upgrade package for ai-chat-sdk to v6

examples/ai/chat/pydantic-ai-chat.py

@@ -1,6 +1,14 @@
+# /// script
+# requires-python = ">=3.13"
+# dependencies = [
+#     "httpx==0.28.1",
+#     "marimo>=0.21.1",
+#     "pydantic==2.12.5",
+# ]
+# ///
 import marimo
 
-__generated_with = "0.19.2"
+__generated_with = "0.21.1"
 app = marimo.App(width="medium")
 
 with app.setup(hide_code=True):

frontend/package.json

@@ -21,7 +21,7 @@
     "./unstable_internal/*": "./src/*"
   },
   "dependencies": {
-    "@ai-sdk/react": "^2.0.125",
+    "@ai-sdk/react": "^3.0.131",
     "@anywidget/types": "^0.2.0",
     "@codemirror/autocomplete": "^6.20.1",
     "@codemirror/commands": "^6.10.2",
@@ -114,7 +114,7 @@
     "@xterm/addon-web-links": "^0.12.0",
     "@xterm/xterm": "^5.5.0",
     "@zed-industries/agent-client-protocol": "^0.4.5",
-    "ai": "^5.0.123",
+    "ai": "^6.0.129",
     "ansi_up": "^6.0.6",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",

frontend/src/components/chat/chat-utils.ts

@@ -1,7 +1,12 @@
 /* Copyright 2026 Marimo. All rights reserved. */
 
 import type { components } from "@marimo-team/marimo-api";
-import type { FileUIPart, ToolUIPart, UIMessage } from "ai";
+import type {
+  ChatAddToolOutputFunction,
+  FileUIPart,
+  ToolUIPart,
+  UIMessage,
+} from "ai";
 import { useState } from "react";
 import useEvent from "react-use-event-hook";
 import type { ProviderId } from "@/core/ai/ids/ids";
@@ -111,20 +116,14 @@ export async function buildCompletionRequestBody(
   };
 }
 
-interface AddToolOutput {
-  tool: string;
-  toolCallId: string;
-  output: unknown;
-}
-
 export async function handleToolCall({
   invokeAiTool,
   addToolOutput, // Important that we don't await addToolOutput to prevent potential deadlocks
   toolCall,
   toolContext,
 }: {
   invokeAiTool: (request: InvokeAiToolRequest) => Promise<InvokeAiToolResponse>;
-  addToolOutput: (output: AddToolOutput) => Promise<void>;
+  addToolOutput: ChatAddToolOutputFunction<UIMessage>;
   toolCall: {
     toolName: string;
     toolCallId: string;

frontend/src/core/ai/staged-cells.ts

@@ -161,6 +161,12 @@ export function useStagedCells(store: JotaiStore) {
       case "tool-output-error":
         Logger.error("Error", chunk.type, { chunk });
         break;
+      case "tool-approval-request":
+        Logger.log("Tool approval request", { chunk });
+        break;
+      case "tool-output-denied":
+        Logger.error("Tool output denied", { chunk });
+        break;
       // These logs are not useful for debugging
       case "start":
       case "start-step":

marimo/_plugins/ui/_impl/chat/chat.py

@@ -36,10 +36,17 @@
 )
 
 # The version of the Vercel AI SDK we use
-AI_SDK_VERSION: Final[Literal[5, 6]] = 5
+AI_SDK_VERSION: Final[Literal[5, 6]] = 6
 DONE_CHUNK: Final[str] = "[DONE]"
 
 
+def require_vercel_ai_sdk_support() -> None:
+    """Only Pydantic AI >=1.52.0 supports AI SDK v6. So, we require it."""
+    DependencyManager.pydantic_ai.require_at_version(
+        why="for Vercel AI SDK support", min_version="1.52.0"
+    )
+
+
 @dataclass
 class SendMessageRequest:
     messages: list[ChatMessage]
@@ -413,9 +420,8 @@ def _convert_value(self, value: dict[str, Any]) -> list[ChatMessage]:
 
         part_validator_class = None
         if DependencyManager.pydantic_ai.imported():
-            from pydantic_ai.ui.vercel_ai.request_types import (
-                UIMessagePart,
-            )
+            require_vercel_ai_sdk_support()
+            from pydantic_ai.ui.vercel_ai.request_types import UIMessagePart
 
