## Why are these changes needed?

The existing run_stream methods used fragile count-based logic (count <=
len(task)) to skip task messages during streaming. This approach was
brittle and broke when team structure changed or task composition
varied, particularly affecting SocietyOfMindAgent's ability to properly
encapsulate inner team messages.

This PR adds an output_task_messages parameter to run_stream methods to
provide explicit control over task message inclusion in streams,
replacing the fragile count-based logic with robust message filtering.

## Related issue number

Closes #6150

## 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 corresponding to the changes introduced in this
PR.
- [x] I've made sure all auto checks have passed.

---------

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

## Why are these changes needed?

This PR addresses critical issues in the AssistantAgent that affect tool
handling:

**Lack of tool call loop functionality**: The agent could not perform
multiple consecutive tool calls in a single turn, limiting its ability
to complete complex multi-step tasks that require chaining tool
operations.

These changes enhance the agent's robustness and capability while
maintaining full backward compatibility through feature flags.

## Related issue number

Closes  #6268

## 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 corresponding to the changes introduced in this
PR.
- [x] I've made sure all auto checks have passed.

---------

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

## Why are these changes needed?

This PR implements unique ID fields for AgentChat messages to enable
proper correlation between streaming chunks and completed messages.
Currently, there's no way to correlate `ModelClientStreamingChunkEvent`
chunks with their eventual completed message, which can lead to
duplicate message display in streaming scenarios.

The implementation adds:
- `id: str` field to `BaseChatMessage` with automatic UUID generation
- `id: str` field to `BaseAgentEvent` with automatic UUID generation  
- `full_message_id: str | None` field to
`ModelClientStreamingChunkEvent` for chunk-to-message correlation

This allows consumers of the streaming API to avoid double-printing
messages by correlating chunks with their final complete message.

## Related issue number

Closes #6317

## 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: #6456

## Why are these changes needed?

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

## Related issue number


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

## Checks

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

## Why are these changes needed?

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

### My Use Case / Problem

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

## Related issue number

Closes #6426

## Checks

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

## Why are these changes needed?
Starting from AutoGen v0.5.5, tools are internally managed through
`StaticWorkbench`.
However, both tools and workbench were being serialized and
deserialized, which caused conflicts during deserialization:
	•	When both are restored, the constructor raises:
```
ValueError: Tools cannot be used with a workbench.
```

The changes address this issue by:
1.	Removing tools from serialization/deserialization:
• tools are now considered internal state of `StaticWorkbench`, and are
no longer serialized.
• Only workbench is serialized, ensuring consistency and avoiding
duplication.
2.	Ensuring logical integrity:
• Since tools are not used directly after initialization, persisting
them separately serves no functional purpose.
• This avoids scenarios where both are populated, violating constructor
constraints.

Summary:

This change prevents tools/workbench conflicts by fully delegating tool
management to `StaticWorkbench` and avoiding unnecessary persistence of
tools themselves.


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

## Related issue number

Closes #6405 

## Checks

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

---------

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

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())
```

Resolves #6295

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

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>

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>

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.

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

Supports agents to carry specific instructions when performing handoff
operations

---------

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

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

Resolves #5919

Resolves #4075

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

How to use it? 

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

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

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

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

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

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

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

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

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

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

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

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

asyncio.run(main())
```

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

---------

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

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

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

## Why are these changes needed?

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

The PR introduces two changes.

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

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

## Related issue number

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

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

## Checks

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

---------

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

<!-- 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. -->

I'm unsure if everyone will agree, but I started to look into adding new
logic and found that refactoring into smaller functions would make it
more maintainable.

There is no change in functionality, only a breakdown into smaller
methods to make it more modular and improve readability. There is a lot
of logic in the method and this refactor breaks it down into context
management, llm call and result processing.

## 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>

Resolves #5562

Resolves #5192

Test

```python
import asyncio
import os
from random import randint
from typing import List
from autogen_core.tools import BaseTool, FunctionTool
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console

async def get_current_time(city: str) -> str:
    return f"The current time in {city} is {randint(0, 23)}:{randint(0, 59)}."

tools: List[BaseTool] = [
    FunctionTool(
        get_current_time,
        name="get_current_time",
        description="Get current time for a city.",
    ),
]

model_client = OpenAIChatCompletionClient(
    model="anthropic/claude-3.5-haiku-20241022",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    model_info={
        "family": "claude-3.5-haiku",
        "function_calling": True,
        "vision": False,
        "json_output": False,
    }
)

agent = AssistantAgent(
    name="Agent",
    model_client=model_client,
    tools=tools,
    system_message= "You are an assistant with some tools that can be used to answer some questions",
)

async def main() -> None:
    await Console(agent.run_stream(task="What is current time of Paris and Toronto?"))

asyncio.run(main())
```

```
---------- user ----------
What is current time of Paris and Toronto?
---------- Agent ----------
I'll help you find the current time for Paris and Toronto by using the get_current_time function for each city.
---------- Agent ----------
[FunctionCall(id='toolu_01NwP3fNAwcYKn1x656Dq9xW', arguments='{"city": "Paris"}', name='get_current_time'), FunctionCall(id='toolu_018d4cWSy3TxXhjgmLYFrfRt', arguments='{"city": "Toronto"}', name='get_current_time')]
---------- Agent ----------
[FunctionExecutionResult(content='The current time in Paris is 1:10.', call_id='toolu_01NwP3fNAwcYKn1x656Dq9xW', is_error=False), FunctionExecutionResult(content='The current time in Toronto is 7:28.', call_id='toolu_018d4cWSy3TxXhjgmLYFrfRt', is_error=False)]
---------- Agent ----------
The current time in Paris is 1:10.
The current time in Toronto is 7:28.
```

