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fix(google): make codeExecutionResult.output optional in response schema
Gemini 3 Flash omits the `output` field in `codeExecutionResult` when code execution produces no text output (e.g., only saves files via PIL). This caused `AI_APICallError: Invalid JSON response` because the Zod schema required `output` to be a string. Changes: - Make `output` field `.nullish()` in the `codeExecutionResult` schema within `getContentSchema()` (affects both response and chunk schemas) - Default missing `output` to empty string in both doGenerate and doStream code paths - Add regression tests for both generateText and streamText with missing output field Closes #12217
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‎.changeset/fix-google-code-execution-optional-output.md‎

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---
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'@ai-sdk/google': patch
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---
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fix(google): make `codeExecutionResult.output` optional in response schema
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Gemini 3 Flash omits the `output` field in `codeExecutionResult` when code execution produces no text output (e.g., only saves files). The Zod response schema now accepts a missing `output` field and defaults it to an empty string.

‎packages/google/src/google-generative-ai-language-model.test.ts‎

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@@ -1519,6 +1519,69 @@ describe('doGenerate', () => {
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`);
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});
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it('should handle code execution result with missing output field', async () => {
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server.urls[TEST_URL_GEMINI_2_0_PRO].response = {
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type: 'json-value',
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body: {
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candidates: [
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{
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content: {
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parts: [
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{
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executableCode: {
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language: 'PYTHON',
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code: "import PIL.Image\nimg = PIL.Image.open('input.png')\nimg.save('output.png')\n",
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},
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},
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{
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codeExecutionResult: {
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outcome: 'OUTCOME_OK',
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},
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},
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],
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role: 'model',
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},
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finishReason: 'STOP',
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},
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],
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},
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};
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const model = provider.languageModel('gemini-2.0-pro');
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const { content } = await model.doGenerate({
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tools: [
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{
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type: 'provider',
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id: 'google.code_execution',
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name: 'code_execution',
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args: {},
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},
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],
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prompt: TEST_PROMPT,
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});
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expect(content).toMatchInlineSnapshot(`
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[
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{
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"input": "{"language":"PYTHON","code":"import PIL.Image\\nimg = PIL.Image.open('input.png')\\nimg.save('output.png')\\n"}",
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"providerExecuted": true,
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"toolCallId": "test-id",
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"toolName": "code_execution",
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"type": "tool-call",
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},
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{
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"result": {
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"outcome": "OUTCOME_OK",
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"output": "",
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},
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"toolCallId": "test-id",
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"toolName": "code_execution",
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"type": "tool-result",
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},
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]
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`);
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});
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it('should return stop finish reason for code execution (provider-executed tool)', async () => {
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server.urls[TEST_URL_GEMINI_2_0_PRO].response = {
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type: 'json-value',
@@ -3152,6 +3215,86 @@ describe('doStream', () => {
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`);
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});
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it('should stream code execution result with missing output field', async () => {
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server.urls[TEST_URL_GEMINI_2_0_PRO].response = {
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type: 'stream-chunks',
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chunks: [
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`data: ${JSON.stringify({
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candidates: [
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{
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content: {
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parts: [
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{
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executableCode: {
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language: 'PYTHON',
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code: "img = PIL.Image.open('input.png')\nimg.save('output.png')\n",
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},
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},
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],
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},
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},
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],
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})}\n\n`,
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`data: ${JSON.stringify({
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candidates: [
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{
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content: {
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parts: [
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{
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codeExecutionResult: {
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outcome: 'OUTCOME_OK',
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},
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},
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],
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},
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finishReason: 'STOP',
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},
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],
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})}\n\n`,
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],
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};
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const model = provider.languageModel('gemini-2.0-pro');
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const { stream } = await model.doStream({
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tools: [
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{
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type: 'provider',
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id: 'google.code_execution',
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name: 'code_execution',
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args: {},
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},
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],
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prompt: TEST_PROMPT,
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});
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const events = await convertReadableStreamToArray(stream);
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const toolEvents = events.filter(
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e => e.type === 'tool-call' || e.type === 'tool-result',
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);
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expect(toolEvents).toMatchInlineSnapshot(`
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[
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{
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"input": "{"language":"PYTHON","code":"img = PIL.Image.open('input.png')\\nimg.save('output.png')\\n"}",
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"providerExecuted": true,
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"toolCallId": "test-id",
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"toolName": "code_execution",
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"type": "tool-call",
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},
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{
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"result": {
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"outcome": "OUTCOME_OK",
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"output": "",
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},
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"toolCallId": "test-id",
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"toolName": "code_execution",
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"type": "tool-result",
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},
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]
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`);
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});
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it('should return stop finish reason for streamed code execution (provider-executed tool)', async () => {
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server.urls[TEST_URL_GEMINI_2_0_PRO].response = {
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type: 'stream-chunks',

‎packages/google/src/google-generative-ai-language-model.ts‎

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@@ -263,7 +263,7 @@ export class GoogleGenerativeAILanguageModel implements LanguageModelV3 {
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toolName: 'code_execution',
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result: {
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outcome: part.codeExecutionResult.outcome,
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output: part.codeExecutionResult.output,
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output: part.codeExecutionResult.output ?? '',
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},
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});
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// Clear the ID after use to avoid accidental reuse.
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toolName: 'code_execution',
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result: {
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outcome: part.codeExecutionResult.outcome,
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output: part.codeExecutionResult.output,
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output: part.codeExecutionResult.output ?? '',
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},
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});
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// Clear the ID after use.
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codeExecutionResult: z
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.object({
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outcome: z.string(),
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output: z.string(),
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output: z.string().nullish(),
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})
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.nullish(),
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text: z.string().nullish(),

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