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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.
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.
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.
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
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
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,
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