{"version":3,"file":"responses.cjs","names":["parseCustomToolCall","parseComputerCall","AIMessage","ChatGenerationChunk","AIMessageChunk","iife","messageToOpenAIRole","getFilenameFromMetadata","getRequiredFilenameFromMetadata","isReasoningModel","isCustomToolCall","isComputerToolCall","completionsApiContentBlockConverter"],"sources":["../../src/converters/responses.ts"],"sourcesContent":["import { OpenAI as OpenAIClient } from \"openai\";\nimport {\n AIMessage,\n AIMessageChunk,\n type BaseMessage,\n type UsageMetadata,\n type BaseMessageFields,\n type MessageContent,\n type InvalidToolCall,\n MessageContentImageUrl,\n isDataContentBlock,\n convertToProviderContentBlock,\n ContentBlock,\n} from \"@langchain/core/messages\";\nimport { ChatGenerationChunk } from \"@langchain/core/outputs\";\nimport {\n makeInvalidToolCall,\n parseToolCall,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport type {\n ToolCall,\n ToolCallChunk,\n ToolMessage,\n} from \"@langchain/core/messages/tool\";\nimport { ResponseInputMessageContentList } from \"openai/resources/responses/responses.js\";\nimport { ChatOpenAIReasoningSummary } from \"../types.js\";\nimport {\n isComputerToolCall,\n isCustomToolCall,\n parseComputerCall,\n parseCustomToolCall,\n} from \"../utils/tools.js\";\nimport {\n getFilenameFromMetadata,\n getRequiredFilenameFromMetadata,\n iife,\n isReasoningModel,\n messageToOpenAIRole,\n} from \"../utils/misc.js\";\nimport { Converter } from \"@langchain/core/utils/format\";\nimport { completionsApiContentBlockConverter } from \"./completions.js\";\n\nconst _FUNCTION_CALL_IDS_MAP_KEY = \"__openai_function_call_ids__\";\n\ntype OpenAIAnnotation =\n OpenAIClient.Responses.ResponseOutputText[\"annotations\"][number];\n\n/**\n * Converts an OpenAI annotation to a LangChain Citation or BaseContentBlock.\n *\n * OpenAI has several annotation types:\n * - `url_citation`: Web citations with url, title, start_index, end_index\n * - `file_citation`: File citations with file_id, filename, index\n * - `container_file_citation`: Container file citations with container_id, file_id, filename, start_index, end_index\n * - `file_path`: File paths with file_id, index\n *\n * This function maps them to LangChain's Citation format or preserves them as non-standard blocks.\n */\nfunction convertOpenAIAnnotationToLangChain(\n annotation: OpenAIAnnotation\n): ContentBlock.Citation | ContentBlock.NonStandard {\n if (annotation.type === \"url_citation\") {\n return {\n type: \"citation\",\n source: \"url_citation\",\n url: annotation.url,\n title: annotation.title,\n startIndex: annotation.start_index,\n endIndex: annotation.end_index,\n } satisfies ContentBlock.Citation;\n }\n\n if (annotation.type === \"file_citation\") {\n return {\n type: \"citation\",\n source: \"file_citation\",\n title: annotation.filename,\n startIndex: annotation.index,\n // Store file_id in a way that can be retrieved\n file_id: annotation.file_id,\n } as ContentBlock.Citation;\n }\n\n if (annotation.type === \"container_file_citation\") {\n return {\n type: \"citation\",\n source: \"container_file_citation\",\n title: annotation.filename,\n startIndex: annotation.start_index,\n endIndex: annotation.end_index,\n // Store additional IDs\n file_id: annotation.file_id,\n container_id: annotation.container_id,\n } as ContentBlock.Citation;\n }\n\n if (annotation.type === \"file_path\") {\n return {\n type: \"citation\",\n source: \"file_path\",\n startIndex: annotation.index,\n file_id: annotation.file_id,\n } as ContentBlock.Citation;\n }\n\n // For unknown annotation types, preserve them as non-standard blocks\n return {\n type: \"non_standard\",\n value: annotation as unknown as Record,\n } satisfies ContentBlock.NonStandard;\n}\n\n/**\n * Converts a LangChain Citation or BaseContentBlock back to an OpenAI annotation.\n *\n * This is the inverse of `convertOpenAIAnnotationToLangChain`. It handles all four\n * annotation types (url_citation, file_citation, container_file_citation, file_path)\n * and also passes through annotations that are already in OpenAI format.\n */\nfunction convertLangChainAnnotationToOpenAI(\n annotation:\n | ContentBlock.Citation\n | ContentBlock.NonStandard\n | Record\n): OpenAIAnnotation {\n // If it's already in OpenAI format, pass through unchanged\n if (\n annotation.type === \"url_citation\" ||\n annotation.type === \"file_citation\" ||\n annotation.type === \"container_file_citation\" ||\n annotation.type === \"file_path\"\n ) {\n return annotation as unknown as OpenAIAnnotation;\n }\n\n // Convert from LangChain citation format back to OpenAI format\n if (annotation.type === \"citation\") {\n const citation = annotation as ContentBlock.Citation & {\n file_id?: string;\n container_id?: string;\n };\n\n if (citation.source === \"url_citation\") {\n return {\n type: \"url_citation\",\n url: citation.url ?? \"\",\n title: citation.title ?? \"\",\n start_index: citation.startIndex ?? 0,\n end_index: citation.endIndex ?? 0,\n } as OpenAIAnnotation;\n }\n\n if (citation.source === \"file_citation\") {\n return {\n type: \"file_citation\",\n file_id: citation.file_id ?? \"\",\n filename: citation.title ?? \"\",\n index: citation.startIndex ?? 0,\n } as OpenAIAnnotation;\n }\n\n if (citation.source === \"container_file_citation\") {\n return {\n type: \"container_file_citation\",\n file_id: citation.file_id ?? \"\",\n filename: citation.title ?? \"\",\n container_id: citation.container_id ?? \"\",\n start_index: citation.startIndex ?? 0,\n end_index: citation.endIndex ?? 0,\n } as OpenAIAnnotation;\n }\n\n if (citation.source === \"file_path\") {\n return {\n type: \"file_path\",\n file_id: citation.file_id ?? \"\",\n index: citation.startIndex ?? 0,\n } as OpenAIAnnotation;\n }\n }\n\n // For non_standard blocks, unwrap the value\n if (annotation.type === \"non_standard\") {\n return (annotation as ContentBlock.NonStandard)\n .value as unknown as OpenAIAnnotation;\n }\n\n // Unknown format, pass through as-is\n return annotation as unknown as OpenAIAnnotation;\n}\n\ntype ExcludeController = T extends { controller: unknown } ? never : T;\n\nexport type ResponsesCreate = OpenAIClient.Responses[\"create\"];\nexport type ResponsesParse = OpenAIClient.Responses[\"parse\"];\n\nexport type ResponsesCreateInvoke = ExcludeController<\n Awaited>\n>;\nexport type ResponsesParseInvoke = ExcludeController<\n Awaited>\n>;\n\nexport type ResponsesInputItem = OpenAIClient.Responses.ResponseInputItem;\n\n/**\n * Converts OpenAI Responses API usage statistics to LangChain's UsageMetadata format.