alfin06/swegent-issues-filtered huggingface dataset:
OpenAI: Web search tool call sources are not included in tool result chunk

### Description

When a web call tool result is received, AI SDK currently enqueues a `tool-result` chunk (irrespective of success or failure - https://github.com/vercel/ai/issues/9270). The input field of this `tool-result` chunk is set to the action returned in the tool call result. However, `sources` property is not defined in the Zod schema of the `search` action type. See:

https://github.com/vercel/ai/blob/418e18f15129e4615f5200c64c535c40b688cff6/packages/openai/src/responses/openai-responses-language-model.ts#L1011-L1025

https://github.com/vercel/ai/blob/418e18f15129e4615f5200c64c535c40b688cff6/packages/openai/src/responses/openai-responses-language-model.ts#L40-L49

So, sources array is completely missed.

### AI SDK Version

- @ai-sdk/openai: 2.0.44
- ai: 5.0.60

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: ai/provider, support, provider/openai
State: closed
针对 Qwen3-VL-4B 微调后的 API 形式启动问题

### Reminder

- [x] I have read the above rules and searched the existing issues.

### System Info

llamafactory 0.9.4.dev0 与 vllm 0.11.0 不兼容，导致以api形式启动的时候，infer_backend 不能为 vllm

### Reproduction

```text
Put your message here.
```


### Others

_No response_

Labels: solved
State: closed
result_as_answer 

Hi,

I tested the new functionality "result_as_answer" and worked fine!
https://github.com/crewAIInc/crewAI/blob/7b53457ef36e14f42af20dcd3abb1ba76883b502/tests/agent_test.py#L728

In the docs it's written:
"...This setup is appropriate if the intention is for the tool to provide a direct answer that does not require further processing by the agent."

But when I print the crew.usage_metrics the output seem to suggest that the agent interacted with the LLM (successful_requests>0):
{'total_tokens': 942, 'prompt_tokens': 581, 'completion_tokens': 361, 'successful_requests': 2}

Imagine the multiply tool, if I set result_as_answer=True, I don't want to lose speed and money interacting with the LLM, just returning the tool output.

It's a bug or have another logic behind?

Thank you and keep up the good work!!!

State: closed
Bug: Bedrock Anthropic reasoning does not work when using additionalModelRequestFields

### Description

With `additionalModelRequestFields` set, reasoning doesn't work anymore.

In https://github.com/vercel/ai/commit/a10bf620f6804f3394913a66b81052d8363fabd4 - filtering out `reasoningConfig` was added, but that leaves potential `additionalModelRequestFields` in, which overwrites the `additionalModelRequestFields` that includes the `reasoningConfig`.

Repro:

```ts
import { bedrock } from "@ai-sdk/amazon-bedrock";
import { stepCountIs, streamText } from "ai";
import "dotenv/config";

async function main() {
  const result = streamText({
    model: bedrock("us.anthropic.claude-3-7-sonnet-20250219-v1:0"),
    prompt: 'How many "r"s are in the word "strawberry"?',
    onError: (error) => console.error(error),
    providerOptions: {
      bedrock: {
        reasoningConfig: { type: "enabled", budgetTokens: 1024 },
        // removing this line below makes reasoning work again
        additionalModelRequestFields: {},
      },
    },
    maxRetries: 0,
    stopWhen: stepCountIs(5),
  });

  for await (const part of result.fullStream) {
    if (part.type === "reasoning-delta") {
      process.stdout.write("\x1b[34m" + part.text + "\x1b[0m");
    } else if (part.type === "text-delta") {
      process.stdout.write(part.text);
    }
  }

  console.log();
  console.log("Warnings:", await result.warnings);
}

main().catch(console.error);
```

### AI SDK Version

```
"@ai-sdk/amazon-bedrock": "^4.0.0-beta.18",
"ai": "^6.0.0-beta.31"
```

also happens on

```
"@ai-sdk/amazon-bedrock": "^3.0.31",
"ai": "^5.0.61"
```

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: bug, ai/provider, provider/anthropic, reproduction provided, provider/amazon-bedrock
State: closed
Confusing error messages, cannot generateObject with grok-4-fast-reasoning

