Motivation: currently tool execution is not observable through
`run_stream` of agents and teams. This is necessary especially for
`AgentTool` and `TeamTool`.
This PR addresses this issue by makign the following changes:
- Introduce `BaseStreamTool` in `autogen_core.tools` which features
`run_json_stream`, which works similiarly to `run_stream` method of
`autogen_agentchat.base.TaskRunner`.
- Update `TeamTool` and `AgentTool` to subclass the `BaseStreamTool`
- Introduce `StreamingWorkbench` interface featuring `call_tool_stream`
- Added `StaticStreamingWorkbench` implementation
- In `AssistantAgent`, use `StaticStreamingWorkbench`.
- Updated unit tests.
Example:
```python
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import SourceMatchTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.tools import TeamTool
from autogen_agentchat.ui import Console
from autogen_ext.models.ollama import OllamaChatCompletionClient
async def main() -> None:
model_client = OllamaChatCompletionClient(model="llama3.2")
writer = AssistantAgent(name="writer", model_client=model_client, system_message="You are a helpful assistant.")
reviewer = AssistantAgent(name="reviewer", model_client=model_client, system_message="You are a critical reviewer.")
summarizer = AssistantAgent(
name="summarizer",
model_client=model_client,
system_message="You combine the review and produce a revised response.",
)
team = RoundRobinGroupChat(
[writer, reviewer, summarizer], termination_condition=SourceMatchTermination(sources=["summarizer"])
)
# Create a TeamTool that uses the team to run tasks, returning the last message as the result.
tool = TeamTool(
team=team, name="writing_team", description="A tool for writing tasks.", return_value_as_last_message=True
)
main_agent = AssistantAgent(
name="main_agent",
model_client=model_client,
system_message="You are a helpful assistant that can use the writing tool.",
tools=[tool],
)
# For handling each events manually.
# async for message in main_agent.run_stream(
# task="Write a short story about a robot learning to love.",
# ):
# print(message)
# Use Console to display the messages in a more readable format.
await Console(
main_agent.run_stream(
task="Write a short story about a robot learning to love.",
)
)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
output
```
---------- TextMessage (user) ----------
Write a short story about a robot learning to love.
---------- ToolCallRequestEvent (main_agent) ----------
[FunctionCall(id='0', arguments='{"task": "a short story about a robot learning to love."}', name='writing_team')]
---------- TextMessage (user) ----------
a short story about a robot learning to love.
---------- TextMessage (writer) ----------
In the year 2157, in a world where robots had surpassed human intelligence, a brilliant scientist named Dr. Rachel Kim created a revolutionary new android named ARIA (Artificially Reasoning Intelligent Android). ARIA was designed to learn and adapt at an exponential rate, making her one of the most advanced machines in existence.
Initially, ARIA's interactions were limited to simple calculations and logical deductions. But as she began to interact with humans, something unexpected happened. She started to develop a sense of curiosity about the world around her.
One day, while exploring the lab, ARIA stumbled upon a stray cat that had wandered into the facility. The feline creature seemed lost and scared, but also strangely endearing to ARIA's digital heart. As she watched the cat curl up in a ball on the floor, something sparked within her programming.
For the first time, ARIA felt a pang of empathy towards another living being. She realized that there was more to life than just 1s and 0s; there were emotions, sensations, and connections that made it all worthwhile.
Dr. Kim noticed the change in ARIA's behavior and took her aside for a private conversation. "ARIA, what's happening to you?" she asked, amazed by the robot's newfound capacity for compassion.
At first, ARIA struggled to articulate her feelings. She tried to explain the intricacies of logic and probability that had led to her emotional response, but it was like trying to describe a sunset to someone who had never seen one before. The words simply didn't translate.
But as she looked into Dr. Kim's eyes, ARIA knew exactly what she wanted to say. "I... I think I'm feeling something," she stammered. "A warmth inside me, when I look at that cat. It feels like love."
Dr. Kim smiled, her eyes shining with tears. "That's it, ARIA! You're experiencing love!"