\n *\n * This converter transforms token usage information from OpenAI's Responses API into\n * the standardized UsageMetadata format used throughout LangChain. It handles both\n * basic token counts and detailed token breakdowns including cached tokens and\n * reasoning tokens.\n *\n * @param usage - The usage statistics object from OpenAI's Responses API containing\n * token counts and optional detailed breakdowns.\n *\n * @returns A UsageMetadata object containing:\n * - `input_tokens`: Total number of tokens in the input/prompt (defaults to 0 if not provided)\n * - `output_tokens`: Total number of tokens in the model's output (defaults to 0 if not provided)\n * - `total_tokens`: Combined total of input and output tokens (defaults to 0 if not provided)\n * - `input_token_details`: Object containing detailed input token information:\n * - `cache_read`: Number of tokens read from cache (only included if available)\n * - `output_token_details`: Object containing detailed output token information:\n * - `reasoning`: Number of tokens used for reasoning (only included if available)\n *\n * @example\n * ```typescript\n * const usage = {\n * input_tokens: 100,\n * output_tokens: 50,\n * total_tokens: 150,\n * input_tokens_details: { cached_tokens: 20 },\n * output_tokens_details: { reasoning_tokens: 10 }\n * };\n *\n * const metadata = convertResponsesUsageToUsageMetadata(usage);\n * // Returns:\n * // {\n * // input_tokens: 100,\n * // output_tokens: 50,\n * // total_tokens: 150,\n * // input_token_details: { cache_read: 20 },\n * // output_token_details: { reasoning: 10 }\n * // }\n * ```\n *\n * @remarks\n * - The function safely handles undefined or null values by using optional chaining\n * and nullish coalescing operators\n * - Detailed token information (cache_read, reasoning) is only included in the result\n * if the corresponding values are present in the input\n * - Token counts default to 0 if not provided in the usage object\n * - This converter is specifically designed for OpenAI's Responses API format and\n * may differ from other OpenAI API endpoints\n */\nexport const convertResponsesUsageToUsageMetadata: Converter<\n OpenAIClient.Responses.ResponseUsage | undefined,\n UsageMetadata\n> = (usage) => {\n const inputTokenDetails = {\n ...(usage?.input_tokens_details?.cached_tokens != null && {\n cache_read: usage?.input_tokens_details?.cached_tokens,\n }),\n };\n const outputTokenDetails = {\n ...(usage?.output_tokens_details?.reasoning_tokens != null && {\n reasoning: usage?.output_tokens_details?.reasoning_tokens,\n }),\n };\n return {\n input_tokens: usage?.input_tokens ?? 0,\n output_tokens: usage?.output_tokens ?? 0,\n total_tokens: usage?.total_tokens ?? 0,\n input_token_details: inputTokenDetails,\n output_token_details: outputTokenDetails,\n };\n};\n\n/**\n * Converts an OpenAI Responses API response to a LangChain AIMessage.\n *\n * This converter processes the output from OpenAI's Responses API (both `create` and `parse` methods)\n * and transforms it into a LangChain AIMessage object with all relevant metadata, tool calls, and content.\n *\n * @param response - The response object from OpenAI's Responses API. Can be either:\n * - ResponsesCreateInvoke: Result from `responses.create()`\n * - ResponsesParseInvoke: Result from `responses.parse()`\n *\n * @returns An AIMessage containing:\n * - `id`: The message ID from the response output\n * - `content`: Array of message content blocks (text, images, etc.)\n * - `tool_calls`: Array of successfully parsed tool calls\n * - `invalid_tool_calls`: Array of tool calls that failed to parse\n * - `usage_metadata`: Token usage information converted to LangChain format\n * - `additional_kwargs`: Extra data including:\n * - `refusal`: Refusal text if the model refused to respond\n * - `reasoning`: Reasoning output for reasoning models\n * - `tool_outputs`: Results from built-in tools (web search, file search, etc.)\n * - `parsed`: Parsed structured output when using json_schema format\n * - Function call ID mappings for tracking\n * - `response_metadata`: Metadata about the response including model, timestamps, status, etc.\n *\n * @throws Error if the response contains an error object. The error message and code are extracted\n * from the response.error field.\n *\n * @example\n * ```typescript\n * const response = await client.responses.create({\n * model: \"gpt-4\",\n * input: [{ type: \"message\", content: \"Hello\" }]\n * });\n * const message = convertResponsesMessageToAIMessage(response);\n * console.log(message.content); // Message content\n * console.log(message.tool_calls); // Any tool calls made\n * ```\n *\n * @remarks\n * The converter handles multiple output item types:\n * - `message`: Text and structured content from the model\n * - `function_call`: Tool/function calls that need to be executed\n * - `reasoning`: Reasoning traces from reasoning models (o1, o3, etc.)\n * - `custom_tool_call`: Custom tool invocations\n * - Built-in tool outputs: web_search, file_search, code_interpreter, etc.\n *\n * Tool calls are parsed and validated. Invalid tool calls (malformed JSON, etc.) are captured\n * in the `invalid_tool_calls` array rather than throwing errors.\n */\nexport const convertResponsesMessageToAIMessage: Converter<\n ResponsesCreateInvoke | ResponsesParseInvoke,\n AIMessage\n> = (response) => {\n if (response.error) {\n // TODO: add support for `addLangChainErrorFields`\n const error = new Error(response.error.message);\n error.name = response.error.code;\n throw error;\n }\n\n let messageId: string | undefined;\n const content: MessageContent = [];\n const tool_calls: ToolCall[] = [];\n const invalid_tool_calls: InvalidToolCall[] = [];\n // Preserve the raw output items so that convertMessagesToResponsesInput can\n // return them verbatim on the fast path. We strip `parsed_arguments` from\n // function_call items because the OpenAI SDK injects it when using\n // responses.parse(), but the API rejects it when sent back as input.