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[];\n \n let input = 0;\n let cached = 0;\n let output = 0;\n\n for (const response of responses) {\n const usage = response?.usage;\n if (!usage) continue;\n\n input += usage.inputTokens ?? 0;\n output += usage.outputTokens ?? 0;\n\n // inputTokensDetails is an array with objects like { cachedTokens: N }\n const details = usage.inputTokensDetails;\n if (Array.isArray(details)) {\n for (const d of details) {\n cached += d?.cachedTokens ?? 0;\n }\n }\n }\n\n return { input, cached, output };\n}\n\n/**\n * Counts tool calls from generated items.\n */\nfunction getToolCalls(runResult: any): Record {\n const counts: Record = {};\n const items: any[] = runResult?.state?._generatedItems ?? [];\n\n for (const item of items) {\n if (item?.type !== \"tool_call_item\") continue;\n\n const rawName = item?.rawItem?.name ?? item?.rawItem?.type ?? \"\";\n const normalized = normalizeToolName(rawName);\n if (normalized) {\n counts[normalized] = (counts[normalized] ?? 0) + 1;\n }\n }\n\n return counts;\n}\n\n/**\n * Normalizes tool names from the SDK format.\n */\nfunction normalizeToolName(name: string): string {\n const s = name.toLowerCase();\n \n if (s.includes(\"web_search\")) return \"web_search\";\n if (s.includes(\"file_search\")) return \"file_search\";\n if (s.includes(\"code_interpreter\")) return \"code_interpreter_session\";\n \n // Strip \"_call\" suffix for unknown tools\n return name.replace(/_call$/i, \"\").trim();\n}\n\n/**\n * Calculates token cost in USD.\n */\nfunction calculateTokenCost(\n tokens: { input: number; cached: number; output: number },\n pricing: ModelPricing\n): number {\n const nonCachedInput = Math.max(0, tokens.input - tokens.cached);\n \n return (\n (nonCachedInput / 1_000_000) * pricing.input +\n (tokens.cached / 1_000_000) * pricing.cachedInput +\n (tokens.output / 1_000_000) * pricing.output\n );\n}\n\n/**\n * Calculates tool cost in USD.\n */\nfunction calculateToolCost(\n toolCalls: Record,\n toolPrices: Record\n): number {\n let cost = 0;\n for (const [tool, count] of Object.entries(toolCalls)) {\n const price = toolPrices[tool];\n if (typeof price === \"number\") {\n cost += price * count;\n }\n }\n return cost;\n}\n\n// ============================================================================\n// Public API\n// ============================================================================\n\n/**\n * Estimates the cost of an @openai/agents SDK RunResult.\n */\nexport function estimateCost(runResult: any, options: EstimateOptions = {}): CostBreakdown {\n const modelPrices = { ...MODEL_PRICES, ...options.modelPrices };\n const toolPrices = { ...TOOL_PRICES, ...options.toolPrices };\n\n const model = getModel(runResult) || options.defaultModel || \"unknown\";\n const usage = getUsage(runResult);\n const toolCalls = getToolCalls(runResult);\n\n const pricing = modelPrices[model];\n if (!pricing) {\n throw new Error(\n `No pricing for model \"${model}\". Add it via options.modelPrices.`\n );\n }\n\n const tokenCostUSD = calculateTokenCost(usage, pricing);\n const toolCostUSD = calculateToolCost(toolCalls, toolPrices);\n\n return {\n model,\n tokens: {\n input: usage.input,\n cachedInput: usage.cached,\n nonCachedInput: Math.max(0, usage.input - usage.cached),\n output: usage.output,\n },\n tokenCostUSD,\n toolCalls,\n toolCostUSD,\n totalCostUSD: tokenCostUSD + toolCostUSD,\n };\n}\n\n// ============================================================================\n// Tests\n// ============================================================================\n\nimport { expect } from \"jsr:@std/expect\";\n\n// Mock data based on actual SDK output from logs\nconst mockSimpleRun = {\n state: {\n _currentAgent: { model: \"gpt-5\" },\n _modelResponses: [{\n usage: {\n inputTokens: 15,\n outputTokens: 7,\n totalTokens: 22,\n inputTokensDetails: [{ cachedTokens: 0 }],\n }\n }],\n _generatedItems: [\n { type: \"reasoning_item\", rawItem: { type: \"reasoning\" } },\n { type: \"message_output_item\", rawItem: { type: \"message\" } },\n ]\n }\n};\n\nconst mockWebSearchRun = {\n state: {\n _currentAgent: { model: \"gpt-5\" },\n _modelResponses: [{\n usage: {\n inputTokens: 69153,\n outputTokens: 3858,\n totalTokens: 73011,\n inputTokensDetails: [{ cachedTokens: 10000 }],\n }\n }],\n _generatedItems: [\n { type: \"reasoning_item\", rawItem: { type: \"reasoning\" } },\n { type: \"tool_call_item\", rawItem: { type: \"hosted_tool_call\", name: \"web_search_call\" } },\n { type: \"tool_call_item\", rawItem: { type: \"hosted_tool_call\", name: \"web_search_call\" } },\n { type: \"message_output_item\", rawItem: { type: \"message\" } },\n ]\n }\n};\n\nconst mockVersionedModelRun = {\n state: {\n _currentAgent: { model: \"gpt-5-2025-08-07\" },\n _modelResponses: [{\n usage: {\n inputTokens: 1000,\n outputTokens: 500,\n inputTokensDetails: [],\n }\n }],\n _generatedItems: []\n }\n};\n\nconst mockMultiResponseRun = {\n state: {\n _currentAgent: { model: \"gpt-4.1-mini\" },\n _modelResponses: [\n { usage: { inputTokens: 100, outputTokens: 50, inputTokensDetails: [{ cachedTokens: 20 }] } },\n { usage: { inputTokens: 200, outputTokens: 100, inputTokensDetails: [{ cachedTokens: 50 }] } },\n ],\n _generatedItems: []\n }\n};\n\n// Run tests\nconsole.log(\"Running cost2.ts tests...\\n\");\n\n// Test 1: Simple run\n{\n const result = estimateCost(mockSimpleRun);\n \n expect(result.model).toBe(\"gpt-5\");\n expect(result.tokens.input).toBe(15);\n expect(result.tokens.output).toBe(7);\n expect(result.tokens.cachedInput).toBe(0);\n expect(result.tokens.nonCachedInput).toBe(15);\n expect(Object.keys(result.toolCalls).length).toBe(0);\n expect(result.toolCostUSD).toBe(0);\n \n // Token cost: (15/1M * $2.50) + (7/1M * $20.00)\n const expectedCost = (15 / 1_000_000) * 2.50 + (7 / 1_000_000) * 20.0;\n expect(result.tokenCostUSD).toBeCloseTo(expectedCost, 10);\n expect(result.totalCostUSD).toBeCloseTo(expectedCost, 10);\n \n console.log(\"✓ Test 1: Simple run - PASSED\");\n}\n\n// Test 2: Web search with cached tokens\n{\n const result = estimateCost(mockWebSearchRun);\n \n expect(result.model).toBe(\"gpt-5\");\n expect(result.tokens.input).toBe(69153);\n expect(result.tokens.cachedInput).toBe(10000);\n expect(result.tokens.nonCachedInput).toBe(59153);\n expect(result.tokens.output).toBe(3858);\n \n expect(result.toolCalls[\"web_search\"]).toBe(2);\n expect(result.toolCostUSD).toBeCloseTo(0.02, 10); // 2 calls * $0.01\n \n // Token cost calculation\n const pricing = MODEL_PRICES[\"gpt-5\"];\n const expectedTokenCost = \n (59153 / 1_000_000) * pricing.input +\n (10000 / 1_000_000) * pricing.cachedInput +\n (3858 / 1_000_000) * pricing.output;\n \n expect(result.tokenCostUSD).toBeCloseTo(expectedTokenCost, 10);\n expect(result.totalCostUSD).toBeCloseTo(expectedTokenCost + 0.02, 10);\n \n console.log(\"✓ Test 2: Web search with cached tokens - PASSED\");\n}\n\n// Test 3: Versioned model name normalization\n{\n const result = estimateCost(mockVersionedModelRun);\n \n expect(result.model).toBe(\"gpt-5\");\n expect(result.tokens.input).toBe(1000);\n expect(result.tokens.output).toBe(500);\n expect(result.tokens.cachedInput).toBe(0);\n \n console.log(\"✓ Test 3: Versioned