Jev with coding agents

What Jev is (and isn’t) when you’re using a coding agent.

If you found TypeSafe while looking for a model to plug into your coding agent, start here. Jev is not a drop-in replacement for the LLM behind Claude Code, Cursor, opencode, Copilot, Muse Spark, Grok Bot, or similar tools. Instead, you can use your coding agent as usual to write code that uses Jev to make decisions.

Jev is not a chat or code-completion LLM

Jev is a System One model. It does not generate text, write code, or hold a conversation. It takes a state and a set of typed questions and returns structured answers your code can use directly:

A choice from a list of options, with per-option probabilities.
A score on a rubric you define.
A noul (0–1) for a true/false statement.

Coding agents rely on an LLM that streams text, calls tools, and edits files based on natural-language instructions. Jev does none of that. There is no model: "jev-latest" setting that turns your coding agent into a Jev-powered agent, because the two systems solve different problems.

What you probably want instead

Pick the row that matches what you were trying to do:

You wanted to…	Do this
Make your coding agent better at writing code that uses TypeSafe	Install the TypeSafe agent skill. It gives Claude Code, Codex, and other agents full context on the Jev API, the primitives, and the patterns so they can generate correct TypeSafe integrations for you.
Use Jev inside an app or agent you’re building — for routing, classification, scoring, guardrails, or any structured decision	Start with the Quick start, then read How to build with TypeSafe and the Patterns for common architectures like confidence routing and intent routing.
Replace or swap the model that powers a coding agent	Jev isn’t the tool for this. Keep using an LLM-based coding agent, and use Jev separately wherever your product needs a fast, calibrated, structured decision.
Try Jev before writing any code	Open the Playground, paste some text as the state, and add a few questions. See the Quick start for a walkthrough.

When Jev is worth reaching for

Even though Jev isn’t a coding-agent LLM, it’s often exactly the right tool inside an agent or app you’re building with a coding agent. Reach for Jev when your code needs to:

Route a request to one of a fixed set of destinations, and know how confident that routing is.
Score something on a rubric (urgency, quality, risk) and branch on the number.
Check whether a statement is true of a document, message, or record before taking an action.
Replace a fragile prompt that asks an LLM to “return JSON” with a call that returns typed values by construction.

If any of that matches what you’re building, the fastest path in is the Quick start, then the primitives reference for the question types.

Next steps

System One — What a System One model is and how it differs from an LLM.
Quick start — Try Jev in the Playground, over HTTP, or with the Python SDK.
Agent skill — Give your coding agent context on the TypeSafe API.
Patterns — Common architectures for building with TypeSafe.