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Set routing policy in routing.decisions[], not in an isolated fallback switch. Each decision combines rules, priority, model references, and an algorithm. For an explicit last-resort route, add a low-priority static decision with an empty AND condition list. If no decision matches, the recipe-wide providers.defaults.model is used. Confidence routing is different: the confidence looper tries candidate models in order and stops when its configured response-confidence threshold is met.
The safe workflow is to start with the canonical YAML schema for the vLLM Semantic Router release you installed, declare every referenced model and model card, validate with the CLI, then start the Router. Threshold numbers are signal- and algorithm-specific; there is no documented universal value that can be copied into every deployment.
Understand the vLLM Semantic Router configuration model
Current documentation describes the canonical v0.3 YAML layout. A recipe owns the routing policy, and its decisions are evaluated using the rules and priorities in routing.decisions[]. The Router then invokes the model selected by the matching decision and its algorithm.
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vllm-sr config migrate --config old-config.yamlKeep the migrated output under version control and compare it with the current release’s canonical example. Configuration can be managed through local YAML and CLI commands, a Dashboard, Helm or vllm-sr serve --target k8s, the Kubernetes Operator, or the routing DSL. The field names and algorithm fragments for the installed release are authoritative when these workflows differ.
How a request is matched
- Select an entrypoint. A virtual model name selects a recipe and therefore its routing policy. A concrete physical provider model name is direct pass-through.
- Evaluate the recipe’s decisions. Each decision supplies rules, a priority, model references, and an algorithm. The matching semantics and priority ordering are those implemented by your installed release.
- Run the selected algorithm. A static algorithm chooses a declared model deterministically. A looper algorithm can examine several candidates and make a runtime choice.
- Apply the recipe default when needed. If no decision matches inside the selected recipe, the documentation says the Router uses
providers.defaults.model.
This distinction matters when troubleshooting. A request sent directly to a physical provider model does not enter the recipe’s signal processing, decisions, route plugins, cache, learning, or session routing. To use those features, send the request to the virtual entrypoint that owns the recipe.
Decision fields and what each one controls
| Field | Purpose | Questions to answer before deployment |
|---|---|---|
name |
Unique label for the decision. | Can an operator identify this route in logs and diagnostics? |
description |
Human explanation of the policy. | Does it state the intended traffic and any fallback role? |
priority |
Controls where the decision sits in matching order. | Is the catch-all lower priority than specific rules? |
rules |
Combines conditions, including their logical operator. | Are conditions expressed using fields supported by this release? |
modelRefs |
References models that the algorithm may select. | Does every referenced name exist in provider and model-card configuration? |
algorithm |
Defines selection behavior, such as static or confidence. |
Are its required, method-specific fields present and valid? |
Do not treat a decision as complete merely because its YAML parses. A model reference can be syntactically valid but unresolved, or bound to a provider that is incompatible with the recipe. Validation catches these classes of errors before startup.
Define providers, defaults, and model cards first
Routing decisions refer to logical model names. Those names must be declared in the provider configuration and in the routing model-card configuration required by the release. Also set a recipe-wide default through providers.defaults.model if you want deterministic behavior when no decision matches.
A minimal planning checklist is:
- Choose the virtual entrypoint clients will call.
- Declare each physical model under the provider model section.
- Create the corresponding routing model-card entries.
- Bind each model to a provider supported by the recipe.
- Set
providers.defaults.modelto a declared model. - Add decisions from most specific policy to broadest policy, while following the release’s priority semantics.
Keep credentials out of YAML. Use the environment-variable references documented for your release and provide secrets through the deployment environment or secret manager.
Add an explicit static fallback
The recipe-wide default is an implicit fallback: it is used when no decision matches. You may instead make fallback visible in the decision list with a final catch-all decision. The CLI README documents this shape:
routing:
decisions:
- name: local-fallback
description: Handle requests that did not match an earlier decision.
priority: 0
rules:
operator: AND
conditions: []
modelRefs:
- model: local-model
algorithm:
type: staticThis fragment must be merged into a complete configuration that declares local-model under providers.models and in routing.modelCards. It is a documented configuration example, not an independently executed test. Run the validator against the finished file and confirm that your installed release still uses the same matching semantics.