---------

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

Resolves #5508

Some LLMs recieve an explicit signal about tool use failures. 

Closes #5273

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?

Currently the way to accomplish RAG behavior with agent chat,
specifically assistant agents is with the memory interface, however
there is no way to configure it via the declarative API.

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

## Related issue number

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

## Checks

- [ ] 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.
- [ ] 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: Victor Dibia <chuvidi2003@gmail.com>

Allow AssistantAgent to drop images when not equipped with a multi-modal model.

Adds a corresponding utility function, which can be used in autogen-ext and teams, to accomplish the same.

Resolves #3983

* introduce `model_client_stream` parameter in `AssistantAgent` to
enable token-level streaming output.
* introduce `ModelClientStreamingChunkEvent` as a type of `AgentEvent`
to pass the streaming chunks to the application via `run_stream` and
`on_messages_stream`. Although this will not affect the inner messages
list in the final `Response` or `TaskResult`.
* handle this new message type in `Console`.

<!-- 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?

Make AssistantAgent and Handoff use BaseTool.  
This ensures that they can be made declarative/serialized

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

## Related issue number

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

## Checks

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

* Pass context between AssistantAgent for handoffs

* Add parallel tool call test

---------

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

* v1, make assistant agent declarative

* make head tail context declarative

* update and formatting

* update assistant, format updates

* make websurfer declarative

* update formatting

* move declarative docs to advanced section

* remove tools until implemented

* minor updates to termination conditions

* update docs

* initial base memroy impl

* update, add example with chromadb

* include mimetype consideration

* add transform method

* update to address feedback, will update after 4681 is merged

* update memory impl,

* remove chroma db, typing fixes

* format, add test

* update uv lock

* update docs

* format updates

* update notebook

* add memoryqueryevent message, yield message for observability.

* minor fixes, make score optional/none

* Update python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py

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

* update tests to improve cov

* refactor, move memory to core.

* format fixxes

* format updates

* format updates

* fix azure notebook import, other fixes

* update notebook, support str query in Memory protocol

* update test

* update cells

* add specific extensible return types to memory query and update_context

---------

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

* Add support for model family specification

* spelling mistake

* lint, etc

* fixes

* Add missing model context attribute

* fix type

* Add test

* imports

---------

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

* Adding ToolCallResultSummaryMessage

* Support for ToolCallResultSummaryMessage

* Added ToolCallSummaryMessage

* ruff format

* Add ToolCallSummaryMessage to ChatMessage

* typing and tests for ToolCallSummaryMessage

* PR Feedback

---------

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

* Define AgentEvent, rename tool call messages to events.

* update doc

* Use AgentEvent | ChatMessage to replace AgentMessage

* Update docs

* update deprecation notice

* remove unused

* fix doc

* format

* feat: add support for list of messages as team task input
* Update society of mind agent to use the list input task
---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* add models.openai namespace

* refactor tools namespace

* update lock file

* revert pyproject changes

* update docs and add cast

* update ext models doc ref

* increase underline

* add reply models namespace

* update imports

* fix test

* linting

* fix missing conflicts

* revert pydantic changes

* rename to replay

* replay

* fix reply

* Fix test

* formatting

* example

---------

Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

* 1 tool call iteration default

* handoff first

* return_only_response

* add and remove tools

* print out tool calls

* pass checks

* fix issues

* add test

* add unit tests

* remove extra print

* Update python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py

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

* documentation and none max_tools_calls

* Always limit # tool call to 1

* Update notebooks for the changing behavior of assistant agent.

* Merge branch 'main' into assistant_Agent_tools

* add reflect_on_tool_use parameter to format the tool call result

* wip

* wip

* fix pyright

* Add unit tests

* Merge remote-tracking branch 'origin/main' into assistant_Agent_tools

* Update with custom formatting of tool call summary

* format

* Merge branch 'main' into assistant_Agent_tools

1. convert dataclass types to pydantic basemodel 
2. add save_state and load_state for ChatAgent
3. state types for AgentChat
---------

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

* Flatten core base and components

* remove extra files

* dont export from deprecated locations

* format

* fmt

* Add task type that are messages to enable multi-modal tasks.

* fix test

* AgentChat pause and resume a task
Resolves #3859

* Add

* Update usage

* Update usage

* WIP to address stateful group chat

* Refactor group chat to add reset and flags for running

* documentation

* Add token usage to messages

* small test edit