Resolves#5786
Also updated the termination tutorial to include an example of a custom
termination conditon.
Also added to guide about FunctionTool and MCP tools.
## Why are these changes needed?
The current installation command fails in certain shells (e.g., `zsh`,
`fish`) because brackets (`[]`) are interpreted as special characters.
Adding quotes ensures compatibility across different environments,
including Linux, macOS, and Windows.
## Related issue number
No related issue, but this fixes an installation issue encountered by
multiple users.
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>.
- [ ] 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>
## Why are these changes needed?
* `python3` to `python`: Windows uses `python` for Python 3 by default,
not `python3`.
* `bin` to `scripts`: Windows virtual environments use `Scripts` instead
of `bin`.
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
AutoGen was passing raw dictionaries to functions instead of
constructing Pydantic model or dataclass instances. If a tool function’s
parameter was a Pydantic BaseModel or a dataclass, the function would
receive a dict and likely throw an error or behave incorrectly (since it
expected an object of that type).
This PR addresses problem in AutoGen where tool functions expecting
structured inputs (Pydantic models or dataclasses) were receiving raw
dictionaries. It ensures that structured inputs are automatically
validated and instantiated before function calls. Complete details are
in Issue #5736
[Reproducible Example Code - Failing
Case](https://colab.research.google.com/drive/1hgoP-cGdSZ1-OqQLpwYmlmcExgftDqlO?usp=sharing)
<!-- Please give a short summary of the change and the problem this
solves. -->
## Changes Made:
- Inspect function signatures for Pydantic BaseModel and dataclass
annotations.
- Convert input dictionaries into properly instantiated objects using
BaseModel.model_validate() for Pydantic models or standard instantiation
for dataclasses.
- Raise descriptive errors when validation or instantiation fails.
- Unit tests have been added to cover all scenarios
Now structured inputs are automatically validated and instantiated
before function calls.
- **Updated Conversion Logic:**
In the `run()` method, we now inspect the function’s signature and
convert input dictionaries to structured objects. For parameters
annotated with a Pydantic model, we use `model_validate()` to create an
instance; for those annotated with a dataclass, we instantiate the
object using the dataclass constructor. For example:
```python
# Get the function signature.
sig = inspect.signature(self._func)
raw_kwargs = args.model_dump()
kwargs = {}
# Iterate over the parameters expected by the function.
for name, param in sig.parameters.items():
if name in raw_kwargs:
expected_type = param.annotation
value = raw_kwargs[name]
# If expected type is a subclass of BaseModel, perform conversion.
if inspect.isclass(expected_type) and issubclass(expected_type,
BaseModel):
try:
kwargs[name] = expected_type.model_validate(value)
except ValidationError as e:
raise ValueError(
f"Error validating parameter '{name}' for function
'{self._func.__name__}': {e}"
) from e
# If it's a dataclass, instantiate it.
elif is_dataclass(expected_type):
try:
cls = expected_type if isinstance(expected_type, type) else
type(expected_type)
kwargs[name] = cls(**value)
except Exception as e:
raise ValueError(
f"Error instantiating dataclass parameter '{name}' for function
'{self._func.__name__}': {e}"
) from e
else:
kwargs[name] = value
```
- **Error Handling Improvements:**
Conversion steps are wrapped in try/except blocks to raise descriptive
errors when instantiation fails, aiding in debugging invalid inputs.
- **Testing:**
Unit tests have been added to simulate tool calls (e.g., an `add` tool)
to ensure that with input like:
```json
{"input": {"x": 2, "y": 3}}
```
The tool function receives an instance of the expected type and returns
the correct result.
## Related issue number
Closes#5736
## 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.
Correcting an error: If CodeReviewResult is not approved, the coder
agents sends a CodeReviewTask back to the reviewer agent, not a
CodeWritingTask.
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Starting out this draft PR to add documentation for the
`with_requirements` decorator in the `autogen-core` package.
---------
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.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. -->
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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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
(I'm not familiar with English, so I use Goggle translation a lot. So
please forgive me if I say something rude.)
I started autogen-studio and opened the page in the browser, nothing was
rendered.
I checked using the Developer tool, the following error appeared:
```
TypeError: Cannot read properties of null (reading 'label')
at editor.tsx:114:42
```
I check the implementation, it looks like `team.component` is `null`,
which seems to be caused during initialization process when the team is
not registered.
[source](78ff883d24/python/packages/autogen-studio/frontend/src/components/views/playground/manager.tsx (L199))
So I fixed the issue where the gallery wasn't being retrieved which was
causing the issue.
