alfin06/swegent-issues-filtered huggingface dataset:
[Bug]: Error occurred while creating agent: table agents has no column named description

### Describe the bug

Autogen Studio:  impossible to create a new agent.  Always get this error, and no agent created.

### Steps to reproduce

I tried to create a team of agents, so tried to instantiate several agents. That didn't work per the error above. Then, duplicating the existing agent still led to the same error.  I am on v0.0.52

### Model Used

gpt4-turbo

### Expected Behavior

An agent should have been instantiated. Any other reasons why this instantiation should fail?

### Screenshots and logs

_No response_

### Additional Information

_No response_

Labels: proj-studio
State: closed
[Bug]: issues and typos in Sarvam integration

### Bug Description

## Bug Description
The integration code for **Sarvam AI** in `llama_index` has multiple typos:

- The class is named `Servam` instead of `Sarvam`.
- The default model is `"servam-m"` instead of `"sarvam-m"`.
- The API base and docstrings reference `servam.ai` instead of `sarvam.ai`.
- The environment variable is `SERVAM_API_KEY` instead of `SARVAM_API_KEY`.

---

## Expected Behavior
All references should correctly use **Sarvam** (matching the official name and domain `sarvam.ai`).

Correct defaults should be:
```python
DEFAULT_API_BASE = "https://api.sarvam.ai/v1"
DEFAULT_MODEL = "sarvam-m"
ENV_VAR = "SARVAM_API_KEY"
```

## Proposed Fix
Rename class to Sarvam.

Update defaults, env vars, and docstrings accordingly.

Update package suggestion in docs:


### I’d be happy to work on this fix — can I take this up?

### Version

github/main

### Steps to Reproduce

## Steps to Reproduce
Open llama_index/llms/servam.py.

Observe incorrect naming (Servam, servam.ai, servam-m).

### Relevant Logs/Tracbacks

```shell

```

Labels: bug, triage
State: closed
[Bug]: QdrantVectorStore does not work with aclient and enable_hybrid

### Bug Description

The `QdrantVectorStore`'s `__init__` method, when `enable_hybrid` is true, calls synchronous methods like `get_default_sparse_doc_encoder` and `use_old_sparse_encoder`. These methods, in turn, attempt to use `self._client` to check for collection existence. However, if only `aclient` is provided and `url`/`api_key` are not passed to the `QdrantVectorStore` constructor, `self._client` remains `None`, raising `AttributeError`.

Qdrant only supports one client accessing a storage folder, so it's not possible to instantiate a separate sync client in local mode.

A workaround (ignore support for old sparse encoder format):
```python
QdrantVectorStore.use_old_sparse_encoder = lambda self, collection_name: False
```

### Version

0.14.3

### Steps to Reproduce

```python
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import AsyncQdrantClient


aclient = AsyncQdrantClient(path="...")
qdrant = QdrantVectorStore(aclient=aclient, collection_name="llamacollection", enable_hybrid=True, fastembed_sparse_model="Qdrant/bm25")
```

### Relevant Logs/Tracbacks

```shell
AttributeError: 'NoneType' object has no attribute 'collection_exists'
```

Labels: bug, triage
State: closed
[Feature Request]: introduce 'verbosity' param into llmconfig

### Is your feature request related to a problem? Please describe.

[1.1 Overview](https://cookbook.openai.com/examples/gpt-5/gpt-5_new_params_and_tools#11-overview)
The verbosity parameter lets you hint to the model to be more or less expansive in its replies.

Values: "low", "medium", "high"

low → terse UX, minimal prose.
medium (default) → balanced detail.
high → verbose, great for audits, teaching, or hand-offs.
Keep prompts stable and use the param rather than rewriting.

### Describe the solution you'd like

_No response_

### Additional context

_No response_

Labels: enhancement
State: closed
[Feature Request]: Add support for "jp" cross-region inference profile for Bedrock Claude Sonnet 4.5 and Haiku 4.5

### Feature Description

In a recent update, support for the global cross-region inference profile prefix was added to llama-index-llms-bedrock-converse ([see PR 20064](https://github.com/run-llama/llama_index/pull/20064)).
Similarly, AWS now provides a "jp" (Japan) cross-region inference profile for Bedrock Claude Sonnet 4.5 and Bedrock Claude Haiku 4.5 models.
I would like to request that llama-index-llms-bedrock-converse support the "jp" cross-region inference profile for these models, allowing users to select the Japan profile as needed.