### Description

```
const model = createXai({
					apiKey: this.env.XAI_API_KEY,
				});
				const { object } = await generateObject({
					model: model.languageModel('grok-4-fast-reasoning'),
					schema: z.object({
						name: z.string(),
					}),
					system: 'Identify the name of the person from the following text',
					messages: [{ role: 'user', content: 'My name is John Doe' }],
					providerOptions: {
						xai: {
							reasoningEffort: 'high',
						} satisfies XaiProviderOptions,
					},
				});
```

Throws a `[ERROR] Bad Request` error. However if i comment out the `providerOptions`, i get another error: `[ERROR] AI SDK Warning: The "responseFormat" setting is not supported by this model - JSON response format schema is not supported`

### AI SDK Version

"@ai-sdk/xai": "^2.0.23",
		"ai": "^5.0.61",

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: ai/provider, support, provider/xai
State: closed
Lazy-load schemas for `ai-sdk/google` provider

- [x] add and run performance test to get initial loading time for compiled code (run 5x to get avg); record the value
  - ➡️ avg 22.3ms
- [x] unify zod schemas into large schemas that can be lazily loaded. derive types from there
- [x] change zod schemas in the provider to be lazy loaded (all models, tools, etc) - search for `zod/v4` to identify files
- [x] rename `X-api-types` file to `X-api` and move response schemas there
- [x] check satisfies with z.infer has been updated to InferValidator
- [x] rerun performance test to get updated number (run 5x to get avg); record the value
  - ➡️ avg 19.5ms (-12.5%)
- [x] add changeset

Labels: feature, ai/provider, provider/google
State: closed
o3使用web_search_preview报错

### 📦 部署环境

Docker

### 📦 部署模式

客户端 Pglite 模式(lobe-chat-pglite 镜像)

### 📌 软件版本

latest

### 💻 系统环境

macOS

### 🌐 浏览器

Chrome

### 🐛 问题描述

o3使用web_search_preview报错，看官方api doc已更新为web_search，需要修正

### 📷 复现步骤

_No response_

### 🚦 期望结果

_No response_

### 📝 补充信息

_No response_

Labels: released, unconfirm
State: closed
启用客户端请求时，自定义服务商未遵循指定请求格式

### 📦 部署环境

Vercel

### 📦 部署模式

服务端模式(lobe-chat-database 镜像)

### 📌 软件版本

latest

### 💻 系统环境

Windows

### 🌐 浏览器

Chrome

### 🐛 问题描述

新建一个请求格式为 Google 的自定义服务商，发现其行为仍然是 OpenAI 类型。（启用客户端请求时）

### 📷 复现步骤

_No response_

### 🚦 期望结果

_No response_

### 📝 补充信息

_No response_

Labels: released, unconfirm
State: closed
infinite client/server loop in case of error during tool calls

### Description

we are noticed that in case if error occurs during tool calls , calling 
```
    stop,
    sendMessage,
    regenerate,
    addToolResult,
    clearError,
    error,
    resumeStream,
  } = useChat<AgentUIMessage>({
    id: storeConversationId,
    sendAutomaticallyWhen:
      lastAssistantMessageIsCompleteWithToolCalls,
```
will cause infinite recursive loop with ```error``` being undefined.

current workaround to use `lastAssistantMessageIsCompleteWithToolCallsAndNoError` in place of `lastAssistantMessageIsCompleteWithToolCalls`
```
function lastAssistantMessageIsCompleteWithToolCallsAndNoError(options: {
  messages: UIMessage[];
}): boolean {
  return (
    //@ts-ignore
    (this.status as ChatStatus) !== "error" &&
    lastAssistantMessageIsCompleteWithToolCalls(options)
  );
}
```

### AI SDK Version

@ai-sdk/react: 2.0.19

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: bug, ai/ui
State: closed
No way to set truncation to auto for Gpt-5 models

### Description

I see in https://github.com/vercel/ai/blob/907007422dfd124c7fa7f7f100cad5ee96d5df39/packages/openai/src/responses/openai-responses-language-model.ts#L1302 `requiredAutoTruncation` is set to false and is never set to true anywhere else, which means this line here https://github.com/vercel/ai/blob/907007422dfd124c7fa7f7f100cad5ee96d5df39/packages/openai/src/responses/openai-responses-language-model.ts#L231 never evaluates to true and hence can never set `truncation` to `true`. 