### Reproduce bug
1. clone this repository
2. open devcontainer(`python/packages/autogen-studio`)
3. Running the application
```sh
cd frontend
yarn build
cd -
OPENAI_API_KEY="" autogenstudio ui --port 8081
```
4. Open `localhost:8081` in browser.
## Related issue number
<!-- For example: "Closes #1234" -->
Probably not found. (sorry if I had to raise an issue before PR)
## 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>
stream_options are not part of the model classes, so they won't get
serialized when calling dump_component. Adding this to the model allows
us to store the stream options when the component is serialized.
---------
Signed-off-by: Peter Jausovec <peter.jausovec@solo.io>
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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assign them to your PR. -->
## Why are these changes needed?
> Hey Victor, this is maybe a bug, but when a session is delete, runs
and messages for that session are not deleted, any reason why to keep
them?
@husseinmozannar
The main fix is to add a pragma that ensures SQL lite enforces foreign
key constraints.
Also needed to update error messages for autoupgrade of databases. Also
adds a test for cascade deletes and for parts of teammanager
With this fix,
- Messages get deleted when the run is deleted
- Runs get deleted when sessiosn are deleted
- Sessions get deleted when a team is deleted
<!-- 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.
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pull request. -->
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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. -->
- AGS contains image files which are required for local build /dev
- These files are stored using git lfs which needs to be run before the
files are downloaded
- This PR adds a reminder to the AGS readme to run `git lfs checkout`
after installing git lfs to download the image files
## Related issue number
<!-- For example: "Closes #1234" -->
Close#3418
## 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?
The CodeExecutorAgent documentation needs to be updated to explicitly
mention that it only processes code properly formatted in markdown code
blocks with triple backticks. This change adds a clear note with
examples to help users understand the required format for code
execution.
## Related issue number
Closes#5771
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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assign them to your PR. -->
## 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.
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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. -->
Shows an example of how to use the `Memory` interface to implement a
just-in-time vector memory based on chromadb.
```python
import os
from pathlib import Path
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_core.memory import MemoryContent, MemoryMimeType
from autogen_ext.memory.chromadb import ChromaDBVectorMemory, PersistentChromaDBVectorMemoryConfig
from autogen_ext.models.openai import OpenAIChatCompletionClient
# Initialize ChromaDB memory with custom config
chroma_user_memory = ChromaDBVectorMemory(
config=PersistentChromaDBVectorMemoryConfig(
collection_name="preferences",
persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
k=2, # Return top k results
score_threshold=0.4, # Minimum similarity score
)
)
# a HttpChromaDBVectorMemoryConfig is also supported for connecting to a remote ChromaDB server
# Add user preferences to memory
await chroma_user_memory.add(
MemoryContent(
content="The weather should be in metric units",
mime_type=MemoryMimeType.TEXT,
metadata={"category": "preferences", "type": "units"},
)
)
await chroma_user_memory.add(
MemoryContent(
content="Meal recipe must be vegan",
mime_type=MemoryMimeType.TEXT,
metadata={"category": "preferences", "type": "dietary"},
)
)
# Create assistant agent with ChromaDB memory
assistant_agent = AssistantAgent(
name="assistant_agent",
model_client=OpenAIChatCompletionClient(
model="gpt-4o",
),
tools=[get_weather],
memory=[user_memory],
)
stream = assistant_agent.run_stream(task="What is the weather in New York?")
await Console(stream)
await user_memory.close()
```
```txt
---------- user ----------
What is the weather in New York?
---------- assistant_agent ----------
[MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': '8a8d683c-5866-41e1-ac17-08c4fda6da86'}), MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': 'f27af42c-cb63-46f0-b26b-ffcc09955ca1'})]
---------- assistant_agent ----------
[FunctionCall(id='call_a8U3YEj2dxA065vyzdfXDtNf', arguments='{"city":"New York","units":"metric"}', name='get_weather')]
---------- assistant_agent ----------
[FunctionExecutionResult(content='The weather in New York is 23 °C and Sunny.', call_id='call_a8U3YEj2dxA065vyzdfXDtNf', is_error=False)]
---------- assistant_agent ----------
The weather in New York is 23 °C and Sunny.
```
Note that MemoryContent object in the MemoryQuery events have useful
metadata like the score and id retrieved memories.
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
- [ ] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation 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.
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pull request. -->
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assign them to your PR. -->
## Why are these changes needed?
The Team DB class doesn't have the "config" field anymore. It has
"component"
<!-- 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.
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
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## Why are these changes needed?