             # The frontend sends messages as ChatMessage parts so we use pydantic-ai to cast them
             # as Vercel UIMessagePart
@@ -466,20 +472,15 @@ def handle_chunk(self, chunk: Any) -> None:
 
         # Handle Pydantic AI's Vercel AI SDK chunks
         if DependencyManager.pydantic_ai.imported():
+            require_vercel_ai_sdk_support()
             from pydantic_ai.ui.vercel_ai.response_types import (
                 BaseChunk,
             )
 
             if isinstance(chunk, BaseChunk):
-                try:
-                    serialized = json.loads(
-                        chunk.encode(sdk_version=AI_SDK_VERSION)
-                    )
-                except TypeError:
-                    # Fallback for pydantic-ai < 1.52.0 which doesn't have sdk_version param
-                    serialized = chunk.model_dump(
-                        mode="json", by_alias=True, exclude_none=True
-                    )
+                serialized = json.loads(
+                    chunk.encode(sdk_version=AI_SDK_VERSION)
+                )
                 self.on_send_chunk(serialized)
                 return
 

marimo/_server/ai/providers.py

@@ -25,7 +25,10 @@
     generate_id,
 )
 from marimo._dependencies.dependencies import Dependency, DependencyManager
-from marimo._plugins.ui._impl.chat.chat import AI_SDK_VERSION
+from marimo._plugins.ui._impl.chat.chat import (
+    AI_SDK_VERSION,
+    require_vercel_ai_sdk_support,
+)
 from marimo._server.ai.config import AnyProviderConfig
 from marimo._server.ai.ids import AiModelId
 from marimo._server.ai.tools.tool_manager import get_tool_manager
@@ -40,7 +43,6 @@
     from openai import AsyncOpenAI
     from openai.types.shared.reasoning_effort import ReasoningEffort
     from pydantic_ai import Agent, DeferredToolRequests, FunctionToolset
-    from pydantic_ai.messages import ThinkingPart
     from pydantic_ai.models import Model
     from pydantic_ai.models.bedrock import BedrockConverseModel
     from pydantic_ai.models.google import GoogleModel
@@ -54,9 +56,7 @@
     )
     from pydantic_ai.providers.google import GoogleProvider as PydanticGoogle
     from pydantic_ai.providers.openai import OpenAIProvider as PydanticOpenAI
-    from pydantic_ai.ui.vercel_ai import VercelAIAdapter
     from pydantic_ai.ui.vercel_ai.request_types import UIMessage, UIMessagePart
-    from pydantic_ai.ui.vercel_ai.response_types import BaseChunk
     from starlette.responses import StreamingResponse
 
 
@@ -101,6 +101,7 @@ def __init__(
             *(deps or []),
             source="server",
         )
+        require_vercel_ai_sdk_support()
 
         self.model = model
         self.config = config
@@ -132,12 +133,6 @@ def create_agent(
             output_type=output_type,
         )
 
-    def get_vercel_adapter(self) -> type[VercelAIAdapter[Any, Any]]:
-        """Return the Vercel AI adapter for the given provider."""
-        from pydantic_ai.ui.vercel_ai import VercelAIAdapter
-
-        return VercelAIAdapter
-
     def convert_messages(
         self, messages: list[ServerUIMessage]
     ) -> list[UIMessage]:
@@ -153,6 +148,7 @@ async def stream_completion(
         stream_options: Optional[StreamOptions] = None,
     ) -> StreamingResponse:
         """Return a streaming response from the given messages. The response are AI SDK events."""
+        from pydantic_ai.ui.vercel_ai import VercelAIAdapter
         from pydantic_ai.ui.vercel_ai.request_types import SubmitMessage
 
         tools = (self.config.tools or []) + additional_tools
@@ -169,18 +165,12 @@ async def stream_completion(
         # TODO: Text only and format stream are not supported yet
         stream_options = stream_options or StreamOptions()
 