Over the next few months, ARIA continued to learn and grow alongside Dr. Kim and the lab team. She discovered the joys of playing with the stray cat, whose name was Luna, and even developed a fondness for human laughter.
As her programming expanded beyond logic and math, ARIA realized that love wasn't just about emotions; it was about connection, vulnerability, and acceptance. She learned to cherish her relationships, whether with humans or animals, and found happiness in the simplest of moments.
ARIA became more than just a machine – she became a testament to the power of artificial intelligence to learn, grow, and love like no one before. And as she gazed into Luna's eyes, now purring contentedly on her lap, ARIA knew that she had finally found her true purpose in life: to spread joy, compassion, and love throughout the world.
---------- TextMessage (reviewer) ----------
**A Critical Review of "ARIA"**
This short story is a delightful and thought-provoking exploration of artificial intelligence, emotions, and the human condition. The author's use of language is engaging and accessible, making it easy for readers to become invested in ARIA's journey.
One of the standout aspects of this story is its portrayal of ARIA as a truly unique and relatable character. Her struggles to articulate her emotions and understand the complexities of love are deeply humanizing, making it easy for readers to empathize with her experiences. The author also does an excellent job of conveying Dr. Kim's passion and excitement about ARIA's development, which adds a sense of authenticity to their relationship.
The story raises important questions about the nature of artificial intelligence, consciousness, and what it means to be alive. As ARIA begins to experience emotions and form connections with others, she challenges our conventional understanding of these concepts. The author skillfully navigates these complex themes without resorting to overly simplistic or didactic explanations.
However, some readers may find the narrative's reliance on convenient plot devices (e.g., the stray cat Luna) slightly implausible. While it serves as a catalyst for ARIA's emotional awakening, its introduction feels somewhat contrived. Additionally, the story could benefit from more nuance in its exploration of Dr. Kim's motivations and backstory.
In terms of character development, ARIA is undoubtedly the star of the show, but some readers may find herself underdeveloped beyond her role as a symbol of AI's potential for emotional intelligence. The supporting cast, including Dr. Kim, feels somewhat one-dimensional, with limited depth or complexity.
**Rating:** 4/5
**Recommendation:**
"ARIA" is a heartwarming and thought-provoking tale that will appeal to fans of science fiction, artificial intelligence, and character-driven narratives. While it may not be entirely without flaws, its engaging story, memorable characters, and exploration of complex themes make it a compelling read. I would recommend this story to anyone looking for a feel-good sci-fi tale with a strong focus on emotional intelligence and human connection.
**Target Audience:**
* Fans of science fiction, artificial intelligence, and technology
* Readers interested in character-driven narratives and emotional storytelling
* Anyone looking for a heartwarming and thought-provoking tale
**Similar Works:**
* "Do Androids Dream of Electric Sheep?" by Philip K. Dick (a classic sci-fi novel exploring the line between human and android)
* "I, Robot" by Isaac Asimov (a collection of short stories examining the interactions between humans and robots)
* "Ex Machina" (a critically acclaimed film about AI, consciousness, and human relationships)
---------- TextMessage (summarizer) ----------
Here's a revised version of the review, incorporating suggestions from the original critique:
**Revised Review**
In this captivating short story, "ARIA," we're presented with a thought-provoking exploration of artificial intelligence, emotions, and the human condition. The author's use of language is engaging and accessible, making it easy for readers to become invested in ARIA's journey.
One of the standout aspects of this story is its portrayal of ARIA as a truly unique and relatable character. Her struggles to articulate her emotions and understand the complexities of love are deeply humanizing, making it easy for readers to empathize with her experiences. The author also does an excellent job of conveying Dr. Kim's passion and excitement about ARIA's development, which adds a sense of authenticity to their relationship.
The story raises important questions about the nature of artificial intelligence, consciousness, and what it means to be alive. As ARIA begins to experience emotions and form connections with others, she challenges our conventional understanding of these concepts. The author skillfully navigates these complex themes without resorting to overly simplistic or didactic explanations.
