ai-sdk-provider-gemini-cli/dist/index.mjs.map

Community AI SDK provider for Google Gemini using the official CLI/SDK

{"version":3,"sources":["../src/gemini-provider.ts","../src/gemini-language-model.ts","../src/client.ts","../src/message-mapper.ts","../src/tool-mapper.ts","../src/error.ts","../src/logger.ts","../src/validation.ts"],"sourcesContent":["import type {\n  ProviderV3,\n  LanguageModelV3,\n  EmbeddingModelV3,\n  ImageModelV3,\n} from '@ai-sdk/provider';\nimport { NoSuchModelError } from '@ai-sdk/provider';\nimport { GeminiLanguageModel } from './gemini-language-model';\nimport type { GeminiProviderOptions } from './types';\nimport { validateAuthOptions } from './validation';\n\nexport interface GeminiProvider extends ProviderV3 {\n  (modelId: string, settings?: Record<string, unknown>): LanguageModelV3;\n  languageModel(\n    modelId: string,\n    settings?: Record<string, unknown>\n  ): LanguageModelV3;\n  chat(modelId: string, settings?: Record<string, unknown>): LanguageModelV3;\n  embeddingModel(modelId: string): EmbeddingModelV3;\n  imageModel(modelId: string): ImageModelV3;\n}\n\n/**\n * Creates a new Gemini provider instance.\n *\n * @param options - Configuration options for the provider\n * @returns A configured provider function\n * @throws Error if authentication options are invalid\n *\n * @example\n * ```typescript\n * // Using API key authentication\n * const gemini = createGeminiProvider({\n *   authType: 'gemini-api-key',\n *   apiKey: process.env.GEMINI_API_KEY\n * });\n *\n * // Use with Vercel AI SDK\n * const model = gemini('gemini-1.5-flash');\n * const result = await generateText({\n *   model,\n *   prompt: 'Hello, world!'\n * });\n * ```\n */\nexport function createGeminiProvider(\n  options: GeminiProviderOptions = {}\n): GeminiProvider {\n  // Validate authentication options\n  const validatedOptions = validateAuthOptions(options);\n\n  // Create the language model factory function\n  const createLanguageModel = (\n    modelId: string,\n    settings?: Record<string, unknown>\n  ) => {\n    return new GeminiLanguageModel({\n      modelId,\n      providerOptions: validatedOptions,\n      settings: {\n        maxOutputTokens: 65536, // 64K output tokens for Gemini 2.5 models\n        ...settings,\n      },\n    });\n  };\n\n  // Create the provider function\n  const provider = Object.assign(\n    function (modelId: string, settings?: Record<string, unknown>) {\n      if (new.target) {\n        throw new Error(\n          'The provider function cannot be called with the new keyword.'\n        );\n      }\n\n      return createLanguageModel(modelId, settings);\n    },\n    {\n      specificationVersion: 'v3' as const,\n      languageModel: createLanguageModel,\n      chat: createLanguageModel,\n      embeddingModel: (modelId: string): never => {\n        throw new NoSuchModelError({\n          modelId,\n          modelType: 'embeddingModel',\n          message: `Gemini provider does not support embedding models.`,\n        });\n      },\n      imageModel: (modelId: string): never => {\n        throw new NoSuchModelError({\n          modelId,\n          modelType: 'imageModel',\n          message: `Gemini provider does not support image models.`,\n        });\n      },\n    }\n  ) as GeminiProvider;\n\n  return provider;\n}\n","import { randomUUID } from 'node:crypto';\nimport type {\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  SharedV3Warning,\n  LanguageModelV3FinishReason,\n  LanguageModelV3FunctionTool,\n  LanguageModelV3StreamPart,\n  LanguageModelV3Content,\n  LanguageModelV3Usage,\n} from '@ai-sdk/provider';\nimport type {\n  ContentGenerator,\n  ContentGeneratorConfig,\n} from '@google/gemini-cli-core';\nimport type {\n  GenerateContentParameters,\n  GenerateContentConfig,\n} from '@google/genai';\n\n/**\n * ThinkingLevel enum for Gemini 3 models.\n * Note: This is defined locally as @google/genai v1.30.0 doesn't export it yet.\n * Values match the official @google/genai v1.34.0 ThinkingLevel enum format.\n * Will be replaced with the official enum when gemini-cli-core upgrades.\n */\nexport enum ThinkingLevel {\n  /** Minimizes latency and cost. Best for simple tasks. */\n  LOW = 'LOW',\n  /** Balanced thinking for most tasks. (Gemini 3 Flash only) */\n  MEDIUM = 'MEDIUM',\n  /** Maximizes reasoning depth. May take longer for first token. */\n  HIGH = 'HIGH',\n  /** Matches \"no thinking\" for most queries. (Gemini 3 Flash only) */\n  MINIMAL = 'MINIMAL',\n}\nimport { initializeGeminiClient } from './client';\nimport { mapPromptToGeminiFormat } from './message-mapper';\nimport { mapGeminiToolConfig, mapToolsToGeminiFormat } from './tool-mapper';\nimport { mapGeminiError } from './error';\nimport type { GeminiProviderOptions, Logger } from './types';\nimport { getLogger, createVerboseLogger } from './logger';\n\nexport interface GeminiLanguageModelOptions {\n  modelId: string;\n  providerOptions: GeminiProviderOptions;\n  settings?: Record<string, unknown> & {\n    logger?: Logger | false;\n    verbose?: boolean;\n  };\n}\n\n/**\n * Input interface for thinkingConfig settings.\n * Supports both Gemini 3 (thinkingLevel) and Gemini 2.5 (thinkingBudget) models.\n */\nexport interface ThinkingConfigInput {\n  /**\n   * Thinking level for Gemini 3 models (gemini-3-pro-preview, gemini-3-flash-preview).\n   * Accepts case-insensitive strings ('high', 'HIGH', 'High') or ThinkingLevel enum.\n   * Valid values: 'low', 'medium', 'high', 'minimal'\n   */\n  thinkingLevel?: string | ThinkingLevel;\n  /**\n   * Token budget for thinking in Gemini 2.5 models.\n   * Common values: 0 (disabled), 512, 8192 (default), -1 (unlimited)\n   */\n  thinkingBudget?: number;\n  /**\n   * Whether to include thinking/reasoning in the response.\n   */\n  includeThoughts?: boolean;\n}\n\n/**\n * Normalize thinkingLevel string to ThinkingLevel enum (case-insensitive).\n * Returns undefined for invalid values, allowing the API to handle validation.\n */\nfunction normalizeThinkingLevel(level: string): ThinkingLevel | undefined {\n  const normalized = level.toUpperCase();\n  switch (normalized) {\n    case 'LOW':\n      return ThinkingLevel.LOW;\n    case 'MEDIUM':\n      return ThinkingLevel.MEDIUM;\n    case 'HIGH':\n      return ThinkingLevel.HIGH;\n    case 'MINIMAL':\n      return ThinkingLevel.MINIMAL;\n    default:\n      return undefined;\n  }\n}\n\n/**\n * Map Gemini finish reasons to Vercel AI SDK finish reasons.