I've also tried setting `truncation` to `true` in `providerOptions` and that seems to be getting dropped. Is there a reason why these extra parameters in `providerOptions` are getting dropped like that? I'd think that if users are providing extra parameters in providerOptions they should just be passed through to the underlying provider as is, no?

### AI SDK Version

    "@ai-sdk/amazon-bedrock": "^3.0.34",
    "@ai-sdk/anthropic": "^2.0.26",
    "@ai-sdk/azure": "^2.0.49",
    "@ai-sdk/cohere": "^2.0.14",
    "@ai-sdk/deepseek": "^1.0.22",
    "@ai-sdk/google": "^2.0.20",
    "@ai-sdk/groq": "^2.0.24",
    "@ai-sdk/mistral": "^2.0.19",
    "@ai-sdk/openai": "^2.0.48",
    "@ai-sdk/openai-compatible": "1.0.18",
    "@ai-sdk/provider": "^2.0.0",
    "@ai-sdk/provider-utils": "^3.0.12",
    "@ai-sdk/xai": "^2.0.25",
    "ai": "^5.0.68"

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: feature, pull request welcome, ai/provider, provider/openai
State: closed
[Request] Add a new reasoning button.

### 🥰 Feature Description

Problem:
- NVIDIA NIM has a new model named 'Deepseek V3.1'. 
- The problem is, is NVIDIA didn't separate it with reasoning and chat.
- Now whenever using the model, it does not use the reasoning. Only the chat.

### 🧐 Proposed Solution

Add a reasoning button. Request reasoning button.

### 📝 Additional Information

_No response_

Labels: released
State: closed
Multi-Flow streaming performance is pretty bad (maybe related to large tool call messages)

### Bug Description

I have a multi-flow setup where an Agent is calling another flow (that has another agent) the called flow/agent is painfully slow. I can bring it up in the playground and it's streaming abnormally slow and I have noticed that there's a single python thread running at 100%. It seems like there's some sort of spin loop happening slowing everything down. 

I'm not really able to figure out how to profile this. 

A few more details:
Flow A is primary flow with basic Chat Input, Agent and Chat Output and has Flow B connected as a tool
Flow B has the same Chat Input, Agent and Chat Output with a Python based tool connected that does return a fair bit of data. 

I thought I had seen in the process of looking at the API that while streaming it keeps streaming the same message over and over with additional data. If this includes the tool call message that message will be bigger and bigger and bigger and then when the agent starts adding to it, it's going to be humongous. 

### Reproduction

Create Flow "Tool" with Chat Input, Agent, Chat Output, connect a tool to Agent that returns a lot of data and agent instructions to parse the data
Create Flow "Test" with Chat Input, Agent, Chat Output, connect Flow "Tool" as a Tool to that agent.
Ask Flow Test about something related to tool data

### Expected behavior

It will work and be usable... 

### Who can help?

_No response_

### Operating System

Linux

### Langflow Version

1.5.0.post2 in Docker

### Python Version

None

### Screenshot

_No response_

### Flow File

_No response_

Labels: bug, jira
State: closed
【Bug】从 OpenRouter 获取模型列表时，报错 "values() must be called

### 📦 Platform

Vercel

### 📦 Deploymenet mode

server db(lobe-chat-database image)

