* Replace on_messages and on_messages_stream with run and run_stream to
unify interface documentation with teams
* Remove magentic-one-cli from homepage as it has not been maintained
and improved for a while.

Finishing up the work on workbench.

```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, McpWorkbench

async def main() -> None:
    params = StdioServerParams(
        command="uvx",
        args=["mcp-server-fetch"],
        read_timeout_seconds=60,
    )

    # You can also use `start()` and `stop()` to manage the session.
    async with McpWorkbench(server_params=params) as workbench:
        model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
        assistant = AssistantAgent(
            name="Assistant",
            model_client=model_client,
            workbench=workbench,
            reflect_on_tool_use=True,
        )
        await Console(assistant.run_stream(task="Go to https://github.com/microsoft/autogen and tell me what you see."))
    
asyncio.run(main())
```

## Why are these changes needed?

| Package | Test time-Origin (Sec) | Test time-Edited (Sec) |

|-------------------------|------------------|-----------------------------------------------|
| autogen-studio          | 1.64             | 1.64 |
| autogen-core            | 6.03             | 6.17 |
| autogen-ext             | 387.15           | 373.40 |
| autogen-agentchat       | 54.20            | 20.67 |


## Related issue number

Related #6361 

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

## Why are these changes needed?
This PR introduces a baseline self-debugging loop to the
`CodeExecutionAgent`.

The loop automatically retries code generation and execution up to a
configurable number of attempts (n) until the execution succeeds or the
retry limit is reached.

This enables the agent to recover from transient failures (e.g., syntax
errors, runtime errors) by using its own reasoning to iteratively
improve generated code—laying the foundation for more robust autonomous
behavior.

## Related issue number

Closes #6207

## Checks

- [x] 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.

---------

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

This PR fixes a bug where `model_context` was either ignored or
explicitly set to `None` during agent deserialization (`_from_config`)
in:

- `AssistantAgent`: `model_context` was serialized but not restored.
- `SocietyOfMindAgent`: `model_context` was neither serialized nor
restored.
- `CodeExecutorAgent`: `model_context` was serialized but not restored.

As a result, restoring an agent from its config silently dropped runtime
context settings, potentially affecting agent behavior.

This patch:
- Adds proper serialization/deserialization of `model_context` using
`.dump_component()` and `load_component(...)`.
- Ensures round-trip consistency when using declarative agent configs.

## Related issue number

Closes #6336 

## 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.

---------

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

This change avoid re-registering a structured message already registered
to the team by a previous agent also included in the team.

This issue occurs when agents share Pydantic models as output format

## Related issue number

Closes #6353

---------

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

The DockerCommandLineCodeExecutor doesn't currently offer GPU support.
By simply using DeviceRequest from the docker python API, these changes
expose GPUs to the docker container and provide the ability to execute
CUDA-accelerated code within autogen.

## Related issue number

Closes: #6302 

## Checks

- [x] 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.

---------

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

## Why are these changes needed?

This PR updates `SelectorGroupChat` to support streaming mode for
`select_speaker`.
It introduces a `streaming` argument — when set to `True`,
`select_speaker` will use `create_streaming()` instead of `create()`.

## Additional context

Some models (e.g., QwQ) only work properly in streaming mode.  
To support them, the prompt selection step in `SelectorGroupChat` must
also run with `streaming=True`.

## Related issue number

Closes #6145

## Checks

- [x] 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.

---------

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

## Why are these changes needed?
`SocietyOfMindAgent` has multiple system message, however many
client/model does not support it.

## Related issue number
Related #6290

---------

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

---------

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

Resolves #6295

Ensure the thought content gets included in handoff message conetxt,
when the only tool call was handoff tool call.

Resolves #6270

Add an option emit_team_events to BaseGroupChat to emit events from
group chat manager through run_stream.
SpeakerSelectedEvent from group chat speaker selection.

Closes #6161

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

Added support for structured message component using the Json to
Pydantic utility functions. Note: also adding the ability to use a
format string for structured messages.

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

## Why are these changes needed?
- To add support for code generation, execution and reflection to
`CodeExecutorAgent`.

## Related issue number
Closes #5824 

## Checks
- [x] 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.

---------

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

Update version

<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->

## 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" -->

## Checks

- [x] 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.

Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>

Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Please refer to #6123  for full context.

