vLLM Semantic Router
The facts below are what the maker publishes about vLLM Semantic Router, checked and dated. Our editors have not written about it yet.
The facts
Each one read from vLLM Semantic Router's own pages, with the date it was checked.
What it does
- Agent use
- Agent harnesses connect to the Router inference endpoint while retaining responsibility for the agent loop, tools, and task state.checked 10 Oct 2026
- API compatibility
- The Quickstart demonstrates requests through an OpenAI-style /v1/chat/completions endpoint.checked 10 Oct 2026
- Client protocols
- The documentation lists OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages as client-facing protocols.checked 11 Oct 2026
- Data retention
- The security guide says replay, response cache, memory, response history, service logs, and provider logs can retain request-derived data, and recommends setting retention and deletion policies for enabled stores.checked 11 Oct 2026
- Deployment choices
- The documentation describes cloud, data center, edge, and enterprise hybrid deployment environments.checked 10 Oct 2026
- Deployment limits
- The Router chooses a model or model pool, while the inference platform handles replica placement, batching, autoscaling, cache locality, and accelerator scheduling.checked 10 Oct 2026
- Deployment locations
- The site says users configure where inference runs, including cloud, private, or edge environments.checked 11 Oct 2026
- Installation
- The quickstart lists Linux, macOS, or WSL2, Python 3.10 or newer, and Docker as requirements, with Podman available as a Linux fallback.checked 11 Oct 2026
- Local and remote models
- It can keep eligible sensitive or offline traffic on local models and allow selected requests to escalate to approved remote models.checked 10 Oct 2026
- Model backends
- The site names vLLM, SGLang, and OpenAI API among the model backends it can route to.checked 11 Oct 2026
- Model routing
- It can route requests by task, capability, modality, context length, and tool requirements across configured model backends.checked 10 Oct 2026
- Open source license
- The public GitHub repository lists an Apache-2.0 license.checked 10 Oct 2026
- Playground
- The project provides an online Playground at app.vllm-sr.ai/playground.checked 10 Oct 2026
- Privacy policy
- The site describes routing private domain knowledge to open models on on-premises GPUs while allowing policy-eligible requests to use external frontier models.checked 11 Oct 2026
- Product
- vLLM Semantic Router describes itself as an open, programmable decision layer for models and compute.checked 11 Oct 2026
- Protocol limitations
- The compatibility matrix says custom free-form tools are not supported for Anthropic Messages, and audio input is supported only for Chat Completions.checked 11 Oct 2026
- Purpose
- vLLM Semantic Router is an open, programmable decision layer that selects or combines models for each call according to policy.checked 10 Oct 2026
- Requirements
- The Quickstart lists Linux, macOS, or WSL2, Python 3.10 or newer, and Docker, with Podman available as a Linux fallback.checked 10 Oct 2026
- Routing
- It selects a model backend per request using signals that can include context, intent, safety, preferences, and system state.checked 11 Oct 2026
- Routing algorithms
- The site lists 16 algorithms: 11 selection algorithms and 5 loopers.checked 11 Oct 2026
- Routing modes
- It offers a Router mode for routing chat to model backends and an Engine mode that disables routing so the application owns the next step.checked 11 Oct 2026
- Routing priorities
- Operators can set quality, latency, and cost priorities for model selection.checked 10 Oct 2026
- Security boundary
- The documentation says the Router complements identity, network segmentation, secrets management, retention policy, and provider governance rather than replacing them.checked 10 Oct 2026
The company
- Project team
- The project team page lists five maintainers responsible for project-wide technical and operational stewardship.checked 11 Oct 2026
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