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NeuroLink

A Juspay TypeScript SDK that treats Jev as a third inference type beside generate and stream: providers declare which kinds they serve, and tryDecide returns typed boolean, choice, or score answers, null when no decision provider is set.

Open github.comjuspay/neurolink
Category
Repos & SDKs
Published by
Community
Author
juspay
Added
2026-09-22
Tagscommunitytypescriptsdkroutingagents

Highlights

  • decide is declared per provider through an inferenceKinds field rather than inferred from behavior; of 40 unified providers, Jev alone serves decide.
  • Every internal Jev caller fails open — tryDecide returns null on any failure — so with no key configured the library behaves exactly as before.
  • Jev is used internally for model routing, context compaction, MCP tool selection, and RAG planning.
  • Routing uses asymmetric confidence thresholds, upgrading at 0.3 and downgrading at 0.6, because a wrong downgrade costs more.
  • A 64-model registry (7 providers, 132 aliases) backs metadata and context-window checks; Ollama, LM Studio, and llama.cpp run fully locally.

Quickstart

ts
import { NeuroLink } from "@juspay/neurolink";
const pipe = new NeuroLink();
const decision = await pipe.tryDecide({
  state: { ticket: "Refund request, $42, first occurrence" },
  questions: {
    autoApprove: { type: "boolean", instructions: "Approve without human review." },
  },
});

Watch out

MIT-licensed and not affiliated with TypeSafe AI; needs at least one provider API key or a local runtime, and Jev decisions require a TypeSafe key.

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From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

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The case against Jev-scored compaction

Theo - t3.gg
Theo - t3.gg
@theo
·

This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more

tamara
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@tamarajtran

found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant

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@isNickMa
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Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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