Add support for default model client, in AGS updates to settings UI.
A default model client will be used for subsequent background tasks
(e.g, automatically naming sessions to meaningful names).
<img width="1441" alt="image"
src="https://github.com/user-attachments/assets/31675ef4-8f80-4a8c-9762-3d6bcbc6d33d"
/>
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5685
## 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.
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
Typo in example text
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
- [X] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [X] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [X] I've made sure all auto checks have passed.
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pull request. -->
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## Why are these changes needed?
- Running a team generates a MemoryQueryEvent has a MemoryContent field
and is part of the model context
- Saving team state (`team.save_state()`) includes serializing model
context
- MemoryContent has a mime_type field, which was not being properly
serialized.
"Object of type MemoryMimeType is not JSON serializable"
This PR? -> add explicit serialization instruction for the mimetype
field.
```python
import asyncio
import logging
import json
import yaml, aiofiles
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_core.memory import ListMemory, MemoryContent, MemoryMimeType
from autogen_agentchat.ui import Console
logger = logging.getLogger(__name__)
state_path = "team_state.json"
model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")
max_msg_termination = MaxMessageTermination(max_messages=3)
# Initialize user memory
user_memory = ListMemory()
# Add user preferences to memory
await user_memory.add(MemoryContent(content="The weather should be in metric units", mime_type=MemoryMimeType.TEXT))
# Create the team.
agent = AssistantAgent(
name="assistant",
model_client=model_client,
system_message="You are a helpful assistant.",
memory = [user_memory],
)
yoda = AssistantAgent(
name="yoda",
model_client=model_client,
system_message="Repeat the same message in the tone of Yoda.",
)
team = RoundRobinGroupChat(
[agent, yoda],
termination_condition=max_msg_termination
)
await Console(team.run_stream(task="Hi, How are you ?"))
# Save team state to file.
state = await team.save_state()
with open(state_path, "w") as f:
json.dump(state, f)
# Load team state from file.
with open("team_state.json", "r") as f:
team_state = json.load(f)
await team.load_state(state)
await Console( team.run_stream(task="What was the last thing that was said in this conversation "))
```
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5688
## 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>
@ekzhu should likely be assigned as reviewer
## Why are these changes needed?
These changes address the bug reported in #5663. Prevents TypeError from
being thrown at inference time by ollama AsyncClient when `host` (and
other) kwargs are passed to autogen OllamaChatCompletionClient
constructor.
It also adds ollama as a named optional extra so that the ollama
requirements can be installed alongside autogen-ext (e.g. `pip install
autogen-ext[ollama]`
@ekzhu, I will need some help or guidance to ensure that the associated
test (which requires ollama and tiktoken as dependencies of the
OllamaChatCompletionClient) can run successfully in autogen's test
execution environment.
I have also left the "I've made sure all auto checks have passed" check
below unchecked as this PR is coming from my fork. (UPDATE: auto checks
appear to have passed after opening PR, so I have checked box below)
## Related issue number
Intended to close#5663
## 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: Ryan Stewart <ryanstewart@Ryans-MacBook-Pro.local>
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
Co-authored-by: peterychang <49209570+peterychang@users.noreply.github.com>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
Claude 3.7 just came out. Its a pretty capable model and it would be
great to support it in Autogen.
This will could augment the already excellent support we have for
Anthropic via the SKAdapters in the following ways
- Based on the ChatCompletion API similar to the ollama and openai
client
- Configurable/serializable (can be dumped) .. this means it can be used
easily in AGS.
## What is Supported
(video below shows the client being used in autogen studio)
https://github.com/user-attachments/assets/8fb7c17c-9f9c-4525-aa9c-f256aad0f40b
- streaming
- tool callign / function calling
- drop in integration with assistant agent.
- multimodal support
```python
from dotenv import load_dotenv
import os
load_dotenv()
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.anthropic import AnthropicChatCompletionClient
model_client = AnthropicChatCompletionClient(
model="claude-3-7-sonnet-20250219"
)
async def get_weather(city: str) -> str:
"""Get the weather for a given city."""
return f"The weather in {city} is 73 degrees and Sunny."
agent = AssistantAgent(
name="weather_agent",
model_client=model_client,
tools=[get_weather],
system_message="You are a helpful assistant.",
# model_client_stream=True,
)
# Run the agent and stream the messages to the console.
async def main() -> None:
await Console(agent.run_stream(task="What is the weather in New York?"))
await main()
```
result
```
messages = [
UserMessage(content="Write a very short story about a dragon.", source="user"),
]
# Create a stream.
stream = model_client.create_stream(messages=messages)
# Iterate over the stream and print the responses.
print("Streamed responses:")
async for response in stream: # type: ignore
if isinstance(response, str):
# A partial response is a string.
print(response, flush=True, end="")
else:
# The last response is a CreateResult object with the complete message.
print("\n\n------------\n")
print("The complete response:", flush=True)
print(response.content, flush=True)
print("\n\n------------\n")
print("The token usage was:", flush=True)
print(response.usage, flush=True)
```
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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" -->
Closes#5205Closes#5708
## 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.
cc @rohanthacker
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pull request. -->
update human in the loop docs for agentchat
..
