Fix classifier sending an invalid reasoningEffort value to OpenAI-family models - #11
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…ily models auto-router-classify.ts passed reasoningEffort: "off" straight through ModelRegistry.complete(), which forwards it verbatim to each API module's own raw request builder. "off" isn't a valid value in any OpenAI-family reasoningEffort enum: openai-completions/-responses/azure-responses only accept minimal..max, and openai-codex-responses accepts "none" instead of "off". This only type-checked because classifyTurnComplexity's `model: Model<Api>` is the broad provider union rather than the specific API a given model actually uses. Verified this is why classification was silently defaulting to "medium" on every turn instead of ever running: routing the classifier through a configured openai-codex-responses model (as happens whenever the medium tier's first model is unhealthy) sent a literally invalid `reasoning.effort: "off"` in the request body, and the model came back with no text content at all - indistinguishable from a genuine "medium" verdict without checking the raw reply. The previous fix in this area (dropping the classifier's maxTokens cap) addressed a real but secondary risk - it didn't touch this root cause, which is why the same empty-reply failure persisted after that merge. There's no reasoningEffort value valid across every provider's raw enum, so the safe fix is to not specify one at all and let each model use its own default; the existing 15s abort timeout still bounds worst-case cost/latency. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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📝 WalkthroughWalkthroughThe classifier now limits output tokens, reports fallback failures, and omits ChangesClassifier failure reporting and request bounds
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to The classifier now avoids invalid reasoning settings and limits response size, but a failed classification reply can still make the warning notification excessively long and obscure the routing message. The PR is mergeable with owner awareness and a bounded follow-up to show only a shortened reply preview. Possibly related PRs
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Inline comments:
In `@extensions/auto-router-classify.ts`:
- Around line 81-94: Add a regression test in the auto-router classification
test suite that verifies modelRegistry.complete() is called without
reasoningEffort or the former fixed token-budget option. Use an
openai-codex-responses model fixture when available, while preserving the
existing reply-parsing assertions.
- Around line 81-93: The classification request currently lacks an explicit
output-token limit because ModelRegistry.complete() uses the provider’s direct
stream() path. Add a provider-compatible token cap to the classification call,
enforce the cap in the provider request layer for the relevant stream path, and
revise the nearby reasoning comment to describe only the client-side timeout
bound rather than cost control. Add request-body tests covering the emitted
token-limit field.
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An empty or unparseable classifier reply already fell back to `medium`, but with nothing distinguishing that from a genuine medium verdict - which is exactly how the reasoningEffort bug fixed earlier in this PR went unnoticed across multiple real turns. classifyTurnComplexity now returns `failed: true` whenever `level` is that fallback rather than an actual parsed answer (empty reply, unparseable reply, or the call erroring out), and routeForPrompt surfaces it as a warning notification rather than routing silently. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…ssion test CodeRabbit correctly flagged that dropping maxTokens entirely (in the prior PR) left the classification call with no bound on output/cost at all - CLASSIFY_TIMEOUT_MS only bounds wall-clock time, not tokens, so a model that reasons at length but still answers quickly could run up real per-turn cost on what's supposed to be a cheap triage call. Reintroduce a cap, but a much larger one (2000, exported as CLASSIFY_MAX_TOKENS) than the previous fixed 20 that was starving reasoning-capable models to an empty reply. Also add the requested regression test asserting the actual options object passed to modelRegistry.complete() has no reasoningEffort field and the new token cap - coverage this suite didn't have before, which is exactly how the invalid reasoningEffort: "off" went unnoticed in the first place. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2000 was an untested guess. Since the classifier no longer sets an explicit reasoningEffort, a reasoning-capable model's default reasoning depth for this trivial a prompt is unmeasured - sized up generously to hedge against that uncertainty rather than tuned from real data. Still negligible next to a real agent turn's own token usage, which is dominated by input context this call never carries. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Actionable comments posted: 1
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Inline comments:
In `@extensions/auto-router.ts`:
- Around line 385-390: Update the failed-classification notification in the
result.failed and ctx.hasUI branch to display a normalized, bounded preview of
result.reply rather than the complete reply, while preserving the full reply for
/usage and retaining the existing classifiedLevel fallback message.