\n *\n * @param geminiReason - The finish reason from Gemini API\n * @returns The corresponding AI SDK finish reason with unified and raw values\n *\n * @remarks\n * Mappings:\n * - 'STOP' -> { unified: 'stop', raw: 'STOP' } (normal completion)\n * - 'MAX_TOKENS' -> { unified: 'length', raw: 'MAX_TOKENS' } (hit token limit)\n * - 'SAFETY'/'RECITATION' -> { unified: 'content-filter', raw } (content filtered)\n * - 'OTHER' -> { unified: 'other', raw: 'OTHER' } (other reason)\n * - undefined -> { unified: 'other', raw: undefined } (no reason provided)\n */\nfunction mapGeminiFinishReason(\n  geminiReason?: string\n): LanguageModelV3FinishReason {\n  switch (geminiReason) {\n    case 'STOP':\n      return { unified: 'stop', raw: geminiReason };\n    case 'MAX_TOKENS':\n      return { unified: 'length', raw: geminiReason };\n    case 'SAFETY':\n    case 'RECITATION':\n      return { unified: 'content-filter', raw: geminiReason };\n    case 'OTHER':\n      return { unified: 'other', raw: geminiReason };\n    default:\n      return { unified: 'other', raw: geminiReason };\n  }\n}\n\n/**\n * Extended ThinkingConfig type that includes thinkingLevel (not yet in @google/genai v1.30.0 types).\n * This is a temporary workaround until the official types are updated.\n * Using Omit to remove any existing thinkingLevel type and replace with our enum.\n */\ntype ExtendedThinkingConfig = Omit<\n  NonNullable<GenerateContentConfig['thinkingConfig']>,\n  'thinkingLevel'\n> & {\n  thinkingLevel?: ThinkingLevel;\n};\n\n/**\n * Build thinkingConfig from user input, normalizing string thinkingLevel to enum.\n */\nfunction buildThinkingConfig(\n  input: ThinkingConfigInput\n): ExtendedThinkingConfig {\n  const config = {} as ExtendedThinkingConfig;\n\n  // Handle thinkingLevel (string or enum)\n  if (input.thinkingLevel !== undefined) {\n    if (typeof input.thinkingLevel === 'string') {\n      const normalized = normalizeThinkingLevel(input.thinkingLevel);\n      if (normalized !== undefined) {\n        config.thinkingLevel = normalized;\n      }\n      // If normalization fails, we skip setting thinkingLevel\n      // and let the API handle any validation errors\n    } else {\n      // Already a ThinkingLevel enum value\n      config.thinkingLevel = input.thinkingLevel;\n    }\n  }\n\n  // Handle thinkingBudget (number)\n  if (input.thinkingBudget !== undefined) {\n    config.thinkingBudget = input.thinkingBudget;\n  }\n\n  // Handle includeThoughts (boolean)\n  if (input.includeThoughts !== undefined) {\n    config.includeThoughts = input.includeThoughts;\n  }\n\n  return config;\n}\n\n/**\n * Prepare generation config with proper handling for JSON mode and thinkingConfig.\n *\n * When JSON response format is requested WITHOUT a schema, we downgrade to\n * text/plain and emit a warning. This aligns with Claude-code provider behavior\n * and prevents raw fenced JSON from leaking to clients.\n *\n * When a schema IS provided, we use native responseJsonSchema for structured output.\n *\n * ThinkingConfig supports both Gemini 3 (thinkingLevel) and Gemini 2.5 (thinkingBudget).\n */\nfunction prepareGenerationConfig(\n  options: LanguageModelV3CallOptions,\n  settings?: Record<string, unknown>\n): {\n  generationConfig: GenerateContentConfig;\n  warnings: SharedV3Warning[];\n} {\n  const warnings: SharedV3Warning[] = [];\n\n  // Extract schema if JSON mode with schema is requested\n  const responseFormat = options.responseFormat;\n  const isJsonMode = responseFormat?.type === 'json';\n  const schema = isJsonMode ? responseFormat.schema : undefined;\n  const hasSchema = isJsonMode && schema !== undefined;\n\n  // JSON without schema: downgrade to text/plain with warning\n  if (isJsonMode && !hasSchema) {\n    warnings.push({\n      type: 'unsupported',\n      feature: 'responseFormat',\n      details:\n        'JSON response format without a schema is not supported. Treating as plain text. Provide a schema for structured output.',\n    });\n  }\n\n  // Handle thinkingConfig from options (call-time) and settings (model-level)\n  // Merge fields: call-time options override settings per-field (like temperature/topP)\n  // Special handling for thinkingLevel: invalid call-time values fall back to settings\n  const settingsThinkingConfig = settings?.thinkingConfig as\n    | ThinkingConfigInput\n    | undefined;\n  const optionsThinkingConfig = (options as Record<string, unknown>)\n    .thinkingConfig as ThinkingConfigInput | undefined;\n\n  // Validate call-time thinkingLevel before merging\n  // If invalid, preserve settings thinkingLevel instead of silently dropping it\n  let effectiveOptionsThinking = optionsThinkingConfig;\n  if (\n    optionsThinkingConfig?.thinkingLevel !== undefined &&\n    typeof optionsThinkingConfig.thinkingLevel === 'string'\n  ) {\n    const normalized = normalizeThinkingLevel(\n      optionsThinkingConfig.thinkingLevel\n    );\n    if (normalized === undefined) {\n      // Invalid thinkingLevel - remove it so settings value is preserved\n      const { thinkingLevel: _, ...rest } = optionsThinkingConfig;\n      effectiveOptionsThinking =\n        Object.keys(rest).length > 0 ? rest : undefined;\n    }\n  }\n\n  const mergedThinkingConfig =\n    settingsThinkingConfig || effectiveOptionsThinking\n      ? { ...settingsThinkingConfig, ...effectiveOptionsThinking }\n      : undefined;\n\n  const thinkingConfig = mergedThinkingConfig\n    ? buildThinkingConfig(mergedThinkingConfig)\n    : undefined;\n\n  const generationConfig: GenerateContentConfig = {\n    temperature:\n      options.temperature ?? (settings?.temperature as number | undefined),\n    topP: options.topP ?? (settings?.topP as number | undefined),\n    topK: options.topK ?? (settings?.topK as number | undefined),\n    maxOutputTokens:\n      options.maxOutputTokens ??\n      (settings?.maxOutputTokens as number | undefined),\n    stopSequences: options.stopSequences,\n    // Only use application/json when we have a schema to enforce it\n    responseMimeType: hasSchema ? 