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

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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] 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

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solves. -->

## Related issue number

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

Closes #5772 
Closes #4742

## Checks

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

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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.

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solves. -->

## Related issue number

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

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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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- [ 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

These changes allow for 2 important use-cases:
1. Add a span for tool calls which will enable tracing of all tool calls
in agent_chat
2. Allow runtimes to pick up global `tracer_providers` if they are
available. This is very helpful because it allows for nested teams/agent
to all use the same tracer.
---------

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

These changes are needed because there is currently no way to get
logging information about Streaming LLM requests/responses.

I decided to put the StreamStart event AFTER the first chunk so there
aren't false positives about connections/auth.

Closes #5730
---------

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

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>

Resolves #5697

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

Resolves #5745

Also made sure to log LLMCallEvent from all builtin model clients, and
added unit test for coverage.

---------

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

Fixes #4821 by adding a `close()` method to all clients.

Additionally:
* The m1 CLI is updated to close the client before exiting.
* The playwrightcontroller is updated to suppress some other unrelated
chatty warnings (e.g,, produced by markitdown when encountering
conversions that require external utilities)

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## Why are these changes needed?

Fixes accessibility issue (34)

## Related issue number

#5634 

## 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: 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 

## Checks

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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
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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 

## Checks

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introduced in this PR.
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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

<!-- 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.
- [ ] 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: 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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<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.

_(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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<https://microsoft.github.io/autogen/>. See
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- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

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.

Resolves #5777

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

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>

AutoGen was passing raw dictionaries to functions instead of
constructing Pydantic model or dataclass instances. If a tool function’s
parameter was a Pydantic BaseModel or a dataclass, the function would
receive a dict and likely throw an error or behave incorrectly (since it
expected an object of that type).

This PR addresses problem in AutoGen where tool functions expecting
structured inputs (Pydantic models or dataclasses) were receiving raw
dictionaries. It ensures that structured inputs are automatically
validated and instantiated before function calls. Complete details are
in Issue #5736

[Reproducible Example Code - Failing
Case](https://colab.research.google.com/drive/1hgoP-cGdSZ1-OqQLpwYmlmcExgftDqlO?usp=sharing)
 
<!-- Please give a short summary of the change and the problem this
solves. -->
## Changes Made:
- Inspect function signatures for Pydantic BaseModel and dataclass
annotations.
- Convert input dictionaries into properly instantiated objects using
BaseModel.model_validate() for Pydantic models or standard instantiation
for dataclasses.
  - Raise descriptive errors when validation or instantiation fails.
  - Unit tests have been added to cover all scenarios

Now structured inputs are automatically validated and instantiated
before function calls.

- **Updated Conversion Logic:**  
In the `run()` method, we now inspect the function’s signature and
convert input dictionaries to structured objects. For parameters
annotated with a Pydantic model, we use `model_validate()` to create an
instance; for those annotated with a dataclass, we instantiate the
object using the dataclass constructor. For example:

  ```python
  # Get the function signature.
  sig = inspect.signature(self._func)
  raw_kwargs = args.model_dump()
  kwargs = {}

  # Iterate over the parameters expected by the function.
  for name, param in sig.parameters.items():
      if name in raw_kwargs:
          expected_type = param.annotation
          value = raw_kwargs[name]
# If expected type is a subclass of BaseModel, perform conversion.
if inspect.isclass(expected_type) and issubclass(expected_type,
BaseModel):
              try:
                  kwargs[name] = expected_type.model_validate(value)
              except ValidationError as e:
                  raise ValueError(
f"Error validating parameter '{name}' for function
'{self._func.__name__}': {e}"
                  ) from e
          # If it's a dataclass, instantiate it.
          elif is_dataclass(expected_type):
              try:
cls = expected_type if isinstance(expected_type, type) else
type(expected_type)
                  kwargs[name] = cls(**value)
              except Exception as e:
                  raise ValueError(
f"Error instantiating dataclass parameter '{name}' for function
'{self._func.__name__}': {e}"
                  ) from e
          else:
              kwargs[name] = value
  ```

- **Error Handling Improvements:**  
Conversion steps are wrapped in try/except blocks to raise descriptive
errors when instantiation fails, aiding in debugging invalid inputs.