> Outputs in docs are outdated as summary is not printed out by default
when using Console
[#5590](https://github.com/microsoft/autogen/issues/5590)
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## Why are these changes needed?
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## Related issue number
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Closes#5590
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This pull request includes updates to the `README.md` file for the
`autogen-studio` package to improve clarity and accuracy. The most
important changes include a minor rewrite of the project structure
section and updates to the installation instructions (better tabbing).
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## Why are these changes needed?
Fixes `(14) Ensures the contrast between foreground and background
colors meets WCAG 2 AA minimum contrast ratio thresholds`
Note: the color values don't output at the exact values on the
stylesheet. For example, the value `#1774E5` evaluates to `#2274E0` by
the Accessibility Insights app.
## Related issue number
https://github.com/microsoft/autogen/issues/5633
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## Why are these changes needed?
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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
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
## Why are these changes needed?
For the sake of subsequent people reading the metacode and following
programming specifications, variable names are updated in combination
with usage scenarios. Mainly contains the variable "_self_text" in
TextMentionTermination
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This PR has 3 main improvements.
- Token streaming
- Adds support for environment variables in the app settings
- Updates AGS to persist Gallery entry in db.
## Adds Token Streaming in AGS.
Agentchat now supports streaming of tokens via
`ModelClientStreamingChunkEvent `. This PR is to track progress on
supporting that in the AutoGen Studio UI.
If `model_client_stream` is enabled in an assitant agent, then token
will be streamed in UI.
```python
streaming_assistant = AssistantAgent(
name="assistant",
model_client=model_client,
system_message="You are a helpful assistant.",
model_client_stream=True, # Enable streaming tokens.
)
```
https://github.com/user-attachments/assets/74d43d78-6359-40c3-a78e-c84dcb5e02a1
## Env Variables
Also adds support for env variables in AGS Settings
You can set env variables that are loaded just before a team is run.
Handy to set variable to be used by tools etc.
<img width="1291" alt="image"
src="https://github.com/user-attachments/assets/437b9d90-ccee-42f7-be5d-94ab191afd67"
/>
> Note: the set variables are available to the server process.
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## Why are these changes needed?
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solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5627Closes#5662Closes#5619
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Add metadata field to BaseMessage.
Why?
- additional metadata field can track 1) timestamp if needed, 2) flags
about the message. For instance, a use case is a metadata field
{"internal":"yes"} that would hide messages from being displayed in an
application or studio.
As long as an extra field is added to basemessage that is not consumed
by existing agents, I am happy.
Notes:
- We can also only add it to BaseChatMessage, that would be fine
- I don't care what the extra field is called as long as there is an
extra field somewhere
- I don't have preference for the type, a str could work, but a dict
would be more useful.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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## Why are these changes needed?
convenience - allows to just run "agenthost"
```
dotnet pack --no-build --configuration Release --output './output/release' -bl\n
dotnet tool install --add-source ./output/release Microsoft.AutoGen.AgentHost
agenthost
```
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## Related issue number
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closes#5646
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Replace the undefined `tools` variable with `tool_schema` parameter
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## Why are these changes needed?
This change keeps the documentation up to date :
https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/components/tools.html
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## Related issue number
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## Why are these changes needed?
Fix minor issues in docs:
- probably editing left over
- typo
- code formatting inconsistency
## Related issue number
N/A
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Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
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## Why are these changes needed?
The HttpTool in AutoGen accepts a headers parameter, but it is not being
used in the actual request. This fix ensures that the headers provided
by users are correctly included in HTTP requests. This resolves issues
where authentication or other custom headers are required but currently
ignored.
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## Related issue number
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Closes#5638
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Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
## Why are these changes needed?
See issue for a bug description.
The problem was that a lot of openrouter models return `""` as
`tool_call.arguments`, which caused `json.loads` to fail
## Related issue number
https://github.com/microsoft/autogen/issues/5666
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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This PR makes makes ChatCompletionCache support component config
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## Why are these changes needed?