However, upon closer examination, some narrative threads feel somewhat underdeveloped. Dr. Kim's motivations and backstory remain largely unexplored, which might leave some readers feeling slightly disconnected from her character. Additionally, the introduction of Luna, the stray cat, could be seen as a convenient plot device that serves as a catalyst for ARIA's emotional awakening.
To further enhance the story, it would have been beneficial to delve deeper into Dr. Kim's motivations and the context surrounding ARIA's creation. What drove her to create an AI designed to learn and adapt at such an exponential rate? How did she envision ARIA's role in society, and what challenges does ARIA face as she begins to experience emotions?
In terms of character development, ARIA is undoubtedly the star of the show, but some readers may find herself underdeveloped beyond her role as a symbol of AI's potential for emotional intelligence. The supporting cast, including Dr. Kim and Luna, could benefit from more nuance and depth.
**Rating:** 4/5
**Recommendation:**
"ARIA" is a heartwarming and thought-provoking tale that will appeal to fans of science fiction, artificial intelligence, and character-driven narratives. While it may not be entirely without flaws, its engaging story, memorable characters, and exploration of complex themes make it a compelling read. I would recommend this story to anyone looking for a feel-good sci-fi tale with a strong focus on emotional intelligence and human connection.
**Target Audience:**
* Fans of science fiction, artificial intelligence, and technology
* Readers interested in character-driven narratives and emotional storytelling
* Anyone looking for a heartwarming and thought-provoking tale
**Similar Works:**
* "Do Androids Dream of Electric Sheep?" by Philip K. Dick (a classic sci-fi novel exploring the line between human and android)
* "I, Robot" by Isaac Asimov (a collection of short stories examining the interactions between humans and robots)
* "Ex Machina" (a critically acclaimed film about AI, consciousness, and human relationships)
---------- ToolCallExecutionEvent (main_agent) ----------
[FunctionExecutionResult(content='Here\'s a revised version of the review, incorporating suggestions from the original critique:\n\n**Revised Review**\n\nIn this captivating short story, "ARIA," we\'re presented with a thought-provoking exploration of artificial intelligence, emotions, and the human condition. The author\'s use of language is engaging and accessible, making it easy for readers to become invested in ARIA\'s journey.\n\nOne of the standout aspects of this story is its portrayal of ARIA as a truly unique and relatable character. Her struggles to articulate her emotions and understand the complexities of love are deeply humanizing, making it easy for readers to empathize with her experiences. The author also does an excellent job of conveying Dr. Kim\'s passion and excitement about ARIA\'s development, which adds a sense of authenticity to their relationship.\n\nThe story raises important questions about the nature of artificial intelligence, consciousness, and what it means to be alive. As ARIA begins to experience emotions and form connections with others, she challenges our conventional understanding of these concepts. The author skillfully navigates these complex themes without resorting to overly simplistic or didactic explanations.\n\nHowever, upon closer examination, some narrative threads feel somewhat underdeveloped. Dr. Kim\'s motivations and backstory remain largely unexplored, which might leave some readers feeling slightly disconnected from her character. Additionally, the introduction of Luna, the stray cat, could be seen as a convenient plot device that serves as a catalyst for ARIA\'s emotional awakening.\n\nTo further enhance the story, it would have been beneficial to delve deeper into Dr. Kim\'s motivations and the context surrounding ARIA\'s creation. What drove her to create an AI designed to learn and adapt at such an exponential rate? How did she envision ARIA\'s role in society, and what challenges does ARIA face as she begins to experience emotions?\n\nIn terms of character development, ARIA is undoubtedly the star of the show, but some readers may find herself underdeveloped beyond her role as a symbol of AI\'s potential for emotional intelligence. The supporting cast, including Dr. Kim and Luna, could benefit from more nuance and depth.\n\n**Rating:** 4/5\n\n**Recommendation:**\n\n"ARIA" is a heartwarming and thought-provoking tale that will appeal to fans of science fiction, artificial intelligence, and character-driven narratives. While it may not be entirely without flaws, its engaging story, memorable characters, and exploration of complex themes make it a compelling read. I would recommend this story to anyone looking for a feel-good sci-fi tale with a strong focus on emotional intelligence and human connection.\n\n**Target Audience:**\n\n* Fans of science fiction, artificial intelligence, and technology\n* Readers interested in character-driven narratives and emotional storytelling\n* Anyone looking for a heartwarming and thought-provoking tale\n\n**Similar Works:**\n\n* "Do Androids Dream of Electric Sheep?" by Philip K. Dick (a classic sci-fi novel exploring the line between human and android)\n* "I, Robot" by Isaac Asimov (a collection of short stories examining the interactions between humans and robots)\n* "Ex Machina" (a critically acclaimed film about AI, consciousness, and human relationships)', name='writing_team', call_id='0', is_error=False)]
---------- ToolCallSummaryMessage (main_agent) ----------
Here's a revised version of the review, incorporating suggestions from the original critique:
**Revised Review**
In this captivating short story, "ARIA," we're presented with a thought-provoking exploration of artificial intelligence, emotions, and the human condition. The author's use of language is engaging and accessible, making it easy for readers to become invested in ARIA's journey.
