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fix(sync): forward delegated model tuning params

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Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
This commit is contained in:
YeonGyu-Kim
2026-04-04 15:40:37 +09:00
parent fd476fe9a1
commit 1fae073009
6 changed files with 281 additions and 16 deletions
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src/tools/delegate-task/subagent-resolver.ts
+34 -15
View File
@@ -14,6 +14,25 @@ import { getAvailableModelsForDelegateTask } from "./available-models"
import type { FallbackEntry } from "../../shared/model-requirements"
import { resolveModelForDelegateTask } from "./model-selection"
import { fuzzyMatchModel } from "../../shared/model-availability"
import type { CategoryConfig } from "../../config/schema"
function applyCategoryParams(
base: DelegatedModelConfig,
config: CategoryConfig | undefined,
): DelegatedModelConfig {
if (!config) {
return base
}
return {
...base,
...(config.reasoningEffort !== undefined ? { reasoningEffort: config.reasoningEffort } : {}),
...(config.temperature !== undefined ? { temperature: config.temperature } : {}),
...(config.top_p !== undefined ? { top_p: config.top_p } : {}),
...(config.maxTokens !== undefined ? { maxTokens: config.maxTokens } : {}),
...(config.thinking !== undefined ? { thinking: config.thinking } : {}),
}
}
export async function resolveSubagentExecution(
args: DelegateTaskArgs,
@@ -104,12 +123,13 @@ Create the work plan directly - that's your job as the planning agent.`,
const agentOverride = agentOverrides?.[agentConfigKey as keyof typeof agentOverrides]
?? (agentOverrides ? Object.entries(agentOverrides).find(([key]) => key.toLowerCase() === agentConfigKey)?.[1] : undefined)
const agentRequirement = AGENT_MODEL_REQUIREMENTS[agentConfigKey]
const agentCategoryModel = agentOverride?.category
? userCategories?.[agentOverride.category]?.model
const agentCategoryConfig = agentOverride?.category
? userCategories?.[agentOverride.category]
: undefined
const agentCategoryModel = agentCategoryConfig?.model
const normalizedAgentFallbackModels = normalizeFallbackModels(
agentOverride?.fallback_models
?? (agentOverride?.category ? userCategories?.[agentOverride.category]?.fallback_models : undefined)
?? agentCategoryConfig?.fallback_models
)
const availableModels = await getAvailableModelsForDelegateTask(client)
@@ -137,17 +157,16 @@ Create the work plan directly - that's your job as the planning agent.`,
if (resolution && !resolutionSkipped) {
const normalized = normalizeModelFormat(resolution.model)
if (normalized) {
const variantToUse = agentOverride?.variant ?? resolution.variant
categoryModel = variantToUse ? { ...normalized, variant: variantToUse } : normalized
const variantToUse = agentOverride?.variant ?? resolution.variant ?? agentCategoryConfig?.variant
const resolvedModel = variantToUse ? { ...normalized, variant: variantToUse } : normalized
categoryModel = applyCategoryParams(resolvedModel, agentCategoryConfig)
}
} else if (resolutionSkipped && (agentOverride?.model ?? agentCategoryModel)) {
const normalized = normalizeModelFormat((agentOverride?.model ?? agentCategoryModel)!)
if (normalized) {
const agentCategoryVariant = agentOverride?.category
? userCategories?.[agentOverride.category]?.variant
: undefined
const variantToUse = agentOverride?.variant ?? agentCategoryVariant
categoryModel = variantToUse ? { ...normalized, variant: variantToUse } : normalized
const variantToUse = agentOverride?.variant ?? agentCategoryConfig?.variant
const resolvedModel = variantToUse ? { ...normalized, variant: variantToUse } : normalized
categoryModel = applyCategoryParams(resolvedModel, agentCategoryConfig)
log("[delegate-task] Cold cache: using explicit user override for subagent", {
agent: agentToUse,
model: agentOverride?.model ?? agentCategoryModel,
@@ -180,11 +199,11 @@ Create the work plan directly - that's your job as the planning agent.`,
categoryModel = {
...categoryModel,
variant: agentOverride?.variant ?? effectiveEntry.variant ?? categoryModel.variant,
reasoningEffort: effectiveEntry.reasoningEffort,
temperature: effectiveEntry.temperature,
top_p: effectiveEntry.top_p,
maxTokens: effectiveEntry.maxTokens,
thinking: effectiveEntry.thinking,
reasoningEffort: effectiveEntry.reasoningEffort ?? categoryModel.reasoningEffort,
temperature: effectiveEntry.temperature ?? categoryModel.temperature,
top_p: effectiveEntry.top_p ?? categoryModel.top_p,
maxTokens: effectiveEntry.maxTokens ?? categoryModel.maxTokens,
thinking: effectiveEntry.thinking ?? categoryModel.thinking,
}
}
}