'application/json' : 'text/plain',\n    // Pass schema directly to Gemini API for native structured output\n    responseJsonSchema: hasSchema ? schema : undefined,\n    toolConfig: mapGeminiToolConfig(options),\n    // Pass thinkingConfig for Gemini 3 (thinkingLevel) or Gemini 2.5 (thinkingBudget)\n    // Cast needed because our ThinkingLevel enum isn't recognized by @google/genai v1.30.0 types\n    thinkingConfig: thinkingConfig as GenerateContentConfig['thinkingConfig'],\n  };\n\n  return { generationConfig, warnings };\n}\n\nexport class GeminiLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3' as const;\n  readonly provider = 'gemini-cli-core';\n  readonly defaultObjectGenerationMode = 'json' as const;\n  readonly supportsImageUrls = false; // CLI Core uses base64 data, not URLs\n  readonly supportedUrls = {}; // No native URL support\n  readonly supportsStructuredOutputs = true; // Native Gemini responseJsonSchema support\n\n  private contentGenerator?: ContentGenerator;\n  private config?: ContentGeneratorConfig;\n  private initPromise?: Promise<void>;\n\n  readonly modelId: string;\n  readonly settings?: Record<string, unknown>;\n  private providerOptions: GeminiProviderOptions;\n  private logger: Logger;\n\n  constructor(options: GeminiLanguageModelOptions) {\n    this.modelId = options.modelId;\n    this.providerOptions = options.providerOptions;\n    this.settings = options.settings;\n\n    // Create logger that respects verbose setting\n    const baseLogger = getLogger(options.settings?.logger);\n    this.logger = createVerboseLogger(\n      baseLogger,\n      options.settings?.verbose ?? false\n    );\n  }\n\n  private async ensureInitialized(): Promise<{\n    contentGenerator: ContentGenerator;\n    config: ContentGeneratorConfig;\n  }> {\n    if (this.contentGenerator && this.config) {\n      return { contentGenerator: this.contentGenerator, config: this.config };\n    }\n\n    if (!this.initPromise) {\n      this.initPromise = this.initialize();\n    }\n\n    await this.initPromise;\n    return { contentGenerator: this.contentGenerator!, config: this.config! };\n  }\n\n  private async initialize(): Promise<void> {\n    try {\n      const { client, config } = await initializeGeminiClient(\n        this.providerOptions,\n        this.modelId\n      );\n      this.contentGenerator = client;\n      this.config = config;\n    } catch (error) {\n      throw new Error(`Failed to initialize Gemini model: ${String(error)}`);\n    }\n  }\n\n  /**\n   * Non-streaming generation method\n   */\n  async doGenerate(options: LanguageModelV3CallOptions): Promise<{\n    content: LanguageModelV3Content[];\n    finishReason: LanguageModelV3FinishReason;\n    usage: LanguageModelV3Usage;\n    rawCall: {\n      rawPrompt: unknown;\n      rawSettings: Record<string, unknown>;\n    };\n    rawResponse?: {\n      body?: unknown;\n    };\n    response?: {\n      id?: string;\n      timestamp?: Date;\n      modelId?: string;\n    };\n    warnings: SharedV3Warning[];\n  }> {\n    this.logger.debug(\n      `[gemini-cli] Starting doGenerate request with model: ${this.modelId}`\n    );\n\n    try {\n      const { contentGenerator } = await this.ensureInitialized();\n\n      // Map the prompt to Gemini format\n      const { contents, systemInstruction } = mapPromptToGeminiFormat(options);\n\n      this.logger.debug(\n        `[gemini-cli] Request mode: ${options.responseFormat?.type === 'json' ? 'object-json' : 'regular'}, response format: ${options.responseFormat?.type ?? 'none'}`\n      );\n\n      this.logger.debug(\n        `[gemini-cli] Converted ${options.prompt.length} messages`\n      );\n\n      // Prepare generation config with proper JSON mode handling\n      // (downgrades to text/plain with warning if JSON requested without schema)\n      const { generationConfig, warnings } = prepareGenerationConfig(\n        options,\n        this.settings\n      );\n\n      // Map tools if provided in regular mode\n      let tools;\n      if (options.tools) {\n        // Filter to only function tools (not provider-defined tools)\n        const functionTools = options.tools.filter(\n          (tool): tool is LanguageModelV3FunctionTool =>\n            tool.type === 'function'\n        );\n        if (functionTools.length > 0) {\n          tools = mapToolsToGeminiFormat(functionTools);\n        }\n      }\n\n      // Create the request parameters\n      const request: GenerateContentParameters = {\n        model: this.modelId,\n        contents,\n        config: {\n          ...generationConfig,\n          systemInstruction: systemInstruction,\n          tools: tools,\n        },\n      };\n\n      // Set up abort handling\n      let abortListener: (() => void) | undefined;\n      if (options.abortSignal) {\n        // Check if already aborted\n        if (options.abortSignal.aborted) {\n          const abortError = new Error('Request aborted');\n          abortError.name = 'AbortError';\n          throw abortError;\n        }\n\n        // Set up listener for abort signal\n        // LIMITATION: The gemini-cli-core library doesn't expose request cancellation\n        // We can only check abort status before/after the request, not cancel in-flight\n        abortListener = () => {\n          // Track abort state - actual cancellation happens via status checks\n        };\n        options.abortSignal.addEventListener('abort', abortListener, {\n          once: true,\n        });\n      }\n\n      // Generate content (new signature requires userPromptId)\n      let response;\n      const startTime = Date.now();\n      try {\n        this.logger.debug('[gemini-cli] Executing generateContent request');\n\n        response = await contentGenerator.generateContent(\n          request,\n          randomUUID()\n        );\n\n        const duration = Date.now() - startTime;\n        this.logger.info(\n          `[gemini-cli] Request completed - Duration: ${duration}ms`\n        );\n\n        // Check if aborted during generation\n        if (options.abortSignal?.aborted) {\n          const abortError = new Error('Request aborted');\n          abortError.name = 'AbortError';\n          throw abortError;\n        }\n      } finally {\n        // Clean up abort listener\n        if (options.abortSignal && abortListener) {\n          options.abortSignal.removeEventListener('abort', abortListener);\n        }\n      }\n\n      // Extract the result\n      const candidate = response.candidates?.