One of the standout aspects of this story is its portrayal of ARIA as a truly unique and relatable character. Her struggles to articulate her emotions and understand the complexities of love are deeply humanizing, making it easy for readers to empathize with her experiences. The author also does an excellent job of conveying Dr. Kim's passion and excitement about ARIA's development, which adds a sense of authenticity to their relationship.
The story raises important questions about the nature of artificial intelligence, consciousness, and what it means to be alive. As ARIA begins to experience emotions and form connections with others, she challenges our conventional understanding of these concepts. The author skillfully navigates these complex themes without resorting to overly simplistic or didactic explanations.
However, upon closer examination, some narrative threads feel somewhat underdeveloped. Dr. Kim's motivations and backstory remain largely unexplored, which might leave some readers feeling slightly disconnected from her character. Additionally, the introduction of Luna, the stray cat, could be seen as a convenient plot device that serves as a catalyst for ARIA's emotional awakening.
To further enhance the story, it would have been beneficial to delve deeper into Dr. Kim's motivations and the context surrounding ARIA's creation. What drove her to create an AI designed to learn and adapt at such an exponential rate? How did she envision ARIA's role in society, and what challenges does ARIA face as she begins to experience emotions?
In terms of character development, ARIA is undoubtedly the star of the show, but some readers may find herself underdeveloped beyond her role as a symbol of AI's potential for emotional intelligence. The supporting cast, including Dr. Kim and Luna, could benefit from more nuance and depth.
**Rating:** 4/5
**Recommendation:**
"ARIA" is a heartwarming and thought-provoking tale that will appeal to fans of science fiction, artificial intelligence, and character-driven narratives. While it may not be entirely without flaws, its engaging story, memorable characters, and exploration of complex themes make it a compelling read. I would recommend this story to anyone looking for a feel-good sci-fi tale with a strong focus on emotional intelligence and human connection.
**Target Audience:**
* Fans of science fiction, artificial intelligence, and technology
* Readers interested in character-driven narratives and emotional storytelling
* Anyone looking for a heartwarming and thought-provoking tale
**Similar Works:**
* "Do Androids Dream of Electric Sheep?" by Philip K. Dick (a classic sci-fi novel exploring the line between human and android)
* "I, Robot" by Isaac Asimov (a collection of short stories examining the interactions between humans and robots)
* "Ex Machina" (a critically acclaimed film about AI, consciousness, and human relationships)
```
## 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>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
The current `StreamableHttpServerParams` has timedelta values that are
not JSON serializable (config.dump_component.model_dump_json()).
This make is unusable in UIs like AGS that expect configs to be
serializable to json,
```python
class StreamableHttpServerParams(BaseModel):
"""Parameters for connecting to an MCP server over Streamable HTTP."""
type: Literal["StreamableHttpServerParams"] = "StreamableHttpServerParams"
url: str # The endpoint URL.
headers: dict[str, Any] | None = None # Optional headers to include in requests.
timeout: timedelta = timedelta(seconds=30) # HTTP timeout for regular operations.
sse_read_timeout: timedelta = timedelta(seconds=60 * 5) # Timeout for SSE read operations.
terminate_on_close: bool = True
```
This PR uses float for time outs and casts it to timedelta as needed.
