Triage & routing

Guardrail + model router for LLM gateways on TypeSafe's Jev (System One model), with an independent accuracy/calibration/latency evaluation. Stdlib Python.

Uses typeful jev, zero sync to pull and sync large repositories for issue triage.

Per-prompt capability router for coding agents: resolves installed skills, MCP servers, agents and commands against your prompt via TypeSafe Jev, and measures whether the injection actually helps.

Type-safe model router. Jev (System One) banks each request to a typed catalog route.

Shift every LLM call to the cheapest model that can handle it. Routing decided by TypeSafe Jev in ~180 ms. No training data. Policy in plain YAML. TypeScript and Python.

Mastra input processor that uses Jev for a typed block decision and moderation category.

Local proxy that picks the Claude model and effort per message using TypeSafe Jev. Routes subagents, leaves your cached main chat alone.

Claude Code mod that routes decisions to TypeSafe's Jev model: ranks installed skills per prompt, and answers the agent's own this-or-that questions when confident.

Open-source LLM router that uses TypeSafe's Jev to pick a model, on top of LiteLLM.

DiffJury — TypeSafe Jev PR risk router + code review coach.

Automatic model routing for Pi using TypeSafe's Jev through Vercel AI Gateway.

Typed, confidence-aware agent skill routing with TypeSafe Jev.

TypeSafe (Jev) skill routing for Hermes Agent: names the one skill worth loading, before the model call. Opt-in, stdlib only, ~$0.001 per routed turn.

MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer.

Semantic tool routing and typed System One decisions for the Pi coding agent using TypeSafe Jev.

Route HTTP requests by meaning. A semantic router for Hono powered by Jev.

Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn.

A lightweight Jev-powered router for models, tools, and subagents.

Route to the cheapest model in claude code for your task using jev-router.

LlamaIndex reranker + router powered by TypeSafe Jev — typed scores/choices, cheaper than LLM-as-judge.

Neon Function proxy for the Neon AI Gateway with TypeSafe Jev routing.

Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.

I made an experimental model router for @pidotdev using Jev as the decision maker. It's not a generic "pick a cheaper model" router. It isolates work into logical threads and prices cache reads/writes, cold context, expected output + the cost of switching back. @CompleteSkeptic @typesafeai This wouldn't of been possible without Jev, it's an amazing primitive to make software with 💪🏻

Using Jev from @typesafeai to improve matching performance, accuracy and cost in our automated @blackbaud RE NXT CRM integration. https://t.co/TVdfZOa3wI

Doc-OCR router using Jev @typesafeai A Jev-powered router that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Result: save cost on # OCR pages + speed https://t.co/ZjXqHSjSGh

my video on the routing tool blew up on YT! i crossed 10k views in less than 19 hours, i still can't believe it - my most viewed video till date yayee have been consistently doing YT and content in the ai space for last 7 months and finally get to feel this😇 i built this tool yesterday called agent router using jev + herdr, i was awake until midnight doing recording, and publishing the video. today it feels all the efforts are worth it

Put together a quick video of using Jev with ReqLLM I cover the new `evaluate/4` method, why I went that route, and make a real API call to Jev to classify an issue https://t.co/wvSoeI07AD

IRS O*NET job classification using Jev (1,016 possibilities) Query: "I scoop scoops and sprinkle sprinkles" Result: 35-3023.00 Fast Food and Counter Workers https://t.co/SSXUqQwNo3

I'm using Jev (from @typesafeai) to categorize expenses for my company! Our office manager used to do this. It's all in Mongolian and we have to do it using bank records. It is very unique to our company, and not something a software would handle easily. We also have about a years worth of Excel files (training data) that I'm using to give Jev guidance on classification (few shot style). It really works, it's stupidly cheap, and when it's not confident it says so. Pretty great! This isn't anything new, classification in ML is extremely "solved". But this is a general classification model