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* Model resolution, scoping, and initial selection\n */\n\nimport type { ThinkingLevel } from \"@earendil-works/pi-agent-core\";\nimport { type Api, type KnownProvider, type Model, modelsAreEqual } from \"@earendil-works/pi-ai\";\nimport chalk from \"chalk\";\nimport { minimatch } from \"minimatch\";\nimport { isValidThinkingLevel } from \"../cli/args.ts\";\nimport { DEFAULT_THINKING_LEVEL } from \"./defaults.ts\";\nimport type { ModelRegistry } from \"./model-registry.ts\";\n\n/** Default model IDs for each known provider */\nexport const defaultModelPerProvider: Record<KnownProvider, string> = {\n\t\"amazon-bedrock\": \"us.anthropic.claude-opus-4-6-v1\",\n\t\"ant-ling\": \"Ring-2.6-1T\",\n\tanthropic: \"claude-opus-4-8\",\n\topenai: \"gpt-5.4\",\n\t\"azure-openai-responses\": \"gpt-5.4\",\n\t\"openai-codex\": \"gpt-5.5\",\n\tnvidia: \"nvidia/nemotron-3-super-120b-a12b\",\n\tdeepseek: \"deepseek-v4-pro\",\n\tgoogle: \"gemini-3.1-pro-preview\",\n\t\"google-vertex\": \"gemini-3.1-pro-preview\",\n\t\"github-copilot\": \"gpt-5.4\",\n\topenrouter: \"moonshotai/kimi-k2.6\",\n\t\"vercel-ai-gateway\": \"zai/glm-5.1\",\n\txai: \"grok-4.20-0309-reasoning\",\n\tgroq: \"openai/gpt-oss-120b\",\n\tcerebras: \"zai-glm-4.7\",\n\tzai: \"glm-5.1\",\n\t\"zai-coding-cn\": \"glm-5.1\",\n\tmistral: \"devstral-medium-latest\",\n\tminimax: \"MiniMax-M2.7\",\n\t\"minimax-cn\": \"MiniMax-M2.7\",\n\tmoonshotai: \"kimi-k2.6\",\n\t\"moonshotai-cn\": \"kimi-k2.6\",\n\thuggingface: \"moonshotai/Kimi-K2.6\",\n\tfireworks: \"accounts/fireworks/models/kimi-k2p6\",\n\ttogether: \"moonshotai/Kimi-K2.6\",\n\topencode: \"kimi-k2.6\",\n\t\"opencode-go\": \"kimi-k2.6\",\n\t\"kimi-coding\": \"kimi-for-coding\",\n\t\"cloudflare-workers-ai\": \"@cf/moonshotai/kimi-k2.6\",\n\t\"cloudflare-ai-gateway\": \"workers-ai/@cf/moonshotai/kimi-k2.6\",\n\txiaomi: \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-cn\": \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-ams\": \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-sgp\": \"mimo-v2.5-pro\",\n};\n\nexport interface ScopedModel {\n\tmodel: Model<Api>;\n\t/** Thinking level if explicitly specified in pattern (e.g., \"model:high\"), undefined otherwise */\n\tthinkingLevel?: ThinkingLevel;\n}\n\n/**\n * Helper to check if a model ID looks like an alias (no date suffix)\n * Dates are typically in format: -20241022 or -20250929\n */\nfunction isAlias(id: string): boolean {\n\t// Check if ID ends with -latest\n\tif (id.endsWith(\"-latest\")) return true;\n\n\t// Check if ID ends with a date pattern (-YYYYMMDD)\n\tconst datePattern = /-\\d{8}$/;\n\treturn !datePattern.test(id);\n}\n\n/**\n * Find an exact model reference match.\n * Supports either a bare model id or a canonical provider/modelId reference.\n * When matching by bare id, ambiguous matches across providers are rejected.\n */\nexport function findExactModelReferenceMatch(\n\tmodelReference: string,\n\tavailableModels: Model<Api>[],\n): Model<Api> | undefined {\n\tconst trimmedReference = modelReference.trim();\n\tif (!trimmedReference) {\n\t\treturn undefined;\n\t}\n\n\tconst normalizedReference = trimmedReference.toLowerCase();\n\n\tconst canonicalMatches = availableModels.filter(\n\t\t(model) => `${model.provider}/${model.id}`.toLowerCase() === normalizedReference,\n\t);\n\tif (canonicalMatches.length === 1) {\n\t\treturn canonicalMatches[0];\n\t}\n\tif (canonicalMatches.length > 1) {\n\t\treturn undefined;\n\t}\n\n\tconst slashIndex = trimmedReference.indexOf(\"/\");\n\tif (slashIndex !== -1) {\n\t\tconst provider = trimmedReference.substring(0, slashIndex).trim();\n\t\tconst modelId = trimmedReference.substring(slashIndex + 1).trim();\n\t\tif (provider && modelId) {\n\t\t\tconst providerMatches = availableModels.filter(\n\t\t\t\t(model) =>\n\t\t\t\t\tmodel.provider.toLowerCase() === provider.toLowerCase() &&\n\t\t\t\t\tmodel.id.toLowerCase() === modelId.toLowerCase(),\n\t\t\t);\n\t\t\tif (providerMatches.length === 1) {\n\t\t\t\treturn providerMatches[0];\n\t\t\t}\n\t\t\tif (providerMatches.length > 1) {\n\t\t\t\treturn undefined;\n\t\t\t}\n\t\t}\n\t}\n\n\tconst idMatches = availableModels.filter((model) => model.id.toLowerCase() === normalizedReference);\n\treturn idMatches.length === 1 ? idMatches[0] : undefined;\n}\n\n/**\n * Try to match a pattern to a model from the available models list.\n * Returns the matched model or undefined if no match found.\n */\nfunction tryMatchModel(modelPattern: string, availableModels: Model<Api>[]): Model<Api> | undefined {\n\tconst exactMatch = findExactModelReferenceMatch(modelPattern, availableModels);\n\tif (exactMatch) {\n\t\treturn exactMatch;\n\t}\n\n\t// No exact match - fall back to partial matching\n\tconst matches = availableModels.filter(\n\t\t(m) =>\n\t\t\tm.id.toLowerCase().includes(modelPattern.toLowerCase()) ||\n\t\t\tm.name?.toLowerCase().includes(modelPattern.toLowerCase()),\n\t);\n\n\tif (matches.length === 0) {\n\t\treturn undefined;\n\t}\n\n\t// Separate into aliases and dated versions\n\tconst aliases = matches.filter((m) => isAlias(m.id));\n\tconst datedVersions = matches.filter((m) => !isAlias(m.id));\n\n\tif (aliases.length > 0) {\n\t\t// Prefer alias - if multiple aliases, pick the one that sorts highest\n\t\taliases.sort((a, b) => b.id.localeCompare(a.id));\n\t\treturn aliases[0];\n\t} else {\n\t\t// No alias found, pick latest dated version\n\t\tdatedVersions.sort((a, b) => b.id.localeCompare(a.id));\n\t\treturn datedVersions[0];\n\t}\n}\n\nexport interface ParsedModelResult {\n\tmodel: Model<Api> | undefined;\n\t/** Thinking level if explicitly specified in pattern, undefined otherwise */\n\tthinkingLevel?: ThinkingLevel;\n\twarning: string | undefined;\n}\n\nfunction buildFallbackModel(provider: string, modelId: string, availableModels: Model<Api>[]): Model<Api> | undefined {\n\tconst providerModels = availableModels.filter((m) => m.provider === provider);\n\tif (providerModels.length === 0) return undefined;\n\n\tconst defaultId = defaultModelPerProvider[provider as KnownProvider];\n\tconst baseModel = defaultId\n\t\t? (providerModels.find((m) => m.id === defaultId) ?? providerModels[0])\n\t\t: providerModels[0];\n\n\treturn {\n\t\t...baseModel,\n\t\tid: modelId,\n\t\tname: modelId,\n\t};\n}\n\n/**\n * Parse a pattern to extract model and thinking level.