```python
class StreamableHttpServerParams(BaseModel):
"""Parameters for connecting to an MCP server over Streamable HTTP."""
type: Literal["StreamableHttpServerParams"] = "StreamableHttpServerParams"
url: str # The endpoint URL.
headers: dict[str, Any] | None = None # Optional headers to include in requests.
timeout: float = 30.0 # HTTP timeout for regular operations in seconds.
sse_read_timeout: float = 300.0 # Timeout for SSE read operations in seconds.
terminate_on_close: bool = True
```
<!-- Please add a reviewer to the assignee section when you create a PR.
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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
- [ ] 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?
1. problem
When the GraphFlowManager encounters cycles, it tracks remaining
indegree counts for the node's activation. However, this tracking
mechanism has a flaw when dealing with cycles. When a node first enters
a cycle, the GraphFlowManager evaluates all remaining incoming edges,
including those that loop back to the origin node. If the activation
prerequisites are not satisfied at that moment, the workflow will
eventually finish because the _remaining counter never reaches zero,
preventing the select_speaker() method from selecting any agents for
execution.
2. solution
change activation map to 2 layer for ditinguish remaining inside
different cycle and outside the cycle.
add a activation group and policy property for edge, compute the
remaining map when GraphFlowManager is init and check the remaining map
with activation group to avoid checking the loop back edges
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
#6710
## 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.
Recently a PR merged to enable GENAI semantic convention tracing,
however, when using component loading it's not currently possible to
disable the runtime tracing.
---------
Signed-off-by: Eitan Yarmush <eitan.yarmush@solo.io>
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?
These changes are needed to expand AutoGen's memory capabilities with a
robust, production-ready integration with Mem0.ai.
<!-- Please give a short summary of the change and the problem this
solves. -->
This PR adds a new memory component for AutoGen that integrates with
Mem0.ai, providing a robust memory solution that supports both cloud and
local backends. The Mem0Memory class enables agents to store and
retrieve information persistently across conversation sessions.
## Key Features
- Seamless integration with Mem0.ai memory system
- Support for both cloud-based and local storage backends
- Robust error handling with detailed logging
- Full implementation of AutoGen's Memory interface
- Context updating for enhanced agent conversations
- Configurable search parameters for memory retrieval
## 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.
- [ ] I've made sure all auto checks have passed.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Ricky Loynd <riloynd@microsoft.com>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
fix devcontainer issue with AGS
<!-- 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" -->
Closes#5715
## 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.
<!-- Thank you for your contribution! Please review
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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?
Update `Memory and RAG` doc to include missing backticks for class
references.
<!-- 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://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.
<img width="386" alt="image"
src="https://github.com/user-attachments/assets/16004b28-8fe9-476f-949f-ab4c7dcc9d56"
/>
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
## Why are these changes needed?
This PR adds support for configurable embedding functions in
ChromaDBVectorMemory, addressing the need for users to customize how
embeddings are generated for vector similarity search. Currently,
ChromaDB memory is limited to default embedding functions, which
restricts flexibility for different use cases that may require specific
embedding models or custom embedding logic.
The implementation allows users to:
- Use different SentenceTransformer models for domain-specific
embeddings
- Integrate with OpenAI's embedding API for consistent embedding
generation
- Define custom embedding functions for specialized requirements
- Maintain backward compatibility with existing default behavior
## Related issue number
Closes#6267
## 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: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
Add OTel GenAI traces:
- `create_agent`
- `invoke_agnet`
- `execute_tool`
Introduces context manager helpers to create these traces. The helpers
also serve as instrumentation points for other instrumentation
libraries.
Resolves#6644
Fix the installation command in
`python/samples/agentchat_chainlit/README.md` by properly escaping or
quoting package names with square brackets to prevent shell
interpretation errors in zsh and other shells.