model name normalization - PASSED\");\n}\n\n// Test 4: Multiple model responses aggregation\n{\n const result = estimateCost(mockMultiResponseRun);\n \n expect(result.model).toBe(\"gpt-4.1-mini\");\n expect(result.tokens.input).toBe(300); // 100 + 200\n expect(result.tokens.output).toBe(150); // 50 + 100\n expect(result.tokens.cachedInput).toBe(70); // 20 + 50\n expect(result.tokens.nonCachedInput).toBe(230);\n \n console.log(\"✓ Test 4: Multiple model responses aggregation - PASSED\");\n}\n\n// Test 5: Custom model pricing\n{\n const customRun = {\n state: {\n _currentAgent: { model: \"my-custom-model\" },\n _modelResponses: [{\n usage: { inputTokens: 1000, outputTokens: 1000, inputTokensDetails: [] }\n }],\n _generatedItems: []\n }\n };\n \n const result = estimateCost(customRun, {\n modelPrices: {\n \"my-custom-model\": { input: 1.0, cachedInput: 0.1, output: 2.0 }\n }\n });\n \n expect(result.model).toBe(\"my-custom-model\");\n // (1000/1M * $1) + (1000/1M * $2) = $0.003\n expect(result.tokenCostUSD).toBeCloseTo(0.003, 10);\n \n console.log(\"✓ Test 5: Custom model pricing - PASSED\");\n}\n\n// Test 6: Unknown model throws error\n{\n const unknownRun = {\n state: {\n _currentAgent: { model: \"unknown-model\" },\n _modelResponses: [{ usage: { inputTokens: 100 } }],\n _generatedItems: []\n }\n };\n \n let threw = false;\n try {\n estimateCost(unknownRun);\n } catch (e) {\n threw = true;\n expect(e.message).toContain(\"No pricing for model\");\n }\n expect(threw).toBe(true);\n \n console.log(\"✓ Test 6: Unknown model throws error - PASSED\");\n}\n\n// Test 7: Tool name normalization\n{\n const toolRun = {\n state: {\n _currentAgent: { model: \"gpt-5\" },\n _modelResponses: [{ usage: { inputTokens: 100, outputTokens: 50, inputTokensDetails: [] } }],\n _generatedItems: [\n { type: \"tool_call_item\", rawItem: { name: \"web_search_call\" } },\n { type: \"tool_call_item\", rawItem: { name: \"file_search_call\" } },\n { type: \"tool_call_item\", rawItem: { type: \"code_interpreter\" } },\n { type: \"tool_call_item\", rawItem: { name: \"my_custom_tool_call\" } },\n ]\n }\n };\n \n const result = estimateCost(toolRun);\n \n expect(result.toolCalls[\"web_search\"]).toBe(1);\n expect(result.toolCalls[\"file_search\"]).toBe(1);\n expect(result.toolCalls[\"code_interpreter_session\"]).toBe(1);\n expect(result.toolCalls[\"my_custom_tool\"]).toBe(1);\n \n // Known tools: $0.01 + $0.0025 + $0.03 = $0.0425\n // Custom tool: $0 (not in price list)\n expect(result.toolCostUSD).toBeCloseTo(0.0425, 10);\n \n console.log(\"✓ Test 7: Tool name normalization - PASSED\");\n}\n\n// Test 8: Empty/malformed input handling\n{\n const emptyResult = estimateCost({}, { \n defaultModel: \"gpt-5\",\n modelPrices: MODEL_PRICES \n });\n \n expect(emptyResult.model).toBe(\"gpt-5\");\n expect(emptyResult.tokens.input).toBe(0);\n expect(emptyResult.tokens.output).toBe(0);\n expect(emptyResult.tokenCostUSD).toBe(0);\n \n console.log(\"✓ Test 8: Empty/malformed input handling - PASSED\");\n}\n\nconsole.log(\"\\n✅ All tests passed!\");\n\n// Print example output\nconsole.log(\"\\n--- Example Output ---\");\nconsole.log(\"Simple run cost:\", estimateCost(mockSimpleRun));\nconsole.log(\"\\nWeb search run cost:\", estimateCost(mockWebSearchRun));\n","/**\n * Cost estimation for @openai/agents SDK runs.\n * Simplified implementation targeting the SDK's actual structure.