awesome-jev README

The project's README from github.com/kydlikebtc/awesome-jev. As one markdown file: README.md.

awesome-jev — Jev Decision Atlas: 1,207 public resources, 1,204 dated HTTP 2xx link records, and 1,121 call-site citation records. Counts describe saved records, not current link availability or passed runtime and performance tests.

 Explore the catalogue ↗   First call   Adapt a project   Independent reports   中文 

Counts describe saved link and evidence records, not current CI passes or runtime tests. About these counts

On this page · full reading map

Patterns · Compatibility · Vetting

What this is

[!NOTE] Not the product, not an SDK, not affiliated with TypeSafe AI, and not a recommendation. Inclusion is a source record, not a runtime or performance endorsement. See what is verified.

What Jev returns

Three primitives. Every pattern below is built out of them, and the asymmetry in the last row is the single most common source of bugs.

Three panels describing the choice, score and noul primitives and what each returns

Input is text only — string, JSON object, or array of text. Context is 64k tokens per request, 32k for the state plus the longest question. Output tokens are free. There are no published weights, so it cannot be run locally. Full cross-platform differences: docs/compatibility.md.

Start here

Six things in reading order. Hand-picked, because "most starred" is not the same as "read this first".

  1. Quickstart

    The canonical first call: one support ticket, one Choice, one Score and one Noul in a single request, in Python, JS and cURL.

  2. Jev 1.13 known limitations

    The most useful page in the docs and the least linked. It explains, among other things, that a Choice over options and one Noul per option answer different questions.

  3. Example: three primitives in one request

    Written from the official API reference and checked field by field against it, but not executed against the live API.

  4. fast-jev-compaction

    Exactly two nouls per tool call: does knowing this call happened still matter, and is the full output still needed verbatim. Despite the word "scored" in its own description, no score primitive is used.

  5. ai-cookbook: Jev track

    The best structured tutorial found. It states plainly that typed output does not guarantee a correct decision, lists the documented weaknesses, and qualifies its own cost illustration rather than selling it.

  6. Hermes Agent: Jev compaction evaluation

    The single most credible row in this catalog. Recall came out below their existing summariser, and at a matched context budget it tied plain recency ordering. Cost was genuinely far lower. Publishing a negative result on a hyped model is rare.

Coverage

Every decision pattern, sized by how many entries this catalogue contains. Use the pattern index below the chart to jump to a section. A zero is a research gap, not a rendering bug.

Horizontal bar chart of how many catalog examples exist for each of the eighteen decision patterns

All 18 patterns have at least one catalogue entry. Coverage does not imply runtime testing or equal maturity. See docs/status.md.

Pattern index · jump to the examples

Decision pattern Decision pattern
Tool selection · 230 Intent routing · 35
Context compaction · 34 Safety gating · 138
Output validation · 134 Retry control · 6
Human escalation · 67 Model routing · 43
Speculative fan-out · 32 Search & ranking · 64
Structured extraction · 16 Classification · 119
ML feature extraction · 8 Document triage · 20
Support triage · 8 Content scoring · 164
Recommendation · 1 Overview · 451

Measured, not claimed

Independent measurement reports in the catalogue, including negative results that help explain where an approach fails. These are the original authors' measurements; this repository has not independently reproduced them. Check each report's dataset, method and model version before comparing results.

By decision pattern

The primary index. Each heading is a decision an agent has to make; the rows are examples of making it. Caveats appear as short tags — the full note for each row is in catalog.json and on the site.

Tool selection

Which tool or action the agent should call next.

10 of 230 shown · all 230 on one page → · filter on the site

↑ Pattern index

Intent routing

Classify what the user wants and send the request down the right branch.

10 of 35 shown · all 35 on one page → · filter on the site

↑ Pattern index

Context compaction

Decide which tool calls and results still matter so stale context can be dropped.

10 of 34 shown · all 34 on one page → · filter on the site

↑ Pattern index

Safety gating

Decide whether an action is safe to run. Defence in depth, never a security boundary.

10 of 138 shown · all 138 on one page → · filter on the site

↑ Pattern index

Output validation

Check a model's output against a rubric before it reaches a user.

10 of 134 shown · all 134 on one page → · filter on the site

↑ Pattern index

Retry control

Decide whether a failed step is worth retrying.

All 6 shown · on its own page · filter on the site

↑ Pattern index

Human escalation

Use calibrated confidence to decide what a person must see.

