Does Jev really stay inside the options I give it?

Yes. You hand Jev your options and it picks one with a confidence value, never answering outside your list, while the LLM keeps doing the writing.

Why does the setup need two different API keys?

Jev runs through OpenRouter while the companion LLMs run through Groq, so the setup needs both keys in .env or Colab secrets.

Can I run the notebook in Colab without an env file?

Yes. Add both keys under Colab Secrets, export them from userdata before the setup cell, then run the notebook top to bottom.

Is the LangChain integration stable for production use?

No. The digest warns the middleware is experimental and early alpha, so expect breaking changes when upgrading the LangChain integration.

Which five examples are in the notebook?

Support ticket triage deciding team, urgency and refund intent; model routing choosing a small, premium or private model per request; reply guardrails deciding whether an AI-written reply is safe to send; tool selection choosing which tool an agent should use and blocking risky tool calls; and a finance inbox check for document type, urgency and bank-details-changed fraud.

What do I need installed before anything runs?

pip install "langchain-typesafe[experimental]" langchain-groq python-dotenv pandas, then a .env file holding OPENROUTER_API_KEY and GROQ_API_KEY, then open jev_decision_guide.ipynb and run it top to bottom. The repository links a one-click Open in Colab badge for the same notebook.

Can I see expected output before spending anything?

Yes, sort of. The single file jev_decision_guide.ipynb ships with outputs captured from a real run, so you can read the answer shapes each example produces before you run anything yourself. The caveat is that those outputs are from one execution, not a test, and the package they came from is the early-alpha integration noted above.

CommunityJev Integrations & Adapters

Jev AI Use Cases

LangChain notebook with runnable Jev examples for ticket triage, model routing, reply guardrails, tool selection and finance inbox checks.

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Stars: 5
Language: Jupyter Notebook
License: MIT
Published by: Community
Author: atliq
Last verified: Oct 2026

atliq/jev-ai-use-cases

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Hands-on LangChain examples of Jev, TypeSafe AI's decision model: support ticket triage, model routing, reply guardrails, tool selection and finance-inbox fraud checks. An LLM writes; Jev decides.

Forks: 0
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

About this project

Practical examples of using Jev (TypeSafe AI's decision model) in real applications, built with LangChain. An LLM writes. Jev decides. You give Jev your options and it picks one, tells you how sure it is, and never answers outside your list. jevdecisionguide.ipynb: the notebook, with outputs from a real run. Create a .env file with your keys (Jev runs through OpenRouter, the LLMs through Groq): Open jevdecisionguide.ipynb and run it top to bottom.

Quoted from the project's README. Read the full README.

Tagscommunity github

Works forJev for Routing Jev for Classification

Used inJev for Software & DevTools

Highlights

Shows triage by team, urgency and refund intent in one decision call pattern.
Demonstrates routing across small, premium or private models per request.
Covers guardrails for reply safety and blocking risky agent tool calls.
Includes finance-inbox fraud checks plus a notebook with real run outputs.

Watch out

Example notebook built on early-alpha langchain-typesafe with experimental middleware, so the API may change. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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