- **Testing:**  
Unit tests have been added to simulate tool calls (e.g., an `add` tool)
to ensure that with input like:
  ```json
  {"input": {"x": 2, "y": 3}}
  ```
The tool function receives an instance of the expected type and returns
the correct result.


## Related issue number
Closes #5736
 
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.

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>

Starting out this draft PR to add documentation for the
`with_requirements` decorator in the `autogen-core` package.

---------

Co-authored-by: Jack Gerrits <jackgerrits@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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<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>

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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

<!-- For example: "Closes #1234" -->
Closes #5607

## 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.

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
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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https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation to
build and test documentation locally.
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introduced in this PR.
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## Why are these changes needed?

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

## 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
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- [X] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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## Why are these changes needed?

- Running a team generates a MemoryQueryEvent has a MemoryContent field
and is part of the model context
- Saving team state (`team.save_state()`) includes serializing model
context
- MemoryContent has a mime_type field, which was not being properly
serialized.
"Object of type MemoryMimeType is not JSON serializable"

This PR? -> add explicit serialization instruction for the mimetype
field.

```python
import asyncio
import logging
import json
import yaml, aiofiles
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_core.memory import ListMemory, MemoryContent, MemoryMimeType
from autogen_agentchat.ui import Console

logger = logging.getLogger(__name__)

state_path = "team_state.json"

model_client =  OpenAIChatCompletionClient(model="gpt-4o-mini")

max_msg_termination = MaxMessageTermination(max_messages=3)
# Initialize user memory
user_memory = ListMemory()

# Add user preferences to memory
await user_memory.add(MemoryContent(content="The weather should be in metric units", mime_type=MemoryMimeType.TEXT))

# Create the team.
agent = AssistantAgent(
    name="assistant",
    model_client=model_client,
    system_message="You are a helpful assistant.",
    memory = [user_memory],
)
yoda = AssistantAgent(
    name="yoda",
    model_client=model_client,
    system_message="Repeat the same message in the tone of Yoda.",
)

team = RoundRobinGroupChat(
    [agent, yoda],
    termination_condition=max_msg_termination
)

await Console(team.run_stream(task="Hi, How are you ?"))
 
# Save team state to file.
state = await team.save_state()
with open(state_path, "w") as f:
    json.dump(state, f)
# Load team state from file.
with open("team_state.json", "r") as f:
    team_state = json.load(f)
    
await team.load_state(state) 
await Console( team.run_stream(task="What was the last thing that was said in this conversation "))
```

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solves. -->

## Related issue number

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

Closes #5688
## Checks

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

Closes #4904 

Does not change default behavior in core.

In agentchat, this change will mean that exceptions that used to be
ignored and result in bugs like the group chat stopping are now reported
out to the user application.

---------

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

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## Why are these changes needed?

Fixes visual accessibility issues `(6) Luminosity contrast ratio of
focus indicator on 'Venv' control is less than 3:1.` and `(20) "Only
color is used to indicate the focus indicator on the 'Venv', and 'conda'
Tab controls."`

## Related issue number

#5633 

before:
<img width="162" alt="image"
src="https://github.com/user-attachments/assets/ac317e82-c82e-4d3b-910b-e9eb5d087340"
/>

![image](https://github.com/user-attachments/assets/2971043a-8b5b-4521-8135-c411c0ecab1f)

after:
<img width="160" alt="image"
src="https://github.com/user-attachments/assets/8053ae77-4d62-491b-9d78-657646ed71a6"
/>

![image](https://github.com/user-attachments/assets/5c3ac2c3-4813-461b-8958-4665860574a0)

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pull request. -->

Claude 3.7 just came out. Its a pretty capable model and it would be
great to support it in Autogen.
This will could augment the already excellent support we have for
Anthropic via the SKAdapters in the following ways

- Based on the ChatCompletion API similar to the ollama and openai
client
- Configurable/serializable (can be dumped) .. this means it can be used
easily in AGS.