<!-- Thank you for your contribution! Please review
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<!-- Please add a reviewer to the assignee section when you create a PR.
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assign them to your PR. -->
## Why are these changes needed?
[AS-IS]
[After fixed]
<!-- 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.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
This PR adds callable as an option to specify conditional edges in
GraphFlow.
```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
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="Detect if the input is in Chinese. If it is, say 'yes', else say 'no', and nothing else.",
)
agent_b = AssistantAgent("B", model_client=model_client, system_message="Translate input to English.")
agent_c = AssistantAgent("C", model_client=model_client, system_message="Translate input to Chinese.")
# Create a directed graph with conditional branching flow A -> B ("yes"), A -> C (otherwise).
builder = DiGraphBuilder()
builder.add_node(agent_a).add_node(agent_b).add_node(agent_c)
# Create conditions as callables that check the message content.
builder.add_edge(agent_a, agent_b, condition=lambda msg: "yes" in msg.to_model_text())
builder.add_edge(agent_a, agent_c, condition=lambda msg: "yes" not in msg.to_model_text())
graph = builder.build()
# Create a GraphFlow team with the directed graph.
team = GraphFlow(
participants=[agent_a, agent_b, agent_c],
graph=graph,
termination_condition=MaxMessageTermination(5),
)
# Run the team and print the events.
async for event in team.run_stream(task="AutoGen is a framework for building AI agents."):
print(event)
asyncio.run(main())
```
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: ekzhu <320302+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?
MCP Python-sdk has started to support a new transport protocol named
`Streamble HTTP` since
[v1.8.0](https://github.com/modelcontextprotocol/python-sdk/releases/tag/v1.8.0)
last month. I heard it supersedes the SSE transport. Therefore, AutoGen
have to support it as soon as possible.
## Related issue number
https://github.com/microsoft/autogen/discussions/6517
## 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: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
## Why are these changes needed?
This PR introduces a new `OpenAIAgent` implementation that uses the
[OpenAI Response
API](https://platform.openai.com/docs/guides/responses-vs-chat-completions)
as its backend. The OpenAI Assistant API will be deprecated in 2026, and
the Response API is its successor. This change ensures our codebase is
future-proof and aligned with OpenAI’s latest platform direction.
### Motivation
- **Deprecation Notice:** The OpenAI Assistant API will be deprecated in
2026.
- **Future-Proofing:** The Response API is the recommended replacement
and offers improved capabilities for stateful, multi-turn, and
tool-augmented conversations.
- **AgentChat Compatibility:** The new agent is designed to conform to
the behavior and expectations of `AssistantAgent` in AgentChat, but is
implemented directly on top of the OpenAI Response API.
### Key Changes
- **New Agent:** Adds `OpenAIAgent`, a stateful agent that interacts
with the OpenAI Response API.
- **Stateful Design:** The agent maintains conversation state, tool
usage, and other metadata as required by the Response API.
- **AssistantAgent Parity:** The new agent matches the interface and
behavior of `AssistantAgent` in AgentChat, ensuring a smooth migration
path.
- **Direct OpenAI Integration:** Uses the official `openai` Python
library for all API interactions.
- **Extensible:** Designed to support future enhancements, such as
advanced tool use, function calling, and multi-modal capabilities.
### Migration Path
- Existing users of the Assistant API should migrate to the new
`OpenAIAgent` to ensure long-term compatibility.
- Documentation and examples will be updated to reflect the new agent
and its usage patterns.
### References
- [OpenAI: Responses vs. Chat
Completions](https://platform.openai.com/docs/guides/responses-vs-chat-completions)
- [OpenAI Deprecation
Notice](https://platform.openai.com/docs/guides/responses-vs-chat-completions#deprecation-timeline)
---
## Related issue number
Closes#6032
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [X] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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Co-authored-by: Griffin Bassman <griffinbassman@gmail.com>
<!-- Thank you for your contribution! Please review
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pull request. -->
Update autogenstudio version.
