Use structured output when available in m1 orchestrator

Co-authored-by: Victor Dibia <victordibia@microsoft.com>

Resolves #6519

---------

Co-authored-by: Victor Dibia <victor.dibia@gmail.com>

resolves: #6456

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Update autogenstudio version. 

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

Closes #6580

<!-- For example: "Closes #1234" -->

## Checks

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introduced in this PR.
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## Why are these changes needed?

The `CodeExecutorAgent` can generate code blocks in various programming
languages, some of which may not be supported by the executor
environment. Adding support for specifying languages to be parsed helps
users ignore unnecessary code blocks, preventing potential execution
errors.

## Related issue number
Closes #6471

## Checks

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introduced in this PR.
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---------

Signed-off-by: Abhijeetsingh Meena <abhijeet040403@gmail.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

## Why are these changes needed?

Enables usage statistics for streaming responses by default.

There is a similar bug in the AzureAI client. Theoretically adding the
parameter
```
model_extras={"stream_options": {"include_usage": True}}
```
should fix the problem, but I'm currently unable to test that workflow

## Related issue number

closes https://github.com/microsoft/autogen/issues/6548

## Checks

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introduced in this PR.
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There have been updates to the azure ai agent foundry sdk
(azure-ai-project). This PR updates the autogen `AzureAIAgent` which
wraps the azure ai agent. A list of some changes

- Update docstring samples to use `endpoint` (instead of connection
string previously)
- Update imports and arguments e.g, from `azure.ai.agents` etc 
- Add a guide in ext docs showing Bing Search Grounding tool example. 
<img width="1423" alt="image"
src="https://github.com/user-attachments/assets/0b7c8fa6-8aa5-4c20-831b-b525ac8243b7"
/>


## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

Closes #6601
<!-- For example: "Closes #1234" -->

## Checks

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introduced in this PR.
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## Why are these changes needed?

The code block fails to execute without the import

## Related issue number

N/A

## Checks

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Co-authored-by: Victor Dibia <victordibia@microsoft.com>

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## Why are these changes needed?

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solves. -->

## Related issue number

resolved https://github.com/microsoft/autogen/issues/6584

## Checks

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introduced in this PR.
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## Why are these changes needed?

I added `created_at` to both BaseChatMessage and BaseAgentEvent classes
that store the time these Pydantic model instances are generated. And
then users will be able to use `created_at` to build up a customized
external persisting state management layer for their case.

## Related issue number


https://github.com/microsoft/autogen/discussions/6169#discussioncomment-13151540

## Checks

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introduced in this PR.
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---------

Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?

Install instructions for Windows are missing or incorrect

## Related issue number

closes https://github.com/microsoft/autogen/issues/6577

## Checks

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introduced in this PR.
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Co-authored-by: Victor Dibia <victordibia@microsoft.com>

## Why are these changes needed?

unnecessary underline shown in the docs
before:
<img width="157" alt="image"
src="https://github.com/user-attachments/assets/37503e10-6b8a-48ee-ae63-d9a12fe3beb5"
/>

after:
<img width="151" alt="image"
src="https://github.com/user-attachments/assets/ea6c1851-3640-4f64-b8ff-91dcc11a6379"
/>


## Related issue number

closes https://github.com/microsoft/autogen/issues/6564

## Checks

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## Why are these changes needed?

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solves. -->

The `LocalCommandLineCodeExecutor `creates temporary files for each code
execution, which can accumulate over time and clutter the filesystem -
especially when a temporary working directory is not used. These changes
introduce an option to automatically delete temporary files after
execution, helping to prevent file system debris, reduce disk usage, and
ensure cleaner runtime environments in long-running or repeated
execution scenarios.

## Related issue number

Closes #4380

## Checks

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introduced in this PR.
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Currently when an error occurs when executing code in docker jupyter
executor, it returns only the error output.

This PR updates the handling of error output to include outputs from
previous code blocks that have been successfully executed.