\n * Handles models with colons in their IDs (e.g., OpenRouter's :exacto suffix).\n *\n * Algorithm:\n * 1. Try to match full pattern as a model\n * 2. If found, return it with \"off\" thinking level\n * 3. If not found and has colons, split on last colon:\n *    - If suffix is valid thinking level, use it and recurse on prefix\n *    - If suffix is invalid, warn and recurse on prefix with \"off\"\n *\n * @internal Exported for testing\n */\nexport function parseModelPattern(\n\tpattern: string,\n\tavailableModels: Model<Api>[],\n\toptions?: { allowInvalidThinkingLevelFallback?: boolean },\n): ParsedModelResult {\n\t// Try exact match first\n\tconst exactMatch = tryMatchModel(pattern, availableModels);\n\tif (exactMatch) {\n\t\treturn { model: exactMatch, thinkingLevel: undefined, warning: undefined };\n\t}\n\n\t// No match - try splitting on last colon if present\n\tconst lastColonIndex = pattern.lastIndexOf(\":\");\n\tif (lastColonIndex === -1) {\n\t\t// No colons, pattern simply doesn't match any model\n\t\treturn { model: undefined, thinkingLevel: undefined, warning: undefined };\n\t}\n\n\tconst prefix = pattern.substring(0, lastColonIndex);\n\tconst suffix = pattern.substring(lastColonIndex + 1);\n\n\tif (isValidThinkingLevel(suffix)) {\n\t\t// Valid thinking level - recurse on prefix and use this level\n\t\tconst result = parseModelPattern(prefix, availableModels, options);\n\t\tif (result.model) {\n\t\t\t// Only use this thinking level if no warning from inner recursion\n\t\t\treturn {\n\t\t\t\tmodel: result.model,\n\t\t\t\tthinkingLevel: result.warning ? undefined : suffix,\n\t\t\t\twarning: result.warning,\n\t\t\t};\n\t\t}\n\t\treturn result;\n\t} else {\n\t\t// Invalid suffix\n\t\tconst allowFallback = options?.allowInvalidThinkingLevelFallback ?? true;\n\t\tif (!allowFallback) {\n\t\t\t// In strict mode (CLI --model parsing), treat it as part of the model id and fail.\n\t\t\t// This avoids accidentally resolving to a different model.\n\t\t\treturn { model: undefined, thinkingLevel: undefined, warning: undefined };\n\t\t}\n\n\t\t// Scope mode: recurse on prefix and warn\n\t\tconst result = parseModelPattern(prefix, availableModels, options);\n\t\tif (result.model) {\n\t\t\treturn {\n\t\t\t\tmodel: result.model,\n\t\t\t\tthinkingLevel: undefined,\n\t\t\t\twarning: `Invalid thinking level \"${suffix}\" in pattern \"${pattern}\". Using default instead.`,\n\t\t\t};\n\t\t}\n\t\treturn result;\n\t}\n}\n\n/**\n * Resolve model patterns to actual Model objects with optional thinking levels\n * Format: \"pattern:level\" where :level is optional\n * For each pattern, finds all matching models and picks the best version:\n * 1. Prefer alias (e.g., claude-sonnet-4-5) over dated versions (claude-sonnet-4-5-20250929)\n * 2. If no alias, pick the latest dated version\n *\n * Supports models with colons in their IDs (e.g., OpenRouter's model:exacto).\n * The algorithm tries to match the full pattern first, then progressively\n * strips colon-suffixes to find a match.\n */\nexport async function resolveModelScope(patterns: string[], modelRegistry: ModelRegistry): Promise<ScopedModel[]> {\n\tconst availableModels = await modelRegistry.getAvailable();\n\tconst scopedModels: ScopedModel[] = [];\n\n\tfor (const pattern of patterns) {\n\t\t// Check if pattern contains glob characters\n\t\tif (pattern.includes(\"*\") || pattern.includes(\"?\") || pattern.includes(\"[\")) {\n\t\t\t// Extract optional thinking level suffix (e.g., \"provider/*:high\")\n\t\t\tconst colonIdx = pattern.lastIndexOf(\":\");\n\t\t\tlet globPattern = pattern;\n\t\t\tlet thinkingLevel: ThinkingLevel | undefined;\n\n\t\t\tif (colonIdx !== -1) {\n\t\t\t\tconst suffix = pattern.substring(colonIdx + 1);\n\t\t\t\tif (isValidThinkingLevel(suffix)) {\n\t\t\t\t\tthinkingLevel = suffix;\n\t\t\t\t\tglobPattern = pattern.substring(0, colonIdx);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Match against \"provider/modelId\" format OR just model ID\n\t\t\t// This allows \"*sonnet*\" to match without requiring \"anthropic/*sonnet*\"\n\t\t\tconst matchingModels = availableModels.filter((m) => {\n\t\t\t\tconst fullId = `${m.provider}/${m.id}`;\n\t\t\t\treturn minimatch(fullId, globPattern, { nocase: true }) || minimatch(m.id, globPattern, { nocase: true });\n\t\t\t});\n\n\t\t\tif (matchingModels.length === 0) {\n\t\t\t\tconsole.warn(chalk.yellow(`Warning: No models match pattern \"${pattern}\"`));\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tfor (const model of matchingModels) {\n\t\t\t\tif (!scopedModels.find((sm) => modelsAreEqual(sm.model, model))) {\n\t\t\t\t\tscopedModels.push({ model, thinkingLevel });\n\t\t\t\t}\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\n\t\tconst { model, thinkingLevel, warning } = parseModelPattern(pattern, availableModels);\n\n\t\tif (warning) {\n\t\t\tconsole.warn(chalk.yellow(`Warning: ${warning}`));\n\t\t}\n\n\t\tif (!model) {\n\t\t\tconsole.warn(chalk.yellow(`Warning: No models match pattern \"${pattern}\"`));\n\t\t\tcontinue;\n\t\t}\n\n\t\t// Avoid duplicates\n\t\tif (!scopedModels.find((sm) => modelsAreEqual(sm.model, model))) {\n\t\t\tscopedModels.push({ model, thinkingLevel });\n\t\t}\n\t}\n\n\treturn scopedModels;\n}\n\nexport interface ResolveCliModelResult {\n\tmodel: Model<Api> | undefined;\n\tthinkingLevel?: ThinkingLevel;\n\twarning: string | undefined;\n\t/**\n\t * Error message suitable for CLI display.\n\t * When set, model will be undefined.\n\t */\n\terror: string | undefined;\n}\n\n/**\n * Resolve a single model from CLI flags.