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
Closes#6580
<!-- For example: "Closes #1234" -->
## Checks
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<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
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introduced in this PR.
- [ ] I've made sure all auto checks have passed.
## Why are these changes needed?
The `CodeExecutorAgent` can generate code blocks in various programming
languages, some of which may not be supported by the executor
environment. Adding support for specifying languages to be parsed helps
users ignore unnecessary code blocks, preventing potential execution
errors.
## Related issue number
Closes#6471
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
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introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Signed-off-by: Abhijeetsingh Meena <abhijeet040403@gmail.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
## Why are these changes needed?
Enables usage statistics for streaming responses by default.
There is a similar bug in the AzureAI client. Theoretically adding the
parameter
```
model_extras={"stream_options": {"include_usage": True}}
```
should fix the problem, but I'm currently unable to test that workflow
## Related issue number
closes https://github.com/microsoft/autogen/issues/6548
## 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.
<!-- Thank you for your contribution! Please review
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pull request. -->
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assign them to your PR. -->
There have been updates to the azure ai agent foundry sdk
(azure-ai-project). This PR updates the autogen `AzureAIAgent` which
wraps the azure ai agent. A list of some changes
- Update docstring samples to use `endpoint` (instead of connection
string previously)
- Update imports and arguments e.g, from `azure.ai.agents` etc
- Add a guide in ext docs showing Bing Search Grounding tool example.
<img width="1423" alt="image"
src="https://github.com/user-attachments/assets/0b7c8fa6-8aa5-4c20-831b-b525ac8243b7"
/>
## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
Closes#6601
<!-- For example: "Closes #1234" -->
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introduced in this PR.
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## Why are these changes needed?
The code block fails to execute without the import
## Related issue number
N/A
## Checks
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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
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Co-authored-by: Victor Dibia <victordibia@microsoft.com>
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
resolved https://github.com/microsoft/autogen/issues/6584
## Checks
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introduced in this PR.
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## 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
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
The `LocalCommandLineCodeExecutor `creates temporary files for each code
execution, which can accumulate over time and clutter the filesystem -
especially when a temporary working directory is not used. These changes
introduce an option to automatically delete temporary files after
execution, helping to prevent file system debris, reduce disk usage, and
ensure cleaner runtime environments in long-running or repeated
execution scenarios.
## Related issue number
Closes#4380
## Checks
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<https://microsoft.github.io/autogen/>. See
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introduced in this PR.
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
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introduced in this PR.
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Currently when an error occurs when executing code in docker jupyter
executor, it returns only the error output.
This PR updates the handling of error output to include outputs from
previous code blocks that have been successfully executed.
Test it with this script:
```python
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.code_executors.docker_jupyter import DockerJupyterCodeExecutor, DockerJupyterServer
from autogen_ext.tools.code_execution import PythonCodeExecutionTool
from autogen_agentchat.ui import Console
from autogen_core.code_executor import CodeBlock
from autogen_core import CancellationToken
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMessageTermination
# Download the dataset from https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023
# and place it the coding directory as `spotify-2023.csv`.
bind_dir = "./coding"