Reference:
 - AWS official documentation for supported cross-region inference profiles: https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html

### Reason

Currently, while the global cross-region inference profile is supported, there is no way to select or utilize the "jp" (Japan) cross-region profile, which can route requests specifically via the Japanese region's optimized resources.
This limitation is problematic for users and workloads that benefit from region-specific inference, such as compliance requirements, latency optimization, or data residency concerns specific to Japan.

### Value of Feature

Adding support for the "jp" cross-region inference profile would:
 - Enable users to leverage lower latency and regional optimization for workloads specific to Japan.
 - Increase compliance with data residency requirements for Japanese customers.

Labels: enhancement, triage
State: closed
[Feature Request]: Support for Anthropic RawMessageDeltaEvent

### Feature Description

Currently, the llama_index integration with Anthropic models ignores the RawMessageDeltaEvent messages returned from the Anthropic streaming API.
These events contain valuable information such as:

* input / output tokens counters
* stop reason (e.g., end_turn, max_tokens, etc.)


For example, when using OpenAI, it is already possible to access the `finish_reason` from streamed events like this:
```
async for event in handler.stream_events():
    if isinstance(event, AgentOutput):
          if choices: = event.raw.get("choices", None):
              for choice in choices:
                 finish_reason = choice.get("finish_reason", None)   
```

I would like to be able to get this information from anthropic as well


### Reason

_No response_

### Value of Feature

This feature would provide access to important metadata from Anthropic responses — including stop reasons and token/billing usage — that are currently lost when RawMessageDeltaEvent is ignored.

It would:

* Improve observability during streaming calls

* Enable cost and rate-limit tracking

* Help developers debug or optimize model interactions

* Bring the Anthropic integration to feature parity with other LLM providers supported by LlamaIndex (e.g., OpenAI)

Labels: enhancement, triage
State: closed
[Bug]: Missing token usage information in GoogleGenAI metadata for MLFlow Tracing

### Bug Description

During my development with the `GoogleGenAI` LLM and MLFlow Tracing, I noticed that token usages are not recorded by the MLFlow llama_index tracer. After investigations, the MLFlow llama_index tracer logs the following error as debug log:

```
DEBUG mlflow.llama_index.tracer: Failed to set TokenUsage to the span: 'prompt_tokens'
Traceback (most recent call last):
  File "...\.venv\Lib\site-packages\mlflow\llama_index\tracer.py", line 454, in _parse_usage
    TokenUsageKey.INPUT_TOKENS: usage["prompt_tokens"],
                                ~~~~~^^^^^^^^^^^^^^^^^
KeyError: 'prompt_tokens'
```

The MLFlow tracer expects the `prompt_tokens`, `completion_tokens` and optionally the `total_tokens` keys to be set in the `additional_kwargs` attribute of `ChatResponse` but are currently missing.

These information must be set by `GoogleGenAI` before returning the response to avoid the error (see the `OpenAI` LLM integration for an example).


### Version

llama-index-llms-google-genai==0.7.1

### Steps to Reproduce

Here is some minimal code to reproduce the error:

```python
import logging
import mlflow
from llama_index.core.base.llms.types import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI

# Set the mlflow llama_index tracer logger to DEBUG
logging.getLogger("mlflow.llama_index.tracer").setLevel(logging.DEBUG)

# Activate llama_index autologging in MLFlow
mlflow.llama_index.autolog()

#  Call the LLM
llm = GoogleGenAI(model="gemini-2.5-flash-lite")
response = llm.chat(
    messages=[ChatMessage(role="user", content="What is the capital of France?")]
)
```

### Relevant Logs/Tracbacks

```shell

```

Labels: bug, triage
State: closed
bug(@ai-sdk/azure): `log_probs` is not be supported for calls to `gpt-4-vision-preview`, causing calls for that deployment to fail entirely

### Description

1. Send a request to Azure OpenAI chat completions, with the deployment pointing to a gpt-4-vision-preview deployment
2. Either specify `logprobs` in the request (defaulting to `false`) or omit it entirely. The `ai-sdk` will send the request with `logprobs` set to false.
3. Receive the following error:

```
error","message":"1 validation error for Request\nbody -> logprobs\n  extra fields not permitted (type=value_error.extra)
```