\n *\n * Supports:\n * - --provider <provider> --model <pattern>\n * - --model <provider>/<pattern>\n * - Fuzzy matching (same rules as model scoping: exact id, then partial id/name)\n *\n * Note: This does not apply the thinking level by itself, but it may *parse* and\n * return a thinking level from \"<pattern>:<thinking>\" so the caller can apply it.\n */\nexport function resolveCliModel(options: {\n\tcliProvider?: string;\n\tcliModel?: string;\n\tmodelRegistry: ModelRegistry;\n}): ResolveCliModelResult {\n\tconst { cliProvider, cliModel, modelRegistry } = options;\n\n\tif (!cliModel) {\n\t\treturn { model: undefined, warning: undefined, error: undefined };\n\t}\n\n\t// Important: use *all* models here, not just models with pre-configured auth.\n\t// This allows \"--api-key\" to be used for first-time setup.\n\tconst availableModels = modelRegistry.getAll();\n\tif (availableModels.length === 0) {\n\t\treturn {\n\t\t\tmodel: undefined,\n\t\t\twarning: undefined,\n\t\t\terror: \"No models available. Check your installation or add models to models.json.\",\n\t\t};\n\t}\n\n\t// Build canonical provider lookup (case-insensitive)\n\tconst providerMap = new Map<string, string>();\n\tfor (const m of availableModels) {\n\t\tproviderMap.set(m.provider.toLowerCase(), m.provider);\n\t}\n\n\tlet provider = cliProvider ? providerMap.get(cliProvider.toLowerCase()) : undefined;\n\tif (cliProvider && !provider) {\n\t\treturn {\n\t\t\tmodel: undefined,\n\t\t\twarning: undefined,\n\t\t\terror: `Unknown provider \"${cliProvider}\". Use --list-models to see available providers/models.`,\n\t\t};\n\t}\n\n\t// If no explicit --provider, try to interpret \"provider/model\" format first.\n\t// When the prefix before the first slash matches a known provider, prefer that\n\t// interpretation over matching models whose IDs literally contain slashes\n\t// (e.g. \"zai/glm-5\" should resolve to provider=zai, model=glm-5, not to a\n\t// vercel-ai-gateway model with id \"zai/glm-5\").\n\tlet pattern = cliModel;\n\tlet inferredProvider = false;\n\n\tif (!provider) {\n\t\tconst slashIndex = cliModel.indexOf(\"/\");\n\t\tif (slashIndex !== -1) {\n\t\t\tconst maybeProvider = cliModel.substring(0, slashIndex);\n\t\t\tconst canonical = providerMap.get(maybeProvider.toLowerCase());\n\t\t\tif (canonical) {\n\t\t\t\tprovider = canonical;\n\t\t\t\tpattern = cliModel.substring(slashIndex + 1);\n\t\t\t\tinferredProvider = true;\n\t\t\t}\n\t\t}\n\t}\n\n\t// If no provider was inferred from the slash, try exact matches without provider inference.\n\t// This handles models whose IDs naturally contain slashes (e.g. OpenRouter-style IDs).\n\tif (!provider) {\n\t\tconst lower = cliModel.toLowerCase();\n\t\tconst exact = availableModels.find(\n\t\t\t(m) => m.id.toLowerCase() === lower || `${m.provider}/${m.id}`.toLowerCase() === lower,\n\t\t);\n\t\tif (exact) {\n\t\t\treturn { model: exact, warning: undefined, thinkingLevel: undefined, error: undefined };\n\t\t}\n\t}\n\n\tif (cliProvider && provider) {\n\t\t// If both were provided, tolerate --model <provider>/<pattern> by stripping the provider prefix\n\t\tconst prefix = `${provider}/`;\n\t\tif (cliModel.toLowerCase().startsWith(prefix.toLowerCase())) {\n\t\t\tpattern = cliModel.substring(prefix.length);\n\t\t}\n\t}\n\n\tconst candidates = provider ? availableModels.filter((m) => m.provider === provider) : availableModels;\n\tconst { model, thinkingLevel, warning } = parseModelPattern(pattern, candidates, {\n\t\tallowInvalidThinkingLevelFallback: false,\n\t});\n\n\tif (model) {\n\t\treturn { model, thinkingLevel, warning, error: undefined };\n\t}\n\n\t// If we inferred a provider from the slash but found no match within that provider,\n\t// fall back to matching the full input as a raw model id across all models.\n\t// This handles OpenRouter-style IDs like \"openai/gpt-4o:extended\" where \"openai\"\n\t// looks like a provider but the full string is actually a model id on openrouter.\n\tif (inferredProvider) {\n\t\tconst lower = cliModel.toLowerCase();\n\t\tconst exact = availableModels.find(\n\t\t\t(m) => m.id.toLowerCase() === lower || `${m.provider}/${m.id}`.toLowerCase() === lower,\n\t\t);\n\t\tif (exact) {\n\t\t\treturn { model: exact, warning: undefined, thinkingLevel: undefined, error: undefined };\n\t\t}\n\t\t// Also try parseModelPattern on the full input against all models\n\t\tconst fallback = parseModelPattern(cliModel, availableModels, {\n\t\t\tallowInvalidThinkingLevelFallback: false,\n\t\t});\n\t\tif (fallback.model) {\n\t\t\treturn {\n\t\t\t\tmodel: fallback.model,\n\t\t\t\tthinkingLevel: fallback.thinkingLevel,\n\t\t\t\twarning: fallback.warning,\n\t\t\t\terror: undefined,\n\t\t\t};\n\t\t}\n\t}\n\n\tif (provider) {\n\t\tconst fallbackModel = buildFallbackModel(provider, pattern, availableModels);\n\t\tif (fallbackModel) {\n\t\t\tconst fallbackWarning = warning\n\t\t\t\t? `${warning} Model \"${pattern}\" not found for provider \"${provider}\". Using custom model id.`\n\t\t\t\t: `Model \"${pattern}\" not found for provider \"${provider}\". Using custom model id.`;\n\t\t\treturn { model: fallbackModel, thinkingLevel: undefined, warning: fallbackWarning, error: undefined };\n\t\t}\n\t}\n\n\tconst display = provider ? `${provider}/${pattern}` : cliModel;\n\treturn {\n\t\tmodel: undefined,\n\t\tthinkingLevel: undefined,\n\t\twarning,\n\t\terror: `Model \"${display}\" not found. Use --list-models to see available models.