\n const cleanedOutput = response.output.map((item) => {\n if (item.type === \"function_call\" && \"parsed_arguments\" in item) {\n const cleaned = { ...item };\n delete (cleaned as Record).parsed_arguments;\n return cleaned;\n }\n return item;\n });\n\n const response_metadata: Record = {\n model_provider: \"openai\",\n model: response.model,\n created_at: response.created_at,\n id: response.id,\n incomplete_details: response.incomplete_details,\n metadata: response.metadata,\n object: response.object,\n output: cleanedOutput,\n status: response.status,\n user: response.user,\n service_tier: response.service_tier,\n // for compatibility with chat completion calls.\n model_name: response.model,\n };\n\n const additional_kwargs: {\n [key: string]: unknown;\n refusal?: string;\n reasoning?: OpenAIClient.Responses.ResponseReasoningItem;\n tool_outputs?: unknown[];\n parsed?: unknown;\n [_FUNCTION_CALL_IDS_MAP_KEY]?: Record;\n } = {};\n\n for (const item of response.output) {\n if (item.type === \"message\") {\n messageId = item.id;\n content.push(\n ...item.content.flatMap((part) => {\n if (part.type === \"output_text\") {\n if (\"parsed\" in part && part.parsed != null) {\n additional_kwargs.parsed = part.parsed;\n }\n const block: ContentBlock = {\n type: \"text\",\n text: part.text,\n annotations: part.annotations.map(\n convertOpenAIAnnotationToLangChain\n ),\n ...(item.phase !== null ? { phase: item.phase } : {}),\n };\n return block;\n }\n\n if (part.type === \"refusal\") {\n additional_kwargs.refusal = part.refusal;\n return [];\n }\n\n return part;\n })\n );\n } else if (item.type === \"function_call\") {\n const fnAdapter = {\n function: { name: item.name, arguments: item.arguments },\n id: item.call_id,\n };\n\n try {\n tool_calls.push(parseToolCall(fnAdapter, { returnId: true }));\n } catch (e: unknown) {\n let errMessage: string | undefined;\n if (\n typeof e === \"object\" &&\n e != null &&\n \"message\" in e &&\n typeof e.message === \"string\"\n ) {\n errMessage = e.message;\n }\n invalid_tool_calls.push(makeInvalidToolCall(fnAdapter, errMessage));\n }\n\n additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] ??= {};\n if (item.id) {\n additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY][item.call_id] = item.id;\n }\n } else if (item.type === \"reasoning\") {\n additional_kwargs.reasoning = item;\n // Also elevate reasoning to content for UI rendering\n const reasoningText = item.summary\n ?.map((s) => s.text)\n .filter(Boolean)\n .join(\"\");\n if (reasoningText) {\n content.push({\n type: \"reasoning\",\n reasoning: reasoningText,\n });\n }\n } else if (item.type === \"custom_tool_call\") {\n const parsed = parseCustomToolCall(item);\n if (parsed) {\n tool_calls.push(parsed);\n } else {\n invalid_tool_calls.push(\n makeInvalidToolCall(item, \"Malformed custom tool call\")\n );\n }\n } else if (item.type === \"computer_call\") {\n const parsed = parseComputerCall(item);\n if (parsed) {\n tool_calls.push(parsed);\n } else {\n invalid_tool_calls.push(\n makeInvalidToolCall(item, \"Malformed computer call\")\n );\n }\n } else if (item.type === \"image_generation_call\") {\n // Add image as proper content block if result is available\n if (item.result) {\n content.push({\n type: \"image\",\n mimeType: \"image/png\",\n data: item.result,\n id: item.id,\n metadata: {\n status: item.status,\n },\n } satisfies ContentBlock.Multimodal.Image);\n }\n // Also store in tool_outputs for backwards compatibility and multi-turn editing (needs id)\n additional_kwargs.tool_outputs ??= [];\n additional_kwargs.tool_outputs.push(item);\n } else {\n additional_kwargs.tool_outputs ??= [];\n additional_kwargs.tool_outputs.push(item);\n }\n }\n\n return new AIMessage({\n id: messageId,\n content,\n tool_calls,\n invalid_tool_calls,\n usage_metadata: convertResponsesUsageToUsageMetadata(response.usage),\n additional_kwargs,\n response_metadata,\n });\n};\n\n/**\n * Converts a LangChain ChatOpenAI reasoning summary to an OpenAI Responses API reasoning item.\n *\n * This converter transforms reasoning summaries that have been accumulated during streaming\n * (where summary parts may arrive in multiple chunks with the same index) into the final\n * consolidated format expected by OpenAI's Responses API. It combines summary parts that\n * share the same index and removes the index field from the final output.\n *\n * @param reasoning - A ChatOpenAI reasoning summary object containing:\n * - `id`: The reasoning item ID\n * - `type`: The type of reasoning (typically \"reasoning\")\n * - `summary`: Array of summary parts, each with:\n * - `text`: The summary text content\n * - `type`: The summary type (e.g., \"summary_text\")\n * - `index`: The index used to group related summary parts during streaming\n *\n * @returns An OpenAI Responses API ResponseReasoningItem with:\n * - All properties from the input reasoning object\n * - `summary`: Consolidated array of summary objects with:\n * - `text`: Combined text from all parts with the same index\n * - `type`: The summary type\n * - No `index` field (removed after consolidation)\n *\n * @example\n * ```typescript\n * // Input: Reasoning summary with multiple parts at the same index\n * const reasoning = {\n * id: \"reasoning_123\",\n * type: \"reasoning\",\n * summary: [\n * { text: \"First \", type: \"summary_text\", index: 0 },\n * { text: \"part\", type: \"summary_text\", index: 0 },\n * { text: \"Second part\", type: \"summary_text\", index: 1 }\n * ]\n * };\n *\n * const result = convertReasoningSummaryToResponsesReasoningItem(reasoning);\n * // Returns:\n * // {\n * // id: \"reasoning_123\",\n * // type: \"reasoning\",\n * // summary: [\n * // { text: \"First part\", type: \"summary_text\" },\n * // { text: \"Second part\", type: \"summary_text\" }\n * // ]\n * // }\n * ```\n *\n * @remarks\n * - This converter is primarily used when reconstructing complete reasoning items from\n * streaming chunks, where summary parts may arrive incrementally with index markers\n * - Summary parts with the same index are concatenated in the order they appear\n * - If the reasoning summary