Jev?

4 use cases so far, for example: Gate agent merges on test output probability; Pick the next tool call from a fixed option list; Score tickets or traces on your own rubric. Each has steps and a copy-paste agent prompt.

What do I need to run Agent Jev?

Python with PyTorch and Transformers installed (the README benchmarks used PyTorch 2.9.1+cu128 and Transformers 4.57.6); A GPU; the README timings were measured on one RTX 5060 with 8 GB; Git access to clone malevrigns/agent-jev and download weights from the linked Hugging Face model repository; A state to evaluate, such as a test runner summary, plus questions you have already phrased as boolean, choice, or score.

What are alternatives to Agent Jev?

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

Is Agent Jev 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.