fix(ai): ported OpenAI API fixes from pi-mono

- Fixed OpenAI Responses API 400 error when switching between models
  (same provider, different model). Omits fc_ prefixed IDs from different
  models to avoid reasoning/function_call pairing validation errors.
  (ported from d327b9c7)

- Fixed 400 errors when reading multiple images via GitHub Copilot's
  Claude models. Batches images from consecutive tool results into a
  single user message to maintain tool_use -> tool_result adjacency.
  (ported from 6289c144)

packages/ai/CHANGELOG.md

@@ -2,6 +2,11 @@
 
 ## [Unreleased]
 
+### Fixed
+
+- Fixed OpenAI Responses API 400 error "function_call without required reasoning item" when switching between models (same provider, different model). The fix omits the `id` field for function_calls from different models to avoid triggering OpenAI's reasoning/function_call pairing validation
+- Fixed 400 errors when reading multiple images via GitHub Copilot's Claude models. Claude requires tool_use -> tool_result adjacency with no user messages interleaved. Images from consecutive tool results are now batched into a single user message
+
 ## [7.0.0] - 2026-01-21
 ### Added
 

packages/ai/src/providers/openai-completions.ts

@@ -23,6 +23,7 @@ import type {
 	ThinkingContent,
 	Tool,
 	ToolCall,
+	ToolResultMessage,
 } from "$ai/types";
 import { AssistantMessageEventStream } from "$ai/utils/event-stream";
 import { parseStreamingJson } from "$ai/utils/json-parse";
@@ -464,7 +465,7 @@ function maybeAddOpenRouterAnthropicCacheControl(
 	}
 }
 
-function convertMessages(
+export function convertMessages(
 	model: Model<"openai-completions">,
 	context: Context,
 	compat: Required<OpenAICompat>,
@@ -481,7 +482,8 @@ function convertMessages(
 
 	let lastRole: string | null = null;
 
-	for (const msg of transformedMessages) {
+	for (let i = 0; i < transformedMessages.length; i++) {
+		const msg = transformedMessages[i];
 		// Some providers (e.g. Mistral/Devstral) don't allow user messages directly after tool results
 		// Insert a synthetic assistant message to bridge the gap
 		if (compat.requiresAssistantAfterToolResult && lastRole === "toolResult" && msg.role === "user") {
@@ -605,55 +607,73 @@ function convertMessages(
 			}
 			params.push(assistantMsg);
 		} else if (msg.role === "toolResult") {
-			// Extract text and image content
-			const textResult = msg.content
-				.filter((c) => c.type === "text")
-				.map((c) => (c as any).text)
-				.join("\n");
-			const hasImages = msg.content.some((c) => c.type === "image");
+			// Batch consecutive tool results and collect all images
+			const imageBlocks: Array<{ type: "image_url"; image_url: { url: string } }> = [];
+			let j = i;
 
-			// Always send tool result with text (or placeholder if only images)
-			const hasText = textResult.length > 0;
-			// Some providers (e.g. Mistral) require the 'name' field in tool results
-			const toolResultMsg: ChatCompletionToolMessageParam = {
-				role: "tool",
-				content: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
-				tool_call_id: normalizeMistralToolId(msg.toolCallId, compat.requiresMistralToolIds),
-			};
-			if (compat.requiresToolResultName && msg.toolName) {
-				(toolResultMsg as any).name = msg.toolName;
-			}
-			params.push(toolResultMsg);
+			for (; j < transformedMessages.length && transformedMessages[j].role === "toolResult"; j++) {
+				const toolMsg = transformedMessages[j] as ToolResultMessage;
 
-			// If there are images and model supports them, send a follow-up user message with images
-			if (hasImages && model.input.includes("image")) {
-				const contentBlocks: Array<
-					{ type: "text"; text: string } | { type: "image_url"; image_url: { url: string } }
-				> = [];
+				// Extract text and image content
+				const textResult = toolMsg.content
+					.filter((c) => c.type === "text")
+					.map((c) => (c as any).text)
+					.join("\n");
+				const hasImages = toolMsg.content.some((c) => c.type === "image");
 
-				// Add text prefix
-				contentBlocks.push({
-					type: "text",
-					text: "Attached image(s) from tool result:",
-				});
+				// Always send tool result with text (or placeholder if only images)
+				const hasText = textResult.length > 0;
+				// Some providers (e.g. Mistral) require the 'name' field in tool results
+				const toolResultMsg: ChatCompletionToolMessageParam = {
+					role: "tool",
+					content: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
+					tool_call_id: normalizeMistralToolId(toolMsg.toolCallId, compat.requiresMistralToolIds),
+				};
+				if (compat.requiresToolResultName && toolMsg.toolName) {
+					(toolResultMsg as any).name = toolMsg.toolName;
+				}
+				params.push(toolResultMsg);
 