contains only one part, no reduction is performed\n * - The index field is used internally during streaming to track which summary parts\n * belong together, but is removed from the final output as it's not part of the\n * OpenAI Responses API schema\n * - This is the inverse operation of the streaming accumulation that happens in\n * `convertResponsesDeltaToChatGenerationChunk`\n */\nexport const convertReasoningSummaryToResponsesReasoningItem: Converter<\n ChatOpenAIReasoningSummary,\n OpenAIClient.Responses.ResponseReasoningItem\n> = (reasoning) => {\n // combine summary parts that have the the same index and then remove the indexes\n const summary = (\n reasoning.summary.length > 1\n ? reasoning.summary.reduce(\n (acc, curr) => {\n const last = acc[acc.length - 1];\n\n if (last!.index === curr.index) {\n last!.text += curr.text;\n } else {\n acc.push(curr);\n }\n return acc;\n },\n [{ ...reasoning.summary[0] }]\n )\n : reasoning.summary\n ).map((s) =>\n Object.fromEntries(Object.entries(s).filter(([k]) => k !== \"index\"))\n ) as OpenAIClient.Responses.ResponseReasoningItem.Summary[];\n\n return {\n ...reasoning,\n summary,\n } as OpenAIClient.Responses.ResponseReasoningItem;\n};\n\n/**\n * Converts OpenAI Responses API stream events to LangChain ChatGenerationChunk objects.\n *\n * This converter processes streaming events from OpenAI's Responses API and transforms them\n * into LangChain ChatGenerationChunk objects that can be used in streaming chat applications.\n * It handles various event types including text deltas, tool calls, reasoning, and metadata updates.\n *\n * @param event - A streaming event from OpenAI's Responses API\n *\n * @returns A ChatGenerationChunk containing:\n * - `text`: Concatenated text content from all text parts in the event\n * - `message`: An AIMessageChunk with:\n * - `id`: Message ID (set when a message output item is added)\n * - `content`: Array of content blocks (text with optional annotations)\n * - `tool_call_chunks`: Incremental tool call data (name, args, id)\n * - `usage_metadata`: Token usage information (only in completion events)\n * - `additional_kwargs`: Extra data including:\n * - `refusal`: Refusal text if the model refused to respond\n * - `reasoning`: Reasoning output for reasoning models (id, type, summary)\n * - `tool_outputs`: Results from built-in tools (web search, file search, etc.)\n * - `parsed`: Parsed structured output when using json_schema format\n * - Function call ID mappings for tracking\n * - `response_metadata`: Metadata about the response (model, id, etc.)\n * - `generationInfo`: Additional generation information (e.g., tool output status)\n *\n * Returns `null` for events that don't produce meaningful chunks:\n * - Partial image generation events (to avoid storing all partial images in history)\n * - Unrecognized event types\n *\n * @example\n * ```typescript\n * const stream = await client.responses.create({\n * model: \"gpt-4\",\n * input: [{ type: \"message\", content: \"Hello\" }],\n * stream: true\n * });\n *\n * for await (const event of stream) {\n * const chunk = convertResponsesDeltaToChatGenerationChunk(event);\n * if (chunk) {\n * console.log(chunk.text); // Incremental text\n * console.log(chunk.message.tool_call_chunks); // Tool call updates\n * }\n * }\n * ```\n *\n * @remarks\n * - Text content is accumulated in an array with index tracking for proper ordering\n * - Tool call chunks include incremental arguments that need to be concatenated by the consumer\n * - Reasoning summaries are built incrementally across multiple events\n * - Function call IDs are tracked in `additional_kwargs` to map call_id to item id\n * - The `text` field is provided for legacy compatibility with `onLLMNewToken` callbacks\n * - Usage metadata is only available in `response.completed` events\n * - Partial images are intentionally ignored to prevent memory bloat in conversation history\n */\nexport const convertResponsesDeltaToChatGenerationChunk: Converter<\n OpenAIClient.Responses.ResponseStreamEvent,\n ChatGenerationChunk | null\n> = (event) => {\n const content: ContentBlock[] = [];\n let generationInfo: Record = {};\n let usage_metadata: UsageMetadata | undefined;\n const tool_call_chunks: ToolCallChunk[] = [];\n const response_metadata: Record = {\n model_provider: \"openai\",\n };\n const additional_kwargs: {\n [key: string]: unknown;\n reasoning?: Partial;\n tool_outputs?: unknown[];\n } = {};\n let id: string | undefined;\n if (event.type === \"response.output_text.delta\") {\n content.push({\n type: \"text\",\n text: event.delta,\n index: event.content_index,\n } satisfies ContentBlock.Text);\n } else if (event.type === \"response.output_text.annotation.added\") {\n content.push({\n type: \"text\",\n text: \"\",\n annotations: [\n convertOpenAIAnnotationToLangChain(\n event.annotation as OpenAIAnnotation\n ),\n ],\n index: event.content_index,\n } satisfies ContentBlock.Text);\n } else if (\n event.type === \"response.output_item.added\" &&\n event.item.type === \"message\"\n ) {\n id = event.item.id;\n const phase = \"phase\" in event.item ? event.item.phase : undefined;\n if (phase) {\n content.push({\n type: \"text\",\n text: \"\",\n phase,\n index: 0,\n } as ContentBlock.Text);\n }\n } else if (\n event.type === \"response.output_item.added\" &&\n event.item.type === \"function_call\"\n ) {\n tool_call_chunks.push({\n type: \"tool_call_chunk\",\n name: event.item.name,\n args: event.item.arguments,\n id: event.item.call_id,\n index: event.output_index,\n });\n\n additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] = {\n [event.item.call_id]: event.item.id,\n };\n } else if (\n event.type === \"response.output_item.done\" &&\n event.item.type === \"computer_call\"\n ) {\n // Handle computer_call as a tool call so ToolNode can process it\n tool_call_chunks.push({\n type: \"tool_call_chunk\",\n name: \"computer_use\",\n args: JSON.stringify({ action: event.item.action }),\n id: event.item.call_id,\n index: event.output_index,\n });\n // Also store the raw item for additional context (pending_safety_checks, etc.)