`,\n\t};\n}\n\nexport interface InitialModelResult {\n\tmodel: Model<Api> | undefined;\n\tthinkingLevel: ThinkingLevel;\n\tfallbackMessage: string | undefined;\n}\n\n/**\n * Find the initial model to use based on priority:\n * 1. CLI args (provider + model)\n * 2. First model from scoped models (if not continuing/resuming)\n * 3. Restored from session (if continuing/resuming)\n * 4. Saved default from settings\n * 5. First available model with valid API key\n */\nexport async function findInitialModel(options: {\n\tcliProvider?: string;\n\tcliModel?: string;\n\tscopedModels: ScopedModel[];\n\tisContinuing: boolean;\n\tdefaultProvider?: string;\n\tdefaultModelId?: string;\n\tdefaultThinkingLevel?: ThinkingLevel;\n\tmodelRegistry: ModelRegistry;\n}): Promise<InitialModelResult> {\n\tconst {\n\t\tcliProvider,\n\t\tcliModel,\n\t\tscopedModels,\n\t\tisContinuing,\n\t\tdefaultProvider,\n\t\tdefaultModelId,\n\t\tdefaultThinkingLevel,\n\t\tmodelRegistry,\n\t} = options;\n\n\tlet model: Model<Api> | undefined;\n\tlet thinkingLevel: ThinkingLevel = DEFAULT_THINKING_LEVEL;\n\n\t// 1. CLI args take priority\n\tif (cliProvider && cliModel) {\n\t\tconst resolved = resolveCliModel({\n\t\t\tcliProvider,\n\t\t\tcliModel,\n\t\t\tmodelRegistry,\n\t\t});\n\t\tif (resolved.error) {\n\t\t\tconsole.error(chalk.red(resolved.error));\n\t\t\tprocess.exit(1);\n\t\t}\n\t\tif (resolved.model) {\n\t\t\treturn { model: resolved.model, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t\t}\n\t}\n\n\t// 2. Use first model from scoped models (skip if continuing/resuming)\n\tif (scopedModels.length > 0 && !isContinuing) {\n\t\treturn {\n\t\t\tmodel: scopedModels[0].model,\n\t\t\tthinkingLevel: scopedModels[0].thinkingLevel ?? defaultThinkingLevel ?? DEFAULT_THINKING_LEVEL,\n\t\t\tfallbackMessage: undefined,\n\t\t};\n\t}\n\n\t// 3. Try saved default from settings\n\tif (defaultProvider && defaultModelId) {\n\t\tconst found = modelRegistry.find(defaultProvider, defaultModelId);\n\t\tif (found) {\n\t\t\tmodel = found;\n\t\t\tif (defaultThinkingLevel) {\n\t\t\t\tthinkingLevel = defaultThinkingLevel;\n\t\t\t}\n\t\t\treturn { model, thinkingLevel, fallbackMessage: undefined };\n\t\t}\n\t}\n\n\t// 4. Try first available model with valid API key\n\tconst availableModels = await modelRegistry.getAvailable();\n\n\tif (availableModels.length > 0) {\n\t\t// Try to find a default model from known providers\n\t\tfor (const provider of Object.keys(defaultModelPerProvider) as KnownProvider[]) {\n\t\t\tconst defaultId = defaultModelPerProvider[provider];\n\t\t\tconst match = availableModels.find((m) => m.provider === provider && m.id === defaultId);\n\t\t\tif (match) {\n\t\t\t\treturn { model: match, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t\t\t}\n\t\t}\n\n\t\t// If no default found, use first available\n\t\treturn { model: availableModels[0], thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t}\n\n\t// 5. No model found\n\treturn { model: undefined, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n}\n\n/**\n * Restore model from session, with fallback to available models\n */\nexport async function restoreModelFromSession(\n\tsavedProvider: string,\n\tsavedModelId: string,\n\tcurrentModel: Model<Api> | undefined,\n\tshouldPrintMessages: boolean,\n\tmodelRegistry: ModelRegistry,\n): Promise<{ model: Model<Api> | undefined; fallbackMessage: string | undefined }> {\n\tconst restoredModel = modelRegistry.find(savedProvider, savedModelId);\n\n\t// Check if restored model exists and still has auth configured\n\tconst hasConfiguredAuth = restoredModel ? modelRegistry.hasConfiguredAuth(restoredModel) : false;\n\n\tif (restoredModel && hasConfiguredAuth) {\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Restored model: ${savedProvider}/${savedModelId}`));\n\t\t}\n\t\treturn { model: restoredModel, fallbackMessage: undefined };\n\t}\n\n\t// Model not found or no API key - fall back\n\tconst reason = !restoredModel ? \"model no longer exists\" : \"no auth configured\";\n\n\tif (shouldPrintMessages) {\n\t\tconsole.error(chalk.yellow(`Warning: Could not restore model ${savedProvider}/${savedModelId} (${reason}).`));\n\t}\n\n\t// If we already have a model, use it as fallback\n\tif (currentModel) {\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Falling back to: ${currentModel.provider}/${currentModel.id}`));\n\t\t}\n\t\treturn {\n\t\t\tmodel: currentModel,\n\t\t\tfallbackMessage: `Could not restore model ${savedProvider}/${savedModelId} (${reason}). Using ${currentModel.provider}/${currentModel.id}.`,\n\t\t};\n\t}\n\n\t// Try to find any available model\n\tconst availableModels = await modelRegistry.getAvailable();\n\n\tif (availableModels.length > 0) {\n\t\t// Try to find a default model from known providers\n\t\tlet fallbackModel: Model<Api> | undefined;\n\t\tfor (const provider of Object.keys(defaultModelPerProvider) as KnownProvider[]) {\n\t\t\tconst defaultId = defaultModelPerProvider[provider];\n\t\t\tconst match = availableModels.find((m) => m.provider === provider && m.id === defaultId);\n\t\t\tif (match) {\n\t\t\t\tfallbackModel = match;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\n\t\t// If no default found, use first available\n\t\tif (!fallbackModel) {\n\t\t\tfallbackModel = availableModels[0];\n\t\t}\n\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Falling back to: ${fallbackModel.provider}/${fallbackModel.id}`));\n\t\t}\n\n\t\treturn {\n\t\t\tmodel: fallbackModel,\n\t\t\tfallbackMessage: `Could not restore model ${savedProvider}/${savedModelId} (${reason}). Using ${fallbackModel.provider}/${fallbackModel.id}.`,\n\t\t};\n\t}\n\n\t// No models available\n\treturn { model: undefined, fallbackMessage: undefined };\n}\n"]}
/home/ubuntu/.local/lib/node_modules/@earendil-works/pi-coding-agent/dist/core/provider-display-names.d.ts.map:{"version":3,"file":"provider-display-names.d.ts","sourceRoot":"","sources":["../../src/core/provider-display-names.ts"],"names":[],"mappings":"AAAA,eAAO,MAAM,+BAA+B,EAAE,MAAM,CAAC,MAAM,EAAE,MAAM,CAkClE,CAAC","sourcesContent":["export const BUILT_IN_PROVIDER_DISPLAY_NAMES: Record<string, string> = {\n\tanthropic: \"Anthropic\",\n\t\"amazon-bedrock\": \"Amazon Bedrock\",\n\t\"ant-ling\": \"Ant Ling\",\n\t\"azure-openai-responses\": \"Azure OpenAI Responses\",\n\tcerebras: \"Cerebras\",\n\t\"cloudflare-ai-gateway\": \"Cloudflare AI Gateway\",\n\t\"cloudflare-workers-ai\": \"Cloudflare Workers AI\",\n\tdeepseek: \"DeepSeek\",\n\tfireworks: \"Fireworks\",\n\tgoogle: \"Google Gemini\",\n\t\"google-vertex\": \"Google Vertex AI\",\n\tgroq: \"Groq\",\n\thuggingface: \"Hugging Face\",\n\t\"kimi-coding\": \"Kimi For Coding\",\n\tmistral: \"Mistral\",\n\tminimax: \"MiniMax\",\n\t\"minimax-cn\": \"MiniMax (China)\",\n\tmoonshotai: \"Moonshot AI\",\n\t\"moonshotai-cn\": \"Moonshot AI (China)\",\n\tnvidia: \"NVIDIA NIM\",\n\topencode: \"OpenCode Zen\",\n\t\"opencode-go\": \"OpenCode Go\",\n\topenai: \"OpenAI\",\n\topenrouter: \"OpenRouter\",\n\ttogether: \"Together AI\",\n\t\"vercel-ai-gateway\": \"Vercel AI Gateway\",\n\txai: \"xAI\",\n\tzai: \"ZAI\",\n\t\"zai-coding-cn\": \"ZAI Coding Plan (China)\",\n\txiaomi: \"Xiaomi MiMo\",\n\t\"xiaomi-token-plan-cn\": \"Xiaomi MiMo Token Plan (China)\",\n\t\"xiaomi-token-plan-ams\": \"Xiaomi MiMo Token Plan (Amsterdam)\",\n\t\"xiaomi-token-plan-sgp\": \"Xiaomi MiMo Token Plan (Singapore)\",\n};\n"]}
