---
title: "Apps with a built-in Jev option"
type: community
source_tier: community
tags: [integrations, apps, vendors, community]
created: 2026-10-05
updated: 2026-10-06
confidence: medium
sources: "6 files, listed under this page in https://jevwiki.ai/index.json (raw/ paths map to their URLs in https://jevwiki.ai/raw/MANIFEST.json); links are inline in the body"
jev_version: "jev-1.13.0"
summary: "Apps that ship Jev as a built-in option (Langfuse, MLflow, n8n, Inbox Zero, MotherDuck and others): what Jev does there, how to turn it on, status; split from framework-integrations 2026-10-05."
---

# Apps with a built-in Jev option

> **TL;DR** Products that ship Jev as a setting or node, so you can use it without writing a client: what Jev decides there, how to turn it on, and each vendor's status. Community tier; vendor claims are labelled. Libraries and frameworks you code against stay on [[ideas/framework-integrations]].

## Apps with a built-in Jev option

| App | What Jev does | Turn it on | Status | Note |
|---|---|---|---|---|
| Langfuse "Jev as a judge" ([docs](https://langfuse.com/docs/evaluation/evaluation-methods/jev-as-a-judge)) | **by Langfuse**, marked experimental ("details of the UI and score format may still" change): a decision-model evaluator; you map observation fields into `state`, add 1 to 50 questions (Langfuse's cap), all answered in one call, each written back as one score (Choice → `CATEGORICAL` option, Score → `NUMERIC` expected level, Yes/no → `NUMERIC` P(true)); probabilities, `confidence` and resolved `model` under `metadata.typesafe`; templates for topic, out-of-scope, frustration and a seven-signal conversation check; runs on live rules, batch evaluation and experiments; execution traces in environment `langfuse-llm-as-a-judge` | Settings → LLM Connections, adapter `typesafe`; upstream TypeSafe, Vercel AI Gateway or OpenRouter, billed by that upstream; models `jev-1.13.0`, `jev-latest` | Langfuse docs · captured 2026-09-28 | Limits (255 options, 2 to 10 levels, ~32k for state plus longest question, input-only pricing) `verified` against [[reference/models-and-pricing]] and [[concepts/score]]. **Stale on access:** its FAQ says access "runs through a waitlist" and links the old `/settings/keys` path; TypeSafe reopened signup on 2026-09-27 and the docs link `/keys` ([[entities/typesafe-console]]). State goes to TypeSafe via the chosen upstream: check its data terms. Build and results: [[ideas/builds-apps]] (Langfuse trace judge) |
| MLflow ([blog part 2](https://www.mlflow.org/blog/jev-llm-judge-part-2), Databricks, 2026-09-25) | **by MLflow**: MLflow 3.17 (unreleased at 2026-09-25) previews Jev in built-in scorers and `make_judge` via `typesafe:/jev-latest`; the probability lands in feedback metadata `typesafe.probability` | MLflow 3.17 | preview | Judge results: [[ideas/head-to-head-retrieval-and-judging]] |
| n8n "TypeSafe AI" node ([announcement](https://community.n8n.io/t/typesafes-jev-is-now-in-n8n-free-on-cloud-via-gateway-credits-until-october-10/317957), Oli Morris, n8n team, 2026-09-30; **vendor**) | **by n8n**, pitched as a "smart If node": operation "Route item by System One question" with State (mapped fields, or the whole item as JSON), Instructions and one Route per outcome, each a named output like If or Switch; Jev's confidence can send unsure items to a person (n8n's linked video gates at 95%) | n8n Cloud (Starter, Pro, new trial): built in, runs on n8n Gateway credits, no TypeSafe account or key; self-hosted or Community Edition: install the TypeSafe AI node with your own key | **free on Cloud until 2026-10-10 23:59 UTC, per n8n**; then n8n's standard Gateway credit