* 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())
```

Add user guide for workbench and mcp

*Also fixed a bug in create_mcp_server_session that included "type" in
the parameters Resolves: #6392

This PR introduces `WorkBench`.

A workbench provides a group of tools that share the same resource and
state. For example, `McpWorkbench` provides the underlying tools on the
MCP server. A workbench allows tools to be managed together and abstract
away the lifecycle of individual tools under a single entity. This makes
it possible to create agents with stateful tools from serializable
configuration (component configs), and it also supports dynamic tools:
tools change after each execution.

Here is how a workbench may be used with AssistantAgent (not included in
this PR):

```python
workbench = McpWorkbench(server_params)
agent = AssistantAgent("assistant", tools=workbench)
result = await agent.run(task="do task...")
```


TODOs:
1. In a subsequent PR, update `AssistantAgent` to use workbench as an
alternative in the `tools` parameter. Use `StaticWorkbench` to manage
individual tools.
2. In another PR, add documentation on workbench.

---------

Co-authored-by: EeS <chiyoung.song@motov.co.kr>
Co-authored-by: Minh Đăng <74671798+perfogic@users.noreply.github.com>

## 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.

- Added the support Azure AI Agent. The new agent is named AzureAIAgent.
- The agent supports Bing search, file search, and Azure search tools.
- Added a Jupiter notebook to demonstrate the usage of the AzureAIAgent.

## What's missing?
- AzureAIAgent support only text message responses
-  Parallel execution for the custom functions. 



## Related issue number


[5545](https://github.com/microsoft/autogen/issues/5545#event-16626859772)

---------

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

DOC: add extentions - autogen-oaiapi and autogen-contextplus

the contextplus is user define autogen model_context.
It discussion in #6217 and #6160

---------

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?

This is an initial exploration of what could be a solution for #6214 .
It implements a simple text canvas using difflib and also a memory
component and a tool component for interacting with the canvas. Still in
early testing but would love feedback on the design.

## 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.
- [ ] 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>
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>

This PR introduces a safer and more controllable execution environment
for LLM code execution in version 0.4 by enabling the use of Jupyter
inside a container. This enhancement addresses security concerns and
provides a more robust execution context. In particular, it allows:

Isolation of code execution via containerized Jupyter environments.

Persistent memory of variables and their values throughout the
conversation.

Memory of code execution results to support more advanced reasoning and
follow-up tasks.

These improvements help build a more interactive and stateful LLM-agent
programming experience, especially for iterative code generation and
debugging scenarios.

## Related issue number

Open #6153

## Checks

- [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>

---------

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

Resolves #6232, #6198

This PR introduces an optional parameter `session` to `mcp_server_tools`
to support reuse of the same session.

```python
import asyncio

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, create_mcp_server_session, mcp_server_tools


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o", parallel_tool_calls=False)  # type: ignore
    params = StdioServerParams(
        command="npx",
        args=["@playwright/mcp@latest"],
        read_timeout_seconds=60,
    )
    async with create_mcp_server_session(params) as session:
        await session.initialize()
        tools = await mcp_server_tools(server_params=params, session=session)
        print(f"Tools: {[tool.name for tool in tools]}")

        agent = AssistantAgent(
            name="Assistant",
            model_client=model_client,
            tools=tools,  # type: ignore
        )

        termination = TextMentionTermination("TERMINATE")
        team = RoundRobinGroupChat([agent], termination_condition=termination)
        await Console(
            team.run_stream(
                task="Go to https://ekzhu.com/, visit the first link in the page, then tell me about the linked page."
            )
        )