\n */\n\n// ============================================================================\n// Types\n// ============================================================================\n\ntype ModelPricing = {\n input: number; // $ per 1M input tokens\n cachedInput: number; // $ per 1M cached input tokens\n output: number; // $ per 1M output tokens\n};\n\ntype CostBreakdown = {\n model: string;\n tokens: {\n input: number;\n cachedInput: number;\n nonCachedInput: number;\n output: number;\n };\n tokenCostUSD: number;\n toolCalls: Record;\n toolCostUSD: number;\n totalCostUSD: number;\n};\n\ntype EstimateOptions = {\n modelPrices?: Record;\n toolPrices?: Record;\n defaultModel?: string;\n};\n\n// ============================================================================\n// Default Pricing (January 2026)\n// ============================================================================\n\nexport const MODEL_PRICES: Record = {\n // GPT-5 series\n \"gpt-5\": { input: 2.50, cachedInput: 0.25, output: 20.0 },\n \"gpt-5-mini\": { input: 0.25, cachedInput: 0.025, output: 2.0 },\n\n // GPT-4.1 series\n \"gpt-4.1\": { input: 2.0, cachedInput: 0.50, output: 8.0 },\n \"gpt-4.1-mini\": { input: 0.40, cachedInput: 0.10, output: 1.60 },\n \"gpt-4.1-nano\": { input: 0.10, cachedInput: 0.025, output: 0.40 },\n\n // GPT-4o series\n \"gpt-4o\": { input: 2.50, cachedInput: 1.25, output: 10.0 },\n \"gpt-4o-mini\": { input: 0.15, cachedInput: 0.075, output: 0.60 },\n\n // Reasoning models\n \"o3\": { input: 2.0, cachedInput: 0.50, output: 8.0 },\n \"o4-mini\": { input: 1.10, cachedInput: 0.275, output: 4.40 },\n\n // Computer use\n \"computer-use-preview\": { input: 3.0, cachedInput: 0.0, output: 12.0 },\n};\n\n// Tool pricing in USD per call\nexport const TOOL_PRICES: Record = {\n \"web_search\": 0.01, // $10 per 1K calls\n \"file_search\": 0.0025, // $2.50 per 1K calls\n \"code_interpreter_session\": 0.03, // $0.03 per session\n};\n\n// ============================================================================\n// Core Implementation\n// ============================================================================\n\n/**\n * Extracts the model name from a RunResult, normalizing versioned names.\n */\nfunction getModel(runResult: any): string {\n const model = runResult?.state?._currentAgent?.model;\n if (typeof model !== \"string\") return \"unknown\";\n \n // Normalize \"gpt-5-2025-08-07\" → \"gpt-5\"\n return model.replace(/-\\d{4}-\\d{2}-\\d{2}$/, \"\");\n}\n\n/**\n * Extracts aggregated token usage from all model responses.\n */\nfunction getUsage(runResult: any): { input: number; cached: number; output: number } {\n const responses: any[] = runResult?.state?._modelResponses ?? [];\n \n let input = 0;\n let cached = 0;\n let output = 0;\n\n for (const response of responses) {\n const usage = response?.usage;\n if (!usage) continue;\n\n input += usage.inputTokens ?? 0;\n output += usage.outputTokens ?? 0;\n\n // inputTokensDetails is an array with objects like { cachedTokens: N }\n const details = usage.inputTokensDetails;\n if (Array.isArray(details)) {\n for (const d of details) {\n cached += d?.cachedTokens ?? 0;\n }\n }\n }\n\n return { input, cached, output };\n}\n\n/**\n * Counts tool calls from generated items.\n */\nfunction getToolCalls(runResult: any): Record {\n const counts: Record = {};\n const items: any[] = runResult?.state?._generatedItems ?? [];\n\n for (const item of items) {\n if (item?.type !== \"tool_call_item\") continue;\n\n const rawName = item?.rawItem?.name ?? item?.rawItem?.type ?? \"\";\n const normalized = normalizeToolName(rawName);\n if (normalized) {\n counts[normalized] = (counts[normalized] ?? 0) + 1;\n }\n }\n\n return counts;\n}\n\n/**\n * Normalizes tool names from the SDK format.