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Why the empty condition list matters
The decision’s AND operator and empty conditions list express a catch-all in the documented example. Its low priority is intended to leave more specific decisions ahead of it. Do not raise its priority above targeted routes, and do not add a condition that accidentally excludes the requests it is meant to catch.
Explicit versus implicit fallback
| Behavior | Configuration location | Operational effect |
|---|---|---|
| Implicit recipe default | providers.defaults.model |
Used when no decision in the selected recipe matches. |
| Explicit catch-all | A final decision in routing.decisions[] |
Appears in the decision policy and can apply its own model references and algorithm. |
Choose one deliberately. An explicit catch-all makes policy review easier, while the recipe default remains an important safety net for unmatched traffic and incomplete decision lists.
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Use static selection when the route is deterministic
The static algorithm selects a model without metrics or learned state. It is appropriate when a rule has already established the destination—for example, when a recipe separates traffic by a stable request property and every matching request should use one model.
Static selection does not evaluate a confidence score and does not try progressively stronger models. If you need escalation based on the generated response, use a looper algorithm and configure its candidate order and method-specific fields according to the release’s algorithm fragment.
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Configure confidence as a looper, not as a global switch
The configuration guide describes confidence as a looper algorithm. It tries candidate models in order and stops when response confidence reaches the configured threshold. This is fundamentally different from static selection, which chooses one declared model directly.
What happens during a confidence route
- The matching decision supplies an ordered set of candidate model references.
- The looper sends the request to the first candidate and obtains the response and the confidence signal defined by that algorithm.
- The Router compares that signal with the configured threshold.
- If the threshold is met, the response is accepted and later candidates are not used.
- If it is not met, the looper advances to the next candidate, subject to the algorithm’s configured limits and release-specific behavior.
The exact confidence field names and method contract are algorithm-specific. The opened configuration guide does not establish one universal calibration method or a threshold that applies to every signal, model family, or workload. Use the algorithm fragment and field reference shipped with the release you deploy rather than copying a value from an unrelated example.
Do not treat example numbers as recommendations
Canonical repository examples contain numeric thresholds attached to distinct signals, including a confidence-based example. Those numbers demonstrate that thresholds are configurable fields; they do not prove that a particular value is suitable for your traffic. A value such as 0.72 should never be presented as a generally correct starting point without knowing the signal definition, model, language mix, and evaluation data.
Tune a threshold with representative traffic
Threshold tuning is an evaluation exercise, not a syntax exercise. Build a representative set of requests that reflects production languages, prompt lengths, domains, safety cases, and peak-time distribution. Record outcomes for each candidate and threshold setting.
| Measure | What it tells you | Trade-off to inspect |
|---|---|---|
| False acceptance | How often a response is accepted even though the quality target was not met. | A threshold that is too permissive can reduce escalations while lowering answer quality. |
| Escalation frequency | How often the looper invokes a later candidate. | More escalations can improve quality but increase latency and model usage. |
| Quality on a labeled set | Whether accepted responses meet your task-specific rubric. | Optimize for the actual task, not a generic confidence interpretation. |
| Latency | Time added by retries or stronger candidates. | Tail latency can worsen sharply when difficult requests traverse several models. |
| Cost or capacity | How candidate order and escalation change resource consumption. | A high threshold may send too much traffic to expensive or scarce capacity. |
Evaluate several threshold values on the same held-out workload, then choose a policy that meets your quality and operational limits. Recheck it after changing models, prompts, providers, languages, or confidence-signal implementation. The Router does not automatically optimize all of these axes for you.
Keep candidate order intentional
A confidence looper’s order is a policy decision. Put the candidate that offers the desired balance of normal-case quality, latency, and capacity first. Place an escalation candidate later when it is more capable but slower or more expensive. If the first model’s confidence signal is not comparable with the second model’s signal, do not assume that one threshold has identical meaning across both.
Document why each candidate appears in the sequence and what should happen when every candidate fails to meet the threshold. The final behavior is release-specific; verify it in the algorithm documentation and test it with the complete configuration rather than inferring it from a static fallback example.
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Understand recipe boundaries and direct model names
A recipe is the unit that owns signals, decisions, route plugins, cache, learning, and session routing. A virtual entrypoint selects that recipe. A concrete physical provider model name bypasses those recipe features and passes the request directly through to the provider.