asyncio.run(main())
``` 

Based on discussion in this thread: #6284, we will consider
serialization and deserialization of MCP server tools when used in this
manner in a separate issue.

This PR also replaces the `json_schema_to_pydantic` dependency with
built-in utils.

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>

minor grammatical fix in docs

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

Update version

adding email agent

## Why are these changes needed?

This PR introduces an AI-powered email assistant that can generate
images, attach files, draft reports, and send emails to multiple
recipients or specific users based on their queries. This feature is
highly beneficial for customer management and email marketing, enhancing
automation and improving efficiency.

## Related issue number

Open #6228 
## 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.

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

Closes #6265

Convert the `Message` and `Resource` dataclasses to Pydantic models in
the `llamaindex-agent` cookbook.

* Replace `dataclass` with `BaseModel` for `Message` and `Resource`
classes.
* Update imports to use `BaseModel` from `pydantic`

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

Add note on how to update modelinfo for new models.

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

## Related issue number

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

Closes #6258

## 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.

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

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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.

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https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

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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>

# Azure AI Search Tool Implementation

This PR adds a new tool for Azure AI Search integration to autogen-ext,
enabling agents to search and retrieve information from Azure AI Search
indexes.

## Why Are These Changes Needed?
AutoGen currently lacks native integration with Azure AI Search, which
is a powerful enterprise search service that supports semantic, vector,
and hybrid search capabilities. This integration enables agents to:
1. Retrieve relevant information from large document collections
2. Perform semantic search with AI-powered ranking
3. Execute vector similarity search using embeddings
4. Combine text and vector approaches for optimal results

This tool complements existing retrieval capabilities and provides a
seamless way to integrate with Azure's search infrastructure.

## Features
- **Multiple Search Types**: Support for text, semantic, vector, and
hybrid search
- **Flexible Configuration**: Customizable search parameters and fields
- **Robust Error Handling**: User-friendly error messages with
actionable guidance
- **Performance Optimizations**: Configurable caching and retry
mechanisms
- **Vector Search Support**: Built-in embedding generation with
extensibility

## Usage Example
```python
from autogen_ext.tools.azure import AzureAISearchTool
from azure.core.credentials import AzureKeyCredential
from autogen import AssistantAgent, UserProxyAgent
# Create the search tool
search_tool = AzureAISearchTool.load_component({
   "provider": "autogen_ext.tools.azure.AzureAISearchTool",
   "config": {
       "name": "DocumentSearch",
       "description": "Search for information in the knowledge base",
       "endpoint": "https://your-service.search.windows.net",
       "index_name": "your-index",
       "credential": {"api_key": "your-api-key"},
       "query_type": "semantic",
       "semantic_config_name": "default"
   }
})
# Create an agent with the search tool
assistant = AssistantAgent(
   "assistant",
   llm_config={"tools": [search_tool]}
)
# Create a user proxy agent
user_proxy = UserProxyAgent(
   "user_proxy",
   human_input_mode="TERMINATE",
   max_consecutive_auto_reply=10,
   code_execution_config={"work_dir": "coding"}
)
# Start the conversation
user_proxy.initiate_chat(
   assistant,
   message="What information do we have about quantum computing in our knowledge base?"
)
```

## Testing
- Added unit tests for all search types (text, semantic, vector, hybrid)
- Added tests for error handling and cancellation
- All tests pass locally

## Documentation
- Added comprehensive docstrings with examples
- Included warnings about placeholder embedding implementation
- Added links to Azure AI Search documentation

## Related issue number

Closes #5419 

## 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>

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>

## Why are these changes needed?

Following the [guide
instructions](https://microsoft.github.io/autogen/stable//user-guide/autogenstudio-user-guide/installation.html#a-install-from-source-manually)
the user will execute the commands in the wrong directory.

## Related issue number

Closes #6165

## Why are these changes needed?
This change addresses a compatibility issue when using Google Gemini
models with AutoGen. Specifically, Gemini returns a 400 INVALID_ARGUMENT
error when receiving a response with an empty "text" parameter.

The root cause is that Gemini does not accept empty string values (e.g.,
"") as valid inputs in the history of the conversation.

To fix this, if the content field is falsy (e.g., None, "", etc.), it is
explicitly replaced with a single whitespace (" "), which prevents the
Gemini model from rejecting the request.

- **Gemini API compatibility:** Gemini models reject empty assistant
messages (e.g., `""`), causing runtime errors. This PR ensures such
messages are safely replaced with whitespace where appropriate.
- **Avoiding regressions:** Applying the empty content workaround **only
to Gemini**, and **only to valid message types**, avoids breaking OpenAI
or other models.
- **Reducing duplication:** Previously, message transformation logic was
scattered and repeated across different message types and models.
Modularizing this pipeline removes that redundancy.
- **Improved maintainability:** With future model variants likely to
introduce more constraints, this modular structure makes it easier to
adapt transformations without writing ad-hoc code each time.
- **Testing for correctness:** The new structure is verified with tests,
ensuring the bug fix is effective and non-intrusive.

## Summary

This PR introduces a **modular transformer pipeline** for message
conversion and **fixes a Gemini-specific bug** related to empty
assistant message content.

### Key Changes

- **[Refactor]** Extracted message transformation logic into a unified
pipeline to:
  - Reduce code duplication
  - Improve maintainability
  - Simplify debugging and extension for future model-specific logic

- **[BugFix]** Gemini models do not accept empty assistant message
content.
- Introduced `_set_empty_to_whitespace` transformer to replace empty
strings with `" "` only where needed
- Applied it **only** to `"text"` and `"thought"` message types, not to
`"tools"` to avoid serialization errors

- **Improved structure for model-specific handling**
- Transformer functions are now grouped and conditionally applied based
on message type and model family
- This design makes it easier to support future models or combinations
(e.g., Gemini + R1)

- **Test coverage added**
- Added dedicated tests to verify that empty assistant content causes
errors for Gemini
  - Ensured the fix resolves the issue without affecting OpenAI models

---

## Motivation

Originally, Gemini-compatible endpoints would fail when receiving
assistant messages with empty content (`""`).
This issue required special handling without introducing brittle, ad-hoc
patches.

In addressing this, I also saw an opportunity to **modularize** the
message transformation logic across models.
This improves clarity, avoids duplication, and simplifies future
adaptations (e.g., different constraints across model families).

---


## 📘 AutoGen Modular Message Transformer: Design & Usage Guide

This document introduces the **new modular transformer system** used in
AutoGen for converting `LLMMessage` instances to SDK-specific message
formats (e.g., OpenAI-style `ChatCompletionMessageParam`).
The design improves **reusability, extensibility**, and
**maintainability** across different model families.

---

### 🚀 Overview

Instead of scattering model-specific message conversion logic across the
codebase, the new design introduces:

- Modular transformer **functions** for each message type
- Per-model **transformer maps** (e.g., for OpenAI-compatible models)
- Optional **conditional transformers** for multimodal/text hybrid
models
- Clear separation between **message adaptation logic** and
**SDK-specific builder** (e.g., `ChatCompletionUserMessageParam`)

---

### 🧱 1. Define Transform Functions

Each transformer function takes:
- `LLMMessage`: a structured AutoGen message
- `context: dict`: metadata passed through the builder pipeline

And returns:
- A dictionary of keyword arguments for the target message constructor
(e.g., `{"content": ..., "name": ..., "role": ...}`)