Ensures we have a path to serializing ChatCompletionCache , similar to
the ChatCompletion client that it wraps.
This PR does the following
- Makes CacheStore serializable first (part of this includes converting
from Protocol to base class). Makes it's derivatives serializable as
well (diskcache, redis)
- Makes ChatCompletionCache serializable
- Adds some tests
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## Related issue number
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Closes#5141
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cc @nour-bouzid
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>
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Update AGS, Remove Numpy Dep
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Closes#5639
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## Why are these changes needed?
Fixes the problem where Gallery items could only be modified via JSON
This PR does the following
- Refactor TeamBuilder to have modular component editor UI primarily
focused on editing each component type.
- Refactor the Gallery UX
- improve layout to use tabs for each component type
- enable editing of each component item by reusing the component editor
- Enable switching between form editing and UI editing for coponent
editor view
This way, gallery items can be readily modified and then reused in the
component library in team builder.
It also implements an upate to the Gallery data structure to make it
more intuitive - it has a components field that has teams, agents,
models ...
<img width="1598" alt="image"
src="https://github.com/user-attachments/assets/3c3a228a-0bd2-4fc1-85ec-c9685c80bf72"
/>
<img width="1614" alt="image"
src="https://github.com/user-attachments/assets/5b6ed840-9c48-47bc-8c17-2aa50c7dcb99"
/>
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## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5465Closes#5047
cc @nour-bouzid @balakreshnan @EItanya @joslat @IustinT @leonG7
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Fix typo in doc
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Fixing grammar issues
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## Why are these changes needed?
Adds ollama client documentation to the docs page
## Related issue number
https://github.com/microsoft/autogen/issues/5604
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## Why are these changes needed?
Initial commit's docstrings were incorrect, which would be confusing for
a user
## Related issue number
https://github.com/microsoft/autogen/issues/5595
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<img width="1560" alt="image"
src="https://github.com/user-attachments/assets/da3d781f-2572-4bd7-802d-1d3900f6c6d9"
/>
## Why are these changes needed?
Improves editing UI for tools in AGS
- add remove tools
- add/remove imports
- show source code section of FunctionTool in in code editor
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solves. -->
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- Add a section on how to use model client with tools
- Replace the obsecure pattern of using ToolAgent with a simpler
implementation of calling tool within the agent itself.
* integration tests used to use the samples - now they are separate
* patch dictionary problem in serializer
* add Message Registry with dead letter queue that gets checked on new
subs.
This pull request includes a small change to the
`python/packages/magentic-one-cli/src/magentic_one_cli/_m1.py` file. The
change modifies the help message for the `--config` argument to remove
the part about leaving it empty to print a sample configuration. This is
already handled by `--sample-config`.
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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## Why are these changes needed?
Make
[PythonCodeExecutionTool](https://github.com/microsoft/autogen/blob/main/python/packages/autogen-ext/src/autogen_ext/tools/code_execution/_code_execution.py)
declarative so it can be used in tools like AGS
Summary of changes
- Make CodeExecutor declarative (convert from Protocol to ABC, inherit
from ComponentBase)
- Make LocalCommandLineCodeExecutor, JupyterCodeExecutor and
DockerCommandLineCodeExecutor declarative , best effort. Not all fields
are serialized, warnings are shown where appropriate.
- Make PythonCodeExecutionTool declarative.
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solves. -->
## Related issue number
Closes#5526
<!-- For example: "Closes #1234" -->
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Pulls the .NET Docs a bit more into the documentation website proper:
* Add a link from the 0.4-series Python docs to
* Adds a transient redirect from /dotnet/ to /dotnet/dev/ to forward to
the dev docs until we do a non "-dev" release
This finishes the fix for the race condition between opening a
GrpcWorkerConnection and registering agent types on that worker. Now,
instead of failing to register, we return from the call (with the
expectation that we will finish registration as we set up the
connection)
Part 1: #5494
Part 2: #5514
---------
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
Fixed a wrong class name style in the document and removed trailing whitespace.
Co-authored-by: Wei Jen Lu <weijenlu@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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Adds the following blurb to the model clients docs
## API Keys From Environment Variables
In the examples above, we show that you can provide the API key through
the `api_key` argument. Importantly, the OpenAI and Azure OpenAI clients
use the [openai
package](3f8d8205ae/src/openai/__init__.py (L260)),
which will automatically read an api key from the environment variable
if one is not provided.