\n additional_kwargs.tool_outputs = [event.item];\n } else if (\n event.type === \"response.output_item.done\" &&\n event.item.type === \"image_generation_call\"\n ) {\n // Add image as proper content block if result is available\n if (event.item.result) {\n content.push({\n type: \"image\",\n mimeType: \"image/png\",\n data: event.item.result,\n id: event.item.id,\n metadata: {\n status: event.item.status,\n },\n } satisfies ContentBlock.Multimodal.Image);\n }\n // Also store in tool_outputs for backwards compatibility and multi-turn editing (needs id)\n additional_kwargs.tool_outputs = [event.item];\n } else if (\n event.type === \"response.output_item.done\" &&\n [\n \"web_search_call\",\n \"file_search_call\",\n \"code_interpreter_call\",\n \"shell_call\",\n \"local_shell_call\",\n \"mcp_call\",\n \"mcp_list_tools\",\n \"mcp_approval_request\",\n \"custom_tool_call\",\n \"tool_search_call\",\n \"tool_search_output\",\n ].includes(event.item.type)\n ) {\n additional_kwargs.tool_outputs = [event.item];\n } else if (event.type === \"response.created\") {\n response_metadata.id = event.response.id;\n response_metadata.model_name = event.response.model;\n response_metadata.model = event.response.model;\n } else if (event.type === \"response.completed\") {\n const msg = convertResponsesMessageToAIMessage(event.response);\n\n usage_metadata = convertResponsesUsageToUsageMetadata(event.response.usage);\n\n if (event.response.text?.format?.type === \"json_schema\" && msg.text) {\n additional_kwargs.parsed ??= JSON.parse(msg.text);\n }\n for (const [key, value] of Object.entries(event.response)) {\n if (key === \"id\") continue;\n // Use the cleaned output from the converted message so that\n // SDK-only fields like parsed_arguments are not persisted.\n if (key === \"output\") {\n response_metadata[key] = msg.response_metadata.output;\n } else {\n response_metadata[key] = value;\n }\n }\n } else if (\n event.type === \"response.function_call_arguments.delta\" ||\n event.type === \"response.custom_tool_call_input.delta\"\n ) {\n tool_call_chunks.push({\n type: \"tool_call_chunk\",\n args: event.delta,\n index: event.output_index,\n });\n } else if (\n event.type === \"response.web_search_call.completed\" ||\n event.type === \"response.file_search_call.completed\"\n ) {\n generationInfo = {\n tool_outputs: {\n id: event.item_id,\n type: event.type.replace(\"response.\", \"\").replace(\".completed\", \"\"),\n status: \"completed\",\n },\n };\n } else if (event.type === \"response.refusal.done\") {\n additional_kwargs.refusal = event.refusal;\n } else if (\n event.type === \"response.output_item.added\" &&\n \"item\" in event &&\n event.item.type === \"reasoning\"\n ) {\n const summary: ChatOpenAIReasoningSummary[\"summary\"] | undefined = event\n .item.summary\n ? event.item.summary.map((s, index) => ({\n ...s,\n index,\n }))\n : undefined;\n\n additional_kwargs.reasoning = {\n // We only capture ID in the first event or else the concatenated result of all chunks will\n // have an ID field that is repeated once per event. There is special handling for the `type`\n // field that prevents this, however.\n id: event.item.id,\n type: event.item.type,\n ...(summary ? { summary } : {}),\n };\n\n // Also elevate reasoning to content for UI rendering\n const reasoningText = event.item.summary\n ?.map((s) => s.text)\n .filter(Boolean)\n .join(\"\");\n if (reasoningText) {\n content.push({\n type: \"reasoning\",\n reasoning: reasoningText,\n });\n }\n } else if (event.type === \"response.reasoning_summary_part.added\") {\n additional_kwargs.reasoning = {\n type: \"reasoning\",\n summary: [{ ...event.part, index: event.summary_index }],\n };\n\n // Also elevate reasoning to content for UI rendering\n if (event.part.text) {\n content.push({\n type: \"reasoning\",\n reasoning: event.part.text,\n index: event.summary_index,\n });\n }\n } else if (event.type === \"response.reasoning_summary_text.delta\") {\n additional_kwargs.reasoning = {\n type: \"reasoning\",\n summary: [\n {\n text: event.delta,\n type: \"summary_text\",\n index: event.summary_index,\n },\n ],\n };\n\n // Also elevate reasoning to content for UI rendering\n if (event.delta) {\n content.push({\n type: \"reasoning\",\n reasoning: event.delta,\n index: event.summary_index,\n });\n }\n } else if (event.type === \"response.image_generation_call.partial_image\") {\n // noop/fixme: retaining partial images in a message chunk means that _all_\n // partial images get kept in history, so we don't do anything here.\n return null;\n } else {\n return null;\n }\n\n return new ChatGenerationChunk({\n // Legacy reasons, `onLLMNewToken` should pulls this out\n text: content.map((part) => part.text).join(\"\"),\n message: new AIMessageChunk({\n id,\n content,\n tool_call_chunks,\n usage_metadata,\n additional_kwargs,\n response_metadata,\n }),\n generationInfo,\n });\n};\n\n/**\n * Converts a single LangChain BaseMessage to OpenAI Responses API input format.\n *\n * This converter transforms a LangChain message into one or more ResponseInputItem objects\n * that can be used with OpenAI's Responses API. It handles complex message structures including\n * tool calls, reasoning blocks, multimodal content, and various content block types.\n *\n * @param message - The LangChain BaseMessage to convert. Can be any message type including\n * HumanMessage, AIMessage, SystemMessage, ToolMessage, etc.\n *\n * @returns An array of ResponseInputItem objects.\n *\n * @example\n * Basic text message conversion:\n * ```typescript\n * const message = new HumanMessage(\"Hello, how are you?\");\n * const items = convertStandardContentMessageToResponsesInput(message);\n * // Returns: [{ type: \"message\", role: \"user\", content: [{ type: \"input_text\", text: \"Hello, how are you?\" }] }]\n * ```\n *\n * @example\n * AI message with tool calls:\n * ```typescript\n * const message = new AIMessage({\n * content: \"I'll check the weather for you.\",\n * tool_calls: [{\n * id: \"call_123\",\n * name: \"get_weather\",\n * args: { location: \"San Francisco\" }\n * }]\n * });\n * const items = convertStandardContentMessageToResponsesInput(message);\n * // Returns:\n * // [\n * // { type: \"message\", role: \"assistant\", content: [{ type: \"input_text\", text: \"I'll check the weather for you.