### 📌 Version

v1.132.11

### 💻 Operating System

Windows

### 🌐 Browser

Firefox

### 🐛 Bug Description

当我在设置中配置 OpenRouter 并尝试获取模型列表时，前端会提示 “Request failed”，同时后端服务会崩溃或报错。 

错误日志 (Error Log): 我查看了部署应用的后端日志，发现了以下关键错误信息。这明確地指向了一个数据库操作问题，似乎是程序在尝试插入一个空的数据列表。

```
2025-09-25T14:38:47.419Z [info] {"level":30,"time":1758811127417,"pid":4,"hostname":"169.254.**.**","msg":"Error in tRPC handler (lambda) on path: aiModel.batchUpdateAiModels, type: mutation"}
2025-09-25T14:38:47.419Z [error] Error [TRPCError]: values() must be called with at least one value
    at PgInsertBuilder.values (.next/server/chunks/60629.js:4122:13)
    at AiModelModel.batchUpdateAiModels (.next/server/chunks/30140.js:556:83)
    at <unknown> (.next/server/chunks/30140.js:6824:33)
    at resolveMiddleware (.next/server/chunks/72581.js:1248:23)
    at callRecursive (.next/server/chunks/72581.js:1283:24)
    at Object.next (.next/server/chunks/72581.js:1289:12)
    at inputValidatorMiddleware (.next/server/chunks/72581.js:1077:15)
    at async callRecursive (.next/server/chunks/72581.js:1283:18)
    at async callRecursive (.next/server/chunks/72581.js:1283:18) {
  code: 'INTERNAL_SERVER_ERROR',
  [cause]: Error: values() must be called with at least one value
      at PgInsertBuilder.values (.next/server/chunks/60629.js:4122:13)
      at AiModelModel.batchUpdateAiModels (.next/server/chunks/30140.js:556:83)
      at <unknown> (.next/server/chunks/30140.js:6824:33)
      at resolveMiddleware (.next/server/chunks/72581.js:1248:23)
      at callRecursive (.next/server/chunks/72581.js:1283:24)
      at Object.next (.next/server/chunks/72581.js:1289:12)
      at inputValidatorMiddleware (.next/server/chunks/72581.js:1077:15)
      at async callRecursive (.next/server/chunks/72581.js:1283:18)
      at async callRecursive (.next/server/chunks/72581.js:1283:18)
}
```

### 📷 Recurrence Steps

复现步骤 (Steps to Reproduce): 
1. 进入 设置 -> AI 服务商 页面。 
2. 在服务商列表中选择 OpenRouter。 
3. 填入有效的 OpenRouter API Key 和 API 代理地址 (https://openrouter.ai/api/v1)。 
4. 点击【获取模型】按钮。 5. 此时，前端界面会立即弹出 “Request failed” 的错误提示。  
```
Request failed Sorry, the server seems to be experiencing some difficulties and is temporarily unable to complete your request. Please try again later.
`values() must be called with at least one value`
```

<img width="1057" height="1001" alt="Image" src="https://github.com/user-attachments/assets/b7d0d4ad-ebaf-4c33-bd68-2977bb6869a9" />

### 🚦 Expected Behavior

_No response_

### 📝 Additional Information

问题具有唯一性：我测试了其他 AI 服务商（例如 Azure OpenAI, Google AI 等），它们的模型列表都可以正常获取和保存。目前只有 OpenRouter 这一个服务商存在此问题。 • 环境信息：我的应用部署在 Vercel 平台，使用的数据库是 Vercel Postgres (NeonDB)

从日志来看，这个问题似乎不是 OpenRouter API 的问题，而是 LobeChat 后端在从 OpenRouter 获取到模型数据后，尝试将这些数据存入自己数据库时（调用 batchUpdateAiModels 函数）的逻辑缺陷导致的。可能是在某些情况下程序没有正确处理空的数据列表，直接将其传递给了数据库的插入操作，从而触发了错误。


Labels: released, unconfirm
State: closed
[gpt-image-1] `revised_prompt` sometimes returns null — causes Zod validation error (`invalid_type`)

### Description

When using the gpt-image-1 model with generateImage in the AI SDK, the API response sometimes includes:

"revised_prompt": null


However, the SDK’s internal Zod schema expects revised_prompt to always be a non-null string. As a result, the SDK throws a validation error:

Error message: [
  {
    "expected": "string",
    "code": "invalid_type",
    "path": ["data", 0, "revised_prompt"],
    "message": "Invalid input: expected string, received null"
  }
]


Because the value is null, it fails validation, even though this seems to be a legitimate response from the backend. In effect, the SDK is not compatible with the real API behavior for gpt-image-1.