## What is Supported 

(video below shows the client being used in autogen studio)

https://github.com/user-attachments/assets/8fb7c17c-9f9c-4525-aa9c-f256aad0f40b



- streaming 
- tool callign / function calling 
- drop in integration with assistant agent. 
- multimodal support

```python

from dotenv import load_dotenv
import os 

load_dotenv()

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.anthropic import AnthropicChatCompletionClient 
model_client =   AnthropicChatCompletionClient(
        model="claude-3-7-sonnet-20250219" 
    )

async def get_weather(city: str) -> str:
    """Get the weather for a given city."""
    return f"The weather in {city} is 73 degrees and Sunny."

 
agent = AssistantAgent(
    name="weather_agent",
    model_client=model_client,
    tools=[get_weather],
    system_message="You are a helpful assistant.", 
    # model_client_stream=True,   
)

# Run the agent and stream the messages to the console.
async def main() -> None:
    await Console(agent.run_stream(task="What is the weather in New York?"))
await main()
```

result 

```
messages = [
    UserMessage(content="Write a very short story about a dragon.", source="user"),
]

# Create a stream.
stream = model_client.create_stream(messages=messages)

# Iterate over the stream and print the responses.
print("Streamed responses:")
async for response in stream:  # type: ignore
    if isinstance(response, str):
        # A partial response is a string.
        print(response, flush=True, end="")
    else:
        # The last response is a CreateResult object with the complete message.
        print("\n\n------------\n")
        print("The complete response:", flush=True)
        print(response.content, flush=True)
        print("\n\n------------\n")
        print("The token usage was:", flush=True)
        print(response.usage, flush=True)
```

 

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## Why are these changes needed?

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solves. -->

## Related issue number

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

Closes #5205 
Closes #5708

## Checks

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



cc @rohanthacker

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update human in the loop docs for agentchat  
..

> Outputs in docs are outdated as summary is not printed out by default
when using Console
[#5590](https://github.com/microsoft/autogen/issues/5590)

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## Why are these changes needed?

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solves. -->

## Related issue number

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Closes #5590

## Checks

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

Fixes `(14) Ensures the contrast between foreground and background
colors meets WCAG 2 AA minimum contrast ratio thresholds`

Note: the color values don't output at the exact values on the
stylesheet. For example, the value `#1774E5` evaluates to `#2274E0` by
the Accessibility Insights app.

## Related issue number

https://github.com/microsoft/autogen/issues/5633

## Checks

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This PR has 3 main improvements. 

- Token streaming 
- Adds support for environment variables in the app settings 
- Updates AGS to persist Gallery entry in db.

## Adds Token Streaming in AGS.  

Agentchat now supports streaming of tokens via
`ModelClientStreamingChunkEvent `. This PR is to track progress on
supporting that in the AutoGen Studio UI.

If `model_client_stream` is enabled in an assitant agent, then token
will be streamed in UI.

```python
streaming_assistant = AssistantAgent(
    name="assistant",
    model_client=model_client,
    system_message="You are a helpful assistant.",
    model_client_stream=True,  # Enable streaming tokens.
)

```

https://github.com/user-attachments/assets/74d43d78-6359-40c3-a78e-c84dcb5e02a1


## Env Variables 
Also adds support for env variables in AGS Settings

You can set env variables that are loaded just before a team is run.
Handy to set variable to be used by tools etc.

<img width="1291" alt="image"
src="https://github.com/user-attachments/assets/437b9d90-ccee-42f7-be5d-94ab191afd67"
/>


> Note: the set variables are available to the server process.

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## Why are these changes needed?

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

## Related issue number

<!-- For example: "Closes #1234" -->
Closes #5627  
Closes #5662 
Closes #5619 

## Checks

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introduced in this PR.
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Resolves #5408

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

Resolves #5610

And address various questions regarding to how to use user proxy agent
and human-in-the-loop.