Support concurrent execution in `GraphFlow`:
- Updated `BaseGroupChatManager.select_speaker` to return a union of a
single string or a list of speaker name strings and added logics to
check for currently activated speakers and only proceed to select next
speakers when all activated speakers have finished.
- Updated existing teams (e.g., `SelectorGroupChat`) with the new
signature, while still returning a single speaker in their
implementations.
- Updated `GraphFlow` to support multiple speakers selected.
- Refactored `GraphFlow` for less dictionary gymnastic by using a queue
and update using `update_message_thread`.
Example: a fan out graph:
```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import DiGraphBuilder, GraphFlow
from autogen_ext.models.openai import OpenAIChatCompletionClient
async def main():
# Initialize agents with OpenAI model clients.
model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
agent_a = AssistantAgent("A", model_client=model_client, system_message="You are a helpful assistant.")
agent_b = AssistantAgent("B", model_client=model_client, system_message="Translate input to Chinese.")
agent_c = AssistantAgent("C", model_client=model_client, system_message="Translate input to Japanese.")
# Create a directed graph with fan-out flow A -> (B, C).
builder = DiGraphBuilder()
builder.add_node(agent_a).add_node(agent_b).add_node(agent_c)
builder.add_edge(agent_a, agent_b).add_edge(agent_a, agent_c)
graph = builder.build()
# Create a GraphFlow team with the directed graph.
team = GraphFlow(
participants=[agent_a, agent_b, agent_c],
graph=graph,
)
# Run the team and print the events.
async for event in team.run_stream(task="Write a short story about a cat."):
print(event)
asyncio.run(main())
```
Resolves:
#6541#6533
## 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?
This PR enhances the `SelectorGroupChat` class by introducing a new
`model_context` parameter to support more context-aware speaker
selection.
### Changes
- Added a `model_context: ChatCompletionContext | None` parameter to
`SelectorGroupChat`.
- Defaulted to `UnboundedChatCompletionContext` when None is provided
like `AssistantAgent`.
- Updated `_select_speaker` to prepend context messages from
`model_context` to the main thread history.
- Refactored history construction into a helper method
`construct_message_history`.
## Related issue number
Closes [Issue #6301](https://github.com/org/repo/issues/6301), enabling
the group chat manager to utilize `model_context` for richer, more
informed speaker selection decisions.
## 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>
## 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>
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>
## 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>
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>
## 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
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
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pull request. -->
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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
- [ ] 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>
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pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
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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.
<!-- Please give a short summary of the change and the problem this
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.
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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pull request. -->
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## 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#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>