### Code example

```typescript
import { createAzure } from '@ai-sdk/azure';
import { streamText } from 'ai';
import dotenv from 'dotenv';

dotenv.config();

// create provider instance via factory
const azure = createAzure({
      resourceName: '<redacted>',
      apiKey: '<redacted>',
})

async function main() {
  const result = await streamText({
    model: azure('gpt-4-vision-preview'), // use a deployment pointing to gpt-4-vision-preview
    prompt: 'Invent a new holiday and describe its traditions.',
  });

  for await (const textPart of result.textStream) {
    process.stdout.write(textPart);
  }
}

main().catch(console.error);
```

### Additional context

`log_probs` is not supported in Azure OpenAI specifically only for `gpt-4-vision-preview`, as documented here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions

![image](https://github.com/vercel/ai/assets/3812012/f08e8e1b-e242-4cec-b78f-e1b5c96b5631)


State: closed
[BUG] Use LTM

### Description

Based on Documentation i am trying to use 

```
long_term_memory=EnhanceLongTermMemory(
        storage=LTMSQLiteStorage(
            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
        )
    ),
```

But it doesn't look like it's working am i missing any import or something is off as these classes doesn't look exist.
Docs link: https://docs.crewai.com/concepts/memory

### Steps to Reproduce

Follow steps from docs: https://docs.crewai.com/concepts/memory

### Expected behavior

Should be able to save result into database

### Screenshots/Code snippets

None

### Operating System

Windows 11

### Python Version

3.11

### crewAI Version

0.100.0

### crewAI Tools Version

latest

### Virtual Environment

Venv

### Evidence

none

### Possible Solution

some class neds to be imported

### Additional context

none

Labels: bug
State: closed
InvalidToolArgumentsError unhandled by useChat, leading to infinite loop

### Description

InvalidToolArgumentsError is unhandled by useChat, leading to an infinite loop of triggerRequest. This is extremely bad as credits can be fully drained easily this way.

The trace is as follows.

Tool arguments are returned and parsed by `parseToolCall` within `streamText`.

https://github.com/vercel/ai/blob/0565cd72934bc22bfc8f7a866abef97c643e6f86/packages/core/core/generate-text/run-tools-transformation.ts#L74-L77

When arguments are malformed, an `InvalidToolArgumentsError` is thrown.

https://github.com/vercel/ai/blob/0565cd72934bc22bfc8f7a866abef97c643e6f86/packages/core/core/generate-text/tool-call.ts#L74-L78

This error is then serialized and streamed back to the client.

https://github.com/vercel/ai/blob/0565cd72934bc22bfc8f7a866abef97c643e6f86/packages/core/core/generate-text/run-tools-transformation.ts#L119-L122

This error is received by `useChat` and is unhandled by `parseComplexResponse`.

https://github.com/vercel/ai/blob/0565cd72934bc22bfc8f7a866abef97c643e6f86/packages/ui-utils/src/parse-complex-response.ts#L34C23-L34C43

Because it is unhandled, `processChatStream` proceeds as normal and no new messages are emitted, leading to `triggerRequest` being called again.

https://github.com/vercel/ai/blob/0565cd72934bc22bfc8f7a866abef97c643e6f86/packages/react/src/use-chat.ts#L292-L349



### Code example

_No response_

### Additional context

I have a tool call defined as

```ts
showBarChart: {
	description:
		'Make a bar plot in matplotlib. The bars are positioned at x. Their dimensions are given by height. Always include the data parameter as a list of JSON objects.',
	parameters: z.object({
		data: z.array(z.record(z.number())).describe('JSON list for which to generate grouped bars'),
		axisKey: z.string().describe('The key to use for the x-axis.'),
		title: z.string().optional().describe('Set a title for the plot.'),
		yLabel: z.string().optional().describe('Set the label for the y-axis.'),
	}),
},
```

For some reason, gpt-4o doesn't return anything for data when it's defined as an array of records with variable keys. However, setting it to have defined keys does return valid data.

```ts
showBarChart: {
	description:
		'Make a bar plot in matplotlib. The bars are positioned at x. Their dimensions are given by height. Always include the data parameter as a list of JSON objects.',
	parameters: z.object({
		data: z
			.array(z.object({ x: z.number(), height: z.number() }))
			.describe('JSON list for which to generate grouped bars'),
		axisKey: z.string().describe('The key to use for the x-axis.'),
		title: z.string().optional().describe('Set a title for the plot.'),
		yLabel: z.string().optional().describe('Set the label for the y-axis.'),
	}),
},
```