That issue outlines several design and behavioral problems with
`SocietyOfMindAgent`.
This DRAFT PR focuses on resolving the most critical and broken
behaviors first.

Here is the error list
🔍 SocietyOfMindAgent: Design Issues and Historical Comparison (v0.2 vs
v0.4+)

### ✅ P1–P4 Regression Issue Table (Updated with Fixes in PR #6142)

| ID | Description | Current v0.4+ Issue | Resolution in PR #6142 | Was
it a problem in v0.2? | Notes |

|-----|-------------|----------------------|--------------------------|----------------------------|-------|
| **P1** | `inner_messages` leaks into outer team termination evaluation
| `Response.inner_messages` is appended to the outer team's
`_message_thread`, affecting termination conditions. Violates
encapsulation. | ✅ `inner_messages` is excluded from `_message_thread`,
avoiding contamination of outer termination logic. | ❌ No | Structural
boundary is now enforced |
| **P2** | Inner team does not execute when outer message history is
empty | In chained executions, if no new outer message exists, no task
is created and the inner team is skipped entirely | ✅ Detects absence of
new outer message and reuses the previous task, passing it via a handoff
message. This ensures the inner team always receives a valid task to
execute | ❌ No | The issue was silent task omission, not summary
failure. Summary succeeds as a downstream effect |
| **P3** | Summary LLM prompt is built from external input only | Prompt
is constructed using external message history, ignoring internal
reasoning | ✅ Prompt construction now uses
`final_response.inner_messages`, restoring internal reasoning as the
source of summarization | ❌ No | Matches v0.2 internal monologue
behavior |
| **P4** | External input is included in summary prompt (possibly
incorrectly) | Outer messages are used in the final LLM summarization
prompt | ✅ Resolved via the same fix as P3; outer messages are no longer
used for summary | ❌ No | Redundant with P3, now fully addressed |


<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->

## Why are these changes needed?

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

## Related issue number
resolve #6123 
Blocked #6168 (Sometimes SoMA send last whitespace message)
related #6187
<!-- 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.
- [ ] 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: Eric Zhu <ekzhu@users.noreply.github.com>

Resolves #5851

* Added GroupChatError event type and terminate a run when an error
occurs in either a participant or the group chat manager
* Raise a RuntimeError from the error message within the group chat run

Resolves #5934

This PR adds ability for `AssistantAgent` to generate a
`StructuredMessage[T]` where `T` is the content type in base model.

How to use?

```python
from typing import Literal

from pydantic import BaseModel

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.ui import Console

# The response format for the agent as a Pydantic base model.
class AgentResponse(BaseModel):
    thoughts: str
    response: Literal["happy", "sad", "neutral"]


# Create an agent that uses the OpenAI GPT-4o model which supports structured output.
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent(
    "assistant",
    model_client=model_client,
    system_message="Categorize the input as happy, sad, or neutral following the JSON format.",
    # Setting the output format to AgentResponse to force the agent to produce a JSON string as response.
    output_content_type=AgentResponse,
)

result = await Console(agent.run_stream(task="I am happy."))

# Check the last message in the result, validate its type, and print the thoughts and response.
assert isinstance(result.messages[-1], StructuredMessage)
assert isinstance(result.messages[-1].content, AgentResponse)
print("Thought: ", result.messages[-1].content.thoughts)
print("Response: ", result.messages[-1].content.response)
await model_client.close()
```

```
---------- user ----------
I am happy.
---------- assistant ----------
{
  "thoughts": "The user explicitly states they are happy.",
  "response": "happy"
}
Thought:  The user explicitly states they are happy.
Response:  happy
```

---------

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

Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Rename the `ChatMessage` and `AgentEvent` base classes to `BaseChatMessage` and `BaseAgentEvent`. 

Bring back the `ChatMessage` and `AgentEvent` as union of built-in concrete types to avoid breaking existing applications that depends on Pydantic serialization. 

Why?

Many existing code uses containers like this:

```python
class AppMessage(BaseModel):
   name: str
   message: ChatMessage 

# Serialization is this:
m = AppMessage(...)
m.model_dump_json()

# Fields like HandoffMessage.target will be lost because it is now treated as a base class without content or target fields.
```

The assumption on `ChatMessage` or `AgentEvent` to be a union of concrete types could be in many existing code bases. So this PR brings back the union types, while keep method type hints such as those on `on_messages` to use the `BaseChatMessage` and `BaseAgentEvent` base classes for flexibility.

