Jev Setup: First Decision in 10 Minutes

Step 1 — Pick an access route

Jev uses a custom API shape rather than chat completions, so an OpenAI-compatible client will not "just work" — that is true on OpenRouter too, where it is exposed through a decisions endpoint rather than the usual chat path.

Step 2 — Install the SDK

# Python 3.10+
pip install typesafe-sdk
# or
uv add typesafe-sdk

# Node 20+
npm install @typesafe-ai/sdk

Then put your key in the environment. The client reads TYPESAFE_API_KEY and defaults to the jev-latest model:

export TYPESAFE_API_KEY="sk-..."

Step 3 — Your first call

One state, three kinds of question, answered in a single parallel pass:

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

client = TypeSafeClient()

response = client.system_one(
    state="The deploy failed twice and customers are seeing 500s on checkout.",
    questions={
        "team": Choice(
            instructions="Which team should own this?",
            criteria={
                "billing": "Payments, invoices, refunds",
                "platform": "Deploys, uptime, infrastructure",
                "other": "Anything that does not fit the options above",
            },
        ),
        "urgent": Noul(instructions="Does this need attention right now?"),
        "severity": Score(
            instructions="Rate customer impact",
            criteria=["Low", "Medium", "High"],
        ),
    },
)

print(response.nouls["urgent"].noul)          # probability it is urgent
print(response.choices["team"].choice)        # selected option
print(response.choices["team"].probabilities) # probability per option
print(response.choices["team"].confidence)    # 0-1

Field names follow the primitive: choice, score and noul carry the value, alongside confidence and a probabilities map. Note the deliberate other option — see the gotchas below.

The raw HTTP call

If you would rather not take an SDK dependency, or you are wiring this into a gateway:

curl -X POST https://api.typesafe.ai/v1/systemone \
  -H "Authorization: Bearer $TYPESAFE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "jev-latest",
    "state": "The deploy failed twice and customers are seeing 500s.",
    "questions": {
      "urgent": { "type": "noul", "instructions": "Does this need attention now?" }
    }
  }'

Shapes change during early access — check the quickstart against your SDK version before you build on the raw payload.

Inside Claude Code

TypeSafe publishes an agent skill as a Claude Code plugin:

claude plugin marketplace add typesafe-ai/skills
claude plugin install typesafe@typesafe-ai

As with any third-party plugin, read it before you enable it — a plugin runs with your session's permissions. Our skills guide has the review checklist, and claude plugin validate (v2.1.281) will now flag .mcp.json entries that would be dropped silently and insecure URLs.

Inside LangChain

pip install langchain-typesafe

from langchain_typesafe import Noul, TypeSafeClassifier

classifier = TypeSafeClassifier()
response = classifier.invoke({
    "state": "The deploy failed twice and customers are seeing 500s...",
    "questions": {
        "urgent": Noul(instructions="Does this need attention now?")
    },
})
urgency = response.nouls["urgent"].noul

The same package ships middleware that puts Jev in the harness loop rather than in your application code — ModelRouterMiddleware and AutoModeMiddleware. Those are covered with worked examples in use cases.

Gotchas worth knowing before production

What it costs to try

Eight playground requests in one tested run used 4,148 input tokens in total. At $0.042 per million that is a fraction of a cent, and output is free. The real budget line in a production system is not Jev — it is whatever you use to assemble the state and to handle the cases Jev routes to a human or to a bigger model. Model both in the cost calculator.