-				// Add images
-				for (const block of msg.content) {
-					if (block.type === "image") {
-						contentBlocks.push({
-							type: "image_url",
-							image_url: {
-								url: `data:${(block as any).mimeType};base64,${(block as any).data}`,
-							},
-						});
+				if (hasImages && model.input.includes("image")) {
+					for (const block of toolMsg.content) {
+						if (block.type === "image") {
+							imageBlocks.push({
+								type: "image_url",
+								image_url: {
+									url: `data:${(block as any).mimeType};base64,${(block as any).data}`,
+								},
+							});
+						}
 					}
 				}
+			}
+
+			i = j - 1;
+
+			// After all consecutive tool results, add a single user message with all images
+			if (imageBlocks.length > 0) {
+				if (compat.requiresAssistantAfterToolResult) {
+					params.push({
+						role: "assistant",
+						content: "I have processed the tool results.",
+					});
+				}
 
 				params.push({
 					role: "user",
-					content: contentBlocks,
+					content: [
+						{
+							type: "text",
+							text: "Attached image(s) from tool result:",
+						},
+						...imageBlocks,
+					],
 				});
+				lastRole = "user";
+			} else {
+				lastRole = "toolResult";
 			}
+			continue;
 		}
 
 		lastRole = msg.role;

packages/ai/src/providers/openai-responses.ts

@@ -507,6 +507,15 @@ function convertMessages(
 			}
 		} else if (msg.role === "assistant") {
 			const output: ResponseInput = [];
+			const assistantMsg = msg as AssistantMessage;
+
+			// Check if this message is from a different model (same provider, different model ID).
+			// For such messages, tool call IDs with fc_ prefix need to be stripped to avoid
+			// OpenAI's reasoning/function_call pairing validation errors.
+			const isDifferentModel =
+				assistantMsg.model !== model.id &&
+				assistantMsg.provider === model.provider &&
+				assistantMsg.api === model.api;
 
 			for (const block of msg.content) {
 				// Do not submit thinking blocks if the completion had an error (i.e. abort)
@@ -535,11 +544,19 @@ function convertMessages(
 				} else if (block.type === "toolCall" && msg.stopReason !== "error") {
 					const toolCall = block as ToolCall;
 					const normalized = normalizeResponsesToolCallId(toolCall.id);
+					const callId = normalized.callId;
+					// For different-model messages, set id to undefined to avoid pairing validation.
+					// OpenAI tracks which fc_xxx IDs were paired with rs_xxx reasoning items.
+					// By omitting the id, we avoid triggering that validation (like cross-provider does).
+					let itemId: string | undefined = normalized.itemId;
+					if (isDifferentModel && itemId?.startsWith("fc_")) {
+						itemId = undefined;
+					}
 					knownCallIds.add(normalized.callId);
 					output.push({
 						type: "function_call",
-						id: normalized.itemId,
-						call_id: normalized.callId,
+						id: itemId,
+						call_id: callId,
 						name: toolCall.name,
 						arguments: JSON.stringify(toolCall.arguments),
 					});

packages/ai/test/openai-completions-tool-result-images.test.ts

@@ -0,0 +1,88 @@
+import { describe, expect, it } from "bun:test";
+import { getModel } from "$ai/models";
+import { convertMessages } from "$ai/providers/openai-completions";
+import type { AssistantMessage, Context, Model, OpenAICompat, ToolResultMessage, Usage } from "$ai/types";
+
+const emptyUsage: Usage = {
+	input: 0,
+	output: 0,
+	cacheRead: 0,
+	cacheWrite: 0,
+	totalTokens: 0,
+	cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
+};
+
+const compat: Required<OpenAICompat> = {
+	supportsStore: true,
+	supportsDeveloperRole: true,
+	supportsReasoningEffort: true,
+	supportsUsageInStreaming: true,
+	maxTokensField: "max_completion_tokens",
+	requiresToolResultName: false,
+	requiresAssistantAfterToolResult: false,
+	requiresThinkingAsText: false,
+	requiresMistralToolIds: false,
+	thinkingFormat: "openai",
+};
+
+function buildToolResult(toolCallId: string, timestamp: number): ToolResultMessage {
+	return {
+		role: "toolResult",
+		toolCallId,
+		toolName: "read",
+		content: [
+			{ type: "text", text: "Read image file [image/png]" },
+			{ type: "image", data: "ZmFrZQ==", mimeType: "image/png" },
+		],
+		isError: false,
+		timestamp,
+	};
+}
+
+describe("openai-completions convertMessages", () => {
+	it("batches tool-result images after consecutive tool results", () => {
+		const baseModel = getModel("openai", "gpt-4o-mini");
+		const model: Model<"openai-completions"> = {
+			...baseModel,
+			api: "openai-completions",
+			input: ["text", "image"],
+		};
+
+		const now = Date.now();
+		const assistantMessage: AssistantMessage = {
+			role: "assistant",
+			content: [
+				{ type: "toolCall", id: "tool-1", name: "read", arguments: { path: "img-1.png" } },
+				{ type: "toolCall", id: "tool-2", name: "read", arguments: { path: "img-2.png" } },
+			],
+			api: model.api,
+			provider: model.provider,
+			model: model.id,
+			usage: emptyUsage,
+			stopReason: "toolUse",
+			timestamp: now,
+		};
+
+		const context: Context = {
+			messages: [
+				{ role: "user", content: "Read the images", timestamp: now - 2 },
+				assistantMessage,
+				buildToolResult("tool-1", now + 1),
+				buildToolResult("tool-2", now + 2),
+			],
+		};
+
+		const messages = convertMessages(model, context, compat);
+		const roles = messages.map((message) => message.role);
+		expect(roles).toEqual(["user", "assistant", "tool", "tool", "user"]);
+
+		const imageMessage = messages[messages.length - 1];
+		expect(imageMessage.role).toBe("user");
+		expect(Array.isArray(imageMessage.content)).toBe(true);
+
+		const imageParts = (imageMessage.content as Array<{ type?: string }>).filter(
+			(part) => part?.type === "image_url",
+		);
+		expect(imageParts.length).toBe(2);
+	});
+});