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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" -->
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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.
<!-- Thank you for your contribution! Please review
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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
- [ ] 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: Jack Gerrits <jackgerrits@users.noreply.github.com>
Fix for LLMCallEvent failing to log "tools" passed to
BaseOpenAIChatCompletionClient in
autogen_ext.models.openai._openai_client.BaseOpenAIChatCompletionClient
This bug creates problems inspecting why a certain tool was selected/not
selected by the LLM as the list of tools available to the LLM is not
present in the logs
## Why are these changes needed?
Added "tools" to the LLMCallEvent to log tools available to the LLM as
these were being missed causing difficulties during debugging LLM tool
calls.
## Related issue number
[<!-- For example: "Closes #1234"
-->](https://github.com/microsoft/autogen/issues/6531)
## 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: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
To add the latest support for using Llama API offerings with AutoGen
## Checks
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build and test documentation locally.
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introduced in this PR.
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---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
Simplified the azure ai search tool and fixed bugs in the code
## Related issue number
"Closes #6430 "
## 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.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
FIX/mistral could not recive name field, so add model transformer for
mistral
## Related issue number
Closes#6147
## Checks
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build and test documentation locally.
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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
…ized properly
## Why are these changes needed?
The exceptions thrown by MCP server tools weren't being serialized
properly - the user would see `[{}, {}, ... {}]` instead of an actual
error/exception message.
## Related issue number
Fixes#6481
## Checks
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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: Peter Jausovec <peter.jausovec@solo.io>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
<!-- Thank you for your contribution! Please review
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pull request. -->
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## Why are these changes needed?
Nice to have functionality
## Related issue number
Closes#6060
## Checks
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<https://microsoft.github.io/autogen/>. See
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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: Eric Zhu <ekzhu@users.noreply.github.com>
<!-- Thank you for your contribution! Please review
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pull request. -->
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## Why are these changes needed?
As-is: Deleting `McpWorkbench` does not close the `McpSession`.
To-be: Deleting `McpWorkbench` now properly closes the `McpSession`.
<!-- 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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<https://microsoft.github.io/autogen/>. See
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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.
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
Some fixes with the AnthropicBedrockChatCompletionClient
- Ensure `AnthropicBedrockChatCompletionClient` exported and can be
imported.
- Update the BedrockInfo keys serialization - client argument can be
string (similar to api key in this ) but exported config should be
Secret
- Replace `AnthropicBedrock` with `AsyncAnthropicBedrock` : client
should be async to work with the ag stack and the BaseAnthropicClient it
inherits from
- Improve `AnthropicBedrockChatCompletionClient` docstring to use the
correct client arguments rather than serialized dict format.
Expect
```python
from autogen_ext.models.anthropic import AnthropicBedrockChatCompletionClient, BedrockInfo
from autogen_core.models import UserMessage, ModelInfo
async def main():
anthropic_client = AnthropicBedrockChatCompletionClient(
model="anthropic.claude-3-5-sonnet-20240620-v1:0",
temperature=0.1,
model_info=ModelInfo(vision=False, function_calling=True,
json_output=False, family="unknown", structured_output=True),
bedrock_info=BedrockInfo(
aws_access_key="<aws_access_key>",
aws_secret_key="<aws_secret_key>",
aws_session_token="<aws_session_token>",
aws_region="<aws_region>",
),
)
# type: ignore
result = await anthropic_client.create([UserMessage(content="What is the capital of France?", source="user")])
print(result)
await main()
```
<!-- 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#6483
## 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>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
Add gpt 4o search models to list of default models
```python
"gpt-4o-mini-search-preview-2025-03-11": {
"vision": False,
"function_calling": True,
"json_output": True,
"family": ModelFamily.GPT_4O,
"structured_output": True,
"multiple_system_messages": True,
},
"gpt-4o-search-preview-2025-03-11": {
"vision": False,
"function_calling": True,
"json_output": True,
"family": ModelFamily.GPT_4O,
"structured_output": True,
"multiple_system_messages": True,
},
```
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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" -->
Closes#6491
## Checks
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build and test documentation locally.
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introduced in this PR.
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## Why are these changes needed?
Our McpWorkbench required properties however mongodb-lens's some tools
do not has it. I will fix it from when properties is None, -> {}
Our McpWorkbench now does not have stop routine with without async with
McpWorkbench(params) as workbench: and lazy init. So, I will adding def
__del__: pass just insert that, It could show error.
## Related issue number
Closes#6425
## Checks
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<https://microsoft.github.io/autogen/>. See
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build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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## Why are these changes needed?
Multimodal message fill context with other routine. However current
`_set_empty_to_whitespace` is fill with context.