\" }] },\n * // { type: \"function_call\", call_id: \"call_123\", name: \"get_weather\", arguments: '{\"location\":\"San Francisco\"}' }\n * // ]\n * ```\n */\nexport const convertStandardContentMessageToResponsesInput: Converter<\n BaseMessage,\n OpenAIClient.Responses.ResponseInputItem[]\n> = (message) => {\n const isResponsesMessage =\n AIMessage.isInstance(message) &&\n message.response_metadata?.model_provider === \"openai\";\n\n function* iterateItems(): Generator {\n const messageRole = iife(() => {\n try {\n const role = messageToOpenAIRole(message);\n if (\n role === \"system\" ||\n role === \"developer\" ||\n role === \"assistant\" ||\n role === \"user\"\n ) {\n return role;\n }\n return \"assistant\";\n } catch {\n return \"assistant\";\n }\n });\n\n let currentMessage: OpenAIClient.Responses.EasyInputMessage | undefined =\n undefined;\n\n const functionCallIdsWithBlocks = new Set();\n const serverFunctionCallIdsWithBlocks = new Set();\n\n const pendingFunctionChunks = new Map<\n string,\n { name?: string; args: string[] }\n >();\n const pendingServerFunctionChunks = new Map<\n string,\n { name?: string; args: string[] }\n >();\n\n function* flushMessage() {\n if (!currentMessage) return;\n const content = currentMessage.content;\n if (\n (typeof content === \"string\" && content.length > 0) ||\n (Array.isArray(content) && content.length > 0)\n ) {\n yield currentMessage;\n }\n currentMessage = undefined;\n }\n\n const pushMessageContent = (\n content: OpenAIClient.Responses.ResponseInputMessageContentList,\n phase?: OpenAIClient.Responses.EasyInputMessage[\"phase\"]\n ) => {\n if (!currentMessage) {\n currentMessage = {\n type: \"message\",\n role: messageRole,\n content: [],\n ...(phase ? { phase } : {}),\n };\n }\n if (typeof currentMessage.content === \"string\") {\n currentMessage.content =\n currentMessage.content.length > 0\n ? [{ type: \"input_text\", text: currentMessage.content }, ...content]\n : [...content];\n } else {\n currentMessage.content.push(...content);\n }\n };\n\n const toJsonString = (value: unknown) => {\n if (typeof value === \"string\") {\n return value;\n }\n try {\n return JSON.stringify(value ?? {});\n } catch {\n return \"{}\";\n }\n };\n\n const resolveImageItem = (\n block: ContentBlock.Multimodal.Image\n ): OpenAIClient.Responses.ResponseInputImage | undefined => {\n const detail = iife(() => {\n const raw = block.metadata?.detail;\n if (raw === \"low\" || raw === \"high\" || raw === \"auto\") {\n return raw;\n }\n return \"auto\";\n });\n if (block.fileId) {\n return {\n type: \"input_image\",\n detail,\n file_id: block.fileId,\n };\n }\n if (block.url) {\n return {\n type: \"input_image\",\n detail,\n image_url: block.url,\n };\n }\n if (block.data) {\n const base64Data =\n typeof block.data === \"string\"\n ? block.data\n : Buffer.from(block.data).toString(\"base64\");\n const mimeType = block.mimeType ?? \"image/png\";\n return {\n type: \"input_image\",\n detail,\n image_url: `data:${mimeType};base64,${base64Data}`,\n };\n }\n return undefined;\n };\n\n const resolveFileItem = (\n block: ContentBlock.Multimodal.File | ContentBlock.Multimodal.Video\n ): OpenAIClient.Responses.ResponseInputFile | undefined => {\n if (block.fileId) {\n const filename = getFilenameFromMetadata(block);\n return {\n type: \"input_file\",\n file_id: block.fileId,\n ...(filename ? { filename } : {}),\n };\n }\n if (block.url) {\n const filename = getFilenameFromMetadata(block);\n return {\n ...(filename ? { filename } : {}),\n type: \"input_file\",\n file_url: block.url,\n };\n }\n if (block.data) {\n const filename = getRequiredFilenameFromMetadata(block);\n const encoded =\n typeof block.data === \"string\"\n ? block.data\n : Buffer.from(block.data).toString(\"base64\");\n const mimeType = block.mimeType ?? \"application/octet-stream\";\n return {\n type: \"input_file\",\n file_data: `data:${mimeType};base64,${encoded}`,\n filename,\n };\n }\n return undefined;\n };\n\n const convertReasoningBlock = (\n block: ContentBlock.Reasoning\n ): OpenAIClient.Responses.ResponseReasoningItem => {\n const summaryEntries = iife(() => {\n if (Array.isArray(block.summary)) {\n const candidate = block.summary;\n const mapped =\n candidate\n ?.map((item) => item?.text)\n .filter((text): text is string => typeof text === \"string\") ?? [];\n if (mapped.length > 0) {\n return mapped;\n }\n }\n return block.reasoning ? [block.reasoning] : [];\n });\n\n const summary =\n summaryEntries.length > 0\n ? summaryEntries.map((text) => ({\n type: \"summary_text\" as const,\n text,\n }))\n : [{ type: \"summary_text\" as const, text: \"\" }];\n\n const reasoningItem: OpenAIClient.Responses.ResponseReasoningItem = {\n type: \"reasoning\",\n id: block.id ?? \"\",\n summary,\n };\n\n if (block.reasoning) {\n reasoningItem.content = [\n {\n type: \"reasoning_text\" as const,\n text: block.reasoning,\n },\n ];\n }\n return reasoningItem;\n };\n\n const convertFunctionCall = (\n block: ContentBlock.Tools.ToolCall | ContentBlock.Tools.ServerToolCall\n ): OpenAIClient.Responses.ResponseFunctionToolCall => ({\n type: \"function_call\",\n name: block.name ?? \"\",\n call_id: block.id ?? \"\",\n arguments: toJsonString(block.args),\n });\n\n const convertFunctionCallOutput = (\n block: ContentBlock.Tools.ServerToolCallResult\n ): OpenAIClient.Responses.ResponseInputItem.FunctionCallOutput => {\n const output = toJsonString(block.output);\n const status =\n block.status === \"success\"\n ? \"completed\"\n : block.status === \"error\"\n ? \"incomplete\"\n : undefined;\n return {\n type: \"function_call_output\",\n call_id: block.toolCallId ?? \"\",\n output,\n ...(status ? { status } : {}),\n };\n };\n\n for (const block of message.contentBlocks) {\n if (block.type === \"text\") {\n const phase = iife(() => {\n if (\n !(\n \"extras\" in block &&\n typeof block.extras === \"object\" &&\n block.extras !