Agent Tooling for Jev — page 8

Voice-driven semantic auto-advance for Slidev, powered by Cloudflare Agents and TypeSafe AI Jev

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

PiJev: a terminal coding agent with Jev in the loop — Jev ranks the repository's files before the first call, picks skills and triages failures; your coding model writes the code. Built on Pi.

Fast System-1 Decision, Arbitration & Safety Engine for Autonomous AI Agents (Powered by TypeSafe Jev)

Comparing LLM-as-judge vs TypeSafe Jev for agent guardrails: same rules, same agent, measured on cost, latency, calibration and coverage.

Keep skill catalogs outside the main LLM context. Jev selects relevant skills through one read-only MCP tool.

pi-thinking-router-jev —— pi 的自适应 Thinking Level Router。在任务起点、推理型失败与稳定窗口咨询 jev 决策模型（未配置时本地 规则兜底），动态选择 low/medium/high/xhigh 推理档位：简单改动降档、复杂 debug 升级、失败可升级、稳定可降级。含防抖钳制 、失败安全回退与 JSONL 决策日志，不切换模型、零侵入 agent loop。

Jev-judged context compaction for DeepSeek Harness: semantic tool-result pruning + deterministic receipt compaction

JEV Smart Router — a Databricks App that uses TypeSafe JEV to pick which model answers each message, then runs inference on the chosen Databricks Foundation Model API endpoint.

Verdict-based context compaction for DeepSeek Harness — replaces lossy LLM summaries with fast keep/truncate/drop decisions from jev-latest; everything kept stays verbatim. Port of tamaratran/fast-jev-compaction.

Don't burn your expensive main model on grep-and-guess grunt work — let jev rank the whole repo, and save the main model for reading the right files and writing the right code.

Email triage PoC: reads a mailbox over IMAP and classifies each message by what it is and what it's about, using TypeSafe System One (Jev) — 11 questions in a single call, decided in Python.

Message triage CLI in Go, built on the Jev decision model from TypeSafe AI. Categorizes messages, scores urgency, and flags low-confidence ones for human review.

Per-turn skill selection for LangChain and deepagents agents: a fast judge picks the few skills a turn needs, so a catalog of hundreds stays out of the prompt.

Smart Codex overlay with JEV model routing, context optimization & Kanban automation. Reduce tokens, keep control

Fast deep research for the pi coding agent: reads up to 100 pages in full per round, Jev keeps only the passages that answer your questions.

Triage Issue Agent for GitHub, built with Eve and Jev.

Jev-powered context curation for Codex. Build compact, traceable handoff context through native plugins and skills.

Effect-based safety gate for AI coding agents' shell commands (OpenCode, Antigravity): fast structural rules, then TypeSafe's Jev or a chat model judges what a command does. Certified with Jev at zero dangerous commands allowed.

Typed model routing for OpenClaw agents, powered by TypeSafe Jev

Open-source Codex plugin for TypeSafe Jev decision consultation, failure diagnosis, and evidence-based completion review

Context compaction for Hermes sessions that never summarises: every tool call is scored by TypeSafe's Jev model, stale calls are dropped, everything kept stays verbatim.

Natural-language MCP tool dispatcher powered entirely by TypeSafe's Jev — no general-purpose LLM. Discovers a simple MCP server's tool signatures at runtime and uses Jev's typed primitives (Choice/Noul) to pick the right tool and extract its arguments straight out of the sentence.

A typed Python framework for controlling Android over ADB with Jev.