intent-routing

1207 public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. Source citations, dated link checks and scheduled call-site text checks; runtime and performance are not independently tested here. EN/中文, JSON schema and platform compatibility.

IntentusNet - Deterministic execution infrastructure for agent and distributed systems, enabling reproducible workflows, reliable intent routing, transport abstraction, and transparent operational control.

Dry-run intent router that connects AI agents to ranked x402 API endpoints.

Lightweight, auditable intent router for LiteLLM and OpenAI-compatible model gateways.

面向生产环境的 AI Agent 动态技能网关与自进化底座：三层级联意图路由(R@1=98%)、8维沙箱评估、8重防御防线、LangGraph状态图旁路与SQLite原子发布状态机。

Cognitive routing core - shared agent memory, intent dispatch and state that survives restarts

A local-first C# reference for intent routing with deterministic guardrails and constrained LLM usage.

On-device dynamic intent routing for Swift: query + actions in, best action + calibrated confidence (or abstention) out. Local, multilingual, benchmarked.

A visual AI intent-routing playground: six agents, tools, and persistent task state. Built with Jev, FastAPI, and Python.

Async, typed intent & tool routing for Python agents: Jev decides, a confidence gate stops weak decisions before they reach a privileged tool.

Intent-routing finance agent over a Turkish and US market data layer. No API key, no database, no account required.

A collection of Generative AI experiments — CV Q&A chatbots, LLM-based intent routing, and streaming API integrations.

A LangGraph rewrite of the dialogue-decision layer from a production voice assistant — with runnable experiments measuring where the framework does NOT help. Runs fully offline.

Military element intent router: Chosen hybrid retrieval + NanoJev decision head + simulated agent dispatch

A simple conversational assistant for canceling a gym membership. Built from a FigJam conversation flow with simple routing, keyword extraction, and fallback handling.

CQUPT AI 学生成长助手 — RAG + 多场景意图路由 + 四层安全护栏，92.9% 评测准确率

Learn JEV, TypeSafe AI's decision model, in ~2.5 hours: one customer-support assistant built up in 12 runnable steps, each compared live with an LLM-only version on speed, cost and accuracy

AI support assistant with intent routing, RAG, conversation memory, citations, and human escalation.

Edge-First AI Agent Intent Routing and Multi-Tier Orchestration for Resource-Constrained IoT Intelligence (IEEE IoT-J Special Issue)

Build a 0.6B parallel decision model that turns states into probability distributions—no output-token decoding needed.