So, error occured.
And, I checked `multimodal_user_transformer_funcs` and I found it, in
this routine, context must not be empty.
Now remove the `_set_empty_to_whitespace` when multimodal message,
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
Closes#6439
## Checks
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build and test documentation locally.
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introduced in this PR.
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Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
Current autogen-ext's test is too slow.
So, I will search slow test case and makes more fast.
[init docker executor function to module
180s->140s](a3cf70bcf8)
[reuse executor at some tests
140s->120s](ca15938afa)
[Remove unnecessary start of docker
120s->110s](61247611e0)
## Related issue number
<!-- For example: "Closes #1234" -->
Part of #6376
## Checks
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build and test documentation locally.
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introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Adding support for Bing grounding citations to the AzureAIAgent.
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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
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<https://microsoft.github.io/autogen/>. See
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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>
Co-authored-by: Dheeraj Bandaru <BandaruDheeraj@users.noreply.github.com>
## Why are these changes needed?
Anthropic models are supported by AWS bedrock. ChatCompletionClient can
be created for anthropic bedrock models using this changes. This enables
the user to do the following
- Add any anthropic models and version from AWS bedrock
- Can use ChatCompletionClient for bedrock anthropic models
## Related issue number
Closes#5226
---------
Co-authored-by: harini.narasimhan <harini.narasimhan@eagleview.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
*Problem*
Previously, in `DockerCommandLineCodeExecutor`, cancellation tasks were
added directly to `self._cancellation_tasks` using
`asyncio.create_task()`:
```python
self._cancellation_tasks.append(asyncio.create_task(self._kill_running_command(command)))
```
This caused issues when cancellation tasks were created from multiple
event loops, leading to loop mismatch errors during executor shutdown.
*Solution*
This PR fixes the issue by introducing a dedicated internal event loop
for managing cancellation tasks.
Cancellation tasks are now scheduled in a fixed event loop using
`asyncio.run_coroutine_threadsafe()`:
```python
future: ConcurrentFuture[None] = asyncio.run_coroutine_threadsafe(
self._kill_running_command(command), self._loop
)
self._cancellation_futures.append(future)
```
*Additional Changes*
- Added detailed logging for easier debugging.
- Ensured clean shutdown of the internal event loop and associated
thread.
*Note*
This change ensures that all cancellation tasks are handled consistently
in a single loop, preventing cross-loop conflicts and improving executor
stability in multi-threaded environments.
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#6395
## 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: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
- Add return_value_as_string for formating result from MCP tool
## Related issue number
- Opened Issue on #6368
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
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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?
> The pytest tests test_local_executor_with_custom_venv and
test_local_executor_with_custom_venv_in_local_relative_path located in
packages/autogen-ext/tests/code_executors/test_commandline_code_executor.py
fail when run on macOS (aarch64) using a Python interpreter managed by
uv (following the project's recommended development setup).
>
> The failure occurs during the creation of a nested virtual environment
using Python's standard venv.EnvBuilder. Specifically, the attempt to
run ensurepip inside the newly created venv fails immediately with a
SIGABRT signal. The root cause appears to be a dynamic library loading
error (dyld error) where the Python executable inside the newly created
venv cannot find its required libpythonX.Y.dylib shared library.
So, when MacOS + uv case, skipping that test.
And, adding uv-venv case
## Related issue number
Closes#6341
## 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.
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
This PR introduces `WorkBench`.
A workbench provides a group of tools that share the same resource and
state. For example, `McpWorkbench` provides the underlying tools on the
MCP server. A workbench allows tools to be managed together and abstract
away the lifecycle of individual tools under a single entity. This makes
it possible to create agents with stateful tools from serializable
configuration (component configs), and it also supports dynamic tools:
tools change after each execution.
Here is how a workbench may be used with AssistantAgent (not included in
this PR):
```python
workbench = McpWorkbench(server_params)
agent = AssistantAgent("assistant", tools=workbench)
result = await agent.run(task="do task...")
```
TODOs:
1. In a subsequent PR, update `AssistantAgent` to use workbench as an
alternative in the `tools` parameter. Use `StaticWorkbench` to manage
individual tools.
2. In another PR, add documentation on workbench.
---------
Co-authored-by: EeS <chiyoung.song@motov.co.kr>
Co-authored-by: Minh Đăng <74671798+perfogic@users.noreply.github.com>
## 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
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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.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.
- Added the support Azure AI Agent. The new agent is named AzureAIAgent.
- The agent supports Bing search, file search, and Azure search tools.
- Added a Jupiter notebook to demonstrate the usage of the AzureAIAgent.