Test it with this script:

```python
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.code_executors.docker_jupyter import DockerJupyterCodeExecutor, DockerJupyterServer
from autogen_ext.tools.code_execution import PythonCodeExecutionTool
from autogen_agentchat.ui import Console
from autogen_core.code_executor import CodeBlock
from autogen_core import CancellationToken
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMessageTermination

# Download the dataset from https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023
# and place it the coding directory as `spotify-2023.csv`.
bind_dir = "./coding"

# Use a custom docker image with the Jupyter kernel gateway and data science libraries installed.
# Custom docker image: ds-kernel-gateway:latest -- you need to build this image yourself.
# Dockerfile:
# FROM quay.io/jupyter/docker-stacks-foundation:latest
# 
# # ensure that 'mamba' and 'fix-permissions' are on the PATH
# SHELL ["/bin/bash", "-o", "pipefail", "-c"]
# 
# # Switch to the default notebook user
# USER ${NB_UID}
# 
# # Install data-science packages + kernel gateway
# RUN mamba install --quiet --yes \
#     numpy \
#     pandas \
#     scipy \
#     matplotlib \
#     scikit-learn \
#     seaborn \
#     jupyter_kernel_gateway \
#     ipykernel \
#     && mamba clean --all -f -y \
#     && fix-permissions "${CONDA_DIR}" \
#     && fix-permissions "/home/${NB_USER}"
# 
# # Allow you to set a token at runtime (or leave blank for no auth)
# ENV TOKEN=""
# 
# # Launch the Kernel Gateway, listening on all interfaces,
# # with the HTTP endpoint for listing kernels enabled
# CMD ["python", "-m", "jupyter", "kernelgateway", \
#     "--KernelGatewayApp.ip=0.0.0.0", \
#     "--KernelGatewayApp.port=8888", \
#     # "--KernelGatewayApp.auth_token=${TOKEN}", \
#     "--JupyterApp.answer_yes=true", \
#     "--JupyterWebsocketPersonality.list_kernels=true"]
# 
# EXPOSE 8888
# 
# WORKDIR "${HOME}"

async def main():
    model = OpenAIChatCompletionClient(model="gpt-4.1")
    async with DockerJupyterServer(
        custom_image_name="ds-kernel-gateway:latest", 
        bind_dir=bind_dir,
    ) as server:
        async with DockerJupyterCodeExecutor(jupyter_server=server) as code_executor:
            await code_executor.execute_code_blocks([
                CodeBlock(code="import pandas as pd\ndf = pd.read_csv('/workspace/spotify-2023.csv', encoding='latin-1')", language="python"),
            ],
                cancellation_token=CancellationToken(),
            )
            tool = PythonCodeExecutionTool(
                executor=code_executor,
            )
            assistant = AssistantAgent(
                "assistant",
                model_client=model,
                system_message="You have access to a Jupyter kernel. Do not write all code at once. Write one code block, observe the output, and then write the next code block.",
                tools=[tool],
            )
            team = RoundRobinGroupChat(
                [assistant],
                termination_condition=TextMessageTermination(source="assistant"),
            )
            task = f"Datafile has been loaded as variable `df`. First preview dataset. Then answer the following question: What is the highest streamed artist in the dataset?"
            await Console(team.run_stream(task=task))

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())
```

You can see the file encoding error gets recovered and the agent
successfully executes the query in the end.

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## Why are these changes needed?

Allows implicit AWS credential setting when using
AnthropicBedrockChatCompletionClient in an instance where you have
already logged into AWS with SSO and credentials are set as environment
variables.

## Related issue number

Closes #6560 

## Checks

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introduced in this PR.
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Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

Fix a grammar error, change "your" to "you".

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solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

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introduced in this PR.
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## Why are these changes needed?

## Prevent Async Event Loop from Running Indefinitely

### Description

This pull request addresses a bug in the
python/packages/autogen-core/src/autogen_core/_single_threaded_agent_runtime.py
`async send_message` function where messages were being queued for
recipients that were not recognized. The current implementation sets an
exception on the future object when the recipient is not found, but
continues to enqueue the message, potentially leading to inconsistent
states.