Labels: bug, ai/ui
State: closed
Anthropic user content inside tool result

### Description

### Title: 
Error with Consecutive User Role Messages After Tool Role Messages in Anthropic API

### Description:
When attempting to place a user role message immediately after a tool role message using the Anthropic API, an error is encountered:

```
AI_APICallError: messages: roles must alternate between "user" and "assistant", but found multiple "user" roles in a row.
```

This issue seems to be caused by the Anthropic API's requirement for roles to alternate strictly between "user" and "assistant". The current behavior does not support having a user role message following a tool role message without an intermediate assistant message.

### Steps to Reproduce:
1. Make an API call using the Anthropic API.
2. Structure the messages in the following order:
   - Tool role 'tool-call' message
   - User role with 'tool-result' message
   - User role with text
3. Execute the API call.

### Expected Behavior:
The API should accept the message structure, possibly by automatically incorporating the user message content within the tool result content or by providing a mechanism to handle such sequences without causing an error.

### Suggested Solution:
Replace this line https://github.com/vercel/ai/blob/main/packages/anthropic/src/convert-to-anthropic-messages-prompt.ts#L74 to this code:
```typescript
if (messages.length > 0 && messages[messages.length-1].role == "user" && messages[messages.length-1].content.find(part => part.type == "tool_result")) {
         messages[messages.length-1].content = messages[messages.length-1].content.concat(anthropicContent);
} else {
         messages.push({role: "user", content: anthropicContent});
}
```

Labels: bug, ai/provider
State: closed
[BUG] Crew.test() requires OpenAI model and does not support specifying model agent uses if different from OpenAI model

### Description

Function `'test()` in `class Crew(BaseModel)` expects as input parameter `openai_model_name: Optional[str] = None,` 

https://github.com/crewAIInc/crewAI/blob/6f4ad532e615a961ae7c5d57da48bb7209e3598b/src/crewai/crew.py#L1125

However, this would not apply if an agent refers to a model different from an OpenAI model; in that case testing would not be performed with the chosen model for the agent, but with an OpenAI model instead.

I'd expect that the model running an agent is the same as testing the agent. Why does `test()` not take a parameter `llm` as e.g. `Agent()` does?

Thank you.

### Steps to Reproduce

See

https://github.com/crewAIInc/crewAI/blob/6f4ad532e615a961ae7c5d57da48bb7209e3598b/src/crewai/crew.py#L1125

### Expected behavior

I'd expect that the model running an agent is the same as testing the agent.

### Screenshots/Code snippets

See

https://github.com/crewAIInc/crewAI/blob/6f4ad532e615a961ae7c5d57da48bb7209e3598b/src/crewai/crew.py#L1125

### Operating System

Windows 11

### Python Version

3.12

### crewAI Version

crewai==0.100.1

### crewAI Tools Version

crewai-tools==0.33.0

### Virtual Environment

Venv

### Evidence

See

https://github.com/crewAIInc/crewAI/blob/6f4ad532e615a961ae7c5d57da48bb7209e3598b/src/crewai/crew.py#L1125

### Possible Solution

Replace parameter `openai_model_name` with `llm` to be able to match the LLM an agent uses to run.

### Additional context

n/a

Labels: bug
State: closed
[Bug]: problems with getting started example code

### Describe the bug

Docs link: https://docs.ag2.ai/0.9.9/docs/quick-start/
Ag2 version: 0.9.9

```python
llm_config = LLMConfig({"api_type": "openai", "model": "gpt-5-nano"})
```
should be 

```python
llm_config = LLMConfig(config_list={"api_type": "openai", "model": "gpt-5-nano"})
``` 



### Steps to reproduce

_No response_

### Model Used

_No response_

### Expected Behavior

_No response_

### Screenshots and logs

_No response_

### Additional Information

_No response_

Labels: bug
State: closed
[Bug]: Documentation for agent groupchat contains bug when groupchat llm_config is used that have functions and tools registered to it

### Describe the Bug

[https://github.com/microsoft/autogen/issues/960#issuecomment-2017254431](url)  from the URL provided. I mentioned that the known issue of groupchat llm_config is used in the both the documentation [https://microsoft.github.io/autogen/docs/notebooks/agentchat_groupchat_RAG/#construct-agents](url) and the sample notebook 'agent_groupchat_RAG.ipynb' and easily reproduces the error of the known issue based on the outstanding issue of issues #274, #252, #960, and #152 regarding function calling in groupchats using pyautogen==0.2.2.