```python
def _set_thought_as_content_gemini(message: LLMMessage, context: Dict[str, Any]) -> Dict[str, str | None]:
    assert isinstance(message, AssistantMessage)
    return {"content": message.thought or " "}
```

---

### 🪢 2. Compose Transformer Pipelines

Multiple transformer functions are composed into a pipeline using
`build_transformer_func()`:

```python
base_user_transformer_funcs: List[Callable[[LLMMessage, Dict[str, Any]], Dict[str, Any]]] = [
    _assert_valid_name,
    _set_name,
    _set_role("user"),
]

user_transformer = build_transformer_func(
    funcs=base_user_transformer_funcs,
    message_param_func=ChatCompletionUserMessageParam
)
```

- The `message_param_func` is the actual constructor for the target
message class (usually from the SDK).
- The pipeline is **ordered** — each function adds or overrides keys in
the builder kwargs.

---

### 🗂️ 3. Register Transformer Map

Each model family maintains a `TransformerMap`, which maps `LLMMessage`
types to transformers:

```python
__BASE_TRANSFORMER_MAP: TransformerMap = {
    SystemMessage: system_transformer,
    UserMessage: user_transformer,
    AssistantMessage: assistant_transformer,
}

register_transformer("openai", model_name_or_family, __BASE_TRANSFORMER_MAP)
```

- `"openai"` is currently required (as only OpenAI-compatible format is
supported now).
- Registration ensures AutoGen knows how to transform each message type
for that model.

---

### 🔁 4. Conditional Transformers (Optional)

When message construction depends on runtime conditions (e.g., `"text"`
vs. `"multimodal"`), use:

```python
conditional_transformer = build_conditional_transformer_func(
    funcs_map=user_transformer_funcs_claude,
    message_param_func_map=user_transformer_constructors,
    condition_func=user_condition,
)
```

Where:

- `funcs_map`: maps condition label → list of transformer functions
```python
user_transformer_funcs_claude = {
    "text": text_transformers + [_set_empty_to_whitespace],
    "multimodal": multimodal_transformers + [_set_empty_to_whitespace],
}
```

- `message_param_func_map`: maps condition label → message builder
```python
user_transformer_constructors = {
    "text": ChatCompletionUserMessageParam,
    "multimodal": ChatCompletionUserMessageParam,
}
```

- `condition_func`: determines which transformer to apply at runtime
```python
def user_condition(message: LLMMessage, context: Dict[str, Any]) -> str:
    if isinstance(message.content, str):
        return "text"
    return "multimodal"
```

---

### 🧪 Example Flow

```python
llm_message = AssistantMessage(name="a", thought="let’s go")
model_family = "openai"
model_name = "claude-3-opus"

transformer = get_transformer(model_family, model_name, type(llm_message))
sdk_message = transformer(llm_message, context={})
```

---

### 🎯 Design Benefits

| Feature | Benefit |
|--------|---------|
| 🧱 Function-based modular design | Easy to compose and test |
| 🧩 Per-model registry | Clean separation across model families |
| ⚖️ Conditional support | Allows multimodal / dynamic adaptation |
| 🔄 Reuse-friendly | Shared logic (e.g., `_set_name`) is DRY |
| 📦 SDK-specific | Keeps message adaptation aligned to builder interface
|

---

### 🔮 Future Direction

- Support more SDKs and formats by introducing new message_param_func
- Global registry integration (currently `"openai"`-scoped)
- Class-based transformer variant if complexity grows



---

## Related issue number
Closes #5762

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

---------

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 adds missing model entries for OpenAI-compatible endpoints,
including gpt-4.5-turbo, gpt-4.5-turbo-preview, and claude-3.5-sonnet.
This improves coverage and avoids potential fallback or mismatch issues
when initializing clients.

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.

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## Why are these changes needed?

- Adds tracing docs page for AgentChat with Jaeger example 
- [x] Runtime tracing: Example code where tracing is done with the
SingleThreaded Runtime, logging all events
- [x] Custom event tracing: Example code logging messages returned from
`team.run_stream()`
- [ ] LLM span tracing .. depends on
https://github.com/microsoft/autogen/issues/5895
 - [ ] [TBD] Distributed tracing 

See
[tracing.ipynb](bdb6ac5315/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb)
here

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

## Related issue number

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

#5992

## Open Questions

@ekzu 
- What is the recommended way to directly log custom events like
LLMCallEvents and ToolCallEvents? LogEventhandlers in user code that
become traced spans?
- Currenltly tool calls and their args are already logged (not sure
where this is done), but LLM call events are not. Should we include
samples on this?

## 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>

## Why are these changes needed?
fix the accessibility issue that screen reader doesn't announce the
theme when it changes

## Related issue number
#5631 (13) (31) (59)

---------

Co-authored-by: peterychang <49209570+peterychang@users.noreply.github.com>

Address some confusion such as #6036

---------

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

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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
If user tab to a code block copy button then hit enter, screen reader
doesn't announce "Copied". This PR fixed this bug.


## Related issue number
#5631 (8)
<!-- 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.

Update AzureContainerCodeExecutor to ACADynamicSessionsCodeExecutor to
match the type name in the target docs

## Why are these changes needed?

Fixes (53) on screen reader issues. A special thanks to @sjay8 for
starting the work on this task

## Related issue number

https://github.com/microsoft/autogen/issues/5631

Adds alt text to images. fixes screen reader issue (41)

## Related issue number

https://github.com/microsoft/autogen/issues/5631

Resolves #6015

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## Why are these changes needed?

Fixes Screen Reader issue (58)

## Related issue number

https://github.com/microsoft/autogen/issues/5631

## 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: Jack Gerrits <jackgerrits@users.noreply.github.com>

Closes #5873
---------

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

Resolves #4108