### Changes Made

- Added a return statement immediately after setting the exception when
the recipient is not found. This ensures that the function exits early,
preventing further processing of the message and avoiding unnecessary
operations.
- This fix also addresses an issue where the asynchronous event loop
could potentially continue running indefinitely without terminating, due
to the future not being properly handled when an unknown recipient is
encountered.

### Impact

This fix prevents messages from being sent to unknown recipients. It
also ensures that the event loop can terminate correctly without being
stuck in an indefinite state.

### Testing

Ensure that the function correctly handles cases where the recipient is
not recognized by returning the exception without enqueuing the message,
and verify that the event loop terminates as expected.


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solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

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introduced in this PR.
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Co-authored-by: Wanfeng Ge （葛万峰） <wf.ge@trip.com>
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

Support concurrent execution in `GraphFlow`:
- Updated `BaseGroupChatManager.select_speaker` to return a union of a
single string or a list of speaker name strings and added logics to
check for currently activated speakers and only proceed to select next
speakers when all activated speakers have finished.
- Updated existing teams (e.g., `SelectorGroupChat`) with the new
signature, while still returning a single speaker in their
implementations.
- Updated `GraphFlow` to support multiple speakers selected. 
- Refactored `GraphFlow` for less dictionary gymnastic by using a queue
and update using `update_message_thread`.

Example: a fan out graph:

```python
import asyncio

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import DiGraphBuilder, GraphFlow
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main():
    # Initialize agents with OpenAI model clients.
    model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
    agent_a = AssistantAgent("A", model_client=model_client, system_message="You are a helpful assistant.")
    agent_b = AssistantAgent("B", model_client=model_client, system_message="Translate input to Chinese.")
    agent_c = AssistantAgent("C", model_client=model_client, system_message="Translate input to Japanese.")

    # Create a directed graph with fan-out flow A -> (B, C).
    builder = DiGraphBuilder()
    builder.add_node(agent_a).add_node(agent_b).add_node(agent_c)
    builder.add_edge(agent_a, agent_b).add_edge(agent_a, agent_c)
    graph = builder.build()

    # Create a GraphFlow team with the directed graph.
    team = GraphFlow(
        participants=[agent_a, agent_b, agent_c],
        graph=graph,
    )

    # Run the team and print the events.
    async for event in team.run_stream(task="Write a short story about a cat."):
        print(event)


asyncio.run(main())
```

Resolves:
#6541 
#6533

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## Why are these changes needed?

**Summary of Change:**
The instruction regarding code block format ("Python code should be
provided in python code blocks, and sh shell scripts should be provided
in sh code blocks for execution") will be moved from
`DEFAULT_AGENT_DESCRIPTION` to `DEFAULT_SYSTEM_MESSAGE`.

**Problem Solved:**
Ensure that the `model_client` receives the correct instructions for
generating properly formatted code blocks. Previously, the instruction
was only included in the agent's description and not passed to the
model_client, leading to potential issues in code generation. By moving
it to `DEFAULT_SYSTEM_MESSAGE`, the `model_client` will now accurately
format code blocks, improving the reliability of code generation.

## Related issue number

Closes #6558 

## Checks

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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
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introduced in this PR.
- [x] I've made sure all auto checks have passed.

Fix for LLMCallEvent failing to log "tools" passed to
BaseOpenAIChatCompletionClient in
autogen_ext.models.openai._openai_client.BaseOpenAIChatCompletionClient

This bug creates problems inspecting why a certain tool was selected/not
selected by the LLM as the list of tools available to the LLM is not
present in the logs

## Why are these changes needed?

Added "tools" to the LLMCallEvent to log tools available to the LLM as
these were being missed causing difficulties during debugging LLM tool
calls.

