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.

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>

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## 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

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solves. -->

## Related issue number

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

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introduced in this PR.
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Signed-off-by: zhanluxianshen <zhanluxianshen@163.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

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.

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.

<!-- Thank you for your contribution! Please review
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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

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<https://microsoft.github.io/autogen/>. See
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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

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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

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

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

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

Resolves #5994

Open AI moved `openai.types.beta.vector_store` to
`openai.types.vector_store`.
https://github.com/openai/openai-python/compare/v1.65.5...v1.66.0

Also fixed unit tests and use parameterized fixture to run all
scenarios.

## 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>

Address questions related to logging of model client calls and reduce
redundant docs.

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## Why are these changes needed?


https://github.com/user-attachments/assets/b649053b-c377-40c7-aa51-ee64af766fc2

<img width="100%" alt="image"
src="https://github.com/user-attachments/assets/03ba1df5-c9a2-4734-b6a2-0eb97ec0b0e0"
/>


## Authentication

This PR implements an experimental authentication feature to enable
personalized experiences (multiple users). Currently, only GitHub
authentication is supported. You can extend the base authentication
class to add support for other authentication methods.

By default authenticatio is disabled and only enabled when you pass in
the `--auth-config` argument when running the application.

### Enable GitHub Authentication

To enable GitHub authentication, create a `auth.yaml` file in your app
directory:

```yaml
type: github
jwt_secret: "your-secret-key"
token_expiry_minutes: 60
github:
  client_id: "your-github-client-id"
  client_secret: "your-github-client-secret"
  callback_url: "http://localhost:8081/api/auth/callback"
  scopes: ["user:email"]
```

Please see the documentation on [GitHub
OAuth](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/authenticating-to-the-rest-api-with-an-oauth-app)
for more details on obtaining the `client_id` and `client_secret`.

To pass in this configuration you can use the `--auth-config` argument
when running the application:

```bash
autogenstudio ui --auth-config /path/to/auth.yaml
```

Or set the environment variable:

```bash
export AUTOGENSTUDIO_AUTH_CONFIG="/path/to/auth.yaml"
```

```{note}
- Authentication is currently experimental and may change in future releases
- User data is stored in your configured database
- When enabled, all API endpoints require authentication except for the authentication endpoints
- WebSocket connections require the token to be passed as a query parameter (`?token=your-jwt-token`)

```

## Related issue number

<!-- For example: "Closes #1234" -->
Closes #4350  

## 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: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>

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## Why are these changes needed?
<img width="1151" alt="image"
src="https://github.com/user-attachments/assets/98bc91ee-749c-4831-b36f-10322979883b"
/>
 
- Update migration guide to cover teachability/rag agents (mention how
similar functionality can be accomplished with AssistantAgent + Memory)
- Update memory docs to explicitly add a text chunking example and a rag
agent

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

## Related issue number

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

Closes #5772 
Closes #4742

## Checks

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

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## Why are these changes needed?

This reverts the base image in AutoGen Studio Dockerfile to `FROM
python:3.10-slim`. This fixes the Docker image build failure due to
conflicting UID with Dev Container's `vscode` user.

## Related issue number

Fixes #5929

## Checks

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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
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![image](https://github.com/user-attachments/assets/73735a47-f3a6-44f6-8e9d-3a5632c8a80f)


## Why are these changes needed?

The [agentchat teams
docs](https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/tutorial/teams.html)
page did not list out the teams currently supported. This is confusing
for readers/uisers as they have to search around to discover that
selector groupchat, swarm and magentic one are available.

This PR adds a list of supported teams to the top of the teams page and
links to the relevant tutorials.

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

## Related issue number

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

## Checks

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<https://microsoft.github.io/autogen/>. See
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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.

Fixed a typo, chroma_user_memory instead of user_memory

## Why are these changes needed?

There's a confusing typo in the documentation.

## Related issue number

None

## Checks

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<https://microsoft.github.io/autogen/>. See
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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: Victor Dibia <victordibia@microsoft.com>

Reverts https://github.com/microsoft/autogen/pull/5857 due to weird
interaction with non-clickable cards

This pull request introduces the integration of the `llama-cpp` library
into the `autogen-ext` package, with significant changes to the project
dependencies and the implementation of a new chat completion client. The
most important changes include updating the project dependencies, adding
a new module for the `LlamaCppChatCompletionClient`, and implementing
the client with various functionalities.

### Project Dependencies:

*
[`python/packages/autogen-ext/pyproject.toml`](diffhunk://#diff-095119d4420ff09059557bd25681211d1772c2be0fbe0ff2d551a3726eff1b4bR34-R38):
Added `llama-cpp-python` as a new dependency under the `llama-cpp`
section.