Reproduction / Minimal Example
import { experimental_generateImage as generateImage } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';

async function test() {
  const openai = createOpenAI({
    apiKey: process.env.OPENAI_API_KEY,
  });
  const resp = await generateImage({
    model: openai.image('gpt-image-1'),
    prompt: 'A futuristic cityscape at sunset',
    n: 1,
    size: '1024x1024',
  });
  console.log(resp);
}

test().catch(err => {
  console.error('Error:', err);
  if (err.cause && Array.isArray(err.cause.errors)) {
    console.error('Zod validation errors:', JSON.stringify(err.cause.errors, null, 2));
  }
  if (err.rawResponse) {
    console.error('Raw response:', JSON.stringify(err.rawResponse, null, 2));
  }
});


Observed API response snippet (rawResponse):

{
  "data": [
    {
      "b64_json": "...base64 data...",
      "revised_prompt": null,
      "url": null
    }
  ],
  "output_format": null,
  "quality": null,
  "size": null,
  "usage": { … }
}


### AI SDK Version

"@ai-sdk/openai": "^2.0.48","ai": "^5.0.68",

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: ai/provider, support, provider/openai, provider/openai-compatible
State: closed
OpenAI provider: Preserve file_id when converting file citations to sources

### Description

The OpenAI responses API returns file citations with **file_id**. This information is lost when converting file citations to sources in the content array. For example https://github.com/vercel/ai/blob/27e8c3aad8e3cdd73f5d8f454cbca92b34580ac2/packages/openai/src/responses/openai-responses-language-model.ts#L478-L486

For our use case, we've implemented our chatbot as an api that adheres to the openai responses API spec, and we've implemented file citations in a way where file_id contains necessary information about the file. It would be nice to preserve the file_id in the source.

### AI SDK Version

"ai": "5.0.17",
"@ai-sdk/openai": "2.0.32",

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: feature, ai/provider, provider/openai
State: closed
a2a server does not return liveOutput for ExecutingToolCall or resultsDisplay for result values

### What happened?

A2A server is filtering almost all result values from scheduled tool results.  This happens in https://github.com/google-gemini/gemini-cli/blob/main/packages/a2a-server/src/agent/task.ts#L446-L456.  Based on the comment it seems like the fields that should be serialized should be allow listed.  This change breaks being able to display errors and tool execution output to users.

### What did you expect to happen?

Requisite fields would be returned by the server.

### Client information

<details>
<summary>Client Information</summary>

Run `gemini` to enter the interactive CLI, then run the `/about` command.

```console
> /about
# paste output here
```

</details>

### Login information

_No response_

### Anything else we need to know?

_No response_

State: closed
🤖 Provider API update - anthropic@2.1.0

### New features

- POST /v1/complete
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001 (hybrid model with near-instant responses and extended thinking)

- POST /v1/messages
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- POST /v1/messages?beta=true
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- POST /v1/messages/batches
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- POST /v1/messages/batches?beta=true
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- GET /v1/messages/batches/{message_batch_id}/results
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- GET /v1/messages/batches/{message_batch_id}/results?beta=true
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- POST /v1/messages/count_tokens
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

- POST /v1/messages/count_tokens?beta=true
  - Added supported model values: claude-haiku-4-5, claude-haiku-4-5-20251001

https://github.com/gr2m/ai-provider-monitor/releases/tag/anthropic%402.1.0


Labels: ai/provider, provider/anthropic, maintenance
State: closed
maxOutputToken value is set for anthropic

### Description

we're setting a default value for `maxOutputToken` to 4096 when initialising args for anthropic doGenerate and doStream.

this in turn returns `too many tokens` error for some models.

set in file `packages/anthropic/src/anthropic-messages-language-model.ts`

### AI SDK Version

_No response_

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: bug, ai/provider, provider/anthropic
State: closed
Support Anthropic's Native MCP Connector API (mcp_servers parameter)

### Description

Anthropic recently released an MCP Connector feature that allows Claude's Messages API to connect directly to remote MCP servers via the mcp_servers parameter (requires anthropic-beta: mcp-client-2025-04-04 header).