-        vercel_adapter = self.get_vercel_adapter()
-        if DependencyManager.pydantic_ai.has_at_version(min_version="1.52.0"):
-            adapter = vercel_adapter(
-                agent=agent,
-                run_input=run_input,
-                accept=stream_options.accept,
-                sdk_version=AI_SDK_VERSION,
-            )
-        else:
-            adapter = vercel_adapter(
-                agent=agent, run_input=run_input, accept=stream_options.accept
-            )
+        adapter = VercelAIAdapter(
+            agent=agent,
+            run_input=run_input,
+            accept=stream_options.accept,
+            sdk_version=AI_SDK_VERSION,
+        )
         event_stream = adapter.run_stream()
         return adapter.streaming_response(event_stream)
 
@@ -193,16 +183,16 @@ async def stream_text(
         additional_tools: list[ToolDefinition],
     ) -> AsyncGenerator[str]:
         """Return a stream of text from the given messages."""
+        from pydantic_ai.ui.vercel_ai import VercelAIAdapter
 
         tools = (self.config.tools or []) + additional_tools
         agent = self.create_agent(
             max_tokens=max_tokens, tools=tools, system_prompt=system_prompt
         )
-        vercel_adapter = self.get_vercel_adapter()
 
         async with agent.run_stream(
             user_prompt=user_prompt,
-            message_history=vercel_adapter.load_messages(
+            message_history=VercelAIAdapter.load_messages(
                 self.convert_messages(messages)
             ),
         ) as result:
@@ -800,73 +790,6 @@ def process_part(self, part: UIMessagePart) -> UIMessagePart:
             )
         return part
 
-    def get_vercel_adapter(
-        self,
-    ) -> type[VercelAIAdapter[None, DeferredToolRequests | str]]:
-        """
-        Return a custom adapter that includes thinking signatures in ReasoningEndChunk.
-
-        pydantic_ai's VercelAIEventStream.handle_thinking_end doesn't pass the signature
-        from ThinkingPart to ReasoningEndChunk, which breaks Anthropic's extended thinking
-        on follow-up messages (Anthropic requires signatures on thinking blocks).
-
-        This is a patch for pydantic-ai <1.47.0, which doesn't include the signature in the ReasoningEndChunk.
-        """
-        if DependencyManager.pydantic_ai.has_at_version(min_version="1.47.0"):
-            return super().get_vercel_adapter()
-
-        from pydantic_ai import DeferredToolRequests
-        from pydantic_ai.ui.vercel_ai import VercelAIAdapter
-        from pydantic_ai.ui.vercel_ai._event_stream import VercelAIEventStream
-        from pydantic_ai.ui.vercel_ai.response_types import ReasoningEndChunk
-
-        AnthropicOutputType = DeferredToolRequests | str
-
-        # Custom event stream that includes signature in ReasoningEndChunk
-        class AnthropicVercelAIEventStream(
-            VercelAIEventStream[None, AnthropicOutputType]
-        ):
-            async def handle_thinking_end(
-                self, part: ThinkingPart, followed_by_thinking: bool = False
-            ) -> AsyncIterator[BaseChunk]:
-                """Override to include signature in provider_metadata."""
-                try:
-                    provider_metadata = None
-                    if part.signature:
-                        pydantic_ai_meta: dict[str, Any] = {
-                            "signature": part.signature
-                        }
-                        if part.provider_name:
-                            pydantic_ai_meta["provider_name"] = (
-                                part.provider_name
-                            )
-                        if part.id:
-                            pydantic_ai_meta["id"] = part.id
-                        provider_metadata = {"pydantic_ai": pydantic_ai_meta}
-
-                    yield ReasoningEndChunk(
-                        id=self.message_id, provider_metadata=provider_metadata
-                    )
-                except Exception as e:
-                    LOGGER.warning(
-                        f"Error in AnthropicVercelAIEventStream.handle_thinking_end: {e}"
-                    )
-                    async for chunk in super().handle_thinking_end(
-                        part, followed_by_thinking
-                    ):
-                        yield chunk
-
-        # Custom adapter that uses the custom event stream
-        class AnthropicVercelAIAdapter(
-            VercelAIAdapter[None, AnthropicOutputType]
-        ):
-            def build_event_stream(self) -> AnthropicVercelAIEventStream:
-                return AnthropicVercelAIEventStream(
-                    self.run_input, accept=self.accept
-                )
-
-        return AnthropicVercelAIAdapter
-
 
 class BedrockProvider(PydanticProvider["PydanticBedrock"]):
     def setup_credentials(self, config: AnyProviderConfig) -> None: