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Jev Projects Added This Week

Discover 46 new open-source Jev projects added this week to bestjev, with verified GitHub repositories, languages, stars, and implementation details.

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GitHub star growth for heyjunpenn/awesome-jev over time
ProjectLanguageAddedDescription
  1. langwatch/langwatch

    Frameworks & integrations
    4,883TypeScript

    LLM evaluation platform whose Instant Evals sends production traces to Jev for batched Noul, Score, and Choice judgments.

  2. TokenRhythm/NeoHorse

    Jev-like models
    1,289Python

    Open 4B decision model with prefill-only Choice, Noul, and Score inference over text or one image, plus native, vLLM, and SGLang runtimes.

  3. feder-cr/jev

    Jev-like models
    1,060Python

    Local 1B CPU decision model with Jev-compatible Noul responses; it returns P(yes) and explicitly rejects Choice and Score.

  4. firelex/jeff

    Jev-like models
    1,052Python

    Local Qwen3.5 and Gemma 4 fine-tunes with a Jev-compatible API for typed Choice, Noul, and Score decisions in one forward pass.

  5. dzhng/jevgrep

    Agent tooling
    864TypeScript

    Semantic code-search CLI for coding agents; Jev selects relevant repository branches, files, and source units for a natural-language behavior query.

  6. qybaihe/mu

    Agent tooling
    325TypeScript

    Coding-agent workspace where Jev or a local Laya judge gates bounded decisions such as context retention, approvals, routing, drift, and inter-agent sharing.

  7. codejunkie99/keel

    Agent tooling
    314Rust

    Local-first coding workspace where Jev or Laya selects an eligible agent/model route and the host validates every choice before execution.

  8. PostHog/jeeves

    Jev-like models
    279Python

    Reasoning Jev-style 9B classifier with a diffusion drafter and compatible API for typed Noul, Choice, and Score decisions.

  9. InternLM/Intern-Decision

    Jev-like models
    253Python

    Multimodal Qwen3.5 decision models that answer multiple typed Choice, Score, and Noul questions with probabilities in one forward pass.

  10. FluidInference/FluidUse

    Browser & computer use
    212Swift

    Apple-silicon computer-use toolkit that runs local typed-decision models to choose form-fill actions from macOS accessibility state.

  11. shengjidaguai-china/goutoujunshi-jev-chat

    Applications
    198Python

    Cross-platform chat assistant where Jev chooses a bounded reply strategy from reviewed conversation context before the user sends any draft.

  12. imikerussell/beebots

    Games & simulations
    153TypeScript

    Paper-trading race where Jev chooses each bee's valid market move and conviction while deterministic code enforces risk limits and execution.

  13. mohit67890/imajev

    Jev-like models
    137Python

    Local multimodal decision models that answer Jev-compatible typed questions over records, text, and up to two images, with explicit abstention.

  14. aaddrick/building-with-typesafe-jev

    Agent tooling
    93Python

    Installable agent skill that teaches Jev typed-decision design, validates live API shapes, and indexes reusable patterns from community projects.

  15. Avinash-jetwani/jevmem

    Agent tooling
    92TypeScript

    Project-memory tooling for coding agents; Jev decides which turns to save, their kind, contradictions, recall relevance, and suspected memory poisoning.

  16. christianmat/jev-pokemon

    Games & simulations
    87TypeScript

    Pokémon Red harness where Jev chooses every menu, navigation, team, and battle action from legal options exposed by the emulator.

About the catalog

What is bestjev?

bestjev is an independent, community-maintained directory of open-source projects built around Jev, TypeSafe AI's System One model for typed decisions.

Each listing links to its original repository and records its category, primary language, GitHub star snapshot, and a localized description, helping developers find verifiable Jev SDKs, agents, integrations, applications, research, and tools.

bestjev is not affiliated with or endorsed by TypeSafe AI.

What is Jev?

Jev is TypeSafe AI's System One model for decisions that software needs to consume. Instead of generating free-form prose, it accepts a state and one or more typed questions, then returns structured answers with calibrated probabilities.

It fits workflows where the option space or scoring rubric is known in advance: routing an agent, validating a tool call, ranking candidates, checking a policy, or deciding whether an action should proceed.

Operational advantage

Applications can set deterministic thresholds, inspect uncertainty, and route low-confidence cases to a person or a larger model.

CHOICE

Pick one option

Select from a known set and return probabilities with confidence.

SCORE

Rate on a rubric

Place an input on an ordered scale with a full distribution.

NOUL

Estimate truth

Return the probability that a statement is true, from zero to one.

How is this catalog verified?

Each entry is checked against public evidence: the upstream repository, project-authored documentation, and visible Jev usage. Stars are a dated discovery snapshot, not a quality ranking.

“Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.”— TypeSafe AI, introducing Jev

Which sources does bestjev use?

Contribute

How to submit a new project

Know a public project that uses Jev? Send us its repository and the evidence needed to verify it. Submissions are reviewed before they appear in the catalog.

Submit a projectOpens a pre-filled GitHub issue.
  1. 01

    Share the repository

    Provide the public GitHub URL and a concise description of what the project does.

  2. 02

    Show how it uses Jev

    Link to code, documentation, a demo, or another public source that makes the Jev integration verifiable.

  3. 03

    We review and publish

    We verify the evidence, choose the right category, and add accepted projects in a future catalog update.

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Guide video: Explore the Showcase

Explore the Showcase

Browse real Jev demos by use case, search the complete collection, and open any card to watch the video with its original source.