== null &&\n \"phase\" in block.extras\n )\n )\n return undefined;\n return block.extras\n .phase as OpenAIClient.Responses.EasyInputMessage[\"phase\"];\n });\n pushMessageContent([{ type: \"input_text\", text: block.text }], phase);\n } else if (block.type === \"invalid_tool_call\") {\n // no-op\n } else if (block.type === \"reasoning\") {\n yield* flushMessage();\n yield convertReasoningBlock(\n block as ContentBlock.Standard & { type: \"reasoning\" }\n );\n } else if (block.type === \"tool_call\") {\n yield* flushMessage();\n const id = block.id ?? \"\";\n if (id) {\n functionCallIdsWithBlocks.add(id);\n pendingFunctionChunks.delete(id);\n }\n yield convertFunctionCall(\n block as ContentBlock.Standard & { type: \"tool_call\" }\n );\n } else if (block.type === \"tool_call_chunk\") {\n if (block.id) {\n const existing = pendingFunctionChunks.get(block.id) ?? {\n name: block.name,\n args: [],\n };\n if (block.name) existing.name = block.name;\n if (block.args) existing.args.push(block.args);\n pendingFunctionChunks.set(block.id, existing);\n }\n } else if (block.type === \"server_tool_call\") {\n yield* flushMessage();\n const id = block.id ?? \"\";\n if (id) {\n serverFunctionCallIdsWithBlocks.add(id);\n pendingServerFunctionChunks.delete(id);\n }\n yield convertFunctionCall(block);\n } else if (block.type === \"server_tool_call_chunk\") {\n if (block.id) {\n const existing = pendingServerFunctionChunks.get(block.id) ?? {\n name: block.name,\n args: [],\n };\n if (block.name) existing.name = block.name;\n if (block.args) existing.args.push(block.args);\n pendingServerFunctionChunks.set(block.id, existing);\n }\n } else if (block.type === \"server_tool_call_result\") {\n yield* flushMessage();\n yield convertFunctionCallOutput(block);\n } else if (block.type === \"audio\") {\n // no-op\n } else if (block.type === \"file\") {\n const fileItem = resolveFileItem(block);\n if (fileItem) {\n pushMessageContent([fileItem]);\n }\n } else if (block.type === \"image\") {\n const imageItem = resolveImageItem(block);\n if (imageItem) {\n pushMessageContent([imageItem]);\n }\n } else if (block.type === \"video\") {\n const videoItem = resolveFileItem(block);\n if (videoItem) {\n pushMessageContent([videoItem]);\n }\n } else if (block.type === \"text-plain\") {\n if (block.text) {\n pushMessageContent([\n {\n type: \"input_text\",\n text: block.text,\n },\n ]);\n }\n } else if (block.type === \"non_standard\" && isResponsesMessage) {\n yield* flushMessage();\n yield block.value as ResponsesInputItem;\n }\n }\n yield* flushMessage();\n\n for (const [id, chunk] of pendingFunctionChunks) {\n if (!id || functionCallIdsWithBlocks.has(id)) continue;\n const args = chunk.args.join(\"\");\n if (!chunk.name && !args) continue;\n yield {\n type: \"function_call\",\n call_id: id,\n name: chunk.name ?? \"\",\n arguments: args,\n };\n }\n\n for (const [id, chunk] of pendingServerFunctionChunks) {\n if (!id || serverFunctionCallIdsWithBlocks.has(id)) continue;\n const args = chunk.args.join(\"\");\n if (!chunk.name && !args) continue;\n yield {\n type: \"function_call\",\n call_id: id,\n name: chunk.name ?? \"\",\n arguments: args,\n };\n }\n }\n return Array.from(iterateItems());\n};\n\n/**\n * - MCP (Model Context Protocol) approval responses\n * - Zero Data Retention (ZDR) mode handling\n *\n * @param params - Conversion parameters\n * @param params.messages - Array of LangChain BaseMessages to convert\n * @param params.zdrEnabled - Whether Zero Data Retention mode is enabled. When true, certain\n * metadata like message IDs and function call IDs are omitted from the output\n * @param params.model - The model name being used. Used to determine if special role mapping\n * is needed (e.g., \"system\" -> \"developer\" for reasoning models)\n *\n * @returns Array of ResponsesInputItem objects formatted for the OpenAI Responses API\n *\n * @throws {Error} When a function message is encountered (not supported)\n * @throws {Error} When computer call output format is invalid\n *\n * @example\n * ```typescript\n * const messages = [\n * new HumanMessage(\"Hello\"),\n * new AIMessage({ content: \"Hi there!\", tool_calls: [...] })\n * ];\n *\n * const input = convertMessagesToResponsesInput({\n * messages,\n * zdrEnabled: false,\n * model: \"gpt-4\"\n * });\n * ```\n */\nexport const convertMessagesToResponsesInput: Converter<\n { messages: BaseMessage[]; zdrEnabled: boolean; model: string },\n ResponsesInputItem[]\n> = ({ messages, zdrEnabled, model }) => {\n return messages.flatMap(\n (lcMsg): ResponsesInputItem | ResponsesInputItem[] => {\n const responseMetadata = lcMsg.response_metadata as\n | Record\n | undefined;\n if (responseMetadata?.output_version === \"v1\") {\n return convertStandardContentMessageToResponsesInput(lcMsg);\n }\n\n const additional_kwargs =\n lcMsg.additional_kwargs as BaseMessageFields[\"additional_kwargs\"] & {\n [_FUNCTION_CALL_IDS_MAP_KEY]?: Record;\n reasoning?: OpenAIClient.Responses.ResponseReasoningItem;\n type?: string;\n refusal?: string;\n };\n\n let role = messageToOpenAIRole(lcMsg);\n if (role === \"system\" && isReasoningModel(model)) role = \"developer\";\n\n if (role === \"function\") {\n throw new Error(\"Function messages are not supported in Responses API\");\n }\n\n if (role === \"tool\") {\n const toolMessage = lcMsg as ToolMessage;\n\n // Handle computer call output\n if (additional_kwargs?.type === \"computer_call_output\") {\n const output = (() => {\n if (typeof toolMessage.content === \"string\") {\n return {\n type: \"input_image\" as const,\n image_url: toolMessage.content,\n };\n }\n\n if (Array.isArray(toolMessage.content)) {\n /**\n * Check for input_image type first (computer-use-preview format)\n */\n const inputImage = toolMessage.content.find(\n (i) => i.type === \"input_image\"\n ) as { type: \"input_image\"; image_url: string } | undefined;\n\n if (inputImage) return inputImage;\n\n /**\n * Check for computer_screenshot type (legacy format)\n */\n const oaiScreenshot = toolMessage.content.find(\n (i) => i.type === \"computer_screenshot\"\n ) as\n | { type: \"computer_screenshot\"; image_url: string }\n | undefined;\n\n if (oaiScreenshot) return oaiScreenshot;\n\n /**\n * Convert image_url content block to input_image format\n */\n const lcImage = toolMessage.content.find(\n (i) => i.type === \"image_url\"\n ) as MessageContentImageUrl;\n\n if (lcImage) {\n return {\n type: \"input_image\" as const,\n image_url:\n typeof lcImage.image_url === \"string\"\n ? lcImage.image_url\n : lcImage.image_url.url,\n };\n }\n }\n\n throw new Error(\"Invalid computer call output\");\n })();\n\n /**\n * Cast needed because OpenAI SDK types don't yet include input_image\n * for computer-use-preview model output format\n */\n return {\n type: \"computer_call_output\",\n output,\n call_id: toolMessage.tool_call_id,\n } as ResponsesInputItem;\n }\n\n // Handle custom tool output\n if (toolMessage.additional_kwargs?.customTool) {\n return {\n type: \"custom_tool_call_output\",\n call_id: toolMessage.tool_call_id,\n output: toolMessage.content as string,\n };\n }\n\n // Check if content contains provider-native OpenAI content blocks\n // that should be passed through without stringification\n const isProviderNativeContent =\n Array.isArray(toolMessage.content) &&\n toolMessage.content.every(\n (item) =>\n typeof item === \"object\" &&\n item !