## Related issue number

[<!-- For example: "Closes #1234"
-->](https://github.com/microsoft/autogen/issues/6531)

## Checks

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build and test documentation locally.
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introduced in this PR.
- [x] I've made sure all auto checks have passed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?

To add the latest support for using Llama API offerings with AutoGen


## Checks

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introduced in this PR.
- [ ] I've made sure all auto checks have passed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?

This change introduces support for dynamic formatting of tool call
summary messages by allowing a user-defined
`tool_call_summary_format_fct`. Instead of relying solely on a static
string template, this function enables runtime generation of summary
messages based on the specific tool call and its result. This provides
greater flexibility and cleaner integration without introducing any
breaking changes.

### My Use Case / Problem

In my use case, I needed concise summaries for successful tool calls and
detailed messages for failures. The existing static summary string
didn't allow conditional formatting, which led to overly verbose success
messages or inconsistent failure outputs. This change allows customizing
summaries per result type, solving that limitation cleanly.

## Related issue number

Closes #6426

## Checks

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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
- [x] I've made sure all auto checks have passed.

---------

Co-authored-by: Chris Wieczorek <Chris.Wieczorek@iav.de>
Co-authored-by: EeS <chiyoung.song@motov.co.kr>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Mehrsa Golestaneh <mehrsa.golestaneh@gmail.com>
Co-authored-by: Mehrsa Golestaneh <mgolestaneh@microsoft.com>
Co-authored-by: Zhenyu <81767213+Dormiveglia-elf@users.noreply.github.com>

## Why are these changes needed?

Add ollama qwen 3 support

## Why are these changes needed?

Simplified the azure ai search tool and fixed bugs in the code


## Related issue number

"Closes #6430 " 

## Checks

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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?
FIX/mistral could not recive name field, so add model transformer for
mistral

## Related issue number
Closes #6147

## Checks

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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

…ized properly

## Why are these changes needed?
The exceptions thrown by MCP server tools weren't being serialized
properly - the user would see `[{}, {}, ... {}]` instead of an actual
error/exception message.

## Related issue number

Fixes #6481 

## Checks

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introduced in this PR.
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---------

Signed-off-by: Peter Jausovec <peter.jausovec@solo.io>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>

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## Why are these changes needed?

Nice to have functionality

## Related issue number

Closes #6060 

## Checks

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<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
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introduced in this PR.
- [x] I've made sure all auto checks have passed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

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pull request. -->

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## Why are these changes needed?
As-is: Deleting `McpWorkbench` does not close the `McpSession`.  
To-be: Deleting `McpWorkbench` now properly closes the `McpSession`.

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solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.

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Some fixes with the AnthropicBedrockChatCompletionClient 

- Ensure `AnthropicBedrockChatCompletionClient` exported and can be
imported.
- Update the BedrockInfo keys serialization - client argument can be
string (similar to api key in this ) but exported config should be
Secret
- Replace `AnthropicBedrock` with `AsyncAnthropicBedrock` : client
should be async to work with the ag stack and the BaseAnthropicClient it
inherits from
- Improve `AnthropicBedrockChatCompletionClient` docstring to use the
correct client arguments rather than serialized dict format.

Expect

```python
from autogen_ext.models.anthropic import AnthropicBedrockChatCompletionClient, BedrockInfo
from autogen_core.models import UserMessage, ModelInfo


async def main():
    anthropic_client = AnthropicBedrockChatCompletionClient(
        model="anthropic.claude-3-5-sonnet-20240620-v1:0",
        temperature=0.1,
        model_info=ModelInfo(vision=False, function_calling=True,
                             json_output=False, family="unknown", structured_output=True),
        bedrock_info=BedrockInfo(
            aws_access_key="<aws_access_key>",
            aws_secret_key="<aws_secret_key>",
            aws_session_token="<aws_session_token>",
            aws_region="<aws_region>",
        ),
    )
    # type: ignore
    result = await anthropic_client.create([UserMessage(content="What is the capital of France?", source="user")])
    print(result)

await main()
```

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

Closes #6483 

## Checks

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build and test documentation locally.
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introduced in this PR.
- [ ] I've made sure all auto checks have passed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

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## Why are these changes needed?