\n */\nfunction normalizeToolName(name: string): string {\n const s = name.toLowerCase();\n \n if (s.includes(\"web_search\")) return \"web_search\";\n if (s.includes(\"file_search\")) return \"file_search\";\n if (s.includes(\"code_interpreter\")) return \"code_interpreter_session\";\n \n // Strip \"_call\" suffix for unknown tools\n return name.replace(/_call$/i, \"\").trim();\n}\n\n/**\n * Calculates token cost in USD.\n */\nfunction calculateTokenCost(\n tokens: { input: number; cached: number; output: number },\n pricing: ModelPricing\n): number {\n const nonCachedInput = Math.max(0, tokens.input - tokens.cached);\n \n return (\n (nonCachedInput / 1_000_000) * pricing.input +\n (tokens.cached / 1_000_000) * pricing.cachedInput +\n (tokens.output / 1_000_000) * pricing.output\n );\n}\n\n/**\n * Calculates tool cost in USD.\n */\nfunction calculateToolCost(\n toolCalls: Record,\n toolPrices: Record\n): number {\n let cost = 0;\n for (const [tool, count] of Object.entries(toolCalls)) {\n const price = toolPrices[tool];\n if (typeof price === \"number\") {\n cost += price * count;\n }\n }\n return cost;\n}\n\n// ============================================================================\n// Public API\n// ============================================================================\n\n/**\n * Estimates the cost of an @openai/agents SDK RunResult.\n */\nexport function estimateCost(runResult: any, options: EstimateOptions = {}): CostBreakdown {\n const modelPrices = { ...MODEL_PRICES, ...options.modelPrices };\n const toolPrices = { ...TOOL_PRICES, ...options.toolPrices };\n\n const model = getModel(runResult) || options.defaultModel || \"unknown\";\n const usage = getUsage(runResult);\n const toolCalls = getToolCalls(runResult);\n\n const pricing = modelPrices[model];\n if (!pricing) {\n throw new Error(\n `No pricing for model \"${model}\". 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Reply 'hi'\");\nconsole.log(result);","label",[150],"b030a98e-efdc-11f0-b5d6-42dde27851f2",["D",1768239157612],1,[29],"bed5eb12-efdb-11f0-a1a1-42dde27851f2","bbca3950-efdb-11f0-82f9-42dde27851f2",8,["D",1768238752696],2,"type ModelTokenPricesPer1M = {\n input: number; // $ per 1M input tokens\n cachedInput: number; // $ per 1M cached input tokens\n output: number; // $ per 1M output tokens\n};\n\ntype UsageLike = {\n input_tokens?: number;\n output_tokens?: number;\n total_tokens?: number;\n input_tokens_details?: { cached_tokens?: number };\n // Some SDKs/versions might camelCase:\n inputTokens?: number;\n outputTokens?: number;\n totalTokens?: number;\n inputTokensDetails?: { cachedTokens?: number };\n};\n\ntype CostBreakdown = {\n model: string;\n tokens: {\n input: number;\n cachedInput: number;\n nonCachedInput: number;\n output: number;\n };\n tokenCostUSD: number;\n toolCalls: Record;\n toolCostUSD: number;\n totalCostUSD: number;\n};\n\ntype EstimateOptions = {\n // Override / extend model prices:\n modelPrices?: Record;\n\n // Tool pricing in USD per call (or per session, depending on tool).\n // You can include your own custom tool names here too.\n toolPricesUSDPerCall?: Record;\n\n // If your run object doesn’t include a model string, fall back to this.\n defaultModel?: string;\n\n // If you use web search “preview” vs “non-preview” and want separate names,\n // normalize them here. If you don’t care, leave as-is.