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NVIDIA-NeMo avatar

NVIDIA-NeMo/Switchyard

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Switchyard

Switchyard is a Rust library for routing large language model requests. For each request it chooses which model and provider should respond, balancing answer quality, cost, and response time rather than relying on one fixed model. It is aimed at coding assistants and agent harnesses, where it picks the best available model for each step and can show the chosen model in a tool's status line.

Routing decisions come from composable strategies. A language model can judge whether a cheaper, more efficient model can handle a task, and that verdict selects the model tier. Execution signals, such as recent tool activity and outcomes, can move an agent to a different model mid-task. When those signals are too uncertain, the classifier sets the default tier. Automatic routing can also be exposed as a named virtual model that agent tools select like any other model.

Switchyard can be embedded as a core library in an existing gateway or agent harness, so the host keeps its own model clients and credentials. Thin adapters attach it to third-party LLM gateways without replacing them, and typed Node.js bindings let TypeScript applications request routing decisions in-process. Requests and responses keep their native OpenAI and Anthropic formats, so existing clients work unchanged while traffic is spread across providers.

A local reference server lets you try routing with your own models and coding agents and confirm it is active. A benchmarking setup compares a full agent, model pool, and routing configuration against a single-model baseline on task success, cost, and response time across the same tasks.

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Features

  • Cost-Quality Model Routing - Selects a model per request by weighing answer quality, cost, and response time rather than relying on one fixed model.
  • Model Routing for Agents - Picks the best available model for each coding assistant step and reroutes to another model mid-task as tool activity changes.
  • Model Request Routing - Chooses which model and provider handles each incoming request so traffic spreads across several models instead of one fixed choice.
  • Tiered Model Cascades - Asks a model whether a cheaper, efficient model can handle a task, then routes the request to the matching model tier.
  • Composable Routing Strategies - Chains independent strategies like task classification and execution-signal analysis, with a classifier setting the default tier when signals are uncertain.
  • Embeddable Model Routing Cores - Ships a standalone routing core that hosts embed to get model decisions while keeping their own clients and credentials.
  • Model Routing Gateways - Implements a Rust library that routes each request to a model and provider, with decisions plugging into existing gateways and agent harnesses.
  • Task-Based Execution Models - Uses a language model to judge whether a cheaper model can handle a task and routes the request to the matching tier.
  • Activity-Driven Model Switching - Moves an agent to a better-suited model mid-task based on recent tool activity and outcomes.
  • Model Request Routing to Hosts - Switches models mid-task by watching recent tool activity and outcome signals so an agent moves to a better-suited model.
  • LLM Gateways - Provides thin adapters that attach routing decisions inside existing third-party LLM gateways without replacing them or changing client connections.
  • Cost-Aware Model Routers - Sends routine tasks to cheaper model tiers using a language model verdict, reserving costlier models for tasks that need them.
  • Model Routing Strategies - Merges task classification with execution signals so a classifier sets the default model tier when signals are too uncertain.
  • Model Aliases - Exposes automatic routing as a named virtual model that agent tools select like any real model while the backend is resolved per request.
  • Multi-Provider LLM Gateways - Attaches to third-party LLM gateways so traffic spreads across providers while clients keep their native OpenAI and Anthropic connections.
  • Routing Baseline Comparisons - Benchmarks a full agent, model pool, and routing configuration against a single-model baseline on success, cost, and latency across identical tasks.
  • API Compatibility Layers - Keeps native OpenAI and Anthropic request and response formats so existing clients work unchanged while traffic is redirected across providers.
  • Embeddable Routing Libraries - Embeds as a core library in an existing gateway or agent harness, leaving model clients and credentials with the host.
  • High-Performance Serving Systems - Rust proxy and library for protocol translation and routing across LLM backends.
3,494 stars·357 forks·Rust·Apache-2.0·2 views

Star history

Star history chart for nvidia-nemo/switchyardStar history chart for nvidia-nemo/switchyard

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does nvidia-nemo/switchyard do?