[0];\n      const responseContent = candidate?.content;\n\n      // Build content array for v3 format\n      const content: LanguageModelV3Content[] = [];\n      let hasToolCalls = false;\n\n      if (responseContent?.parts) {\n        for (const part of responseContent.parts) {\n          if (part.text) {\n            // With native responseJsonSchema, the output is already clean JSON\n            content.push({\n              type: 'text',\n              text: part.text,\n            });\n          } else if (part.functionCall) {\n            hasToolCalls = true;\n            content.push({\n              type: 'tool-call',\n              toolCallId: randomUUID(),\n              toolName: part.functionCall.name || '',\n              input: JSON.stringify(part.functionCall.args || {}),\n            } as LanguageModelV3Content);\n          }\n        }\n      }\n\n      // Calculate token usage\n      const inputTokens = response.usageMetadata?.promptTokenCount || 0;\n      const outputTokens = response.usageMetadata?.candidatesTokenCount || 0;\n      const totalTokens = inputTokens + outputTokens;\n\n      const usage: LanguageModelV3Usage = {\n        inputTokens: {\n          total: inputTokens,\n          noCache: undefined,\n          cacheRead: undefined,\n          cacheWrite: undefined,\n        },\n        outputTokens: {\n          total: outputTokens,\n          text: undefined,\n          reasoning: undefined,\n        },\n      };\n\n      this.logger.debug(\n        `[gemini-cli] Token usage - Input: ${inputTokens}, Output: ${outputTokens}, Total: ${totalTokens}`\n      );\n\n      // Determine finish reason - use 'tool-calls' if tools were called\n      const finishReason = hasToolCalls\n        ? ({\n            unified: 'tool-calls',\n            raw: candidate?.finishReason,\n          } as LanguageModelV3FinishReason)\n        : mapGeminiFinishReason(candidate?.finishReason);\n      this.logger.debug(`[gemini-cli] Finish reason: ${finishReason.unified}`);\n\n      return {\n        content,\n        finishReason,\n        usage,\n        rawCall: {\n          rawPrompt: { contents, systemInstruction, generationConfig, tools },\n          rawSettings: generationConfig as Record<string, unknown>,\n        },\n        rawResponse: {\n          body: response,\n        },\n        response: {\n          id: randomUUID(),\n          timestamp: new Date(),\n          modelId: this.modelId,\n        },\n        warnings,\n      };\n    } catch (error) {\n      this.logger.debug(\n        `[gemini-cli] Error during doGenerate: ${error instanceof Error ? error.message : String(error)}`\n      );\n      throw mapGeminiError(error);\n    }\n  }\n\n  /**\n   * Streaming generation method\n   */\n  async doStream(options: LanguageModelV3CallOptions): Promise<{\n    stream: ReadableStream<LanguageModelV3StreamPart>;\n    rawCall: {\n      rawPrompt: unknown;\n      rawSettings: Record<string, unknown>;\n    };\n  }> {\n    this.logger.debug(\n      `[gemini-cli] Starting doStream request with model: ${this.modelId}`\n    );\n\n    try {\n      const { contentGenerator } = await this.ensureInitialized();\n\n      // Map the prompt to Gemini format\n      const { contents, systemInstruction } = mapPromptToGeminiFormat(options);\n\n      this.logger.debug(\n        `[gemini-cli] Stream mode: ${options.responseFormat?.type === 'json' ? 'object-json' : 'regular'}, response format: ${options.responseFormat?.type ?? 'none'}`\n      );\n\n      this.logger.debug(\n        `[gemini-cli] Converted ${options.prompt.length} messages for streaming`\n      );\n\n      // Prepare generation config with proper JSON mode handling\n      // (downgrades to text/plain with warning if JSON requested without schema)\n      const { generationConfig, warnings } = prepareGenerationConfig(\n        options,\n        this.settings\n      );\n\n      // Map tools if provided in regular mode\n      let tools;\n      if (options.tools) {\n        // Filter to only function tools (not provider-defined tools)\n        const functionTools = options.tools.filter(\n          (tool): tool is LanguageModelV3FunctionTool =>\n            tool.type === 'function'\n        );\n        if (functionTools.length > 0) {\n          tools = mapToolsToGeminiFormat(functionTools);\n        }\n      }\n\n      // Create the request parameters\n      const request: GenerateContentParameters = {\n        model: this.modelId,\n        contents,\n        config: {\n          ...generationConfig,\n          systemInstruction: systemInstruction,\n          tools: tools,\n        },\n      };\n\n      // Set up abort handling\n      let abortListener: (() => void) | undefined;\n      if (options.abortSignal) {\n        // Check if already aborted\n        if (options.abortSignal.aborted) {\n          const abortError = new Error('Request aborted');\n          abortError.name = 'AbortError';\n          throw abortError;\n        }\n\n        // Set up listener for abort signal\n        // LIMITATION: The gemini-cli-core library doesn't expose stream cancellation\n        // We can only check abort status during iteration, not cancel the underlying stream\n        abortListener = () => {\n          // Track abort state - actual cancellation happens via status checks\n        };\n        options.abortSignal.addEventListener('abort', abortListener, {\n          once: true,\n        });\n      }\n\n      // Create streaming response (new signature requires userPromptId)\n      let streamResponse;\n      try {\n        this.logger.debug(\n          '[gemini-cli] Starting generateContentStream request'\n        );\n\n        streamResponse = await contentGenerator.generateContentStream(\n          request,\n          randomUUID()\n        );\n\n        // Check if aborted during stream creation\n        if (options.abortSignal?.aborted) {\n          const abortError = new Error('Request aborted');\n          abortError.name = 'AbortError';\n          throw abortError;\n        }\n      } catch (error) {\n        // Clean up abort listener on error\n        if (options.abortSignal && abortListener) {\n          options.abortSignal.removeEventListener('abort', abortListener);\n        }\n        throw error;\n      }\n\n      // Capture modelId, logger, and warnings for use in stream\n      const modelId = this.modelId;\n      const logger = this.logger;\n      const streamWarnings = warnings;\n\n      // Transform the stream to AI SDK v6 format\n      const stream = new ReadableStream<LanguageModelV3StreamPart>({\n        async start(controller) {\n          try {\n            // Check for abort signal in stream\n            if (options.abortSignal?.aborted) {\n              const abortError = new Error('Request aborted');\n              abortError.name = 'AbortError';\n              controller.error(abortError);\n              return;\n            }\n            let totalInputTokens = 0;\n            let totalOutputTokens = 0;\n\n            // Track text streaming lifecycle - stable id per text block\n            let textPartId: string | undefined;\n            let hasToolCalls = false;\n\n            // Emit stream-start event with any warnings\n            controller.enqueue({\n              type: 'stream-start',\n              warnings: streamWarnings,\n            });\n\n            const streamStartTime = Date.now();\n            logger.debug('[gemini-cli] Stream started, processing chunks');\n\n            for await (const chunk of streamResponse) {\n              // Check if aborted during streaming\n              if (options.abortSignal?.aborted) {\n                const abortError = new Error('Request aborted');\n                abortError.name = 'AbortError';\n                controller.error(abortError);\n                return; // Return after error to prevent further processing\n              }\n\n              const candidate = chunk.candidates?.[0];\n              const content = candidate?.content;\n\n              // Update token counts if available\n              if (chunk.usageMetadata) {\n                totalInputTokens = chunk.usageMetadata.promptTokenCount || 0;\n                totalOutputTokens =\n                  chunk.usageMetadata.candidatesTokenCount || 0;\n              }\n\n              if (content?.parts) {\n                for (const part of content.parts) {\n                  if (part.text) {\n                    // Emit text-start if this is the first text chunk\n                    if (!textPartId) {\n                      textPartId = randomUUID();\n                      controller.enqueue({\n                        type: 'text-start',\n                        id: textPartId,\n                      });\n                    }\n\n                    // Stream text delta with stable id\n                    controller.enqueue({\n                      type: 'text-delta',\n                      id: textPartId,\n                      delta: part.text,\n                    });\n                  } else if (part.functionCall) {\n                    hasToolCalls = true;\n                    // Emit tool call as a single event\n                    controller.enqueue({\n                      type: 'tool-call',\n                      toolCallId: randomUUID(),\n                      toolName: part.functionCall.name || '',\n                      input: JSON.stringify(part.functionCall.args || {}),\n                    });\n                  }\n                }\n              }\n\n              if (candidate?.finishReason) {\n                const duration = Date.now() - streamStartTime;\n                logger.info(\n                  `[gemini-cli] Stream completed - Duration: ${duration}ms`\n                );\n\n                logger.debug(\n                  `[gemini-cli] Stream token usage - Input: ${totalInputTokens}, Output: ${totalOutputTokens}, Total: ${totalInputTokens + totalOutputTokens}`\n                );\n\n                // Close text part if it was opened\n                if (textPartId) {\n                  controller.enqueue({\n                    type: 'text-end',\n                    id: textPartId,\n                  });\n                }\n\n                // Determine finish reason - use 'tool-calls' if tools were called\n                const finishReason = hasToolCalls\n                  ? ({\n                      unified: 'tool-calls',\n                      raw: candidate.finishReason,\n                    } as LanguageModelV3FinishReason)\n                  : mapGeminiFinishReason(candidate.finishReason);\n                logger.debug(\n                  `[gemini-cli] Stream finish reason: ${finishReason.unified}`\n                );\n\n                // Emit response metadata\n                controller.enqueue({\n                  type: 'response-metadata',\n                  id: randomUUID(),\n                  timestamp: new Date(),\n                  modelId: modelId,\n                });\n\n                // Emit finish event\n                controller.enqueue({\n                  type: 'finish',\n                  finishReason,\n                  usage: {\n                    inputTokens: {\n                      total: totalInputTokens,\n                      noCache: undefined,\n                      cacheRead: undefined,\n                      cacheWrite: undefined,\n                    },\n                    outputTokens: {\n                      total: totalOutputTokens,\n                      text: undefined,\n                      reasoning: undefined,\n                    },\n                  },\n                });\n              }\n            }\n\n            logger.debug('[gemini-cli] Stream finalized, closing stream');\n            controller.close();\n          } catch (error) {\n            logger.debug(\n              `[gemini-cli] Error during doStream: ${error instanceof Error ? error.message : String(error)}`\n            );\n            controller.error(mapGeminiError(error));\n          } finally {\n            // Clean up abort listener\n            if (options.abortSignal && abortListener) {\n              options.abortSignal.removeEventListener('abort', abortListener);\n            }\n          }\n        },\n        cancel: () => {\n          // Clean