\n normalizeToolName?: (rawNameOrType: string) => string;\n};\n\n// Prices are “$ per 1M tokens” for model token usage. :contentReference[oaicite:2]{index=2}\nexport const DEFAULT_MODEL_PRICES_PER_1M: Record = {\n // Priority tier excerpt (add/remove as you use them):\n \"gpt-5.2\": { input: 3.5, cachedInput: 0.35, output: 28.0 },\n \"gpt-5.1\": { input: 2.5, cachedInput: 0.25, output: 20.0 },\n \"gpt-5\": { input: 2.5, cachedInput: 0.25, output: 20.0 },\n \"gpt-5-mini\": { input: 0.45, cachedInput: 0.045, output: 3.6 },\n\n \"gpt-4.1\": { input: 3.5, cachedInput: 0.875, output: 14.0 },\n \"gpt-4.1-mini\": { input: 0.7, cachedInput: 0.175, output: 2.8 },\n \"gpt-4.1-nano\": { input: 0.2, cachedInput: 0.05, output: 0.8 },\n\n \"gpt-4o\": { input: 4.25, cachedInput: 2.125, output: 17.0 },\n \"gpt-4o-mini\": { input: 0.25, cachedInput: 0.125, output: 1.0 },\n\n \"o3\": { input: 3.5, cachedInput: 0.875, output: 14.0 },\n \"o4-mini\": { input: 2.0, cachedInput: 0.5, output: 8.0 },\n\n // Tool-specific model sometimes used directly:\n \"computer-use-preview\": { input: 3.0, cachedInput: 0.0, output: 12.0 }, // cachedInput not listed in this row; treat as 0/unused :contentReference[oaicite:3]{index=3}\n};\n\n// Built-in tool call pricing in USD per call. :contentReference[oaicite:4]{index=4}\nexport const DEFAULT_TOOL_PRICES_USD_PER_CALL: Record = {\n // Web search tool calls are priced per 1K calls; tokens from retrieved content are billed as model input. :contentReference[oaicite:5]{index=5}\n \"web_search\": 10.0 / 1000,\n \"web_search_preview_reasoning\": 10.0 / 1000,\n \"web_search_preview_non_reasoning\": 25.0 / 1000,\n\n // File search tool call is priced per 1K calls. Storage is separate and not inferable from a run result alone. :contentReference[oaicite:6]{index=6}\n \"file_search\": 2.5 / 1000,\n\n // Code Interpreter is priced per session/container (not per call).\n // We can’t reliably infer “sessions” from a generic run object, so:\n // - by default we count each appearance as 1 “session”\n // - you can override by passing toolPricesUSDPerCall or your own counter logic\n // Pricing: $0.03 / session. :contentReference[oaicite:7]{index=7}\n \"code_interpreter_session\": 0.03,\n};\n\nfunction pickFirstString(...candidates: any[]): string | undefined {\n for (const c of candidates) if (typeof c === \"string\" && c.trim()) return c;\n return undefined;\n}\n\nfunction getModelFromRunLike(runLike: any, fallback?: string): string {\n // Common with @openai/agents: result.state._currentAgent.model\n // Your earlier note: result.state._content.usage exists (that _content is a Responses API response).\n const model =\n pickFirstString(\n runLike?.state?._currentAgent?.model,\n runLike?.state?.currentAgent?.model,\n runLike?.state?._lastAgent?.model,\n runLike?.lastAgent?.model,\n runLike?.model,\n runLike?._currentAgent?.model\n ) ?? fallback;\n\n return model ?? \"unknown\";\n}\n\nfunction getUsageFromRunLike(runLike: any): UsageLike {\n // Try the common “Responses API response” shape often stashed on state._content\n const u =\n runLike?.state?._content?.usage ??\n runLike?._content?.usage ??\n runLike?.usage ??\n // Sometimes rawResponses[...].usage exists; if so, sum them:\n undefined;\n\n if (u) return u as UsageLike;\n\n // If we have rawResponses, aggregate usage fields best-effort\n const rawResponses: any[] =\n runLike?.rawResponses ??\n runLike?.state?.rawResponses ??\n runLike?.state?._rawResponses ??\n [];\n\n if (Array.isArray(rawResponses) && rawResponses.length) {\n let input = 0, output = 0, total = 0, cached = 0;\n\n for (const r of rawResponses) {\n const ru: UsageLike | undefined = r?.usage;\n if (!ru) continue;\n\n const inTok = ru.input_tokens ?? ru.inputTokens ?? 0;\n const outTok = ru.output_tokens ?? ru.outputTokens ?? 0;\n const totTok = ru.total_tokens ?? ru.totalTokens ?? 0;\n const cTok =\n ru.input_tokens_details?.cached_tokens ??