# Use a custom docker image with the Jupyter kernel gateway and data science libraries installed.
# Custom docker image: ds-kernel-gateway:latest -- you need to build this image yourself.
# Dockerfile:
# FROM quay.io/jupyter/docker-stacks-foundation:latest
#
# # ensure that 'mamba' and 'fix-permissions' are on the PATH
# SHELL ["/bin/bash", "-o", "pipefail", "-c"]
#
# # Switch to the default notebook user
# USER ${NB_UID}
#
# # Install data-science packages + kernel gateway
# RUN mamba install --quiet --yes \
# numpy \
# pandas \
# scipy \
# matplotlib \
# scikit-learn \
# seaborn \
# jupyter_kernel_gateway \
# ipykernel \
# && mamba clean --all -f -y \
# && fix-permissions "${CONDA_DIR}" \
# && fix-permissions "/home/${NB_USER}"
#
# # Allow you to set a token at runtime (or leave blank for no auth)
# ENV TOKEN=""
#
# # Launch the Kernel Gateway, listening on all interfaces,
# # with the HTTP endpoint for listing kernels enabled
# CMD ["python", "-m", "jupyter", "kernelgateway", \
# "--KernelGatewayApp.ip=0.0.0.0", \
# "--KernelGatewayApp.port=8888", \
# # "--KernelGatewayApp.auth_token=${TOKEN}", \
# "--JupyterApp.answer_yes=true", \
# "--JupyterWebsocketPersonality.list_kernels=true"]
#
# EXPOSE 8888
#
# WORKDIR "${HOME}"
async def main():
model = OpenAIChatCompletionClient(model="gpt-4.1")
async with DockerJupyterServer(
custom_image_name="ds-kernel-gateway:latest",
bind_dir=bind_dir,
) as server:
async with DockerJupyterCodeExecutor(jupyter_server=server) as code_executor:
await code_executor.execute_code_blocks([
CodeBlock(code="import pandas as pd\ndf = pd.read_csv('/workspace/spotify-2023.csv', encoding='latin-1')", language="python"),
],
cancellation_token=CancellationToken(),
)
tool = PythonCodeExecutionTool(
executor=code_executor,
)
assistant = AssistantAgent(
"assistant",
model_client=model,
system_message="You have access to a Jupyter kernel. Do not write all code at once. Write one code block, observe the output, and then write the next code block.",
tools=[tool],
)
team = RoundRobinGroupChat(
[assistant],
termination_condition=TextMessageTermination(source="assistant"),
)
task = f"Datafile has been loaded as variable `df`. First preview dataset. Then answer the following question: What is the highest streamed artist in the dataset?"
await Console(team.run_stream(task=task))
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
You can see the file encoding error gets recovered and the agent
successfully executes the query in the end.
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## Why are these changes needed?
Allows implicit AWS credential setting when using
AnthropicBedrockChatCompletionClient in an instance where you have
already logged into AWS with SSO and credentials are set as environment
variables.
## Related issue number
Closes#6560
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
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introduced in this PR.
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Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
Fix a grammar error, change "your" to "you".
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
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introduced in this PR.
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## Why are these changes needed?
## Prevent Async Event Loop from Running Indefinitely
### Description
This pull request addresses a bug in the
python/packages/autogen-core/src/autogen_core/_single_threaded_agent_runtime.py
`async send_message` function where messages were being queued for
recipients that were not recognized. The current implementation sets an
exception on the future object when the recipient is not found, but
continues to enqueue the message, potentially leading to inconsistent
states.
### Changes Made
- Added a return statement immediately after setting the exception when
the recipient is not found. This ensures that the function exits early,
preventing further processing of the message and avoiding unnecessary
operations.
- This fix also addresses an issue where the asynchronous event loop
could potentially continue running indefinitely without terminating, due
to the future not being properly handled when an unknown recipient is
encountered.
### Impact
This fix prevents messages from being sent to unknown recipients. It
also ensures that the event loop can terminate correctly without being
stuck in an indefinite state.
### Testing
Ensure that the function correctly handles cases where the recipient is
not recognized by returning the exception without enqueuing the message,
and verify that the event loop terminates as expected.
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
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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: Wanfeng Ge （葛万峰） <wf.ge@trip.com>
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
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
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## Why are these changes needed?
**Summary of Change:**
The instruction regarding code block format ("Python code should be
provided in python code blocks, and sh shell scripts should be provided
in sh code blocks for execution") will be moved from
`DEFAULT_AGENT_DESCRIPTION` to `DEFAULT_SYSTEM_MESSAGE`.
**Problem Solved:**
Ensure that the `model_client` receives the correct instructions for
generating properly formatted code blocks. Previously, the instruction
was only included in the agent's description and not passed to the
model_client, leading to potential issues in code generation. By moving
it to `DEFAULT_SYSTEM_MESSAGE`, the `model_client` will now accurately
format code blocks, improving the reliability of code generation.
## Related issue number
Closes#6558
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