Token limited model context is currently broken because it is importing
from extensions.

This fix removed the imports and updated the model context
implementation to use model client directly.

In the future, the model client's token counting should cache results
from model API to provide accurate counting.

This PR refactored `AgentEvent` and `ChatMessage` union types to
abstract base classes. This allows for user-defined message types that
subclass one of the base classes to be used in AgentChat.

To support a unified interface for working with the messages, the base
classes added abstract methods for:
- Convert content to string
- Convert content to a `UserMessage` for model client
- Convert content for rendering in console.
- Dump into a dictionary
- Load and create a new instance from a dictionary

This way, all agents such as `AssistantAgent` and `SocietyOfMindAgent`
can utilize the unified interface to work with any built-in and
user-defined message type.

This PR also introduces a new message type, `StructuredMessage` for
AgentChat (Resolves #5131), which is a generic type that requires a
user-specified content type.

You can create a `StructuredMessage` as follow:

```python

class MessageType(BaseModel):
  data: str
  references: List[str]

message = StructuredMessage[MessageType](content=MessageType(data="data", references=["a", "b"]), source="user")

# message.content is of type `MessageType`. 
```

This PR addresses the receving side of this message type. To produce
this message type from `AssistantAgent`, the work continue in #5934.

Added unit tests to verify this message type works with agents and
teams.

Take the output of the tool and use that to create the HandoffMessage.
[discussion is
here](https://github.com/microsoft/autogen/discussions/6067#discussion-8117177)

Supports agents to carry specific instructions when performing handoff
operations

---------

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

This PR introduces a metadata field in AssistantAgentConfig, allowing
applications to assign and track identity information for agents.
The metadata field is a Dict[str, str] and is included in the
configuration for proper serialization.


---------

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

AssistantAgent initiation allows one to pass in a model_context, but
there isn't a "public: way to get the existing model_context created by
default.

<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->

## Why are these changes needed?

Resolve #5953 

## Related issue number
#5953 

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

## Checks

- [x] 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.

I have run all [common
tasks](https://github.com/microsoft/autogen/blob/main/python/README.md#common-tasks),
got below errors which I think it is due to no OpenAI API Key is in my
environment variables. Can we ignore them or do I need to buy one?
```
=================================================== short test summary info =================================================== 
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_basic_entity_creation - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_upsert_operations - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_delete_operations - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_load_from_file - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_load_from_directory - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_create_team - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_run_stream - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
=========================================== 3 passed, 5 warnings, 7 errors in 9.07s ===========================================
```

Co-authored-by: Leonardo Pinheiro <leosantospinheiro@gmail.com>

## Summary of Changes
- Added 'candidate_func' to 'SelectorGroupChat' to narrow-down the pool
of candidate speakers.
- Introduced a test in tests/test_group_chat_endpoint.py to validate its
functionality.
- Updated the selector group chat user guide with an example
demonstrating 'candidate_func'.

## Why are these changes needed?
- These changes adds a new parameter `candidate_func` to
`SelectorGroupChat` that helps user narrow-down the set of agents for
speaker selection, allowing users to automatically select next speaker
from a smaller pool of agents.

## Related issue number
Closes #5828

## Checks
- [x] 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.

---------

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

Resolves #5919

1. Add `on_pause` and `on_resume` API to `ChatAgent` to support pausing
behavior when running `on_message` concurrently.
2. Add `GroupChatPause` and `GroupChatResume` RPC events and handle them
in `ChatAgentContainer`.
3. Add `pause` and `resume` API to `BaseGroupChat` to allow for this
behavior accessible from the public API.
4. Improve `SequentialRoutedAgent` class to customize which message
types are sequentially handled, making it possible to have concurrent
handling for some messages (e.g., `GroupChatPause`).
5. Added unit tests. 

See `test_group_chat_pause_resume.py` for how to use this feature. 

What is the difference between pause/resume vs. termination and restart?
- Pause and resume issue direct RPC calls to the participanting agents
of a team while they are running, allowing putting the on-going
generation or actions on hold. This is useful when an agent's turn takes
a long time and multiple steps to complete, and user/application wants
to re-evaluate whether it is worth continue the step or cancel. This
also allows user/application to pause individual agents and resuming
them independently from the team API.
- Termination and restart requires the whole team to comes to a
full-stop, and termination conditions are checked in between agents'
turns. So termination can only happen when no agent is working on its
turn. It is possible that a termination condition has reached well
before the team is terminated, if the agent is taking a long time to
generate a response.

