5 posts tagged with "vllm"

Semantic Router can now verify grounded responses with a generative span detector served by vLLM. The new endpoint detector backend runs LettuceDetect v2 against every fact-checkable answer: unsupported spans are located to the character, typed against a hallucination taxonomy, and explained — in one call, before the response reaches the user.

The models come out of a joint paper between KR Labs and the Semantic Router team, Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents (arXiv:2607.00895). This post walks through the paper — the benchmark, the taxonomy, the models, and what they score — and then through the integration that puts the detector into the serving stack.

The Feature Everyone Wants and Almost Nobody Has​

Cursor's Auto mode is deceptively simple: the developer types, and the IDE chooses whether a prompt deserves a frontier model or something faster and cheaper. It is easy to stop noticing — until moving to an open tool where every request starts with a model dropdown.

Agent systems that span multiple models — a local endpoint for coding, a frontier cloud model for deep reasoning, and a fast general-purpose model for everyday tasks — all face the same routing question: how should each request be directed to the right backend?

Many deployments start with a lightweight Python proxy or keyword matcher in front of the gateway. That approach works at small scale, but misroutes grow quickly as traffic, languages, and task types diversify. This post shows how vLLM Semantic Router running as an Envoy ExtProc sidecar inside AgentGateway replaces that pattern with semantic, config-driven routing.

Most agent systems start with a simple idea: call model: auto and let the inference layer pick the right model. That is useful, but it is not enough for long-running agents.

A coding agent can begin with architecture work, call tools, receive short tool outputs, continue with "fix that", then ask a privacy-sensitive question in the same user session. The latest message may look simple, but the route cannot be chosen from the latest message alone. The router also has to know whether this is a safe moment to switch models.

This guide shows how to deploy that pattern on AMD ROCm with vLLM Semantic Router. You will start one ROCm vLLM backend, serve the agentic routing recipe, open the dashboard, validate the OpenAI-compatible API, and use Inferoa to experience route decisions and Router Learning behavior from an agent client.

Agentic routing is not only choosing a model. It is choosing when to keep one.

Running vLLM Semantic Router on AMD Developer Cloud is not just about bringing up one more inference endpoint. It is about turning it into a routed multi-tier system that can classify requests, choose a semantic lane, and make replay and Insights immediately useful.

This post walks through the practical path: start the ROCm backend on an AMD Developer Cloud instance, install vLLM-SR, import the reference profile, and validate the deployment end to end.