/home/ubuntu/.local/lib/node_modules/@earendil-works/pi-coding-agent/dist/core/model-resolver.js:    minimax: "MiniMax-M2.7",
/home/ubuntu/.local/lib/node_modules/@earendil-works/pi-coding-agent/dist/core/model-resolver.js:    "minimax-cn": "MiniMax-M2.7",
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* Model resolution, scoping, and initial selection\n */\n\nimport type { ThinkingLevel } from \"@earendil-works/pi-agent-core\";\nimport { type Api, type KnownProvider, type Model, modelsAreEqual } from \"@earendil-works/pi-ai\";\nimport chalk from \"chalk\";\nimport { minimatch } from \"minimatch\";\nimport { isValidThinkingLevel } from \"../cli/args.ts\";\nimport { DEFAULT_THINKING_LEVEL } from \"./defaults.ts\";\nimport type { ModelRegistry } from \"./model-registry.ts\";\n\n/** Default model IDs for each known provider */\nexport const defaultModelPerProvider: Record<KnownProvider, string> = {\n\t\"amazon-bedrock\": \"us.anthropic.claude-opus-4-6-v1\",\n\t\"ant-ling\": \"Ring-2.6-1T\",\n\tanthropic: \"claude-opus-4-8\",\n\topenai: \"gpt-5.4\",\n\t\"azure-openai-responses\": \"gpt-5.4\",\n\t\"openai-codex\": \"gpt-5.5\",\n\tnvidia: \"nvidia/nemotron-3-super-120b-a12b\",\n\tdeepseek: \"deepseek-v4-pro\",\n\tgoogle: \"gemini-3.1-pro-preview\",\n\t\"google-vertex\": \"gemini-3.1-pro-preview\",\n\t\"github-copilot\": \"gpt-5.4\",\n\topenrouter: \"moonshotai/kimi-k2.6\",\n\t\"vercel-ai-gateway\": \"zai/glm-5.1\",\n\txai: \"grok-4.20-0309-reasoning\",\n\tgroq: \"openai/gpt-oss-120b\",\n\tcerebras: \"zai-glm-4.7\",\n\tzai: \"glm-5.1\",\n\t\"zai-coding-cn\": \"glm-5.1\",\n\tmistral: \"devstral-medium-latest\",\n\tminimax: \"MiniMax-M2.7\",\n\t\"minimax-cn\": \"MiniMax-M2.7\",\n\tmoonshotai: \"kimi-k2.6\",\n\t\"moonshotai-cn\": \"kimi-k2.6\",\n\thuggingface: \"moonshotai/Kimi-K2.6\",\n\tfireworks: \"accounts/fireworks/models/kimi-k2p6\",\n\ttogether: \"moonshotai/Kimi-K2.6\",\n\topencode: \"kimi-k2.6\",\n\t\"opencode-go\": \"kimi-k2.6\",\n\t\"kimi-coding\": \"kimi-for-coding\",\n\t\"cloudflare-workers-ai\": \"@cf/moonshotai/kimi-k2.6\",\n\t\"cloudflare-ai-gateway\": \"workers-ai/@cf/moonshotai/kimi-k2.6\",\n\txiaomi: \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-cn\": \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-ams\": \"mimo-v2.5-pro\",\n\t\"xiaomi-token-plan-sgp\": \"mimo-v2.5-pro\",\n};\n\nexport interface ScopedModel {\n\tmodel: Model<Api>;\n\t/** Thinking level if explicitly specified in pattern (e.g., \"model:high\"), undefined otherwise */\n\tthinkingLevel?: ThinkingLevel;\n}\n\n/**\n * Helper to check if a model ID looks like an alias (no date suffix)\n * Dates are typically in format: -20241022 or -20250929\n */\nfunction isAlias(id: string): boolean {\n\t// Check if ID ends with -latest\n\tif (id.endsWith(\"-latest\")) return true;\n\n\t// Check if ID ends with a date pattern (-YYYYMMDD)\n\tconst datePattern = /-\\d{8}$/;\n\treturn !datePattern.test(id);\n}\n\n/**\n * Find an exact model reference match.\n * Supports either a bare model id or a canonical provider/modelId reference.\n * When matching by bare id, ambiguous matches across providers are rejected.\n */\nexport function findExactModelReferenceMatch(\n\tmodelReference: string,\n\tavailableModels: Model<Api>[],\n): Model<Api> | undefined {\n\tconst trimmedReference = modelReference.trim();\n\tif (!trimmedReference) {\n\t\treturn undefined;\n\t}\n\n\tconst normalizedReference = trimmedReference.toLowerCase();\n\n\tconst canonicalMatches = availableModels.filter(\n\t\t(model) => `${model.provider}/${model.id}`.toLowerCase() === normalizedReference,\n\t);\n\tif (canonicalMatches.length === 1) {\n\t\treturn canonicalMatches[0];\n\t}\n\tif (canonicalMatches.length > 1) {\n\t\treturn undefined;\n\t}\n\n\tconst slashIndex = trimmedReference.indexOf(\"/\");\n\tif (slashIndex !== -1) {\n\t\tconst provider = trimmedReference.substring(0, slashIndex).trim();\n\t\tconst modelId = trimmedReference.substring(slashIndex + 1).trim();\n\t\tif (provider && modelId) {\n\t\t\tconst providerMatches = availableModels.filter(\n\t\t\t\t(model) =>\n\t\t\t\t\tmodel.provider.toLowerCase() === provider.toLowerCase() &&\n\t\t\t\t\tmodel.id.toLowerCase() === modelId.toLowerCase(),\n\t\t\t);\n\t\t\tif (providerMatches.length === 1) {\n\t\t\t\treturn providerMatches[0];\n\t\t\t}\n\t\t\tif (providerMatches.length > 1) {\n\t\t\t\treturn undefined;\n\t\t\t}\n\t\t}\n\t}\n\n\tconst idMatches = availableModels.filter((model) => model.id.toLowerCase() === normalizedReference);\n\treturn idMatches.length === 1 ? idMatches[0] : undefined;\n}\n\n/**\n * Try to match a pattern to a model from the available models list.\n * Returns the matched model or undefined if no match found.