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…default The classifier previously omitted reasoningEffort entirely, letting each provider fall back to whatever it does by default - reasonable as a safe stopgap, but not what the medium tier's own config says a given model should run at. routeForPrompt now resolves the same effort the classifier model would actually be dispatched at for real work (its own configured override, or the tier name) via the existing resolveEffort() helper, and passes that through. The raw per-API reasoningEffort field is still provider-specific (that's the whole reason "off" broke things), so classifyTurnComplexity only forwards it for APIs verified to accept the full AutoRouterEffortLevel vocabulary (the OpenAI family), and never forwards "off" even there since it's invalid everywhere. Every other provider keeps using its own default, same as before. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…ication If a reasoning model hits CLASSIFY_MAX_TOKENS before ever producing its answer, that's directly actionable (raise the cap, or this model needs less reasoning effort) - but it was indistinguishable from any other unusable reply. classifyTurnComplexity now checks the response's stopReason and names truncation explicitly when that's what happened. Also addresses review: a failed reply can now legitimately run up to CLASSIFY_MAX_TOKENS long, and was being interpolated into the warning notification whole - long enough to flood the UI and bury the warning itself. Reused (and exported) the existing truncateForLog helper that already bounds this same text for /usage, rather than adding a second copy of the same logic. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…ot cause of the state-file leak Two independent findings from a live retest after PR #11 merged: 1. The classifier still came back empty for gpt-5.3-codex-spark even with a valid, model-configured effort ("max") and the higher token cap. Root cause: Model.thinkingLevelMap's own doc says "missing keys use provider defaults" - in practice an unmapped level is forwarded to the raw API as its own literal name, and "max" is pi's own extended vocabulary that several models (this one included, per its own map only covering xhigh and minimal) don't actually understand despite it type-checking. This is exactly why the very first footer-badge mismatch existed: Pi's own real-turn dispatch already knows to clamp such a model down to what it actually supports (observably "xhigh"), but the classifier's raw completion call bypassed that entirely. Now mirrors it: "max" clamps to "xhigh" unless the model's own thinkingLevelMap explicitly confirms support. 2. My earlier per-test-file "wait out the debounce before teardown" fixes only protected each file against its *own* later teardown - they did nothing against a *different* test file's beforeEach changing the same process-wide PI_CODING_AGENT_DIR while an earlier file's save was still pending, which is exactly how the real ~/.pi/agent/auto-router-state.json kept getting corrupted with test fixture data even after those fixes landed. Root fix: AutoRouterHealthStore now pins its target path once, at construction, instead of re-resolving the env var on every debounced flush - making each instance immune to any later change to that var from anywhere, not just careful test teardown. This makes the ad-hoc per-file debounce waits unnecessary; removed them (verified corruption no longer reproduces, twice, with the real full test suite run twice against the actual global state file). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Summary
Follow-up to #10. That PR fixed the footer display and dropped the classifier's
maxTokens: 20cap as a plausible (but ultimately secondary) fix for empty classifier replies. After merging, retesting still showed the exact same failure - classification silently defaulting tomediumon every turn - so I dug further.Root cause:
auto-router-classify.tspassedreasoningEffort: "off"straight throughModelRegistry.complete(), which forwards it verbatim to whichever API module's raw request builder handles that model."off"is not a valid value in any OpenAI-familyreasoningEffortenum:openai-completions/openai-responses/azure-openai-responsesonly acceptminimal | low | medium | high | xhigh | max, andopenai-codex-responsesaccepts"none"instead of"off". This only type-checked becauseclassifyTurnComplexity'smodel: Model<Api>parameter is the broad provider union rather than the specific API a given model actually uses. Verified in the wild: whenever the classifier landed on a configuredopenai-codex-responsesmodel, it sent a literally invalidreasoning.effort: "off"and got back a response with no text content at all - indistinguishable from a genuine "medium" verdict without checking the raw reply.Changes:
classifyTurnComplexitynow reasons at the same effort its model would actually be dispatched at for real work in the medium tier - its own configuredeffortoverride, or the tier name itself - resolved via the existingresolveEffort()helper, instead of an unrelated provider default.reasoningEffortfield is still provider-specific (that's the whole reason"off"broke things), so it's only forwarded for APIs verified to accept the fullAutoRouterEffortLevelvocabulary (openai-completions/openai-responses/azure-openai-responses/openai-codex-responses), and never forwarded as"off"even there since that's invalid everywhere. Every other provider (Anthropic, Z.ai, MiniMax, OpenCode Go, ...) keeps using its own default, same as before - and Mistral specifically would break the same way "off" did if this were forwarded blindly, since its own enum is only"none" | "high".CLASSIFY_MAX_TOKENS = 8000, exported) - droppingmaxTokensentirely left the call with no bound on output/cost at all, since the 15s abort timeout only bounds wall-clock time, not tokens. Sized generously to hedge against unmeasured reasoning depth (the old fixed 20 is what silently starved replies to empty) while still capping the worst case - and still negligible next to a real agent turn's own token usage, which is dominated by input context this call never carries.classifyTurnComplexitynow returnsfailed: truewheneverlevelis the medium fallback because the call errored, timed out, or returned nothing parseable - rather than that being silently indistinguishable from a genuine medium verdict, which is exactly how this bug went undetected across several real turns.routeForPromptsurfaces it as a warning notification instead of routing silently.complete()have noreasoningEffortfor "off" or unlisted-API models, do have it for known-safe-API models, and always carry the token cap.Test plan
bun test(full suite) - 347/347 passbunx tsc --noEmit- clean