Switchyard is a Rust library for routing large language model requests. For each request it chooses which model and provider should respond, balancing answer quality, cost, and response time rather than relying on one fixed model. It is aimed at coding assistants and agent harnesses, where it picks the best available model for each step and can show the chosen model in a tool's status line.

What are the main features of nvidia-nemo/switchyard?

The main features of nvidia-nemo/switchyard are: Cost-Quality Model Routing, Model Routing for Agents, Model Request Routing, Tiered Model Cascades, Composable Routing Strategies, Embeddable Model Routing Cores, Model Routing Gateways, Task-Based Execution Models.

Which projects share features with nvidia-nemo/switchyard?

Projects with overlapping indexed features include: experientiallabs/experiential — Experiential is an open-source LLM gateway and router. It exposes OpenAI-compatible and Anthropic-compatible APIs in… lemony-ai/cascadeflow — Cascadeflow is a runtime for AI agents that cuts language model spend by trying an inexpensive drafter model first,… ombharatiya/ai-system-design-guide — This is a written reference for designing, building and operating production AI systems, aimed at engineers who work… codelion/optillm — OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost,… yetone/magpie — Magpie is a local gateway for coding agents. It points tools such as Claude Code, Codex and Gemini CLI at models from… vllm-project/semantic-router.

Projects sharing features with Switchyard

These projects share indexed features with Switchyard. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • experientiallabs/experientialexperientiallabs avatar

    experientiallabs/experiential

    15,193View on GitHub↗

    Experiential is an open-source LLM gateway and router. It exposes OpenAI-compatible and Anthropic-compatible APIs in front of hosted models, models reached with your own provider keys, and local models, all under one base URL and one bearer key. Existing OpenAI and Anthropic SDK clients, as well as coding agents, can be pointed at it by changing only the endpoint. The project states that usage carries zero markup. What sets it apart is that routing is learned from your own traffic. It captures traces from desktop coding agents and imports past activity from supported monitoring tools, databas

    Python
    View on GitHub↗15,193
  • lemony-ai/cascadeflowlemony-ai avatar

    lemony-ai/cascadeflow

    3,926View on GitHub↗

    Cascadeflow is a runtime for AI agents that cuts language model spend by trying an inexpensive drafter model first, validating the answer, and escalating to a stronger verifier only when quality falls short. It also works as a budget and policy enforcement layer inside the agent loop, applying spending caps, compliance rules, and allow, switch-model, deny-tool, or stop actions at each step, with an audit trail of the decisions taken. Each call reports the model used, its cost, and the savings. What sets it apart is how it plugs into existing code. It can run as a local gateway that speaks the

    Pythonagentaianthropic
    View on GitHub↗3,926
  • ombharatiya/ai-system-design-guideombharatiya avatar

    ombharatiya/ai-system-design-guide

    4,200View on GitHub↗

    This is a written reference for designing, building and operating production AI systems, aimed at engineers who work with large language models. It is organized as chapters, worked case studies and interview-preparation material rather than as installable software. The central topics are tool-using LLM agents, retrieval-augmented generation, cost and model selection, evaluation and observability, and security and compliance, with explicit trade-offs among latency, cost, accuracy and safety. The agent material covers control loops such as ReAct and plan-and-execute, graph-based orchestration,

    agentic-aiagentic-workflowai
    View on GitHub↗4,200
  • codelion/optillmcodelion avatar

    codelion/optillm

    4,164View on GitHub↗

    OptiLLM is an inference proxy and gateway router that directs prompts to specific language models based on cost, performance, and provider health. It functions as a middleware layer designed to optimize requests through intelligent routing, load balancing, and context management. The project provides specialized capabilities for data protection by anonymizing personally identifiable information before requests reach a model. It also acts as a reasoning orchestrator and tool integration layer, using inference-time loops and self-reflection to improve accuracy while connecting models to externa

    Python
    View on GitHub↗4,164
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