10 of 67 shown · all 67 on one page → · filter on the site

↑ Pattern index

Model routing

Pick which downstream model or tier should handle a request.

10 of 43 shown · all 43 on one page → · filter on the site

↑ Pattern index

Speculative fan-out

Pack many questions — including speculative ones — into one request and let code pick what mattered.

10 of 32 shown · all 32 on one page → · filter on the site

↑ Pattern index

Search & ranking

Score or re-rank candidates from a cheaper retrieval step.

10 of 64 shown · all 64 on one page → · filter on the site

↑ Pattern index

Structured extraction

Pull typed fields out of messy text by choosing among candidates rather than generating them.

10 of 16 shown · all 16 on one page → · filter on the site

↑ Pattern index

Classification

Put an item into a taxonomy, including deep hierarchies walked with probabilities.

10 of 119 shown · all 119 on one page → · filter on the site

↑ Pattern index

ML feature extraction

Turn free text into numeric features for a classical downstream model.

All 8 shown · on its own page · filter on the site

↑ Pattern index

Document triage

Classify and route incoming documents, invoices and forms.

10 of 20 shown · all 20 on one page → · filter on the site

↑ Pattern index

Support triage

Route support tickets and conversations by intent and urgency.

All 8 shown · on its own page · filter on the site

↑ Pattern index

Content scoring

Score quality, risk or relevance on an ordered scale.

10 of 164 shown · all 164 on one page → · filter on the site

↑ Pattern index

Recommendation

Choose what to surface next, fast enough for a live conversation.

All 1 shown · on its own page · filter on the site

↑ Pattern index

Overview

Surveys the model or the space rather than one pattern.

10 of 451 shown · all 451 on one page → · filter on the site

↑ Pattern index

By resource kind

The same rows grouped by what you will find when you open the link.

Kind Examples What you will find
Official docs 31 Vendor documentation, cookbooks and pattern pages.
SDK 94 Client libraries, official and community.
Integration 34 A gateway, framework or platform route to the model.
Snippet 4 Small runnable examples in this repository.
Project 653 An application or library that calls Jev in anger.
Plugin 238 Editor, agent and MCP integrations you can install.
Tutorial 9 Step-by-step material with code.
Benchmark 70 Measurement. Check whether it is independent or vendor-reported.
Article 12 Explainers, analysis and launch coverage.
Video 3 Walkthroughs and reviews.
Discussion 2 Threads worth reading, including the sceptical ones.
Jev-like alternative 57 Independent reimplementations. These do NOT call Jev.

Also in this repo

The parts that are not the catalog.

Preview the searchable catalogue

Searchable Jev catalogue with curated paths, filters, dated source evidence and entry cards

Filter by clicking a bar. Two more views: primitives · compatibility. Every filter and entry is a shareable URL.

File What it is
docs/patterns.md Every pattern defined, each with an explicit when NOT to use this.
docs/compatibility.md Model string, field names, request shape, endpoint and env var differ per platform. This is that table.
docs/vetting.md What to check before trusting a row, and the one mistake most people make.
docs/status.md What week one of this ecosystem actually looked like, gaps included.
docs/method.md How the catalog was built, what was excluded, and where it is weakest.
docs/sources.md Where every row came from, and the licence position.
examples/ Four runnable examples. One deliberately leaves the threshold policy to you.
schema/entry.schema.json What a catalog entry may contain.
.claude-plugin/ Install the skill and the MCP server together in Claude Code: /plugin marketplace add kydlikebtc/awesome-jev, then /plugin install awesome-jev@awesome-jev.
src/awesome_jev_mcp/ An MCP server, so an agent can query the catalogue instead of reading it. Caveats travel with every result, and so does how current the data is.
skills/awesome-jev/ An agent skill: the facts that generated Jev code most often gets wrong, and the design rules worth following.
scripts/verify_claims.py Re-reads every cited call site weekly, so a primitive claim is checkable rather than asserted.
scripts/refresh_metadata.py Re-reads stars, licences and archive status from the GitHub API and opens a PR.

What is verified, and what is not

What the tags mean

Tag Means
not Jev itself Does not call Jev at all. A compatible API does not imply compatible calibration, so thresholds do not transfer.
shadow mode Wired in but deliberately inert — nothing it returns reaches a user-visible decision.
early access Needs waitlist access to run.
code untested The code was read, not executed.
one commit One commit, so maintenance is unlikely.
no licence No LICENSE file, whatever a README badge claims. A blocker for reuse.
3rd-party key Needs a key for a service other than TypeSafe.
vendor numbers Repeats the vendor's own benchmarks rather than an independent measurement.
unverified claims Makes measurement claims that could not be checked.
AI-written Reads as machine-generated content.
marketing Published to sell something as much as to explain.
paywall Behind a paywall or a metered reader.
archived Development has visibly stopped.