\n */\nfunction tryMatchModel(modelPattern: string, availableModels: Model<Api>[]): Model<Api> | undefined {\n\tconst exactMatch = findExactModelReferenceMatch(modelPattern, availableModels);\n\tif (exactMatch) {\n\t\treturn exactMatch;\n\t}\n\n\t// No exact match - fall back to partial matching\n\tconst matches = availableModels.filter(\n\t\t(m) =>\n\t\t\tm.id.toLowerCase().includes(modelPattern.toLowerCase()) ||\n\t\t\tm.name?.toLowerCase().includes(modelPattern.toLowerCase()),\n\t);\n\n\tif (matches.length === 0) {\n\t\treturn undefined;\n\t}\n\n\t// Separate into aliases and dated versions\n\tconst aliases = matches.filter((m) => isAlias(m.id));\n\tconst datedVersions = matches.filter((m) => !isAlias(m.id));\n\n\tif (aliases.length > 0) {\n\t\t// Prefer alias - if multiple aliases, pick the one that sorts highest\n\t\taliases.sort((a, b) => b.id.localeCompare(a.id));\n\t\treturn aliases[0];\n\t} else {\n\t\t// No alias found, pick latest dated version\n\t\tdatedVersions.sort((a, b) => b.id.localeCompare(a.id));\n\t\treturn datedVersions[0];\n\t}\n}\n\nexport interface ParsedModelResult {\n\tmodel: Model<Api> | undefined;\n\t/** Thinking level if explicitly specified in pattern, undefined otherwise */\n\tthinkingLevel?: ThinkingLevel;\n\twarning: string | undefined;\n}\n\nfunction buildFallbackModel(provider: string, modelId: string, availableModels: Model<Api>[]): Model<Api> | undefined {\n\tconst providerModels = availableModels.filter((m) => m.provider === provider);\n\tif (providerModels.length === 0) return undefined;\n\n\tconst defaultId = defaultModelPerProvider[provider as KnownProvider];\n\tconst baseModel = defaultId\n\t\t? (providerModels.find((m) => m.id === defaultId) ?? providerModels[0])\n\t\t: providerModels[0];\n\n\treturn {\n\t\t...baseModel,\n\t\tid: modelId,\n\t\tname: modelId,\n\t};\n}\n\n/**\n * Parse a pattern to extract model and thinking level.\n * Handles models with colons in their IDs (e.g., OpenRouter's :exacto suffix).\n *\n * Algorithm:\n * 1. Try to match full pattern as a model\n * 2. If found, return it with \"off\" thinking level\n * 3. If not found and has colons, split on last colon:\n *    - If suffix is valid thinking level, use it and recurse on prefix\n *    - If suffix is invalid, warn and recurse on prefix with \"off\"\n *\n * @internal Exported for testing\n */\nexport function parseModelPattern(\n\tpattern: string,\n\tavailableModels: Model<Api>[],\n\toptions?: { allowInvalidThinkingLevelFallback?: boolean },\n): ParsedModelResult {\n\t// Try exact match first\n\tconst exactMatch = tryMatchModel(pattern, availableModels);\n\tif (exactMatch) {\n\t\treturn { model: exactMatch, thinkingLevel: undefined, warning: undefined };\n\t}\n\n\t// No match - try splitting on last colon if present\n\tconst lastColonIndex = pattern.lastIndexOf(\":\");\n\tif (lastColonIndex === -1) {\n\t\t// No colons, pattern simply doesn't match any model\n\t\treturn { model: undefined, thinkingLevel: undefined, warning: undefined };\n\t}\n\n\tconst prefix = pattern.substring(0, lastColonIndex);\n\tconst suffix = pattern.substring(lastColonIndex + 1);\n\n\tif (isValidThinkingLevel(suffix)) {\n\t\t// Valid thinking level - recurse on prefix and use this level\n\t\tconst result = parseModelPattern(prefix, availableModels, options);\n\t\tif (result.model) {\n\t\t\t// Only use this thinking level if no warning from inner recursion\n\t\t\treturn {\n\t\t\t\tmodel: result.model,\n\t\t\t\tthinkingLevel: result.warning ? undefined : suffix,\n\t\t\t\twarning: result.warning,\n\t\t\t};\n\t\t}\n\t\treturn result;\n\t} else {\n\t\t// Invalid suffix\n\t\tconst allowFallback = options?.allowInvalidThinkingLevelFallback ?? true;\n\t\tif (!allowFallback) {\n\t\t\t// In strict mode (CLI --model parsing), treat it as part of the model id and fail.\n\t\t\t// This avoids accidentally resolving to a different model.\n\t\t\treturn { model: undefined, thinkingLevel: undefined, warning: undefined };\n\t\t}\n\n\t\t// Scope mode: recurse on prefix and warn\n\t\tconst result = parseModelPattern(prefix, availableModels, options);\n\t\tif (result.model) {\n\t\t\treturn {\n\t\t\t\tmodel: result.model,\n\t\t\t\tthinkingLevel: undefined,\n\t\t\t\twarning: `Invalid thinking level \"${suffix}\" in pattern \"${pattern}\". Using default instead.`,\n\t\t\t};\n\t\t}\n\t\treturn result;\n\t}\n}\n\n/**\n * Resolve model patterns to actual Model objects with optional thinking levels\n * Format: \"pattern:level\" where :level is optional\n * For each pattern, finds all matching models and picks the best version:\n * 1. Prefer alias (e.g., claude-sonnet-4-5) over dated versions (claude-sonnet-4-5-20250929)\n * 2. If no alias, pick the latest dated version\n *\n * Supports models with colons in their IDs (e.g., OpenRouter's model:exacto).\n * The algorithm tries to match the full pattern first, then progressively\n * strips colon-suffixes to find a match.\n */\nexport async function resolveModelScope(patterns: string[], modelRegistry: ModelRegistry): Promise<ScopedModel[]> {\n\tconst availableModels = await modelRegistry.getAvailable();\n\tconst scopedModels: ScopedModel[] = [];\n\n\tfor (const pattern of patterns) {\n\t\t// Check if pattern contains glob characters\n\t\tif (pattern.includes(\"*\") || pattern.includes(\"?\") || pattern.includes(\"[\")) {\n\t\t\t// Extract optional thinking level suffix (e.g., \"provider/*:high\")\n\t\t\tconst colonIdx = pattern.lastIndexOf(\":\");\n\t\t\tlet globPattern = pattern;\n\t\t\tlet thinkingLevel: ThinkingLevel | undefined;\n\n\t\t\tif (colonIdx !== -1) {\n\t\t\t\tconst suffix = pattern.substring(colonIdx + 1);\n\t\t\t\tif (isValidThinkingLevel(suffix)) {\n\t\t\t\t\tthinkingLevel = suffix;\n\t\t\t\t\tglobPattern = pattern.substring(0, colonIdx);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Match against \"provider/modelId\" format OR just model ID\n\t\t\t// This allows \"*sonnet*\" to match without requiring \"anthropic/*sonnet*\"\n\t\t\tconst matchingModels = availableModels.filter((m) => {\n\t\t\t\tconst fullId = `${m.provider}/${m.id}`;\n\t\t\t\treturn minimatch(fullId, globPattern, { nocase: true }) || minimatch(m.id, globPattern, { nocase: true });\n\t\t\t});\n\n\t\t\tif (matchingModels.length === 0) {\n\t\t\t\tconsole.warn(chalk.yellow(`Warning: No models match pattern \"${pattern}\"`));\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tfor (const model of matchingModels) {\n\t\t\t\tif (!scopedModels.find((sm) => modelsAreEqual(sm.model, model))) {\n\t\t\t\t\tscopedModels.push({ model, thinkingLevel });\n\t\t\t\t}\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\n\t\tconst { model, thinkingLevel, warning } = parseModelPattern(pattern, availableModels);\n\n\t\tif (warning) {\n\t\t\tconsole.warn(chalk.yellow(`Warning: ${warning}`));\n\t\t}\n\n\t\tif (!model) {\n\t\t\tconsole.warn(chalk.yellow(`Warning: No models match pattern \"${pattern}\"`));\n\t\t\tcontinue;\n\t\t}\n\n\t\t// Avoid duplicates\n\t\tif (!scopedModels.find((sm) => modelsAreEqual(sm.model, model))) {\n\t\t\tscopedModels.push({ model, thinkingLevel });\n\t\t}\n\t}\n\n\treturn scopedModels;\n}\n\nexport interface ResolveCliModelResult {\n\tmodel: Model<Api> | undefined;\n\tthinkingLevel?: ThinkingLevel;\n\twarning: string | undefined;\n\t/**\n\t * Error message suitable for CLI display.