### New Module:

*
[`python/packages/autogen-ext/src/autogen_ext/models/llama_cpp/__init__.py`](diffhunk://#diff-42ae3ba17d51ca917634c4ea3c5969cf930297c288a783f8d9c126f2accef71dR1-R8):
Introduced the `LlamaCppChatCompletionClient` class and handled import
errors with a descriptive message for missing dependencies.

### Implementation of `LlamaCppChatCompletionClient`:

*
`python/packages/autogen-ext/src/autogen_ext/models/llama_cpp/_llama_cpp_completion_client.py`:
- Added the `LlamaCppChatCompletionClient` class with methods to
initialize the client, create chat completions, detect and execute
tools, and handle streaming responses.
- Included detailed logging for debugging purposes and implemented
methods to count tokens, track usage, and provide model information.…d
chat capabilities

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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" -->

## Checks

- [X ] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation 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: aribornstein <x@x.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

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## Why are these changes needed?

Add anthropic docs

- Add api docs 
- Add sample code + usage in agent chat user guide

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

## Related issue number

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

Closes #5856 

## Checks

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## Why are these changes needed?

Fixes accessibility issue (34)

## Related issue number

#5634 

## 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: Ryan Sweet <rysweet@microsoft.com>

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## Why are these changes needed?

Long module names get cut off in autodocs. Fixes (3). I would love to
break the words on dots and underscores, but it doesn't look like theres
a CSS option for that

before:

![image](https://github.com/user-attachments/assets/c2e425db-a197-4174-9af8-1f7a805df307)

after:

![image](https://github.com/user-attachments/assets/dcfc261a-61ca-4072-a28f-3b51e428ef7f)


## Related issue number

#5634 

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Update the outdated ollama usage doc.

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## Why are these changes needed?

(Partially?) fixes accessibility issue (19). Question out to
accessibility team whether its enough.

Migrating to 16.0 for accessibility fixes. Not moving to 16.1 yet
because of a weird change to the 'Show Source' link's appearance

## Related issue number

#5630 

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

Fix issue here in this discussion -
https://github.com/microsoft/autogen/discussions/4208#discussioncomment-12394408

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## Why are these changes needed?

Fix bug in AGS UI where frontend crashes because the default team config
is null

- update /teams endpoint to always return a default team if none is
found for the user
- update UI to check for team before rendering 
- also update run_id type to be autoincrement int (similar to team id)
instead of uuid. This helps side step the migration failed errors
related to UUID type when using an sqlite backend

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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: Ryan Sweet <rysweet@microsoft.com>

Fix a warning that has been resolved in the latest release.

Added notice for user who are not using Jypter Notebook for the example,
to wrap with asyncio

## Why are these changes needed?

Keyboard focus location was being lost after a copy event. Header anchor
was also not selectable while hidden

Fixes (2), (4), (11), (35)

## Related issue number

#5630 

## Checks

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_(EXPERIMENTAL, RESEARCH IN PROGRESS)_

In 2023 AutoGen introduced [Teachable
Agents](https://microsoft.github.io/autogen/0.2/blog/2023/10/26/TeachableAgent/)
that users could teach new facts, preferences and skills. But teachable
agents were limited in several ways: They could only be
`ConversableAgent` subclasses, they couldn't learn a new skill unless
the user stated (in a single turn) both the task and how to solve it,
and they couldn't learn on their own. **Task-Centric Memory** overcomes
these limitations, allowing users to teach arbitrary agents (or teams)
more flexibly and reliably, and enabling agents to learn from their own
trial-and-error experiences.

This PR is large and complex. All of the files are new, and most of the
added components depend on the others to run at all. But the review
process can be accelerated if approached in the following order.
1. Start with the [Task-Centric Memory
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/packages/autogen-ext/src/autogen_ext/task_centric_memory).
1. Install the memory extension locally, since it won't be in pypi until
it's merged. In the `agentic_memory` branch, and the `python/packages`
directory:
        - `pip install -e autogen-agentchat`
        - `pip install -e autogen-ext[openai]`
        - `pip install -e autogen-ext[task-centric-memory]`
2. Run the Quickstart sample code, then immediately open the
`./pagelogs/quick/0 Call Tree.html` file in a browser to view the work
in progress.
    3. Click through the web page links to see the details.
2. Continue through the rest of the main README to get a high-level
overview of the architecture.
3. Read through the [code samples
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/samples/task_centric_memory),
running each of the 4 code samples while viewing their page logs.
4. Skim through the 4 code samples, along with their corresponding yaml
config files:
    1. `chat_with_teachable_agent.py`
    2. `eval_retrieval.py`
    3. `eval_teachability.py`
    4. `eval_learning_from_demonstration.py`
    5. `eval_self_teaching.py`
6. Read `task_centric_memory_controller.py`, referring back to the
previously generated page logs as needed. This is the most important and
complex file in the PR.
7. Read the remaining core files.
    1. `_task_centric_memory_bank.py`
    2. `_string_similarity_map.py`
    3. `_prompter.py`
8. Read the supporting files in the utils dir.
    1. `teachability.py`
    2. `apprentice.py`
    3. `grader.py`
    4. `page_logger.py`
    5. `_functions.py`

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## Why are these changes needed?