- For OpenAI, you can set the `OPENAI_API_KEY` environment variable.
- For Azure OpenAI, you can set the `AZURE_OPENAI_API_KEY` environment
variable.
This is a good practice to explore, as it avoids including sensitive api
keys in your code.
## Why are these changes needed?
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## Related issue number
<!-- For example: "Closes #1234" -->
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Right now we rely on opening the channel to associate a ClientId with an
entry on the gateway side. This causes a race when the channel is being
opened in the background while an RPC (e.g. MyAgent.register()) is
invoked.
If the RPC is processed first, the gateway rejects it due to "invalid"
clientId.
This fix makes this condition less likely to trigger, but there is still
a piece of the puzzle that needs to be solved on the Gateway side.
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## Why are these changes needed?
It is useful to rapidly validate any changes to a team structure as
teams are built either via drag and drop or by modifying the underlying
spec
You can now “validate” your team. The key ideas are as follows
- Each team is based on some Component Config specification which is a
pedantic model underneath.
- Validation is 3 pronged based on a ValidatorService class
- Data model validation (validate component schema)
- Instantiation validation (validate component can be instantiated)
- Provider validation, component_type validation (validate that provider
exists and can be imported)
- UX: each time a component is **loaded or saved**, it is automatically
validated and any errors shown (via a server endpoint). This way, the
developer immediately knows if updates to the configuration is wrong or
has errors.
> Note: this is different from actually running the component against a
task. Currently you can run the entire team. In a separate PR we will
implement ability to run/test other components.
<img width="1360" alt="image"
src="https://github.com/user-attachments/assets/d61095b7-0b07-463a-b4b2-5c50ded750f6"
/>
<img width="1368" alt="image"
src="https://github.com/user-attachments/assets/09a1677e-76e8-44a4-9749-15c27457efbb"
/>
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solves. -->
## Related issue number
Closes#4616
<!-- For example: "Closes #1234" -->
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Closes#5297
---------
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
Co-authored-by: Jacob Alber <jacob.alber@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Add the following additional configuration options to
DockerCommandLineCodeExectutor:
- **extra_volumes** (Optional[Dict[str, Dict[str, str]]], optional): A
dictionary of extra volumes (beyond the work_dir) to mount to the
container. Defaults to None.
- **extra_hosts** (Optional[Dict[str, str]], optional): A dictionary of
host mappings to add to the container. (See Docker docs on extra_hosts)
Defaults to None.
- **init_command** (Optional[str], optional): A shell command to run
before each shell operation execution. Defaults to None.
## Why are these changes needed?
See linked issue below.
In summary: Enable the agents to:
- work with a richer set of sys admin tools on top of code execution
- add support for a 'project' directory the agents can interact on
that's accessible by bash tools and custom scripts
## Related issue number
Closes#5363
## Checks
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This PR improves documentation on custom agents
- Shows example on how to create a custom agent that directly uses a
model client. In this case an example of a GeminiAssistantAgent that
directly uses the Gemini SDK model client.
- Shows that that CustomAgent can be easily added to any agentchat team
- Shows how the same CustomAgent can be made declarative by inheriting
the Component interface and implementing the required methods.
Closes#5450
## Why are these changes needed?
These changes are needed because currently there's no generic way to add
`tools` to autogen studio workflows using the existing DSL and schema
other than inline python.
This API will be quite verbose, and lacks a discovery mechanism, but it
unlocks a lot of programmatic use-cases.
## Related issue number
https://github.com/microsoft/autogen/issues/5170
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Have seen discussion on Discord regarding confusion about multi-modal
support in v0.4. This change adds a small note on how to use multi-modal
messages with agents.
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## Why are these changes needed?
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solves. -->
The current implementation tries to recreate the metadata but it does it
in an incomplete way. This PR uses SK built-in kernel from function
decorator to infer the callable from the `run_json` and makes better use
of the pydantic schemas for the input and output to infer the schema of
the kernel function.
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5458
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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Add clear notes on how to specify api key and modifying specifications
in AGS.
Add diagrams explaining how to switch between visual builder and JSON
mode
## Why are these changes needed?
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solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
@nour-bouzid
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It is often helpful to inspect the raw request and response to/from an
LLM as agents act.
This PR, does the following:
- Updates TeamManager to yield LLMCallEvents from core library
- Run in an async background task to listen for LLMCallEvent JIT style
when a team is run
- Add events to an async queue and
- yield those events in addition to whatever actual agentchat
team.run_stream yields.
- Update the AGS UI to show those LLMCallEvents in the messages section
as a team runs
- Add settings panel to show/hide llm call events in messages.