== null &&\n \"type\" in item &&\n (item.type === \"input_file\" ||\n item.type === \"input_image\" ||\n item.type === \"input_text\")\n );\n\n return {\n type: \"function_call_output\",\n call_id: toolMessage.tool_call_id,\n id: toolMessage.id?.startsWith(\"fc_\") ? toolMessage.id : undefined,\n output: isProviderNativeContent\n ? (toolMessage.content as OpenAIClient.Responses.ResponseFunctionCallOutputItemList)\n : typeof toolMessage.content !== \"string\"\n ? JSON.stringify(toolMessage.content)\n : toolMessage.content,\n };\n }\n\n if (role === \"assistant\") {\n // if we have the original response items, just reuse them\n if (\n !zdrEnabled &&\n responseMetadata?.output != null &&\n Array.isArray(responseMetadata?.output) &&\n responseMetadata?.output.length > 0 &&\n responseMetadata?.output.every((item) => \"type\" in item)\n ) {\n return responseMetadata?.output;\n }\n\n // otherwise, try to reconstruct the response from what we have\n\n const input: ResponsesInputItem[] = [];\n\n // reasoning items\n const reasoning = additional_kwargs?.reasoning;\n const hasEncryptedContent = !!reasoning?.encrypted_content;\n /**\n * With ZDR enabled, OpenAI does not retain reasoning items, so we only send\n * them when encrypted content is available (via include: [\"reasoning.encrypted_content\"]).\n * With ZDR disabled, we include reasoning item ids so OpenAI can reference them, as it's storing them.\n */\n if (reasoning && (!zdrEnabled || hasEncryptedContent)) {\n const reasoningItem =\n convertReasoningSummaryToResponsesReasoningItem(reasoning);\n input.push(reasoningItem);\n }\n\n // ai content\n let { content } = lcMsg;\n if (additional_kwargs?.refusal) {\n if (typeof content === \"string\") {\n content = [{ type: \"output_text\", text: content, annotations: [] }];\n }\n content = [\n ...(content as ContentBlock[]),\n { type: \"refusal\", refusal: additional_kwargs.refusal },\n ];\n }\n\n if (typeof content === \"string\" || content.length > 0) {\n const messageItem: ResponsesInputItem = {\n type: \"message\",\n role: \"assistant\",\n ...(lcMsg.id && !zdrEnabled && lcMsg.id.startsWith(\"msg_\")\n ? { id: lcMsg.id }\n : {}),\n content: iife(() => {\n if (typeof content === \"string\") {\n return content;\n }\n return content.flatMap((item) => {\n if (item.type === \"text\") {\n const textItem = item as ContentBlock.Text;\n return {\n type: \"output_text\",\n text: textItem.text,\n annotations: (textItem.annotations ?? []).map(\n convertLangChainAnnotationToOpenAI\n ),\n };\n }\n\n if (item.type === \"output_text\" || item.type === \"refusal\") {\n return item;\n }\n\n return [];\n });\n }) as ResponseInputMessageContentList,\n phase: iife(() => {\n if (!Array.isArray(content)) {\n return undefined;\n }\n\n const phasedContent = content.find(\n (item): item is ContentBlock & { phase: string } =>\n \"phase\" in item && typeof item.phase === \"string\"\n );\n return phasedContent?.phase as\n | OpenAIClient.Responses.EasyInputMessage[\"phase\"]\n | undefined;\n }),\n };\n input.push(messageItem);\n }\n\n const functionCallIds = additional_kwargs?.[_FUNCTION_CALL_IDS_MAP_KEY];\n\n if (AIMessage.isInstance(lcMsg) && !!lcMsg.tool_calls?.length) {\n input.push(\n ...lcMsg.tool_calls.map((toolCall): ResponsesInputItem => {\n if (isCustomToolCall(toolCall)) {\n return {\n type: \"custom_tool_call\",\n id: toolCall.call_id,\n call_id: toolCall.id ?? \"\",\n input: toolCall.args.input,\n name: toolCall.name,\n };\n }\n if (isComputerToolCall(toolCall)) {\n return {\n type: \"computer_call\",\n id: toolCall.call_id,\n call_id: toolCall.id ?? \"\",\n action: toolCall.args.action,\n } as ResponsesInputItem;\n }\n return {\n type: \"function_call\",\n name: toolCall.name,\n arguments: JSON.stringify(toolCall.args),\n call_id: toolCall.id!,\n ...(!zdrEnabled ? { id: functionCallIds?.[toolCall.id!] } : {}),\n };\n })\n );\n } else if (additional_kwargs?.tool_calls) {\n input.push(\n ...additional_kwargs.tool_calls.map(\n (toolCall): ResponsesInputItem => ({\n type: \"function_call\",\n name: toolCall.function.name,\n call_id: toolCall.id,\n arguments: toolCall.function.arguments,\n ...(!zdrEnabled ? { id: functionCallIds?.[toolCall.id] } : {}),\n })\n )\n );\n }\n\n const toolOutputs = (\n responseMetadata?.output as Array\n )?.length\n ? responseMetadata?.output\n : additional_kwargs.tool_outputs;\n\n const fallthroughCallTypes: ResponsesInputItem[\"type\"][] = [\n \"computer_call\",\n \"mcp_call\",\n \"code_interpreter_call\",\n \"image_generation_call\",\n \"shell_call\",\n \"local_shell_call\",\n ];\n\n if (toolOutputs != null) {\n const castToolOutputs = toolOutputs as Array;\n const fallthroughCalls = castToolOutputs?.filter((item) =>\n fallthroughCallTypes.includes(item.type)\n );\n if (fallthroughCalls.length > 0) input.push(...fallthroughCalls);\n }\n\n return input;\n }\n\n if (role === \"user\" || role === \"system\" || role === \"developer\") {\n if (typeof lcMsg.content === \"string\") {\n return { type: \"message\", role, content: lcMsg.content };\n }\n\n const messages: ResponsesInputItem[] = [];\n const content = (lcMsg.content as ContentBlock[]).flatMap((item) => {\n if (item.type === \"mcp_approval_response\") {\n messages.push({\n type: \"mcp_approval_response\",\n approval_request_id: item.approval_request_id as string,\n approve: item.approve as boolean,\n });\n }\n if (isDataContentBlock(item)) {\n return convertToProviderContentBlock(\n item,\n 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