## What's missing?
- AzureAIAgent support only text message responses
- Parallel execution for the custom functions.
## Related issue number
[5545](https://github.com/microsoft/autogen/issues/5545#event-16626859772)
---------
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
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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: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
`convert_tools` failed if Optional args were used in tools (the `type`
field doesn't exist in that case and `anyOf` must be used).
This uses the `anyOf` field to pick the first non-null type to use.
## Related issue number
Fixes#6323
## 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.
---------
Signed-off-by: Peter Jausovec <peter.jausovec@solo.io>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
## Why are these changes needed?
I was getting the following exception when doing tool calls with
anthropic - the exception was coming form the `__str__` in
`LLMStreamStartEvent`.
```
('Object of type ToolUseBlock is not JSON serializable',)
```
The issue is that when creating the LLMStreamStartevent in the
`create_stream`, the messages weren't being serialized first.
## Related issue number
Signed-off-by: Peter Jausovec <peter.jausovec@solo.io>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
The current implementation of consecutive `SystemMessage` merging
applies only to models where `model_info.family` starts with
`"gemini-"`.
Since PR #6327 introduced the `multiple_system_messages` field in
`model_info`, we can now generalize this logic by checking whether the
field is explicitly set to `False`.
This change replaces the hardcoded family check with a conditional that
merges consecutive `SystemMessage` blocks whenever
`multiple_system_messages` is set to `False`.
Test cases that previously depended on the `"gemini"` model family have
been updated to reflect this configuration flag, and renamed accordingly
for clarity.
In addition, for consistency across conditional logic, a follow-up PR is
planned to refactor the Claude-specific transformation condition
(currently implemented via `create_args.get("model",
"unknown").startswith("claude-")`)
to instead use the existing `is_claude()`.
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. -->
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## Why are these changes needed?
This is an initial exploration of what could be a solution for #6214 .
It implements a simple text canvas using difflib and also a memory
component and a tool component for interacting with the canvas. Still in
early testing but would love feedback on the design.
## 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>
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>
This PR introduces a safer and more controllable execution environment
for LLM code execution in version 0.4 by enabling the use of Jupyter
inside a container. This enhancement addresses security concerns and
provides a more robust execution context. In particular, it allows:
Isolation of code execution via containerized Jupyter environments.
Persistent memory of variables and their values throughout the
conversation.
Memory of code execution results to support more advanced reasoning and
follow-up tasks.
These improvements help build a more interactive and stateful LLM-agent
programming experience, especially for iterative code generation and
debugging scenarios.
## Related issue number
Open #6153
## Checks
- [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 fixes a bug where the underlying azure `SearchClient` was being
closed prematurely due to use of `async with client` : inside the tool's
run method. this caused the users to encounter errors "HTTP transport
has already been closed"
## Related issue number
Closes#6308 "
## 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.
- [X] I've made sure all auto checks have passed.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Resolves#6232, #6198
This PR introduces an optional parameter `session` to `mcp_server_tools`
to support reuse of the same session.
```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, create_mcp_server_session, mcp_server_tools
async def main() -> None:
model_client = OpenAIChatCompletionClient(model="gpt-4o", parallel_tool_calls=False) # type: ignore
params = StdioServerParams(
command="npx",
args=["@playwright/mcp@latest"],
read_timeout_seconds=60,
)
async with create_mcp_server_session(params) as session:
await session.initialize()
tools = await mcp_server_tools(server_params=params, session=session)
print(f"Tools: {[tool.name for tool in tools]}")
agent = AssistantAgent(
name="Assistant",
model_client=model_client,
tools=tools, # type: ignore
)
termination = TextMentionTermination("TERMINATE")
team = RoundRobinGroupChat([agent], termination_condition=termination)
await Console(
team.run_stream(
task="Go to https://ekzhu.com/, visit the first link in the page, then tell me about the linked page."
)
)
asyncio.run(main())
```
Based on discussion in this thread: #6284, we will consider
serialization and deserialization of MCP server tools when used in this
manner in a separate issue.
This PR also replaces the `json_schema_to_pydantic` dependency with
built-in utils.
## Description
This PR pins opentelemetry-proto version to >=1.28.0, which uses
protobuf > 5.0, < 6.0 to generate protobuf files.
## Related issue number
Closes#6304
<!-- Thank you for your contribution! Please review
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pull request. -->
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An app can pass untyped dicts to set configuration options of various
Task-Centric Memory classes. But tools like pyright can complain about
the loose typing. This PR exposes 4 TypedDict classes that apps can
optionally use.
<!-- 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.
- [ ] I've made sure all auto checks have passed.