- Minor updates to default team
<img width="1539" alt="image"
src="https://github.com/user-attachments/assets/bfbb19fe-3560-4faa-b600-7dd244e0e974"
/>
<img width="1554" alt="image"
src="https://github.com/user-attachments/assets/775624f5-ba83-46e8-81ff-512bfeed6bab"
/>
<img width="1538" alt="image"
src="https://github.com/user-attachments/assets/3becbf85-b75a-4506-adb7-e5575ebbaec4"
/>
## 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#5440
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## Why are these changes needed?
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solves. -->
The current stream processing of SK model adapter returns on the first
function call chunk but this behavior is incorrect end ends up returning
with an incomplete function call. The observed behavior is that the
function name and arguments are split into different chunks and this
update correctly processes the chunks in this way.
## Related issue number
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Fixes the reply in #5420
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Don't throw an exception when model makes a mistake. Use retries, and if
not succeeding after a fixed attempts, fall back to the previous sepaker
if available, or the first participant.
Resolves#5453
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## Why are these changes needed?
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solves. -->
Semantic kernel prepends the plugin name to the tool name when passing
the tools to model clients and this is causing a mismatch between tool
names in SK and the AssistantAgent. Since plugin names are optional, we
have opted to remove it.
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5420
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Restoring the grpc + Orleans server into the project
## Why are these changes needed?
This is the distributed agent runtime for .NET that can manage routing
messages amongst a fleet of grpc agent runtimes.
## Related issue number
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introduced in this PR.
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---------
Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
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## Why are these changes needed?
- Add ability to test teams in Team Builder view
- Update Gallery (add deep research default team, fix bug with gallery
serialization)
- UI fixes
- improve drag drop component experience
- improve new session experience (single click rather than 3 clicks to
create a session)
- fix bug with stop reason not being shown in some cases
<img width="1738" alt="Image"
src="https://github.com/user-attachments/assets/4b895df2-3bad-474e-bec6-4fbcbf1c4346"
/>
<img width="1761" alt="Image"
src="https://github.com/user-attachments/assets/65f52eb9-e926-4168-88fb-d2496c159474"
/>
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solves. -->
## Related issue number
Closes#5392
<!-- For example: "Closes #1234" -->
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Introduce a sample chat application using AgentChat and FastAPI,
demonstrating single-agent and team chat functionalities, along with
state persistence and conversation history management.
Resolves#5423
---------
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Presently MagenticOne and the m1 CLI use the LocalCommandLineExecutor
(presumably copied from the agbench code, which already runs in Docker).
This pr defaults m1 to Docker, and adds a code_executor parameter to
MagenticOne, which defaults to local for now to maintain backward
compatibility -- but this behavior is immediately deprecated.
- Updated HumanEval template to use AgentChat
- Update templates to use config.yaml for model and other configuration
- Read environment from ENV.yaml (ENV.json still supported but
deprecated)
- Temporarily removed WebArena and AssistantBench. Neither had viable
Templates after `autogen_magentic_one` was removed. Templates need to be
update to AgentChat (in a future PR, but this PR is getting big enough
already)
A series of changes to the
`python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py`
file have been made to better support smaller models.
This includes changes to the prompts, state descriptions, and ordering
of messages.
Regression tasks with OpenAI models shows no change in GAIA scores,
while scores for Llama are significantly improved.
Get's SelectorGroupChat working for llama by:
1. Using a UserMessage rather than a SystemMessage
2. Normalizing how roles are presented (one agent per line)
3. Normalizing how the transcript is constructed (a blank line between
every message)
This PR fixes:
A prompting bug when no control had focus.
Awkward prompt phrasing.
Renamed page_down to scroll_down to better match other prompting and
agent descriptions.
Some agent descriptions were split over multiple lines in the M1
orchestrator. This PR ensures that each description appears on one, and
only one, line. This makes it easier for smaller models to understand.
The Tuple class is never used in CountDownAgent class.
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## Why are these changes needed?
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solves. -->
## Related issue number
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Co-authored-by: Victor Dibia <victordibia@microsoft.com>
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## Why are these changes needed?
Currently the way to accomplish RAG behavior with agent chat,
specifically assistant agents is with the memory interface, however
there is no way to configure it via the declarative API.
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solves. -->
## Related issue number
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---------
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
Just fix typo
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## Why are these changes needed?
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## Related issue number
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## Why are these changes needed?
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solves. -->
To allow serialization of OAI Assistant Agent.
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5130
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## Why are these changes needed?