up abort listener on cancel\n          if (options.abortSignal && abortListener) {\n            options.abortSignal.removeEventListener('abort', abortListener);\n          }\n        },\n      });\n\n      return {\n        stream,\n        rawCall: {\n          rawPrompt: { contents, systemInstruction, generationConfig, tools },\n          rawSettings: generationConfig as Record<string, unknown>,\n        },\n      };\n    } catch (error) {\n      this.logger.debug(\n        `[gemini-cli] Error creating stream: ${error instanceof Error ? error.message : String(error)}`\n      );\n      throw mapGeminiError(error);\n    }\n  }\n}\n","import { randomUUID } from 'node:crypto';\nimport type {\n  ContentGenerator,\n  ContentGeneratorConfig,\n} from '@google/gemini-cli-core';\nimport {\n  createContentGenerator,\n  createContentGeneratorConfig,\n  AuthType,\n} from '@google/gemini-cli-core';\nimport type { GeminiProviderOptions } from './types';\n\nexport interface GeminiClient {\n  client: ContentGenerator;\n  config: ContentGeneratorConfig;\n  sessionId: string;\n}\n\n/**\n * Initializes the Gemini client with the provided authentication options\n */\nexport async function initializeGeminiClient(\n  options: GeminiProviderOptions,\n  modelId: string\n): Promise<GeminiClient> {\n  // Map our auth types to Gemini CLI Core auth types\n  let authType: AuthType | undefined;\n\n  if (options.authType === 'api-key' || options.authType === 'gemini-api-key') {\n    authType = AuthType.USE_GEMINI;\n  } else if (options.authType === 'vertex-ai') {\n    authType = AuthType.USE_VERTEX_AI;\n  } else if (\n    options.authType === 'oauth' ||\n    options.authType === 'oauth-personal'\n  ) {\n    authType = AuthType.LOGIN_WITH_GOOGLE;\n  } else if (options.authType === 'google-auth-library') {\n    // Google Auth Library is not directly supported by AuthType enum\n    // We'll need to handle this differently or use a default\n    authType = AuthType.USE_GEMINI;\n  }\n\n  // Generate a stable session ID for this provider instance\n  const sessionId = randomUUID();\n\n  // Phase 1: Core config methods with safe defaults\n  const baseConfig = {\n    // Required methods (currently working)\n    getModel: () => modelId,\n    getProxy: () =>\n      options.proxy ||\n      process.env.HTTP_PROXY ||\n      process.env.HTTPS_PROXY ||\n      undefined,\n    getUsageStatisticsEnabled: () => false, // Disable telemetry by default\n    getContentGeneratorConfig: () => ({\n      authType: authType, // Keep as AuthType | undefined for consistency\n      model: modelId,\n      apiKey: 'apiKey' in options ? options.apiKey : undefined,\n      vertexai: options.authType === 'vertex-ai' ? true : undefined,\n      proxy: options.proxy,\n    }),\n\n    // Core safety methods - most likely to be called\n    getSessionId: () => sessionId,\n    getDebugMode: () => false,\n    getTelemetryEnabled: () => false,\n    getTargetDir: () => process.cwd(),\n    getFullContext: () => false,\n    getIdeMode: () => false,\n    getCoreTools: () => [],\n    getExcludeTools: () => [],\n    getMaxSessionTurns: () => 100,\n    getFileFilteringRespectGitIgnore: () => true,\n\n    // OAuth-specific methods (required for LOGIN_WITH_GOOGLE auth)\n    isBrowserLaunchSuppressed: () => false, // Allow browser launch for OAuth flow\n\n    // NEW in 0.20.0 - JIT Context & Memory\n    getContextManager: () => undefined,\n    getGlobalMemory: () => '',\n    getEnvironmentMemory: () => '',\n\n    // NEW in 0.20.0 - Hook System\n    getHookSystem: () => undefined,\n\n    // NEW in 0.20.0 - Model Availability Service (replaces getUseModelRouter)\n    getModelAvailabilityService: () => undefined,\n\n    // NEW in 0.20.0 - Shell Timeout (default: 2 minutes)\n    getShellToolInactivityTimeout: () => 120000,\n\n    // NEW in 0.20.0 - Experiments (async getter)\n    getExperimentsAsync: () => Promise.resolve(undefined),\n  };\n\n  // Phase 2: Proxy wrapper to catch any unknown method calls\n  const configMock = new Proxy(baseConfig, {\n    get(target, prop) {\n      if (prop in target) {\n        return target[prop as keyof typeof target];\n      }\n\n      // Log unknown method calls (helps identify what else might be needed)\n      if (typeof prop === 'string') {\n        // Handle different method patterns\n        if (\n          prop.startsWith('get') ||\n          prop.startsWith('is') ||\n          prop.startsWith('has')\n        ) {\n          if (process.env.DEBUG) {\n            console.warn(\n              `[ai-sdk-provider-gemini-cli] Unknown config method called: ${prop}()`\n            );\n          }\n\n          // Return safe defaults based on method prefix and naming patterns\n          return () => {\n            // Boolean methods (is*, has*)\n            if (prop.startsWith('is') || prop.startsWith('has')) {\n              return false; // Safe default for boolean checks\n            }\n\n            // Getter methods (get*)\n            if (prop.startsWith('get')) {\n              // Return undefined for most unknown methods (safest default)\n              if (prop.includes('Enabled') || prop.includes('Mode')) {\n                return false; // Booleans default to false\n              }\n              if (\n                prop.includes('Registry') ||\n                prop.includes('Client') ||\n                prop.includes('Service') ||\n                prop.includes('Manager')\n              ) {\n                return undefined; // Objects/services default to undefined\n              }\n              if (prop.includes('Memory')) {\n                return ''; // Memory methods return empty string\n              }\n              if (prop.includes('Timeout')) {\n                return 120000; // Timeout methods default to 2 minutes\n              }\n              if (prop.includes('Config') || prop.includes('Options')) {\n                return {}; // Config objects default to empty\n              }\n              if (prop.includes('Command') || prop.includes('Path')) {\n                return undefined; // Strings default to undefined\n              }\n              return undefined; // Default fallback\n            }\n\n            return undefined; // Fallback for any other pattern\n          };\n        }\n      }\n\n      return undefined;\n    },\n  });\n\n  // Create the configuration\n  const config = await createContentGeneratorConfig(\n    configMock as unknown as Parameters<typeof createContentGeneratorConfig>[0],\n    authType\n  );\n\n  // Apply additional configuration based on auth type\n  if (\n    (options.authType === 'api-key' || options.authType === 'gemini-api-key') &&\n    options.apiKey\n  ) {\n    config.apiKey = options.apiKey;\n  } else if (options.authType === 'vertex-ai' && options.vertexAI) {\n    config.vertexai = true;\n    // Note: Vertex AI project/location configuration might need to be\n    // handled through environment variables or other means\n  }\n\n  // Create content generator - pass the configMock as the second parameter and sessionId\n  const client = await createContentGenerator(\n    config,\n    configMock as unknown as Parameters<typeof createContentGenerator>[1],\n    sessionId\n  );\n\n  return { client, config, sessionId };\n}\n","import type {\n  LanguageModelV3CallOptions,\n  LanguageModelV3FilePart,\n  LanguageModelV3Message,\n} from '@ai-sdk/provider';\nimport type { Content, Part } from '@google/genai';\n\nexport interface GeminiPromptResult {\n  contents: Content[];\n  systemInstruction?: Content;\n}\n\n/**\n * Maps Vercel AI SDK messages to Gemini format\n *\n * Note: Schema is now passed directly via responseJsonSchema in the generation config,\n * so we no longer inject schema instructions into the prompt.\n */\nexport function mapPromptToGeminiFormat(\n  options: LanguageModelV3CallOptions\n): GeminiPromptResult {\n  const messages = options.prompt;\n  const contents: Content[] = [];\n  let systemInstruction: Content | undefined;\n\n  for (const message of messages) {\n    switch (message.role) {\n      case 'system':\n        // Gemini uses a separate systemInstruction field\n        systemInstruction = {\n          role: 'user',\n          parts: [{ text: message.content }],\n        };\n        break;\n\n      case 'user':\n        contents.push(mapUserMessage(message));\n        break;\n\n      case 'assistant':\n        contents.push(mapAssistantMessage(message));\n        break;\n\n      case 'tool': {\n        // Tool results in v6 have typed output union\n        const parts: Part[] = [];\n        for (const part of message.content) {\n          if (part.type === 'tool-result') {\n            // Handle new ToolResultOutput union types in v6\n            const output = part.output;\n            let resultValue: Record<string, unknown>;\n\n            if (output.type === 'text' || output.type === 'error-text') {\n              resultValue = { result: output.value };\n            } else if (output.type === 'json' || output.type === 'error-json') {\n              // JSON values can be objects, arrays, strings, numbers, booleans, or null\n              // Gemini expects an object, so wrap non-object values\n              const jsonValue = output.value;\n              if (\n                jsonValue !== null &&\n                typeof jsonValue === 'object' &&\n                !Array.isArray(jsonValue)\n              ) {\n                resultValue = jsonValue as Record<string, unknown>;\n              } else {\n                resultValue = { result: jsonValue };\n              }\n            } else if (output.type === 'execution-denied') {\n              resultValue = {\n                result: `[Execution denied${output.reason ? `: ${output.reason}` : ''}]`,\n              };\n            } else if (output.type === 'content') {\n              // Handle content array - extract text parts\n              const textContent = output.value\n                .filter(\n                  (p): p is { type: 'text'; text: string } => p.type === 'text'\n                )\n                .map((p) => p.text)\n                .join('\\n');\n              resultValue = { result: textContent };\n            } else {\n              resultValue = { result: '[Unknown output type]' };\n            }\n\n            parts.push({\n              functionResponse: {\n                name: part.toolName,\n                response: resultValue,\n              },\n            });\n          }\n        }\n        contents.push({\n          role: 'user',\n          parts,\n        });\n        break;\n      }\n    }\n  }\n\n  return { contents, systemInstruction };\n}\n\n/**\n * Maps a user message to Gemini format\n */\nfunction mapUserMessage(\n  message: LanguageModelV3Message & { role: 'user' }\n): Content {\n  const parts: Part[] = [];\n\n  for (const part of message.content) {\n    switch (part.type) {\n      case 'text':\n        parts.push({ text: part.text });\n        break;\n\n      case 'file': {\n        // Handle file parts (images, PDF, audio, video)\n        const mediaType = part.mediaType || 'application/octet-stream';\n        if (\n          mediaType.startsWith('image/') ||\n          mediaType.startsWith('audio/') ||\n          mediaType.startsWith('video/') ||\n          mediaType === 'application/pdf'\n        ) {\n          parts.push(mapFilePart(part));\n        } else {\n          throw new Error(`Unsupported file type: ${mediaType}`);\n        }\n        break;\n      }\n    }\n  }\n\n  return { role: 'user', parts };\n}\n\n/**\n * Maps an assistant message to Gemini format\n */\nfunction mapAssistantMessage(\n  message: LanguageModelV3Message & { role: 'assistant' }\n): Content {\n  const parts: Part[] = [];\n\n  for (const part of message.content) {\n    switch (part.type) {\n      case 'text':\n        parts.push({ text: part.text });\n        break;\n\n      case 'tool-call':\n        // In v5, tool calls have input as an object already\n        parts.push({\n          functionCall: {\n            name: part.toolName,\n            args: (part.input || {}) as Record<string, unknown>,\n          },\n        });\n        break;\n    }\n  }\n\n  return { role: 'model', parts };\n}\n\n/**\n * Maps a file part to Gemini format\n */\nfunction mapFilePart(part: LanguageModelV3FilePart): Part {\n  if (part.data instanceof URL) {\n    throw new Error(\n      'URL files are not supported by Gemini CLI Core. Please provide base64-encoded data.'