Will the changes made in
https://github.com/microsoft/autogen/pull/5853/files and this PR need to
be ported to the worker_runtime as well?
Resolves https://github.com/microsoft/autogen/issues/5894

## Related issue number

https://github.com/microsoft/autogen/issues/5894

## Checks

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introduced in this PR.
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## Why are these changes needed?

This PR improves keyboard accessibility by ensuring that header
links/icons have visual feedback (underline and color change) on both
hover and keyboard focus states. Also adds smooth scaling animation.

## Related issue number

Related issue: #6090 

## Checks

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- [☑️] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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Co-authored-by: peterychang <49209570+peterychang@users.noreply.github.com>

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Add gpt 4o search models to list of default models

```python
"gpt-4o-mini-search-preview-2025-03-11": {
        "vision": False,
        "function_calling": True,
        "json_output": True,
        "family": ModelFamily.GPT_4O,
        "structured_output": True,
        "multiple_system_messages": True,
    },
"gpt-4o-search-preview-2025-03-11": {
        "vision": False,
        "function_calling": True,
        "json_output": True,
        "family": ModelFamily.GPT_4O,
        "structured_output": True,
        "multiple_system_messages": True,
    },
```

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

Closes #6491

## Checks

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build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

## Related issue number

Closes #6486

## Why are these changes needed?

This pull request adds new samples that integrates the Autogen Core API
with Chainlit. It closely follows the structure of the
Agentchat+Chainlit sample and provides examples for using a single agent
and multiple agents in a groupchat.
## Related issue number


Closes: #5345

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?
This PR enhances the `SelectorGroupChat` class by introducing a new
`model_context` parameter to support more context-aware speaker
selection.

### Changes
- Added a `model_context: ChatCompletionContext | None` parameter to
`SelectorGroupChat`.
- Defaulted to `UnboundedChatCompletionContext` when None is provided
like `AssistantAgent`.
- Updated `_select_speaker` to prepend context messages from
`model_context` to the main thread history.
- Refactored history construction into a helper method
`construct_message_history`.

## Related issue number
Closes [Issue #6301](https://github.com/org/repo/issues/6301), enabling
the group chat manager to utilize `model_context` for richer, more
informed speaker selection decisions.

## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.

---------

Signed-off-by: Abhijeetsingh Meena <abhijeet040403@gmail.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?
Our McpWorkbench required properties however mongodb-lens's some tools
do not has it. I will fix it from when properties is None, -> {}
Our McpWorkbench now does not have stop routine with without async with
McpWorkbench(params) as workbench: and lazy init. So, I will adding def
__del__: pass just insert that, It could show error.

## Related issue number

Closes #6425

## Checks

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build and test documentation locally.
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introduced in this PR.
- [x] I've made sure all auto checks have passed.

## Why are these changes needed?

Multimodal message fill context with other routine. However current
`_set_empty_to_whitespace` is fill with context.
So, error occured.

And, I checked `multimodal_user_transformer_funcs` and I found it, in
this routine, context must not be empty.
Now remove the `_set_empty_to_whitespace` when multimodal message,
<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

Closes #6439 

## Checks

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build and test documentation locally.
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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Added `flush=True` to the `aprint` call when handling
`ModelClientStreamingChunkEvent` message to ensure each chunk is
immediately displayed as it arrives.

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## Why are these changes needed?
When handling `ModelClientStreamingChunkEvent` message, streaming chunks
weren't guaranteed to be displayed immediately, as Python's stdout might
buffer output without an explicit flush instruction. This could cause
visual delays between when `chunk_event` objects are added to the
message queue and when users actually see the content rendered in the
console.
<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number
None

<!-- For example: "Closes #1234" -->

## Checks

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introduced in this PR.
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## Why are these changes needed?