Links that stopped resolving, kept so a dead reference stays searchable instead of vanishing.

Example Why
jev-atlas Retired 2026-09-24: the repository returns 404 on both the API and the web while its owner's account still exists — deleted or made private. Kept here so the reference stays searchable. HTTP 404
jev-mac-voice Retired 2026-09-24: the repository returns 404 on both the API and the web while its owner's account still exists — deleted or made private. Kept here so the reference stays searchable. HTTP 404

Machine-readable data

One entry per example, validated against a JSON Schema on every push.

File What it is
catalog.json 1207 entries
retired.json 2 retired
compat.json The platform matrix behind docs/compatibility.md
patterns.json The decision taxonomy both generators and the MCP server read
collections.json Bilingual editorial paths, selection reasons and limitations
schema/entry.schema.json One entry's shape
llms.txt For agents, with the caveats spelled out

Contributing and licence

Corrections take priority over additions — a wrong row costs more than a missing one. See CONTRIBUTING.md; the bar is could a reader act on this row without opening the link?

Code in scripts/, site/ and examples/ is MIT. Catalog metadata is CC0-1.0, with a per-row license field. Linked works keep their own licences — repo_license records what each declares.

Maintenance checks: lint · Scheduled link checks · Call-site text checks

Jev Decision Atlas · ↑ Back to top · 中文

awesome-jev (kydlikebtc/awesome-jev)

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602 ·GitHub
Awesome-jev
这是什么？一个为 Jev（TypeSafe AI 的 System One 决策模型）整理的索引，收录了 1207 个公开资源，按照每个资源所展示的决策类型进行分类。这是一个独立的目录，不是产品本身，也不是 SDK。
那 Jev 本身是什么？TypeSafe AI 推出的一个决策模型：你传入状态和带类型的问题，它返回带校准置信度的带类型答案——速度快、成本低，足以放在智能体（agent）的内部循环中使用。目录中的每种模式都建立在三个基础原语之上。
我该怎么开始使用这个目录？从“Start here”部分开始：六个精选资源，按阅读顺序排列——quickstart、Jev 1.13 已知限制页面、一个三原语示例，然后是三个经过评估的推荐，其中包括一个负面结果。
要真正使用 Jev，我需要什么？只需要文本——一个字符串、JSON 对象或数组；每次请求有 64k token 的上下文（状态加最长的问题共 32k）。输出 token 是免费的，但没有公开的模型权重，所以 Jev 只能通过 API 运行，无法在本地运行。
这里面的内容有在运行时真正验证过的吗？没有——那些数量统计的是已保存的链接和证据记录，并不是 CI 通过或运行时测试；覆盖面广不代表成熟度相同，而且链接中的测量数据都是原作者自己的结果，这里没有复现过。
Jev 的三个原语是什么？Choice、Score 和 Noul——三种带类型的判断，能把自然语言描述的状态转化为分类、打分和路由决策。目录特别指出最后一个原语的不对称性是最常见的 bug 来源，并说明针对选项列表的一个 Choice，与每个选项各用一个 Noul，回答的是不同的问题。
为什么按决策模式来组织，而不是一个平铺的链接列表？作者认为你这周读的任何单个资源都是一次性的，而决策模式才是长期有价值的部分。覆盖数为零的模式会被标记为研究空白，而不是页面渲染错误。
这个目录是双语的吗？机器可读吗？是的——全文提供英文和中文，带有 JSON schema、机器可读的数据部分，以及 docs/compatibility.md 中的平台兼容性说明。
What are similar projects?awesome-jev-live, awesome-jev-projects, awesome-jev, AnyJev, awesome-mcp-servers, Harness-RSI, StartLux-Decision, ai-agents-the-definitive-guide
Which topics is it in?typesafe-ai, system-one, calibration, json-schema, decision-model, jev, awesome-lists, llm-evaluation, bilingual, agent-tools, classification, tool-calling
Creator
kydlikebtc
Size
11.6 MB
Stars
602
Updated
CommitOct 15
Age
27d
Commits · 90 Days