Resolves: #5881

Modify `BaseGroupChat.save_state` to not require the team to be stopped
first. The `save_state` method is read-only. While it may retrieve an
inconsistent state when the team is running, we made a notice to it's
API doc.

Resolves: #5880

Resolves #4075

1. Introduce custom runtime parameter for all AgentChat teams
(RoundRobinGroupChat, SelectorGroupChat, etc.). This is done by making
sure each team's topics are isolated from other teams, and decoupling
state from agent identities. Also, I removed the closure agent from the
BaseGroupChat and use the group chat manager agent to relay messages to
the output message queue.
2. Added unit tests to test scenarios with custom runtimes by using
pytest fixture
3. Refactored existing unit tests to use ReplayChatCompletionClient with
a few improvements to the client.
4. Fix a one-liner bug in AssistantAgent that caused deserialized agent
to have handoffs.

How to use it? 

```python
import asyncio
from autogen_core import SingleThreadedAgentRuntime
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_ext.models.replay import ReplayChatCompletionClient

async def main() -> None:
    # Create a runtime
    runtime = SingleThreadedAgentRuntime()
    runtime.start()

    # Create a model client.
    model_client = ReplayChatCompletionClient(
        ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
    )

    # Create agents
    agent1 = AssistantAgent("assistant1", model_client=model_client, system_message="You are a helpful assistant.")
    agent2 = AssistantAgent("assistant2", model_client=model_client, system_message="You are a helpful assistant.")

    # Create a termination condition
    termination_condition = TextMentionTermination("10", sources=["assistant1", "assistant2"])

    # Create a team
    team = RoundRobinGroupChat([agent1, agent2], runtime=runtime, termination_condition=termination_condition)

    # Run the team
    stream = team.run_stream(task="Count to 10.")
    async for message in stream:
        print(message)
    
    # Save the state.
    state = await team.save_state()

    # Load the state to an existing team.
    await team.load_state(state)

    # Run the team again
    model_client.reset()
    stream = team.run_stream(task="Count to 10.")
    async for message in stream:
        print(message)

    # Create a new team, with the same agent names.
    agent3 = AssistantAgent("assistant1", model_client=model_client, system_message="You are a helpful assistant.")
    agent4 = AssistantAgent("assistant2", model_client=model_client, system_message="You are a helpful assistant.")
    new_team = RoundRobinGroupChat([agent3, agent4], runtime=runtime, termination_condition=termination_condition)

    # Load the state to the new team.
    await new_team.load_state(state)

    # Run the new team
    model_client.reset()
    new_stream = new_team.run_stream(task="Count to 10.")
    async for message in new_stream:
        print(message)
    
    # Stop the runtime
    await runtime.stop()

asyncio.run(main())
```

TODOs as future PRs:
1. Documentation.
2. How to handle errors in custom runtime when the agent has exception?

---------

Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

Resolves #5731

Resolves #5782

---------

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

Resolves #5777

Add function call termination condition.

Useful as an alternative to TextMentionTermination for models with tool
call capability.

Closes #5732 
---------

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

<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->

## Why are these changes needed?

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

The PR introduces two changes.

The first change is adding a name attribute to
`FunctionExecutionResult`. The motivation is that semantic kernel
requires it for their function result interface and it seemed like a
easy modification as `FunctionExecutionResult` is always created in the
context of a `FunctionCall` which will contain the name. I'm unsure if
there was a motivation to keep it out but this change makes it easier to
trace which tool the result refers to and also increases api
compatibility with SK.

The second change is an update to how messages are mapped from autogen
to semantic kernel, which includes an update/fix in the processing of
function results.

## Related issue number

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

Related to #5675 but wont fix the underlying issue of anthropic
requiring tools during AssistantAgent reflection.

## 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.
- [ ] 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: Leonardo Pinheiro <lpinheiro@microsoft.com>

## Why are these changes needed?

The CodeExecutorAgent documentation needs to be updated to explicitly
mention that it only processes code properly formatted in markdown code
blocks with triple backticks. This change adds a clear note with
examples to help users understand the required format for code
execution.

## Related issue number

Closes #5771


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