Requesting to add a new example folder under Python samples so that
AutoGen(0.4+) users can easily find this comprehensive example of using
agents and Groupchats to build a multi-agent data management system for
Azure postgreSQL. Readme contains link to a repo with comprehensive
multiagent postgreSQL data management example

## Related issue number

N/A

## Checks
N/A (only a readme file)
- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
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build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

Co-authored-by: Mehrsa Golestaneh <mgolestaneh@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?
❗ Before

Previously, GraphFlow.__init__() modified the inner_chats and
termination_condition for internal execution logic (e.g., constructing
_StopAgent or composing OrTerminationCondition).
However, these modified values were also used during dump_component(),
meaning the serialized config no longer matched the original inputs.

As a result:
1. dump_component() → load_component() → dump_component() produced
non-idempotent configs.
2. Internal-only constructs like _StopAgent were mistakenly serialized,
even though they should only exist in runtime.

⸻

✅ After

This patch changes the behavior to:
• Store original inner_chats and termination_condition as-is at
initialization.
	•	During to_config(), serialize only the original unmodified versions.
	•	Avoid serializing _StopAgent or other dynamically built agents.
• Ensure deserialization (from_config) produces a logically equivalent
object without additional nesting or duplication.

This ensures that:
• GraphFlow.dump_component() → load_component() round-trip produces
consistent, minimal configs.
• Internal execution logic and serialized component structure are
properly separated.
<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->
Closes #6431 

## Checks

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introduced in this PR.
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## Why are these changes needed?
Current autogen-ext's test is too slow.
So, I will search slow test case and makes more fast.

[init docker executor function to module
180s->140s](a3cf70bcf8)
[reuse executor at some tests
140s->120s](ca15938afa)
[Remove unnecessary start of docker
120s->110s](61247611e0)

## Related issue number

<!-- For example: "Closes #1234" -->
Part of #6376

## Checks

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- [x] I've made sure all auto checks have passed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Documentation for Graph based workflow. I kept this separate from pull
request #6333 so that you can just merge in the code without the
documentation changes if needed.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Closes #4623 

### Add Directed Graph-based Group Chat Execution Engine
(`DiGraphGroupChat`)

This PR introduces a new graph-based execution framework for Autogen
agent teams, located under `autogen_agentchat/teams/_group_chat/_graph`.

**Key Features:**

- **`DiGraphGroupChat`**: A new group chat implementation that executes
agents based on a user-defined directed graph (DAG or cyclic with exit
conditions).
- **`AGGraphBuilder`**: A fluent builder API to programmatically
construct graphs.
- **`MessageFilterAgent`**: A wrapper to restrict what messages an agent
sees before invocation, supporting per-source and per-position
filtering.

**Capabilities:**

- Supports sequential, parallel, conditional, and cyclic workflows.
- Enables fine-grained control over both execution order and message
context.
- Compatible with existing Autogen agents and runtime interfaces.

**Tests:**

- Located in `autogen_agentchat/tests/test_group_chat_graph.py`
- Includes unit and integration tests covering:
  - Graph validation
  - Execution paths
  - Conditional routing
  - Loops with exit conditions
  - Message filtering

Let me know if anything needs refactoring or if you'd like the
components split further.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Leonardo Pinheiro <leosantospinheiro@gmail.com>

## Why are these changes needed?

This PR fixes the issue of multiple `<h1>` headers in the Distributed
Agent Runtime documentation page. The page has more than one `<h1>`
which violates semantic HTML structure. This fix downgrades the inner
section headings (e.g., "Cross-Language Runtimes", "Next Steps") to
`<h2>`.

## Related issue number

Related issue: [#6090](https://github.com/microsoft/autogen/issues/6090)

## Checks

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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

Closes #6366


## Checks

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Co-authored-by: Victor Dibia <victordibia@microsoft.com>

Adding support for Bing grounding citations to the AzureAIAgent.

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## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

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introduced in this PR.
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---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Dheeraj Bandaru <BandaruDheeraj@users.noreply.github.com>