Adds sidebar and breadcrumb selection and focus indicators to high
contrast modes.
Fixes (46), (55), (56)

## Related issue number

 #5633 

This change has no affect on normal color modes, but adds selection and
focus indicators to high contrast modes. I'm not sure how to get rid of
the double bars on nested links, but thats a minor issue

before:

![image](https://github.com/user-attachments/assets/62e7ce65-c3f0-4160-8260-b4153a4a2835)

after:

![image](https://github.com/user-attachments/assets/581ed2a2-5f7c-43c8-a675-2f6c57c6a251)

## Why are these changes needed?

Current webpage theme does not highlight code output boxes. Issues (5)
and (29)

## Related issue number

#5630 

## Checks

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introduced in this PR.
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Resolves #5786

Also updated the termination tutorial to include an example of a custom
termination conditon.

Also added to guide about FunctionTool and MCP tools.

## Why are these changes needed?

The current installation command fails in certain shells (e.g., `zsh`,
`fish`) because brackets (`[]`) are interpreted as special characters.
Adding quotes ensures compatibility across different environments,
including Linux, macOS, and Windows.

## Related issue number

No related issue, but this fixes an installation issue encountered by
multiple users.

## Checks

- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?

* `python3` to `python`: Windows uses `python` for Python 3 by default,
not `python3`.
* `bin` to `scripts`: Windows virtual environments use `Scripts` instead
of `bin`.

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

Correcting an error: If CodeReviewResult is not approved, the coder
agents sends a CodeReviewTask back to the reviewer agent, not a
CodeWritingTask.

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. -->

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

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

---------

Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>

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## Why are these changes needed?

Make FileSurfer and CodeExecAgent Declarative.
These agent presents are used as part of magentic one and having them
declarative is a precursor to their use in AGS.

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solves. -->

## Related issue number

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Closes #5607

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## Why are these changes needed?

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solves. -->
Shows an example of how to use the `Memory` interface to implement a
just-in-time vector memory based on chromadb.

```python
import os
from pathlib import Path

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_core.memory import MemoryContent, MemoryMimeType
from autogen_ext.memory.chromadb import ChromaDBVectorMemory, PersistentChromaDBVectorMemoryConfig
from autogen_ext.models.openai import OpenAIChatCompletionClient

# Initialize ChromaDB memory with custom config
chroma_user_memory = ChromaDBVectorMemory(
    config=PersistentChromaDBVectorMemoryConfig(
        collection_name="preferences",
        persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
        k=2,  # Return top  k results
        score_threshold=0.4,  # Minimum similarity score
    )
)
# a HttpChromaDBVectorMemoryConfig is also supported for connecting to a remote ChromaDB server

# Add user preferences to memory
await chroma_user_memory.add(
    MemoryContent(
        content="The weather should be in metric units",
        mime_type=MemoryMimeType.TEXT,
        metadata={"category": "preferences", "type": "units"},
    )
)

await chroma_user_memory.add(
    MemoryContent(
        content="Meal recipe must be vegan",
        mime_type=MemoryMimeType.TEXT,
        metadata={"category": "preferences", "type": "dietary"},
    )
)


# Create assistant agent with ChromaDB memory
assistant_agent = AssistantAgent(
    name="assistant_agent",
    model_client=OpenAIChatCompletionClient(
        model="gpt-4o",
    ),
    tools=[get_weather],
    memory=[user_memory],
)

stream = assistant_agent.run_stream(task="What is the weather in New York?")
await Console(stream)

await user_memory.close()
```

```txt
 ---------- user ----------
What is the weather in New York?
---------- assistant_agent ----------
[MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': '8a8d683c-5866-41e1-ac17-08c4fda6da86'}), MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': 'f27af42c-cb63-46f0-b26b-ffcc09955ca1'})]
---------- assistant_agent ----------
[FunctionCall(id='call_a8U3YEj2dxA065vyzdfXDtNf', arguments='{"city":"New York","units":"metric"}', name='get_weather')]
---------- assistant_agent ----------
[FunctionExecutionResult(content='The weather in New York is 23 °C and Sunny.', call_id='call_a8U3YEj2dxA065vyzdfXDtNf', is_error=False)]
---------- assistant_agent ----------
The weather in New York is 23 °C and Sunny.
```

Note that MemoryContent object in the MemoryQuery events have useful
metadata like the score and id retrieved memories.

## Related issue number

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


## Checks

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introduced in this PR.
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