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Validate before starting the Router
Use the documented two-command sequence after every substantial policy change:
vllm-sr config validate --config config.yaml
vllm-sr serve --config config.yamlValidation checks schema errors, unresolved model references, incompatible recipe boundaries, invalid provider bindings, and unsupported plugin or algorithm settings before startup. Treat a successful process start as a separate concern: the provider still needs reachable endpoints, valid credentials, sufficient capacity, and compatible model behavior.
A practical validation checklist
- Run validation with the same configuration file and environment-variable set that production will use.
- Confirm every name in
modelRefsexists in provider and model-card declarations. - Confirm
providers.defaults.modelresolves to a usable model. - Check that the virtual entrypoint maps to the intended recipe.
- Review priorities so a catch-all cannot mask specific decisions.
- Verify the selected algorithm’s fields against the installed release’s fragment.
- Start the Router only after validation succeeds.
Troubleshoot unmatched and unexpected routes
| Symptom | Likely cause | What to check |
|---|---|---|
| Traffic always reaches the default model | No decision matches, or the client is using a direct physical model name. | Inspect the virtual entrypoint, rule inputs, priorities, and providers.defaults.model. |
| The fallback decision never runs | Its priority or conditions do not form a catch-all under the installed matching semantics. | Compare with the documented low-priority, empty-AND-conditions shape and validate the full file. |
| Validation reports an unresolved reference | A model name appears in modelRefs but is missing from provider or model-card configuration. |
Make the names identical and confirm provider bindings. |
| Confidence routing behaves like static routing | The decision uses static, or the request bypasses the recipe. |
Use the confidence algorithm on the recipe decision and call the virtual entrypoint. |
| Escalation is unexpectedly frequent | The threshold is too demanding for the current signal, or the first model’s confidence is poorly calibrated. | Measure on representative traffic and review the signal-specific algorithm documentation. |
| Startup fails after a schema change | The file uses an older schema or an unsupported plugin or algorithm field. | Migrate old YAML, validate with the installed binary, and remove fields not supported by that release. |
Version and migration notes
The v0.3 CLI README identifies canonical YAML and removes vllm-sr init. Use vllm-sr config migrate --config old-config.yaml for older files, then inspect the generated structure instead of assuming every legacy field has a one-to-one replacement.
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A v0.4 Hermes release announcement is dated September 24, 2026. If you run that or another newer release, use its bundled configuration guide for confidence fields and matching details. Version labels alone do not guarantee that an example copied from another release has the same algorithm contract.
End-to-end implementation sequence
- Identify the installed release. Read its CLI help and configuration guide, especially the algorithm fragment for confidence routing.
- Migrate legacy YAML if necessary. Run the explicit migration command and review the result.
- Declare models. Add provider models, routing model cards, provider bindings, and a usable recipe default.
- Create specific decisions. Define their rules, priorities, model references, and algorithm types.
- Choose fallback policy. Keep the recipe default, add an explicit static catch-all, or use both intentionally.
- Add confidence routing only where it solves a measured problem. Order candidates and set a signal-specific threshold from evaluation data.
- Point clients at the virtual entrypoint. Direct physical model names bypass recipe routing.
- Validate. Run
vllm-sr config validate --config config.yamland resolve every reported issue. - Serve. Start with
vllm-sr serve --config config.yaml, then observe route selection, escalation, latency, quality, and capacity. - Re-evaluate after changes. Recalibrate thresholds when models, providers, prompts, traffic mix, or confidence methods change.
What a robust policy looks like
A robust policy has explicit, testable intent: targeted rules handle known traffic; a low-priority catch-all prevents silent gaps; providers.defaults.model covers unmatched recipe requests; and confidence looping is limited to decisions where measured escalation improves outcomes. Every model reference resolves, every credential is supplied outside YAML, and the same release-specific validator is run before startup.
The central design choice is not a magic threshold. It is deciding which requests need deterministic selection, which benefit from sequential confidence-based escalation, and what should happen when no rule matches. Once those choices are encoded in the current schema and validated against representative traffic, routing behavior becomes observable and maintainable rather than an accidental side effect of model names or rule order.
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