\n\t * When set, model will be undefined.\n\t */\n\terror: string | undefined;\n}\n\n/**\n * Resolve a single model from CLI flags.\n *\n * Supports:\n * - --provider <provider> --model <pattern>\n * - --model <provider>/<pattern>\n * - Fuzzy matching (same rules as model scoping: exact id, then partial id/name)\n *\n * Note: This does not apply the thinking level by itself, but it may *parse* and\n * return a thinking level from \"<pattern>:<thinking>\" so the caller can apply it.\n */\nexport function resolveCliModel(options: {\n\tcliProvider?: string;\n\tcliModel?: string;\n\tmodelRegistry: ModelRegistry;\n}): ResolveCliModelResult {\n\tconst { cliProvider, cliModel, modelRegistry } = options;\n\n\tif (!cliModel) {\n\t\treturn { model: undefined, warning: undefined, error: undefined };\n\t}\n\n\t// Important: use *all* models here, not just models with pre-configured auth.\n\t// This allows \"--api-key\" to be used for first-time setup.\n\tconst availableModels = modelRegistry.getAll();\n\tif (availableModels.length === 0) {\n\t\treturn {\n\t\t\tmodel: undefined,\n\t\t\twarning: undefined,\n\t\t\terror: \"No models available. Check your installation or add models to models.json.\",\n\t\t};\n\t}\n\n\t// Build canonical provider lookup (case-insensitive)\n\tconst providerMap = new Map<string, string>();\n\tfor (const m of availableModels) {\n\t\tproviderMap.set(m.provider.toLowerCase(), m.provider);\n\t}\n\n\tlet provider = cliProvider ? providerMap.get(cliProvider.toLowerCase()) : undefined;\n\tif (cliProvider && !provider) {\n\t\treturn {\n\t\t\tmodel: undefined,\n\t\t\twarning: undefined,\n\t\t\terror: `Unknown provider \"${cliProvider}\". Use --list-models to see available providers/models.`,\n\t\t};\n\t}\n\n\t// If no explicit --provider, try to interpret \"provider/model\" format first.\n\t// When the prefix before the first slash matches a known provider, prefer that\n\t// interpretation over matching models whose IDs literally contain slashes\n\t// (e.g. \"zai/glm-5\" should resolve to provider=zai, model=glm-5, not to a\n\t// vercel-ai-gateway model with id \"zai/glm-5\").\n\tlet pattern = cliModel;\n\tlet inferredProvider = false;\n\n\tif (!provider) {\n\t\tconst slashIndex = cliModel.indexOf(\"/\");\n\t\tif (slashIndex !== -1) {\n\t\t\tconst maybeProvider = cliModel.substring(0, slashIndex);\n\t\t\tconst canonical = providerMap.get(maybeProvider.toLowerCase());\n\t\t\tif (canonical) {\n\t\t\t\tprovider = canonical;\n\t\t\t\tpattern = cliModel.substring(slashIndex + 1);\n\t\t\t\tinferredProvider = true;\n\t\t\t}\n\t\t}\n\t}\n\n\t// If no provider was inferred from the slash, try exact matches without provider inference.\n\t// This handles models whose IDs naturally contain slashes (e.g. OpenRouter-style IDs).\n\tif (!provider) {\n\t\tconst lower = cliModel.toLowerCase();\n\t\tconst exact = availableModels.find(\n\t\t\t(m) => m.id.toLowerCase() === lower || `${m.provider}/${m.id}`.toLowerCase() === lower,\n\t\t);\n\t\tif (exact) {\n\t\t\treturn { model: exact, warning: undefined, thinkingLevel: undefined, error: undefined };\n\t\t}\n\t}\n\n\tif (cliProvider && provider) {\n\t\t// If both were provided, tolerate --model <provider>/<pattern> by stripping the provider prefix\n\t\tconst prefix = `${provider}/`;\n\t\tif (cliModel.toLowerCase().startsWith(prefix.toLowerCase())) {\n\t\t\tpattern = cliModel.substring(prefix.length);\n\t\t}\n\t}\n\n\tconst candidates = provider ? availableModels.filter((m) => m.provider === provider) : availableModels;\n\tconst { model, thinkingLevel, warning } = parseModelPattern(pattern, candidates, {\n\t\tallowInvalidThinkingLevelFallback: false,\n\t});\n\n\tif (model) {\n\t\treturn { model, thinkingLevel, warning, error: undefined };\n\t}\n\n\t// If we inferred a provider from the slash but found no match within that provider,\n\t// fall back to matching the full input as a raw model id across all models.\n\t// This handles OpenRouter-style IDs like \"openai/gpt-4o:extended\" where \"openai\"\n\t// looks like a provider but the full string is actually a model id on openrouter.\n\tif (inferredProvider) {\n\t\tconst lower = cliModel.toLowerCase();\n\t\tconst exact = availableModels.find(\n\t\t\t(m) => m.id.toLowerCase() === lower || `${m.provider}/${m.id}`.toLowerCase() === lower,\n\t\t);\n\t\tif (exact) {\n\t\t\treturn { model: exact, warning: undefined, thinkingLevel: undefined, error: undefined };\n\t\t}\n\t\t// Also try parseModelPattern on the full input against all models\n\t\tconst fallback = parseModelPattern(cliModel, availableModels, {\n\t\t\tallowInvalidThinkingLevelFallback: false,\n\t\t});\n\t\tif (fallback.model) {\n\t\t\treturn {\n\t\t\t\tmodel: fallback.model,\n\t\t\t\tthinkingLevel: fallback.thinkingLevel,\n\t\t\t\twarning: fallback.warning,\n\t\t\t\terror: undefined,\n\t\t\t};\n\t\t}\n\t}\n\n\tif (provider) {\n\t\tconst fallbackModel = buildFallbackModel(provider, pattern, availableModels);\n\t\tif (fallbackModel) {\n\t\t\tconst fallbackWarning = warning\n\t\t\t\t? `${warning} Model \"${pattern}\" not found for provider \"${provider}\". Using custom model id.`\n\t\t\t\t: `Model \"${pattern}\" not found for provider \"${provider}\". Using custom model id.