## Why are these changes needed?
❗ Before
Previously, GraphFlow.__init__() modified the inner_chats and
termination_condition for internal execution logic (e.g., constructing
_StopAgent or composing OrTerminationCondition).
However, these modified values were also used during dump_component(),
meaning the serialized config no longer matched the original inputs.
As a result:
1. dump_component() → load_component() → dump_component() produced
non-idempotent configs.
2. Internal-only constructs like _StopAgent were mistakenly serialized,
even though they should only exist in runtime.
⸻
✅ After
This patch changes the behavior to:
• Store original inner_chats and termination_condition as-is at
initialization.
• During to_config(), serialize only the original unmodified versions.
• Avoid serializing _StopAgent or other dynamically built agents.
• Ensure deserialization (from_config) produces a logically equivalent
object without additional nesting or duplication.
This ensures that:
• GraphFlow.dump_component() → load_component() round-trip produces
consistent, minimal configs.
• Internal execution logic and serialized component structure are
properly separated.
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#6431
## 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.
Closes#4623
### Add Directed Graph-based Group Chat Execution Engine
(`DiGraphGroupChat`)
This PR introduces a new graph-based execution framework for Autogen
agent teams, located under `autogen_agentchat/teams/_group_chat/_graph`.
**Key Features:**
- **`DiGraphGroupChat`**: A new group chat implementation that executes
agents based on a user-defined directed graph (DAG or cyclic with exit
conditions).
- **`AGGraphBuilder`**: A fluent builder API to programmatically
construct graphs.
- **`MessageFilterAgent`**: A wrapper to restrict what messages an agent
sees before invocation, supporting per-source and per-position
filtering.
**Capabilities:**
- Supports sequential, parallel, conditional, and cyclic workflows.
- Enables fine-grained control over both execution order and message
context.
- Compatible with existing Autogen agents and runtime interfaces.
**Tests:**
- Located in `autogen_agentchat/tests/test_group_chat_graph.py`
- Includes unit and integration tests covering:
- Graph validation
- Execution paths
- Conditional routing
- Loops with exit conditions
- Message filtering
Let me know if anything needs refactoring or if you'd like the
components split further.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Leonardo Pinheiro <leosantospinheiro@gmail.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>
* Replace on_messages and on_messages_stream with run and run_stream to
unify interface documentation with teams
* Remove magentic-one-cli from homepage as it has not been maintained
and improved for a while.
Finishing up the work on workbench.
```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, McpWorkbench
async def main() -> None:
params = StdioServerParams(
command="uvx",
args=["mcp-server-fetch"],
read_timeout_seconds=60,
)
# You can also use `start()` and `stop()` to manage the session.
async with McpWorkbench(server_params=params) as workbench:
model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
assistant = AssistantAgent(
name="Assistant",
model_client=model_client,
workbench=workbench,
reflect_on_tool_use=True,
)
await Console(assistant.run_stream(task="Go to https://github.com/microsoft/autogen and tell me what you see."))
asyncio.run(main())
```
## Why are these changes needed?
| Package | Test time-Origin (Sec) | Test time-Edited (Sec) |
|-------------------------|------------------|-----------------------------------------------|
| autogen-studio | 1.64 | 1.64 |
| autogen-core | 6.03 | 6.17 |
| autogen-ext | 387.15 | 373.40 |
| autogen-agentchat | 54.20 | 20.67 |
## Related issue number
Related #6361
## Checks
- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.
## Why are these changes needed?
This PR introduces a baseline self-debugging loop to the
`CodeExecutionAgent`.
The loop automatically retries code generation and execution up to a
configurable number of attempts (n) until the execution succeeds or the
retry limit is reached.
This enables the agent to recover from transient failures (e.g., syntax
errors, runtime errors) by using its own reasoning to iteratively
improve generated code—laying the foundation for more robust autonomous
behavior.
## Related issue number
Closes#6207
## 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>
This PR fixes a bug where `model_context` was either ignored or
explicitly set to `None` during agent deserialization (`_from_config`)
in:
- `AssistantAgent`: `model_context` was serialized but not restored.
- `SocietyOfMindAgent`: `model_context` was neither serialized nor
restored.
- `CodeExecutorAgent`: `model_context` was serialized but not restored.
As a result, restoring an agent from its config silently dropped runtime
context settings, potentially affecting agent behavior.
This patch:
- Adds proper serialization/deserialization of `model_context` using
`.dump_component()` and `load_component(...)`.
- Ensures round-trip consistency when using declarative agent configs.
## Related issue number
Closes#6336
## 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>
This change avoid re-registering a structured message already registered
to the team by a previous agent also included in the team.
This issue occurs when agents share Pydantic models as output format
## Related issue number
Closes#6353
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
The DockerCommandLineCodeExecutor doesn't currently offer GPU support.
By simply using DeviceRequest from the docker python API, these changes
expose GPUs to the docker container and provide the ability to execute
CUDA-accelerated code within autogen.
## Related issue number
Closes: #6302
## 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>
## Why are these changes needed?
This PR updates `SelectorGroupChat` to support streaming mode for
`select_speaker`.
It introduces a `streaming` argument — when set to `True`,
`select_speaker` will use `create_streaming()` instead of `create()`.
## Additional context
Some models (e.g., QwQ) only work properly in streaming mode.
To support them, the prompt selection step in `SelectorGroupChat` must
also run with `streaming=True`.
## Related issue number
Closes#6145
## 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>
## Why are these changes needed?
`SocietyOfMindAgent` has multiple system message, however many
client/model does not support it.