As stated, this is confusing to the end user trying to use the documentation and sample notebook to learn the autogen framework.

The correct behavior should be that no error is produced when the groupchat uses the llm_config and as per @ekzhu  and @afourney "Don't share llm_config among different agents. You can share the config list, but LLM configs get modified as you register functions"

My solution was to copy the llm_config and pop out the function and tools registration, which worked fine for the agent groupchat when calling the 'call_rag_chat' function, but instead, both the sample and the documentation need to show separate llm_config being used for the groupchat when functions and tools are being used. 

### Steps to reproduce

using autogen version 0.2.20, run the sample notebook 'agent_groupchat_RAG.ipynb' and call the function 'call_rag_chat'.

### Model Used

mistral 7B instruct v2

### Expected Behavior

No error message should occur, if the llm_config is passed to the groupchat, there should be check that it has functions or tools registered to it, if so, create a new instance of the llm_config without the functions and tools registered and this will automatically fix it but for demonstration purposes just add another llm_config called 'llm_config_groupchat' that is associated with the config_list, that way it doesn't have any registered functions or tools with it.

### Screenshots and logs

_No response_

### Additional Information

autogen 0.2.20
ubuntu 22.04
python 3.11.1

Labels: documentation
State: closed
Improve protocol incompatibility related errors

In particular in the cases where the user will try to use a new sdk without re-registering. This is important to guide user through registration in these cases.

Labels: bug, invoker
State: closed
Only the Last Router Triggers Events, Others Are Ignored

### Description

The problem is that even if all the routers are triggered and now need to trigger the event afterward, only the last router will activate it, while the rest won't route anything, even if their conditions are true.
```
@start()
def scan_medical(self):
    print("Scanning medical data")
    result = (
        MedicalScannerCrew()
        .crew()
        .kickoff(inputs={"data": str(self.state.patient_data)})
    )
    print("Scan result: ", result)
    self.state.total_token_usage += result.token_usage.total_tokens
    print("Medical data obtained", result)
    self.state.scan_medical = result.raw
    print("Saving medical scan data")
    with open("medical_scan.md", "w") as f:
        f.write(result.raw)

@router(scan_medical)
def diagnose_conditions(self):
    print("Diagnosing medical conditions")
    result = MedicalDiagnosisCrew().crew().kickoff(inputs={"data": self.state.scan_medical})
    self.state.diagnosed_conditions = result.raw
    print("Diagnosed conditions: ", self.state.diagnosed_conditions)

@router(diagnose_conditions)
def diabetes_router(self):
    print("Diabetes Router")
    if "D" in self.state.diagnosed_conditions:
        print("Diabetes detected")
        return "diabetes"

@listen("diabetes")
def diabetes_analysis(self):
    print("Performing detailed diabetes analysis")

@router(diagnose_conditions)
def hypertension_router(self):
    print("Hypertension Router")
    if "H" in self.state.diagnosed_conditions:
        print("Hypertension detected")
        return "hypertension"

@listen("hypertension")
def hypertension_analysis(self):
    print("Performing detailed hypertension analysis")

@router(diagnose_conditions)
def anemia_router(self):
    print("Anemia Router")
    if "A" in self.state.diagnosed_conditions:
        print("Anemia detected")
        return "anemia"

@listen("anemia")
def anemia_analysis(self):
    print("Performing detailed anemia analysis")
```
 only anemia_analysis work but others no even if all the conditions true
### Steps to Reproduce

use the same code

### Expected behavior

all the routers should activate the next node if the condition is true

### Screenshots/Code snippets

in description

### Operating System

Ubuntu 20.04

### Python Version

3.11

### crewAI Version

0.100.1

### crewAI Tools Version

0.32.0

### Virtual Environment

Venv

### Evidence

see description

### Possible Solution

none

### Additional context

none

Labels: bug
State: closed