\n ru.inputTokensDetails?.cachedTokens ??\n 0;\n\n input += inTok;\n output += outTok;\n total += totTok;\n cached += cTok;\n }\n\n return {\n input_tokens: input,\n output_tokens: output,\n total_tokens: total,\n input_tokens_details: { cached_tokens: cached },\n };\n }\n\n return {};\n}\n\nfunction tokensToUSD(tokens: number, pricePer1M: number): number {\n return (tokens / 1_000_000) * pricePer1M;\n}\n\nfunction bumpCount(map: Record, key: string, delta = 1) {\n map[key] = (map[key] ?? 0) + delta;\n}\n\nfunction defaultNormalizeToolName(raw: string): string {\n // Normalize common “type” strings from Responses/Agents outputs to stable names.\n const s = raw.toLowerCase();\n\n if (s.includes(\"web_search\")) return \"web_search\";\n if (s.includes(\"file_search\")) return \"file_search\";\n\n // Agents SDK hosted tool might show \"code_interpreter\" or \"codeInterpreter\"\n if (s.includes(\"code_interpreter\")) return \"code_interpreter_session\";\n\n // If you have your own tools, keep as-is (trimmed)\n return raw.trim();\n}\n\nfunction extractToolCallsFromRunLike(\n runLike: any,\n normalizeToolName: (s: string) => string\n): Record {\n const counts: Record = {};\n\n // @openai/agents: result.output is “model data” items, can include tool calls.\n const outputItems: any[] =\n (Array.isArray(runLike?.output) && runLike.output) ||\n (Array.isArray(runLike?.state?.output) && runLike.state.output) ||\n // Responses API response: response.output is array of items like {type:\"web_search_call\", ...}\n (Array.isArray(runLike?.state?._content?.output) && runLike.state._content.output) ||\n (Array.isArray(runLike?._content?.output) && runLike._content.output) ||\n [];\n\n for (const item of outputItems) {\n const type = pickFirstString(item?.type, item?.rawItem?.type);\n const name = pickFirstString(item?.name, item?.rawItem?.name);\n\n // Responses API often encodes tool usage in the item type (e.g. \"web_search_call\").\n // Agents SDK hosted tools may have rawItem.type === \"hosted_tool_call\" and rawItem.name like \"web_search\".\n const signal = name ?? type;\n if (!signal) continue;\n\n // Only count “call” items, not results/messages.\n const isCallish =\n (type && type.toLowerCase().includes(\"call\")) ||\n (type && type.toLowerCase().includes(\"tool\")) ||\n (name && true);\n\n if (!isCallish) continue;\n\n const normalized = normalizeToolName(signal);\n bumpCount(counts, normalized, 1);\n }\n\n // Some runs keep richer “newItems” with explicit RunToolCallItem objects.\n const newItems: any[] =\n (Array.isArray(runLike?.newItems) && runLike.newItems) ||\n (Array.isArray(runLike?.state?.newItems) && runLike.state.newItems) ||\n [];\n\n for (const it of newItems) {\n const type = pickFirstString(it?.type, it?.rawItem?.type);\n const name = pickFirstString(it?.name, it?.rawItem?.name);\n const signal = name ?? type;\n if (!signal) continue;\n\n const isToolCall =\n (type && type.toLowerCase().includes(\"tool\")) ||\n (type && type.toLowerCase().includes(\"call\")) ||\n (name && true);\n\n if (!isToolCall) continue;\n\n const normalized = normalizeToolName(signal);\n bumpCount(counts, normalized, 1);\n }\n\n return counts;\n}\n\nexport function estimateAgentsRunCostUSD(runLike: any, opts: EstimateOptions = {}): CostBreakdown {\n const modelPrices = { ...DEFAULT_MODEL_PRICES_PER_1M, ...(opts.modelPrices ?? {}) };\n const toolPrices = { ...DEFAULT_TOOL_PRICES_USD_PER_CALL, ...(opts.toolPricesUSDPerCall ?? {}) };\n const normalizeToolName = opts.normalizeToolName ?? defaultNormalizeToolName;\n\n const model = getModelFromRunLike(runLike, opts.defaultModel);\n const usage = getUsageFromRunLike(runLike);\n\n const inputTokens = usage.input_tokens ?? usage.inputTokens ?? 