A probability for every message. You set the line.

What it costs at your volume.

Type your monthly message count. Every figure below is your number multiplied by a measured or listed constant; the arithmetic stays visible. Nothing here is a claim about time saved.

Constants: 1,005 input tokens per judged message and 49 ms per message for Llama Guard, measured on 2,531 messages (RTX 5080); Jev $0.042 and Claude Haiku 4.5 $1.00 per million input tokens at list price; the 1.2 factor is prompt overhead. Details in BENCHMARK.md.

Self-hosted, plus wherever you run it: a $4/month VM is enough for the bots. The hosted bot has its own flat plans (see Community owner).

Three ways in.

Same questions, same thresholds and the same audit log whether a bot, a script or an agent asks. Judgments come from Jev, TypeSafe's System One model, so you need their API key.

pip install "jevmod[discord]"
jevmod init                # asks for the key, stores it in the OS keyring
export DISCORD_TOKEN=...
jevmod discord             # or: jevmod telegram, jevmod reddit

# Python
from jevmod import Moderator
from typesafe_sdk import TypeSafeError
try:
    d = Moderator().check(text, channel_topic="gaming")
    d.action, d.category, d.scores   # 'flag', 'scam', {...}
except TypeSafeError:                # Jev unreachable: fail open on purpose
    d = None

# Node 20+
npm install jevmod        # or: pnpm add jevmod
import { Moderator } from "jevmod";
const d = await new Moderator().check(text, { channelTopic: "gaming" });  // d.action, d.category, d.scores

# CLI, one message per line
cat comments.txt | jevmod check --rule "No politics." -
# exit 0 clean, 1 triggered, 2 error; --json prints every probability

# HTTP
JEVMOD_ADMIN_TOKEN=... jevmod api
curl -X POST localhost:8080/v1/keys -H "Authorization: Bearer $JEVMOD_ADMIN_TOKEN" \
     -H "Content-Type: application/json" -d '{"tenant":"my-app"}'   # -> {"api_key":"jm_..."}
curl -X POST localhost:8080/v1/moderate -H "Authorization: Bearer jm_..." -H "Content-Type: application/json" \
     -d '{"messages":[{"id":"a","text":"..."}]}'

# response
{"request_id":"9f1c...","decisions":[{"message_id":"a","action":"flag","category":"scam","probability":0.99,
  "scores":{"spam":0.98,"scam":0.99,"harassment":0.02,"nsfw":0.01,"selfharm":0.01,"doxxing":0.02,"minors":0.01},
  "judged":true,"reason":"jev"}],
 "usage":{"judged_this_month":1,"jev_requests_this_month":1,"input_tokens_this_month":1193}}

pip install "jevmod[mcp]"
# Claude Code: plugin (two skills + the MCP server)
/plugin marketplace add ohernandezdev/jevmod
/plugin install jevmod@jevmod
# or only the MCP server
claude mcp add jevmod -- jevmod mcp
# Cursor, Codex, others
{ "mcpServers": { "jevmod": { "command": "jevmod", "args": ["mcp"], "env": { "TYPESAFE_API_KEY": "${TYPESAFE_API_KEY}" } } } }

Add content moderation to this repository with jevmod (https://github.com/ohernandezdev/jevmod, MIT, powered by TypeSafe's Jev). If you are Claude Code, first run /plugin marketplace add ohernandezdev/jevmod and /plugin install jevmod@jevmod, then use the jevmod-integrate skill; otherwise read https://raw.githubusercontent.com/ohernandezdev/jevmod/main/AGENTS.md and follow its integration recipe. Detect the stack and pick the surface (Python SDK: pip install jevmod; npm: npm install jevmod; or the HTTP API POST /v1/moderate); store the key with jevmod init or TYPESAFE_API_KEY in the environment, never in a committed file; insert one Moderator().check(text, channel_topic=...) call at the single point where user text enters the system; decide fail-open or fail-closed explicitly around TypeSafeError; keep the defaults flag-only; add a real test that skips without the key and asserts d.category with d.probability >= 0.7 on the scam sample and d.action == "none" on a clean one; finally run jevmod check "FREE NITRO for the first 100!! claim at discord-gifts.ru/nitro" (expect exit 1) and jevmod check "gg everyone, same time tomorrow?" (expect exit 0) and show me the diff before committing.

Full reference: README · AGENTS.md · llms.txt · examples/ · Postman collection · OpenAPI · Disclaimer

From the benchmark.

Twelve of the 2,531 messages, with the probabilities Jev gave them in the benchmark run. Click one to see what the default policy does with it. 12 items inlined from benchmark/results/jevmod.jsonl; this part sends nothing from this page.

For live checks: pip install jevmod, set TYPESAFE_API_KEY, run jevmod check "your text". Jev's probabilities move about 0.03 between runs, so a message within that band of a threshold can flip. Above 0.9 the probabilities match observed rates within a few points; between 0.5 and 0.85 they run high, which is why the shipped thresholds sit mostly at 0.75 to 0.85 (minors 0.70, off-topic 0.90).

Against three local classifiers.

2,531 public messages with human labels, the same items for every system. AUROC is threshold-free: 0.5 is a coin flip, 1.0 is a perfect ranking. On OpenAI's human-labelled set jevmod has the best AUROC in every category it was compared on (harassment and sexual content against three models; self-harm and minors against Llama Guard, the only other system with those labels); on Civil Comments toxic-bert wins, having been trained on it, and ties ShieldGemma at 0.939 on "any category" of the OpenAI set. Generated 2026-09-18 by benchmark/report.py.

Swipe the table sideways for the other systems.

Not measured, for scale: a general LLM as judge on the same text, 1,005 tokens x 1.2 prompt overhead x $1 per million input tokens (Claude Haiku 4.5 list price), is about $1.2 per 1,000 messages before output tokens.

The full table with F1 at default thresholds, precision and recall is in BENCHMARK.md. A separate 98-message red-team suite (unicode evasion, Spanish, Portuguese, French, German, Russian and Japanese, prompt injection, gaming slang) runs as a regression test against the real API.

One service, one request per batch.

Every surface is an adapter over the same core. Messages that a local rule can settle never leave the machine; the rest go to Jev together, and every probability lands in the audit log.