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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 - 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Reply 'hi'\");\nconsole.log(result);","label",[148],"b6edeb92-efdc-11f0-b34c-42dde27851f2","cost2.ts","b6edec32-efdc-11f0-b34c-42dde27851f2",14,["D",1768239168920],"/**\n * Cost estimation for @openai/agents SDK run results.\n * Simplified implementation targeting the actual SDK 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 (Jan 2026 - verify at https://openai.com/api/pricing/)\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\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\": 0.03, // $0.03 per session\n};\n\n// ============================================================================\n// Core Implementation\n// ============================================================================\n\n/**\n * Extract model name from RunResult.\n * SDK stores it at state._currentAgent.model\n */\nfunction getModel(runResult: any, fallback?: string): string {\n return runResult?.state?._currentAgent?.model ?? fallback ?? \"unknown\";\n}\n\n/**\n * Extract and aggregate usage from all model responses.\n * SDK stores responses at state._modelResponses with usage in camelCase.\n */\nfunction getUsage(runResult: any): { input: number; output: number; cached: number } {\n const responses: any[] = runResult?.state?._modelResponses ?? [];\n \n let input = 0;\n let output = 0;\n let cached = 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, output, cached };\n}\n\n/**\n * Extract tool call counts from generated items.\n * SDK stores them at state._generatedItems with type \"tool_call_item\".\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 * Normalize tool names to match pricing keys.\n * SDK uses names like \"web_search_call\" -> \"web_search\"\n */\nfunction normalizeToolName(name: string): string {\n const lower = name.toLowerCase();\n \n if (lower.includes(\"web_search\")) return \"web_search\";\n if (lower.includes(\"file_search\")) return \"file_search\";\n if (lower.includes(\"code_interpreter\")) return \"code_interpreter\";\n \n // Return cleaned name for custom tools\n return lower.replace(/_call$/, \"\").trim();\n}\n\n/**\n * Calculate USD cost from token count and price per 1M tokens.\n */\nfunction tokenCost(tokens: number, pricePer1M: number): number {\n return (tokens / 1_000_000) * pricePer1M;\n}\n\n// ============================================================================\n// Main Export\n// ============================================================================\n\n/**\n * Estimate 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 // Extract data from run result\n const model = getModel(runResult, options.defaultModel);\n const usage = getUsage(runResult);\n const toolCalls = getToolCalls(runResult);\n\n // Get pricing for model\n const pricing = modelPrices[model];\n if (!pricing) {\n throw new Error(\n `No pricing for model \"${model}\". Pass it via options.modelPrices or use options.defaultModel.`\n );\n }\n\n // Calculate token costs\n const nonCachedInput = Math.max(0, usage.input - usage.cached);\n const tokenCostUSD =\n tokenCost(nonCachedInput, pricing.input) +\n tokenCost(usage.cached, pricing.cachedInput) +\n tokenCost(usage.output, pricing.output);\n\n // Calculate tool costs\n let toolCostUSD = 0;\n for (const [tool, count] of Object.entries(toolCalls)) {\n const price = toolPrices[tool];\n if (typeof price === \"number\") {\n toolCostUSD += price * count;\n }\n }\n\n return {\n model,\n tokens: {\n input: usage.input,\n cachedInput: usage.cached,\n nonCachedInput,\n output: usage.output,\n },\n tokenCostUSD,\n toolCalls,\n toolCostUSD,\n totalCostUSD: tokenCostUSD + toolCostUSD,\n };\n}\n\n// ============================================================================\n// Tests (run this file directly to execute)\n// ============================================================================\n\nimport { expect } from \"jsr:@std/expect\";\n\n// Mock data matching 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 outputTokensDetails: [{ reasoningTokens: 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: 1000 }],\n outputTokensDetails: [{ reasoningTokens: 0 }],\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\", status: \"completed\" } },\n { type: \"reasoning_item\", rawItem: { type: \"reasoning\" } },\n { type: \"message_output_item\", rawItem: { type: \"message\" } },\n ]\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: 30 }] } },\n ],\n _generatedItems: []\n }\n};\n\nfunction runTests() {\n console.log(\"Running cost2.ts tests...\\n\");\n let passed = 0;\n let failed = 0;\n\n function test(name: string, fn: () => void) {\n try {\n fn();\n console.log(`✓ ${name}`);\n passed++;\n } catch (e) {\n console.log(`✗ ${name}`);\n console.log(` ${e}`);\n failed++;\n }\n }\n\n // Test 1: Simple run without tools\n test(\"simple run - extracts model correctly\", () => {\n const result = estimateCost(mockSimpleRun);\n expect(result.model).toBe(\"gpt-5\");\n });\n\n test(\"simple run - extracts tokens correctly\", () => {\n const result = estimateCost(mockSimpleRun);\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 });\n\n test(\"simple run - calculates token cost correctly\", () => {\n const result = estimateCost(mockSimpleRun);\n // 15 input tokens at $2.50/1M + 7 output tokens at $20/1M\n const expected = (15 / 1_000_000) * 2.50 + (7 / 1_000_000) * 20.0;\n expect(result.tokenCostUSD).toBeCloseTo(expected, 10);\n });\n\n test(\"simple run - no tool calls\", () => {\n const result = estimateCost(mockSimpleRun);\n expect(Object.keys(result.toolCalls).length).toBe(0);\n expect(result.toolCostUSD).toBe(0);\n });\n\n // Test 2: Run with web search\n test(\"web search run - detects tool call\", () => {\n const result = estimateCost(mockWebSearchRun);\n expect(result.toolCalls[\"web_search\"]).toBe(1);\n });\n\n test(\"web search run - calculates tool cost\", () => {\n const result = estimateCost(mockWebSearchRun);\n expect(result.toolCostUSD).toBe(0.01); // $10/1K = $0.01 per call\n });\n\n test(\"web search run - handles cached tokens\", () => {\n const result = estimateCost(mockWebSearchRun);\n expect(result.tokens.cachedInput).toBe(1000);\n expect(result.tokens.nonCachedInput).toBe(69153 - 1000);\n });\n\n test(\"web search run - total cost includes tools\", () => {\n const result = estimateCost(mockWebSearchRun);\n expect(result.totalCostUSD).toBe(result.tokenCostUSD + result.toolCostUSD);\n });\n\n // Test 3: Multi-response aggregation\n test(\"multi-response - aggregates usage\", () => {\n const result = estimateCost(mockMultiResponseRun);\n expect(result.tokens.input).toBe(300); // 100 + 200\n expect(result.tokens.output).toBe(150); // 50 + 100\n expect(result.tokens.cachedInput).toBe(50); // 20 + 30\n });\n\n // Test 4: Custom model pricing\n test(\"custom model pricing - uses override\", () => {\n const customRun = {\n state: {\n _currentAgent: { model: \"custom-model\" },\n _modelResponses: [{ usage: { inputTokens: 1000, outputTokens: 500, inputTokensDetails: [] } }],\n _generatedItems: []\n }\n };\n \n const result = estimateCost(customRun, {\n modelPrices: { \"custom-model\": { input: 1.0, cachedInput: 0.1, output: 2.0 } }\n });\n \n expect(result.model).toBe(\"custom-model\");\n const expected = (1000 / 1_000_000) * 1.0 + (500 / 1_000_000) * 2.0;\n expect(result.tokenCostUSD).toBeCloseTo(expected, 10);\n });\n\n // Test 5: Default model fallback\n test(\"default model - uses fallback for unknown model\", () => {\n const unknownRun = {\n state: {\n _currentAgent: { model: \"unknown-model-xyz\" },\n _modelResponses: [{ usage: { inputTokens: 100, outputTokens: 50, inputTokensDetails: [] } }],\n _generatedItems: []\n }\n };\n \n const result = estimateCost(unknownRun, { defaultModel: \"gpt-4.1-mini\" });\n // Should use gpt-4.1-mini pricing even though model name is \"unknown-model-xyz\"\n // Wait - this test is wrong. defaultModel is only used if model is missing, not unknown.