0;\n const outputTokens = usage.output_tokens ?? usage.outputTokens ?? 0;\n const cachedTokens =\n usage.input_tokens_details?.cached_tokens ??\n usage.inputTokensDetails?.cachedTokens ??\n 0;\n\n const nonCachedInput = Math.max(0, inputTokens - cachedTokens);\n\n const price = modelPrices[model];\n if (!price) {\n throw new Error(\n `No pricing configured for model \"${model}\". Add it via opts.modelPrices (prices are $ per 1M tokens).`\n );\n }\n\n const tokenCostUSD =\n tokensToUSD(nonCachedInput, price.input) +\n tokensToUSD(cachedTokens, price.cachedInput) +\n tokensToUSD(outputTokens, price.output);\n\n const toolCalls = extractToolCallsFromRunLike(runLike, normalizeToolName);\n\n let toolCostUSD = 0;\n for (const [toolName, count] of Object.entries(toolCalls)) {\n const perCall = toolPrices[toolName];\n if (typeof perCall === \"number\") toolCostUSD += perCall * count;\n // If a tool isn't in toolPrices, we assume $0 (custom tools often have external costs you must supply).\n }\n\n return {\n model,\n tokens: {\n input: inputTokens,\n cachedInput: cachedTokens,\n nonCachedInput,\n output: outputTokens,\n },\n tokenCostUSD,\n toolCalls,\n toolCostUSD,\n totalCostUSD: tokenCostUSD + toolCostUSD,\n };\n}",[31],"62ae120a-efd7-11f0-8671-42dde27851f2","README.md","019bb321-90d8-7149-add7-4c4cd87898a9",["D",1768236880010],"# OpenAI Agents Template\n\nA Val Town template for using the OpenAI Agents SDK.\n\n## Get Started\n\n1. **Remix this val**\n1. **Set the `OPENAI_API_KEY` environment variable.** Get your OpenAI API key\n here: \n1. **Customize your agent:** Edit the agent configuration in `main.ts`. You can\n provide custom `instructions`, choose a different model, or add custom tools.\n1. **Use your agent:** Click `Run` on [`main.ts`](./main.ts)\n\n## Learn More\n\n- [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-js/)\n- [OpenAI Agents Quickstart](https://openai.github.io/openai-agents-js/guides/quickstart/)\n- [Val Town Documentation](https://docs.val.town/)",[171],"pulls","branches","forks","envVars",{"_183":161,"_185":65},"connections",{"_183":65,"_184":65},"count","unfilledCount","emptyCount",{"_45":170,"_34":171,"_32":172,"_151":-5,"_43":44,"_42":161,"_153":25,"_40":200,"_13":65,"_35":20,"_37":174,"_24":27,"_157":-5,"_191":201,"_28":202},{"_45":163,"_34":31,"_32":164,"_151":-5,"_43":44,"_42":165,"_153":27,"_40":197,"_13":167,"_35":36,"_37":168,"_24":27,"_157":-5,"_191":198,"_28":199},{"_45":159,"_34":29,"_32":33,"_151":-5,"_43":44,"_42":14,"_153":27,"_40":194,"_13":161,"_35":36,"_37":39,"_24":27,"_157":-5,"_191":195,"_28":196},{"_45":149,"_34":150,"_32":60,"_151":-5,"_43":44,"_42":152,"_153":25,"_40":190,"_13":155,"_35":36,"_37":156,"_24":27,"_157":-5,"_191":192,"_28":193},["D",1768236968140],"children",[],[150],["D",1768239157612],[],[29],["D",1768238752696],[],[31],["D",1768236880010],[],[171],["D",1768236880010],{"_103":102,"_110":106},{"_103":102,"_104":-5,"_105":106,"_18":58,"_107":108,"_109":27},{"_13":88,"_34":89,"_45":44,"_90":46,"_91":92,"_93":94,"_95":52,"_96":97,"_98":99,"_100":47,"_101":102},"ctx",{"_81":-5,"_132":240,"_241":242,"_243":244,"_245":27,"_246":27,"_247":27,"_248":25,"_249":-5,"_250":-7,"_251":-7,"_252":65,"_253":27,"_254":27,"_255":-5,"_256":27,"_257":27,"_258":27,"_259":-5},"ENV",{"_211":212,"_213":214,"_215":89,"_216":217,"_218":219,"_220":221,"_222":223,"_224":225,"_226":227,"_228":229,"_230":231,"_232":233,"_234":235,"_236":237,"_238":239},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v10