`;\n\t\t\treturn { model: fallbackModel, thinkingLevel: undefined, warning: fallbackWarning, error: undefined };\n\t\t}\n\t}\n\n\tconst display = provider ? `${provider}/${pattern}` : cliModel;\n\treturn {\n\t\tmodel: undefined,\n\t\tthinkingLevel: undefined,\n\t\twarning,\n\t\terror: `Model \"${display}\" not found. Use --list-models to see available models.`,\n\t};\n}\n\nexport interface InitialModelResult {\n\tmodel: Model<Api> | undefined;\n\tthinkingLevel: ThinkingLevel;\n\tfallbackMessage: string | undefined;\n}\n\n/**\n * Find the initial model to use based on priority:\n * 1. CLI args (provider + model)\n * 2. First model from scoped models (if not continuing/resuming)\n * 3. Restored from session (if continuing/resuming)\n * 4. Saved default from settings\n * 5. First available model with valid API key\n */\nexport async function findInitialModel(options: {\n\tcliProvider?: string;\n\tcliModel?: string;\n\tscopedModels: ScopedModel[];\n\tisContinuing: boolean;\n\tdefaultProvider?: string;\n\tdefaultModelId?: string;\n\tdefaultThinkingLevel?: ThinkingLevel;\n\tmodelRegistry: ModelRegistry;\n}): Promise<InitialModelResult> {\n\tconst {\n\t\tcliProvider,\n\t\tcliModel,\n\t\tscopedModels,\n\t\tisContinuing,\n\t\tdefaultProvider,\n\t\tdefaultModelId,\n\t\tdefaultThinkingLevel,\n\t\tmodelRegistry,\n\t} = options;\n\n\tlet model: Model<Api> | undefined;\n\tlet thinkingLevel: ThinkingLevel = DEFAULT_THINKING_LEVEL;\n\n\t// 1. CLI args take priority\n\tif (cliProvider && cliModel) {\n\t\tconst resolved = resolveCliModel({\n\t\t\tcliProvider,\n\t\t\tcliModel,\n\t\t\tmodelRegistry,\n\t\t});\n\t\tif (resolved.error) {\n\t\t\tconsole.error(chalk.red(resolved.error));\n\t\t\tprocess.exit(1);\n\t\t}\n\t\tif (resolved.model) {\n\t\t\treturn { model: resolved.model, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t\t}\n\t}\n\n\t// 2. Use first model from scoped models (skip if continuing/resuming)\n\tif (scopedModels.length > 0 && !isContinuing) {\n\t\treturn {\n\t\t\tmodel: scopedModels[0].model,\n\t\t\tthinkingLevel: scopedModels[0].thinkingLevel ?? defaultThinkingLevel ?? DEFAULT_THINKING_LEVEL,\n\t\t\tfallbackMessage: undefined,\n\t\t};\n\t}\n\n\t// 3. Try saved default from settings\n\tif (defaultProvider && defaultModelId) {\n\t\tconst found = modelRegistry.find(defaultProvider, defaultModelId);\n\t\tif (found) {\n\t\t\tmodel = found;\n\t\t\tif (defaultThinkingLevel) {\n\t\t\t\tthinkingLevel = defaultThinkingLevel;\n\t\t\t}\n\t\t\treturn { model, thinkingLevel, fallbackMessage: undefined };\n\t\t}\n\t}\n\n\t// 4. Try first available model with valid API key\n\tconst availableModels = await modelRegistry.getAvailable();\n\n\tif (availableModels.length > 0) {\n\t\t// Try to find a default model from known providers\n\t\tfor (const provider of Object.keys(defaultModelPerProvider) as KnownProvider[]) {\n\t\t\tconst defaultId = defaultModelPerProvider[provider];\n\t\t\tconst match = availableModels.find((m) => m.provider === provider && m.id === defaultId);\n\t\t\tif (match) {\n\t\t\t\treturn { model: match, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t\t\t}\n\t\t}\n\n\t\t// If no default found, use first available\n\t\treturn { model: availableModels[0], thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n\t}\n\n\t// 5. No model found\n\treturn { model: undefined, thinkingLevel: DEFAULT_THINKING_LEVEL, fallbackMessage: undefined };\n}\n\n/**\n * Restore model from session, with fallback to available models\n */\nexport async function restoreModelFromSession(\n\tsavedProvider: string,\n\tsavedModelId: string,\n\tcurrentModel: Model<Api> | undefined,\n\tshouldPrintMessages: boolean,\n\tmodelRegistry: ModelRegistry,\n): Promise<{ model: Model<Api> | undefined; fallbackMessage: string | undefined }> {\n\tconst restoredModel = modelRegistry.find(savedProvider, savedModelId);\n\n\t// Check if restored model exists and still has auth configured\n\tconst hasConfiguredAuth = restoredModel ? modelRegistry.hasConfiguredAuth(restoredModel) : false;\n\n\tif (restoredModel && hasConfiguredAuth) {\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Restored model: ${savedProvider}/${savedModelId}`));\n\t\t}\n\t\treturn { model: restoredModel, fallbackMessage: undefined };\n\t}\n\n\t// Model not found or no API key - fall back\n\tconst reason = !restoredModel ? \"model no longer exists\" : \"no auth configured\";\n\n\tif (shouldPrintMessages) {\n\t\tconsole.error(chalk.yellow(`Warning: Could not restore model ${savedProvider}/${savedModelId} (${reason}).`));\n\t}\n\n\t// If we already have a model, use it as fallback\n\tif (currentModel) {\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Falling back to: ${currentModel.provider}/${currentModel.id}`));\n\t\t}\n\t\treturn {\n\t\t\tmodel: currentModel,\n\t\t\tfallbackMessage: `Could not restore model ${savedProvider}/${savedModelId} (${reason}). Using ${currentModel.provider}/${currentModel.id}.`,\n\t\t};\n\t}\n\n\t// Try to find any available model\n\tconst availableModels = await modelRegistry.getAvailable();\n\n\tif (availableModels.length > 0) {\n\t\t// Try to find a default model from known providers\n\t\tlet fallbackModel: Model<Api> | undefined;\n\t\tfor (const provider of Object.keys(defaultModelPerProvider) as KnownProvider[]) {\n\t\t\tconst defaultId = defaultModelPerProvider[provider];\n\t\t\tconst match = availableModels.find((m) => m.provider === provider && m.id === defaultId);\n\t\t\tif (match) {\n\t\t\t\tfallbackModel = match;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\n\t\t// If no default found, use first available\n\t\tif (!fallbackModel) {\n\t\t\tfallbackModel = availableModels[0];\n\t\t}\n\n\t\tif (shouldPrintMessages) {\n\t\t\tconsole.log(chalk.dim(`Falling back to: ${fallbackModel.provider}/${fallbackModel.id}`));\n\t\t}\n\n\t\treturn {\n\t\t\tmodel: fallbackModel,\n\t\t\tfallbackMessage: `Could not restore model ${savedProvider}/${savedModelId} (${reason}). Using ${fallbackModel.provider}/${fallbackModel.id}.`,\n\t\t};\n\t}\n\n\t// No models available\n\treturn { model: undefined, fallbackMessage: undefined };\n}\n"]}