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.
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>
* Decouple model_context from AssistantAgent
* add UnboundedBufferedChatCompletionContext to mimic pervious model_context behaviour on AssistantAgent
* moving unbounded buffered chat to a different file
* fix model_context assertions in test_group_chat
* Refactor model context, introduce states
* fixes
* update
---------
Co-authored-by: aditya.kurniawan <aditya.kurniawan@core42.ai>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
* 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>
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>
