switchyard

How to use it

• Through an existing gateway: Use an available gateway integration.
• Embed libsy in your harness: Add libsy to a service you operate.
• Evaluate Switchyard locally: Compare routing configurations with your own model pool using the demo server.

Through an existing gateway

Relay 0.8.x and 0.9.0 can lose upstream error status and details when the plugin is enabled, even for models outside its routes. Review the upstream error compatibility note before enabling the plugin.

LiteLLM and NeMo Relay run in your own deployment. Follow each gateway's guidance for credentials and service operation.

Embed libsy in your harness

Algorithm::run_stream hands model calls and the final routing outcome back to your application. In this development-API outline, comments mark the work your code supplies:

let mut steps = algorithm.run_stream(request, models, state);
while let Some(step) = steps.next().await {
    match step? {
        Step::CallModel(call) => {
            // Send call.request through your LLM client.
            // Give the call future to call.respond(...).await.
        }
        Step::CallDecision(call) => {
            // Send call.request through your decision-model client.
            // Give its typed response future to call.respond(...).await.
        }
        Step::Done(outcome) => {
            match outcome.response {
                Some(response) => {
                    // Return this answer; no final LLM call is needed.
                }
                None => {
                    // Send outcome.request through your client for the final answer,
                    // or pass it to your gateway to make that call later.
                }
            }
        }
    }
}

• Inputs: supply an algorithm, model pool, request, and state.
• Calls: your clients handle model calls. Stage returns a selection; other algorithms may return an answer.
• Start here: Rust quickstart · Python bindings.

Evaluate Switchyard locally

The standalone server is provided only for demos and local evaluation. It is not a supported production deployment path.

Evaluate routing locally with the standalone server and your own model pool, then verify routing is active.

The experimental desktop and terminal quickstart covers local installation, coding-tool connections, sessions, usage, recovery, and uninstall.

Agent-specific guides are available for pi and Oh My Pi.

Routing algorithms

Start with Auto. Choose Task, Execution, or Composite when you need more control. These names describe routing choices. The configuration keys are unchanged.

Auto is a fixed preset in v0.3.0. It does not compare strategies at runtime. The TOML runner supports type = "auto". In Rust, use StageRouterConfig::new(PickerMode::EfficientFirst, 0.5) for the same preset.

The routing overview lists all strategies, including the Plan/Execute configuration for Stage, escalation, advisor, sub-agent, and random. Integration support varies: check the gateway guide before choosing an algorithm.

Prefill routing is designed for fast, efficient model selection using a model's prefill activations and a classifier trained on your own evaluation data.

Evaluation and reference

Compare benchmark results and configs for Terminal-Bench 2.1 and DeepSWE. For latency and routing overhead testing, see Soak Testing.

Further reading

• Benchmark setup and profiles: run your own baseline and routing comparisons.
• Mechanics of Libsy: steps, model calls, and routing outcomes.
• TOML schema: configuration fields and defaults.
• Rust crate reference: core and optional integration crates.
• Installation: package and platform requirements.

Components

Pre-1.0 software. APIs, configuration, and routing behavior can change between releases. Pin the version you integrate.

What’s new

Features and integrations recently merged into main.

• 2026-10-16 · Route between Sonnet and Opus in Claude Code
  Use your Claude login for automatic routing on Linux x86_64 and see the selected model in the status line. Setup guide →

• 2026-10-15 · Use decision models in composite routing
  Use Mercury Decide to judge tasks and Stage routing to adjust model selection as tools run. Example configuration →

• 2026-10-13 · Use Jev to choose between models
  Configure Jev as a task judge with candidate descriptions and a probability cutoff. Configuration guide →

• 2026-10-12 · Bring Switchyard to TypeScript and Pi
  Select models in Node.js or try automatic routing through Pi’s virtual models. TypeScript bindings → · Pi example →

Community and license

Report an issue · Contribute · Code of conduct

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