Switch from Jev to Curva: a step-by-step guide

To switch from Jev to Curva, run a Curva server, create an API key, and point your existing client at it: Curva serves `POST /v1/systemone`, which accepts Jev's `criteria` request shape, so a client built for Jev keeps working after you change two environment variables. Then replay logged traffic through Curva with a shadow test, read where the two disagree, and move traffic once the numbers say so. This guide walks the steps in order, says what was verified and what wasn't, and lists the differences to expect before you cut over.

Jev by TypeSafe AI is a hosted "System One" model: unstructured state in, typed probabilistic decisions out. Curva gives the same kind of output from any LLM you choose, on your own servers. It is free to use under the Curva Free License; you pay only your model provider.

Run Curva and create a key

curva serve --addr 127.0.0.1:7777
curva keys create --name my-app          # prints curva_… once
export TYPESAFE_BASE_URL=http://127.0.0.1:7777
export TYPESAFE_API_KEY=curva_...

`pip install curva-ai` installs the `curva` binary. The server needs a model provider key, such as `OPENROUTER_API_KEY`, or a local model. Curva doesn't ship a model of its own, so pick one before you compare and write it down: every number you measure belongs to that model.

Two details about keys. Once any key exists, every route except `/health` needs one, and Jev's SDK always sends one. And a server started without keys stays open until it is restarted, so restart it after creating the first key. For production, the Docker image `ghcr.io/itsmohitrohilla/curva` runs the same binary.

Point the client at /v1/systemone

You have two ways to switch from Jev.

**Keep Jev's Python SDK.** Set `TYPESAFE_BASE_URL` to your Curva server and `TYPESAFE_API_KEY` to a Curva key, as above. Nothing else changes. This was verified with `typesafe-sdk` 0.7.2 against `curva serve`: `system_one`, all three primitives with `criteria`, dict questions, `extra_body`, `response.choices/scores/nouls`, `usage`, `models.list()`, and 401 and 422 errors all behaved. Jev's TypeScript SDK uses the same wire contract and should work the same way, but it was not run. Test it before you rely on it.

**Move to Curva's SDK.** Its Python client accepts the same style, so most code survives an import change:

from curva import Curva as TypeSafeClient, CurvaError as TypeSafeError, Choice, Score, Noul

with TypeSafeClient(base_url="http://127.0.0.1:7777", api_key="curva_...") as client:
    result = client.system_one(
        state={"ticket": "I was charged twice for order A-104"},
        questions={
            "department": Choice(instructions="Which team",
                                 criteria={"billing": "payments, refunds", "technical": "bugs"}),
            "frustration": Score(instructions="How frustrated", criteria=["Calm", "Frustrated", "Angry"]),
            "refund_requested": Noul(instructions="Customer explicitly asks for a refund"),
        },
    )
    print(result.choices["department"].choice)

Pass `base_url` and `api_key` by keyword. Also drop any `model=` argument that names jev-latest: through Curva's own SDK a model name is sent to the provider as is, and your provider doesn't know that name. On `/v1/systemone`, a name without a provider prefix means the server's default model.

Questions in the criteria shape

If you call the HTTP API yourself, the body stays in the `criteria` shape:

{
  "state": {"ticket": "I was charged twice for order A-104"},
  "model": "curva-latest",
  "questions": {
    "team": {"type": "choice", "instructions": "Which team?",
             "criteria": {"billing": "payments, refunds", "technical": "bugs", "sales": null}},
    "anger": {"type": "score", "instructions": "How frustrated?",
              "criteria": ["calm", "annoyed", "angry"]},
    "refund": {"type": "noul", "instructions": "Asks for a refund",
               "criteria": {"true": "explicitly asks for money back", "false": "anything else"}}
  }
}

How Curva reads each part:

The response is Curva's normal response plus `usage` and a `type` on every answer. A Score's `probabilities` and `legend` are keyed `"0"`, `"1"` and so on, as Jev's SDK expects.

Shadow-test on logged traffic

Don't move traffic on faith. Log a day of Jev's decisions, one per line, with its answers as labels in feedback format (option key, level index, or true and false):

{"state": {"ticket": "I was charged twice"}, "labels": {"team": "billing", "refund_requested": true}}

Then replay it:

curva shadow traffic.jsonl -q questions.json -o shadow.jsonl

`questions.json` holds the same questions in Curva's `/v1/decide` format, with `options` and `levels` instead of `criteria`. The Markdown report shows agreement per question, agreement by Curva's confidence band, and the share of traffic you could hand to Curva at or above each band. The most confident disagreements come first. In each one, one of the two systems is clearly wrong, so read them. Agreement is not accuracy: label a sample of the disagreements yourself. The run is resumable, so a quota error costs nothing but time.

Differences: headers, error codes, usage

Know these before you cut over:

Latency is the big one. If you need sub-second answers, test a fast provider such as Groq, a local model, `rules` for the easy cases, and `debias: "auto"`, which drops to one call once a question has shown no position bias.

Turn on what Jev didn't have

Once traffic flows, add the parts Curva adds:

The checklist to switch from Jev

Next steps

The docs have the [switching guide](https://itsmohitrohilla.github.io/curva-docs/guides/switching/) and [shadow mode](https://itsmohitrohilla.github.io/curva-docs/guides/shadow/). For measured accuracy, speed and cost side by side, read [a self-hosted Jev alternative](/blog/jev-alternative-self-hosted/) or the [Curva vs Jev page](/curva/vs-jev/). For the shadow test in depth, see [shadow-test an LLM classifier](/blog/shadow-test-llm-classifier/), and for what feedback buys you, [LLM classification confidence scores you can act on](/blog/llm-classification-confidence-scores/).