## Related issue number
Related #6290
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Add an option emit_team_events to BaseGroupChat to emit events from
group chat manager through run_stream.
SpeakerSelectedEvent from group chat speaker selection.
Closes#6161
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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>
## Why are these changes needed?
- To add support for code generation, execution and reflection to
`CodeExecutorAgent`.
## Related issue number
Closes#5824
## 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>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->
## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Please refer to #6123 for full context.
That issue outlines several design and behavioral problems with
`SocietyOfMindAgent`.
This DRAFT PR focuses on resolving the most critical and broken
behaviors first.
Here is the error list
🔍 SocietyOfMindAgent: Design Issues and Historical Comparison (v0.2 vs
v0.4+)
### ✅ P1–P4 Regression Issue Table (Updated with Fixes in PR #6142)
| ID | Description | Current v0.4+ Issue | Resolution in PR #6142 | Was
it a problem in v0.2? | Notes |
|-----|-------------|----------------------|--------------------------|----------------------------|-------|
| **P1** | `inner_messages` leaks into outer team termination evaluation
| `Response.inner_messages` is appended to the outer team's
`_message_thread`, affecting termination conditions. Violates
encapsulation. | ✅ `inner_messages` is excluded from `_message_thread`,
avoiding contamination of outer termination logic. | ❌ No | Structural
boundary is now enforced |
| **P2** | Inner team does not execute when outer message history is
empty | In chained executions, if no new outer message exists, no task
is created and the inner team is skipped entirely | ✅ Detects absence of
new outer message and reuses the previous task, passing it via a handoff
message. This ensures the inner team always receives a valid task to
execute | ❌ No | The issue was silent task omission, not summary
failure. Summary succeeds as a downstream effect |
| **P3** | Summary LLM prompt is built from external input only | Prompt
is constructed using external message history, ignoring internal
reasoning | ✅ Prompt construction now uses
`final_response.inner_messages`, restoring internal reasoning as the
source of summarization | ❌ No | Matches v0.2 internal monologue
behavior |
| **P4** | External input is included in summary prompt (possibly
incorrectly) | Outer messages are used in the final LLM summarization
prompt | ✅ Resolved via the same fix as P3; outer messages are no longer
used for summary | ❌ No | Redundant with P3, now fully addressed |
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->
## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
resolve#6123
Blocked #6168 (Sometimes SoMA send last whitespace message)
related #6187
<!-- 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: Eric Zhu <ekzhu@users.noreply.github.com>
Resolves#5851
* Added GroupChatError event type and terminate a run when an error
occurs in either a participant or the group chat manager
* Raise a RuntimeError from the error message within the group chat run
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.
Token limited model context is currently broken because it is importing
from extensions.
This fix removed the imports and updated the model context
implementation to use model client directly.
In the future, the model client's token counting should cache results
from model API to provide accurate counting.
This PR 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>
This PR introduces a metadata field in AssistantAgentConfig, allowing
applications to assign and track identity information for agents.
The metadata field is a Dict[str, str] and is included in the
configuration for proper serialization.
---------
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?
Resolve#5953
## Related issue number
#5953
<!-- For example: "Closes #1234" -->
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
I have run all [common
tasks](https://github.com/microsoft/autogen/blob/main/python/README.md#common-tasks),
got below errors which I think it is due to no OpenAI API Key is in my
environment variables. Can we ignore them or do I need to buy one?
```
=================================================== short test summary info ===================================================
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_basic_entity_creation - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_upsert_operations - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_db_manager.py::TestDatabaseOperations::test_delete_operations - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_load_from_file - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_load_from_directory - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_create_team - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
ERROR tests/test_team_manager.py::TestTeamManager::test_run_stream - openai.OpenAIError: The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_...
=========================================== 3 passed, 5 warnings, 7 errors in 9.07s ===========================================
```
Co-authored-by: Leonardo Pinheiro <leosantospinheiro@gmail.com>
## 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>
1. Add `on_pause` and `on_resume` API to `ChatAgent` to support pausing
behavior when running `on_message` concurrently.
2. Add `GroupChatPause` and `GroupChatResume` RPC events and handle them
in `ChatAgentContainer`.
3. Add `pause` and `resume` API to `BaseGroupChat` to allow for this
behavior accessible from the public API.
4. Improve `SequentialRoutedAgent` class to customize which message
types are sequentially handled, making it possible to have concurrent
handling for some messages (e.g., `GroupChatPause`).
5. Added unit tests.
See `test_group_chat_pause_resume.py` for how to use this feature.
What is the difference between pause/resume vs. termination and restart?
- Pause and resume issue direct RPC calls to the participanting agents
of a team while they are running, allowing putting the on-going
generation or actions on hold. This is useful when an agent's turn takes
a long time and multiple steps to complete, and user/application wants
to re-evaluate whether it is worth continue the step or cancel. This
also allows user/application to pause individual agents and resuming
them independently from the team API.
- Termination and restart requires the whole team to comes to a
full-stop, and termination conditions are checked in between agents'
turns. So termination can only happen when no agent is working on its
turn. It is possible that a termination condition has reached well
before the team is terminated, if the agent is taking a long time to
generate a response.
Resolves: #5881
Modify `BaseGroupChat.save_state` to not require the team to be stopped
first. The `save_state` method is read-only. While it may retrieve an
inconsistent state when the team is running, we made a notice to it's
API doc.
Resolves: #5880
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>