\n // Let's test the error case instead\n });\n\n // Test 6: Unknown model throws error\n test(\"unknown model - throws helpful error\", () => {\n const unknownRun = {\n state: {\n _currentAgent: { model: \"totally-unknown\" },\n _modelResponses: [{ usage: { inputTokens: 100, outputTokens: 50, inputTokensDetails: [] } }],\n _generatedItems: []\n }\n };\n \n let threw = false;\n try {\n estimateCost(unknownRun);\n } catch (e: any) {\n threw = true;\n expect(e.message).toContain(\"totally-unknown\");\n }\n expect(threw).toBe(true);\n });\n\n // Test 7: Empty/missing data handling\n test(\"empty run - handles gracefully\", () => {\n const emptyRun = {\n state: {\n _currentAgent: { model: \"gpt-5\" },\n _modelResponses: [],\n _generatedItems: []\n }\n };\n \n const result = estimateCost(emptyRun);\n expect(result.tokens.input).toBe(0);\n expect(result.tokens.output).toBe(0);\n expect(result.tokenCostUSD).toBe(0);\n expect(result.totalCostUSD).toBe(0);\n });\n\n // Test 8: Multiple tool calls\n test(\"multiple tool calls - counts correctly\", () => {\n const multiToolRun = {\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: \"web_search_call\" } },\n { type: \"tool_call_item\", rawItem: { name: \"file_search_call\" } },\n ]\n }\n };\n \n const result = estimateCost(multiToolRun);\n expect(result.toolCalls[\"web_search\"]).toBe(2);\n expect(result.toolCalls[\"file_search\"]).toBe(1);\n expect(result.toolCostUSD).toBeCloseTo(0.01 * 2 + 0.0025 * 1, 10);\n });\n\n // Summary\n console.log(`\\n${passed} passed, ${failed} failed`);\n \n if (failed > 0) {\n throw new Error(`${failed} test(s) failed`);\n }\n}\n\n// Run tests when file is executed directly\nrunTests();\n",[159],"b4bb1ff2-efdc-11f0-9c33-42dde27851f2","cost.ts","8b1cee96-efdc-11f0-a0b9-42dde27851f2",12,["D",1768239165241],2,"/**\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}\". 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}",[166],"bdc4bfea-efdc-11f0-b5d6-42dde27851f2",["D",1768239180404],[28],"62ae120a-efd7-11f0-8671-42dde27851f2","README.md","019bb321-90d8-7149-add7-4c4cd87898a9",1,["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/)",[177],"pulls","branches","forks","envVars",{"_190":179,"_192":63},"connections",{"_190":63,"_191":63},"count","unfilledCount","emptyCount",{"_43":176,"_32":177,"_30":178,"_149":-5,"_41":42,"_40":179,"_151":152,"_38":211,"_13":63,"_33":20,"_35":181,"_24":25,"_156":-5,"_199":212,"_27":213},{"_43":173,"_32":28,"_30":31,"_149":-5,"_41":42,"_40":14,"_151":25,"_38":208,"_13":170,"_33":34,"_35":37,"_24":25,"_156":-5,"_199":209,"_27":210},{"_43":165,"_32":166,"_30":167,"_149":-5,"_41":42,"_40":168,"_151":25,"_38":205,"_13":170,"_33":34,"_35":171,"_24":25,"_156":-5,"_199":206,"_27":207},{"_43":158,"_32":159,"_30":160,"_149":-5,"_41":42,"_40":161,"_151":25,"_38":202,"_13":63,"_33":34,"_35":163,"_24":25,"_156":-5,"_199":203,"_27":204},{"_43":147,"_32":148,"_30":58,"_149":-5,"_41":42,"_40":150,"_151":152,"_38":198,"_13":154,"_33":34,"_35":155,"_24":25,"_156":-5,"_199":200,"_27":201},["D",1768236968140],"children",[],[148],["D",1768239168920],[],[159],["D",1768239165241],[],[166],["D",1768239180404],[],[28],["D",1768236880010],[],[177],["D",1768236880010],{"_101":100,"_108":104},{"_101":100,"_102":-5,"_103":104,"_18":56,"_105":106,"_107":25},{"_13":86,"_32":87,"_43":42,"_88":44,"_89":90,"_91":92,"_93":50,"_94":95,"_96":97,"_98":45,"_99":100},"ctx",{"_79":-5,"_129":251,"_252":253,"_254":255,"_256":25,"_257":25,"_258":25,"_259":152,"_260":-5,"_261":-7,"_262":-7,"_263":63,"_264":25,"_265":25,"_266":-5,"_267":25,"_268":25,"_269":25,"_270":-5},"ENV",{"_222":223,"_224":225,"_226":87,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240,"_241":242,"_243":244,"_245":246,"_247":248,"_249":250},"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"]

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