\n    );\n  }\n\n  // Extract mime type and base64 data\n  const mimeType = part.mediaType || 'application/octet-stream';\n  let base64Data: string;\n\n  if (typeof part.data === 'string') {\n    // Already base64 encoded\n    base64Data = part.data;\n  } else if (part.data instanceof Uint8Array) {\n    // Convert Uint8Array to base64\n    base64Data = Buffer.from(part.data).toString('base64');\n  } else {\n    throw new Error('Unsupported file format');\n  }\n\n  return {\n    inlineData: {\n      mimeType,\n      data: base64Data,\n    },\n  };\n}\n","import type {\n  LanguageModelV3CallOptions,\n  LanguageModelV3FunctionTool,\n  LanguageModelV3ToolChoice,\n} from '@ai-sdk/provider';\nimport {\n  Tool,\n  FunctionDeclaration,\n  Schema,\n  ToolConfig,\n  FunctionCallingConfigMode,\n} from '@google/genai';\nimport { z } from 'zod';\n\n// Type for JSON Schema objects with common properties\ninterface JsonSchemaObject {\n  $schema?: string;\n  $ref?: string;\n  $defs?: unknown;\n  definitions?: unknown;\n  properties?: Record<string, unknown>;\n  items?: unknown;\n  additionalProperties?: unknown;\n  allOf?: unknown[];\n  anyOf?: unknown[];\n  oneOf?: unknown[];\n  [key: string]: unknown;\n}\n\n/**\n * Maps Vercel AI SDK tools to Gemini format\n */\nexport function mapToolsToGeminiFormat(\n  tools: LanguageModelV3FunctionTool[]\n): Tool[] {\n  const functionDeclarations: FunctionDeclaration[] = [];\n\n  for (const tool of tools) {\n    functionDeclarations.push({\n      name: tool.name,\n      description: tool.description,\n      parameters: convertToolParameters(tool.inputSchema),\n    });\n  }\n\n  return [{ functionDeclarations }];\n}\n\n/**\n * Attempts to convert a Zod schema to JSON Schema using available methods\n */\nfunction convertZodToJsonSchema(zodSchema: z.ZodSchema): unknown {\n  // Try Zod v4's native toJSONSchema function first (if available)\n  const zodWithToJSONSchema = z as unknown as {\n    toJSONSchema?: (schema: z.ZodSchema) => unknown;\n  };\n\n  if (\n    zodWithToJSONSchema.toJSONSchema &&\n    typeof zodWithToJSONSchema.toJSONSchema === 'function'\n  ) {\n    try {\n      // Zod v4 uses z.toJSONSchema(schema) as a standalone function\n      return zodWithToJSONSchema.toJSONSchema(zodSchema);\n    } catch {\n      // Method exists but failed, try fallback\n    }\n  }\n\n  // Try zod-to-json-schema for Zod v3 compatibility\n  try {\n    // Lazy load zod-to-json-schema to avoid import errors with Zod v4\n    // eslint-disable-next-line @typescript-eslint/no-require-imports, @typescript-eslint/no-unsafe-assignment\n    const zodToJsonSchemaModule = require('zod-to-json-schema');\n    // eslint-disable-next-line @typescript-eslint/no-unsafe-member-access, @typescript-eslint/no-unsafe-call\n    return zodToJsonSchemaModule.zodToJsonSchema(zodSchema);\n  } catch {\n    // zod-to-json-schema not available or not compatible\n  }\n\n  // No conversion method available\n  console.warn(\n    'Unable to convert Zod schema to JSON Schema. ' +\n      'For Zod v3, install zod-to-json-schema. ' +\n      'For Zod v4, use z.toJSONSchema() function.'\n  );\n\n  // Return a basic object schema as fallback\n  return { type: 'object' };\n}\n\n/**\n * Converts tool parameters from Zod schema or JSON schema to Gemini format\n */\nfunction convertToolParameters(parameters: unknown): Schema {\n  // If it's already a plain object (JSON schema), clean it\n  if (isJsonSchema(parameters)) {\n    return cleanJsonSchema(parameters as JsonSchemaObject) as Schema;\n  }\n\n  // If it's a Zod schema, convert to JSON schema first\n  if (isZodSchema(parameters)) {\n    const jsonSchema = convertZodToJsonSchema(parameters as z.ZodSchema);\n    return cleanJsonSchema(jsonSchema as JsonSchemaObject) as Schema;\n  }\n\n  // Return a basic schema if we can't identify the format\n  return parameters as Schema;\n}\n\n/**\n * Checks if an object is a JSON schema\n */\nfunction isJsonSchema(obj: unknown): boolean {\n  return (\n    typeof obj === 'object' &&\n    obj !== null &&\n    ('type' in obj || 'properties' in obj || '$schema' in obj)\n  );\n}\n\n/**\n * Checks if an object is a Zod schema\n */\nfunction isZodSchema(obj: unknown): obj is z.ZodTypeAny {\n  return (\n    typeof obj === 'object' &&\n    obj !== null &&\n    '_def' in obj &&\n    typeof (obj as z.ZodTypeAny)._def === 'object'\n  );\n}\n\n/**\n * Cleans JSON schema for Gemini compatibility\n * Removes $schema and other metadata that Gemini doesn't support\n */\nfunction cleanJsonSchema(schema: JsonSchemaObject): JsonSchemaObject {\n  if (typeof schema !== 'object' || schema === null) {\n    return schema;\n  }\n\n  const cleaned = { ...schema };\n\n  // Remove $schema property\n  delete cleaned.$schema;\n  delete cleaned.$ref;\n  delete cleaned.$defs;\n  delete cleaned.definitions;\n\n  // Recursively clean nested schemas\n  if (cleaned.properties && typeof cleaned.properties === 'object') {\n    const cleanedProps: Record<string, unknown> = {};\n    for (const [key, value] of Object.entries(cleaned.properties)) {\n      cleanedProps[key] = cleanJsonSchema(value as JsonSchemaObject);\n    }\n    cleaned.properties = cleanedProps;\n  }\n\n  if (cleaned.items) {\n    cleaned.items = cleanJsonSchema(cleaned.items as JsonSchemaObject);\n  }\n\n  if (\n    cleaned.additionalProperties &&\n    typeof cleaned.additionalProperties === 'object'\n  ) {\n    cleaned.additionalProperties = cleanJsonSchema(\n      cleaned.additionalProperties as JsonSchemaObject\n    );\n  }\n\n  // Clean arrays\n  for (const key of ['allOf', 'anyOf', 'oneOf'] as const) {\n    const arrayProp = cleaned[key];\n    if (Array.isArray(arrayProp)) {\n      cleaned[key] = arrayProp.map((item) =>\n        cleanJsonSchema(item as JsonSchemaObject)\n      );\n    }\n  }\n\n  if (cleaned.properties && cleaned.type === undefined) {\n    cleaned.type = 'object';\n  }\n\n  return cleaned;\n}\n\n/**\n * Maps Vercel AI SDK tool config options to Gemini format\n */\nexport function mapGeminiToolConfig(\n  options: LanguageModelV3CallOptions\n): ToolConfig | undefined {\n  if (options.toolChoice) {\n    // Restrict allowed function names when a specific tool is forced.\n    // Gemini expects that when forcing a tool call, the function name is\n    // provided via `allowedFunctionNa