rates | Node name and Route operation match TypeSafe's own node (`@typesafe-ai/n8n-nodes-typesafe-ai`, Evaluate and Route; first on npm 2026-09-29, our check 2026-10-01; [[entities/github-repos]]); the post does not say Cloud runs that same package (inferred, not confirmed). On Cloud your state goes through n8n's Gateway to TypeSafe: check n8n's data terms. $42 per Btok input, output free `verified` ([[reference/models-and-pricing]]); "238x lower" than Fable 5.1 is TypeSafe's claim. n8n's advice matches the wiki's: a clean rule (`amount > 10000`) stays an If node; text output needs an LLM. Community nodes: [[ideas/tools-and-integrations]]. An n8n email-routing build (Gmail to Jev to Slack): [[ideas/builds-business]]. P06, P22 |
| [Inbox Zero](https://github.com/elie222/inbox-zero) (elie222; open-source email assistant, 12314★ on 2026-09-23; [decision-model code at `de44248`](https://github.com/elie222/inbox-zero/tree/de44248153af42a224b28e0395ea7454c945d318/apps/web/utils/decision-model)) | optional "specialized decision model": Choice over the user's rules, cold-email yes/no, sender categories, thread status, unsubscribe pages, reply-memory selection; raw fetch to `/v1/systemone`, 30 s timeout | operator sets `DEFAULT_DECISION_MODEL=typesafe:<model>` and `TYPESAFE_API_KEY`; `DEFAULT_DECISION_MODEL_ENABLED` (default false) decides whether users who have not chosen get it; users with their own AI key never get it by default; per-user Settings toggle | merged to main 2026-09-19 and 2026-09-21, **no tagged release** (last web tag 2026-05-18); whether the hosted app runs it is not stated | our code read 2026-09-24, not captured (`unverified` beyond that). Any error, or a rule Choice under 0.3 (`MIN_CHOICE_CONFIDENCE`, compared with the answer's `confidence`, or with the chosen rule's probability when it pools content rules; our code read 2026-09-25), falls back to the normal LLM path. Its pricing table lists `jev-latest` at $42 per billion input tokens, output free (= $0.042/Mtok; our code read 2026-09-24; equals the list price, [[reference/models-and-pricing]]). P22 |
| MotherDuck `prompt_jev()` ([walkthrough](https://motherduck.com/blog/jev-for-analytics/), [@mehd_io](https://x.com/mehd_io/status/2104910528837521649), 2026-09-29; [docs](https://motherduck.com/docs/sql-reference/motherduck-sql-reference/ai-functions/prompt-jev/); **vendor**) | **by MotherDuck**: a SQL function returning a struct with `.choice`, `.confidence`, `.probabilities`; Choice and Score. Walkthrough: gpt-5-mini proposed 7 labels from 40 sampled CFPB complaints (7.6 s), then Jev classified 100,000 rows in 82.2 s; gpt-5-nano via `prompt()` took 6 min 9 s for 10,000 rows (Jev 12.5 s). Mean confidence 0.83; 65.8% of rows ≥ 0.8, 10.9% below 0.5. On 300 rows: GPT-5 vs Opus 5.5 agree 72.7%, Jev vs GPT-5 77.3%, Jev vs Opus 74.0%; Jev matches on 90.4% of the 218 rows where both agree, 96.9% of the 161 of those at ≥ 0.8. MotherDuck's launch benchmark (100k short news articles): Jev 40 s, $0.50 vs LLMs 18–32 min, $1.58–$37.58 | `prompt_jev(text, question, ...)` in SQL | paid plans | text goes to TypeSafe; check data handling. Agreement is with other models, not labels. `unverified` |

DataGOL (document sorting): Jev available as of 2026-10-05; vendor claims only: [[ideas/head-to-head]].
## Related

- [[ideas/framework-integrations]] — framework-shipped Jev support you code against
- [[ideas/platforms-and-gateways]] — hosted routes to Jev
- [[ideas/head-to-head-retrieval-and-judging]] — judge comparisons
- [[reference/http-api]] — the wire contract these apps call

## Sources

Files in frontmatter `sources:`, captured by 2026-10-05; original URLs inline.