Adding support for Anthropic's native connector would enable:
  - Direct MCP server connections via Claude's Messages API
  - OAuth bearer token authentication for MCP servers
  - Multiple simultaneous MCP server connections
  - Tool access without local MCP client implementation

Reference: https://docs.claude.com/en/docs/agents-and-tools/mcp-connector

### AI SDK Version

"ai": "^5.0.59",
"@ai-sdk/anthropic": "^2.0.22",

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: feature, ai/provider, provider/anthropic, ai/mcp
State: closed
Support for Custom Context Attachments in UserMessage

### Description

There currently isn’t a straightforward way to store and rehydrate additional context attachments within a UserMessage in the AI SDK.
This becomes relevant when building advanced chat UIs (e.g., similar to [Cursor’s `<PromptInput />` component](https://ai-sdk.dev/elements/components/prompt-input#cursor-style)), where a message may include not only text or files, but also structured context such as URLs that the system needs to fetch, JSON data, or tool toggles (like enabling webSearch for a specific prompt).

Existing mechanisms (FilePart, custom data parts, Message.metadata) only partially address this and don’t provide a consistent way to persist, rehydrate, and send non-file or text attachments.

**Use Case**

<img width="1476" height="442" alt="Image" src="https://github.com/user-attachments/assets/a36fa54c-4beb-4df3-9693-ba0ba4e3bca1" />

In chat applications where users can attach extra context to a message — such as files, URLs, structured JSON, or tool toggles - it would be helpful if these attachments could be:

- Stored alongside the user message
- Displayed again when editing that message, and
- Optionally included when converting messages to model input.

At the moment:

- Custom parts are stripped out by `convertToModelMessages()`.
- metadata is not sent to the model and is mainly used for auxiliary data like timestamps or model info.
- Using `prepareStep()` to call a custom tool (e.g., `getContext(attachmentIds)`) results in that fetched context being stored in the assistant message rather than the user message, meaning those attachments cannot later be removed or modified.

**What's would be helpful**

It would be valuable to have a more flexible pattern to:

1. Persist arbitrary contextual data alongside UserMessage objects.
2. Rehydrate and display those attachments when editing messages.
3. Optionally include this data in the conversion process for model messages.
4. Achieve this without manually overriding internal conversion utilities like `converToModelMessages()`

**Potential solution options**

- Extending `Message.parts` to accept additional part types beyond text and file (e.g., data, tool, or url).
- Allowing custom serialization logic to map rich `UserMessage` structures to `ModelMessage`.
- Introducing middleware or hooks that can modify message payloads before conversion while retaining the ability to rehydrate those parts.
- Providing a mechanism to selectively include certain metadata fields in model messages when appropriate.

**References**
- https://github.com/vercel/ai/discussions/2820#discussioncomment-14616465
- https://ai-sdk.dev/docs/reference/ai-sdk-core/model-message
- https://ai-sdk.dev/docs/ai-sdk-ui/message-metadata#common-use-cases

### AI SDK Version

- [ai@5.0.76](https://github.com/vercel/ai/releases/tag/ai%405.0.76)

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

Labels: feature, ai/core
State: closed
Subsequent chats in topic branch are switched back to main topic

### 📦 Platform

Self hosting Docker

### 📦 Deploymenet mode

server db(lobe-chat-database image)

### 📌 Version

v1.136.1

### 💻 Operating System

Windows

### 🌐 Browser

Edge

### 🐛 Bug Description

打开一个话题的分支后，第一次聊天正常，继续在分支中对话，我的用户信息和回答都会到主话题里。并且使用的是主话题的上下文（？）

### 📷 Recurrence Steps

打开一个话题的分支，第一次对话后，继续在分支中对话

### 🚦 Expected Behavior

_No response_

### 📝 Additional Information

_No response_

Labels: released
State: closed