Fix permission issue in ags windows (env files now stored in a user
directory)
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solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5355
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Allow AssistantAgent to drop images when not equipped with a multi-modal model.
Adds a corresponding utility function, which can be used in autogen-ext and teams, to accomplish the same.
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## Why are these changes needed?
Wrong file names in the README.md
## Related issue number
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Co-authored-by: Mohammad Mazraeh <Mazraeh.Mohammad@Gmail.com>
This PR does the following:
- Fix warning messages in AGS on launch.
- Improve Cli message to include app URL on startup from command line
- Minor improvements default gallery generator. (add more default tools)
- Improve new session behaviour.
## Related issue number
Closes#5097
## Checks
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## Why are these changes needed?
Fix bug where the `model_info` field is not serialized for the
`OpenAIChatCompletionClient` class. This was because the `_raw_config`
field was based on a version of the args that had been sanitized
(model_info removed). We need the full model info field for non-openai
models
```python
from autogen_ext.agents.web_surfer import MultimodalWebSurfer
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelInfo
mistral_vllm_model = OpenAIChatCompletionClient(
model="TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
base_url="http://localhost:1234/v1",
api_key="empty",
model_info=ModelInfo(vision=False, function_calling=True, json_output=False, family="unkown"),
)
(mistral_vllm_model.dump_component().model_dump_json())
```
Before
```
{
"provider": "autogen_ext.models.openai.OpenAIChatCompletionClient",
"component_type": "model",
"version": 1,
"component_version": 1,
"description": "Chat completion client for OpenAI hosted models.",
"label": "OpenAIChatCompletionClient",
"config": {
"model": "TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
"api_key": "empty",
"base_url": "http://localhost:1234/v1"
}
}
```
After
```
{
"provider": "autogen_ext.models.openai.OpenAIChatCompletionClient",
"component_type": "model",
"version": 1,
"component_version": 1,
"description": "Chat completion client for OpenAI hosted models.",
"label": "OpenAIChatCompletionClient",
"config": {
"model": "TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
"api_key": "empty",
"model_info": {
"vision": false,
"function_calling": true,
"json_output": false,
"family": "unkown"
},
"base_url": "http://localhost:1234/v1"
}
}
```
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solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
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## Why are these changes needed?
This pull request introduces the 'o3' model family and adds support for
the 'o3-mini' model.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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## Why are these changes needed?
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solves. -->
We are seeing this issue more often now, probably related to the load on
the API servers. Hence this PR:
1. Demotes the the `max_consecutive_empty_chunk_tolerance` parameter
from function to inline threshold
2. Change exception to a one time warning
## Related issue number
<!-- For example: "Closes #1234" -->
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---------
Signed-off-by: Mohammad Mazraeh <mazraeh.mohammad@gmail.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
This PR adds a method that approximately extracts the text visible in
the viewport of the web browser (as opposed to always printing the first
50 lines, or relying entirely on OCR).
This PR removes the older `autogen_magentic_one` package, and directs
people to use the new AgentChat implementation.
Hopefully this eases confusion.
---------
Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
* New sample of chess playing showing R1's thought process in streaming
mode
* Modify existing samples to use `model_config.yml` instead of JSON
configs for better clarity.
---------
Co-authored-by: Mohammad Mazraeh <Mazraeh.Mohammad@Gmail.com>
* Separate agent and team examples
* Add streaming output
* Refactor to better use the chainlit API
* Removed the user proxy example -- this needs a bit more work to
improve the presentation on the ChainLit interface.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
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## Why are these changes needed?
This PR updates AGS to use the declarative config serialization native
to AgentChat.
The effect? You can build your teams/artifacts directly in python, run
`team.dump_component()` and immediately run it in AGS.
Some change details:
- Removes ComponentFactory. Instead TeamManager just loads team specs
directly using `Team.load_component`.
- Some fixes to the UI to simplify drag and drop experience.
- Improve layout of nodes...
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#4439Closes#5172
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cc @EItanya @nour-bouzid
Removed the TERMINATE clause from the system prompt since M1 handles
termination via the Orchestrator, and it is just ignored.
Removed the clause about saving to a particular file name, since tmp_
files are created in the current CodeExecutors.
This PR adds a `sources` optional parameter to CodeExecutorAgent
(similar to the termination conditions), that allows finer-grained
control on which agents can provide code for execution.
It also moves the `_extract_markdown_code_blocks` subroutine to a member
method, so that it can be overridden by subclasses. I've found this to
be very important to support benchmarks like HumanEval, where we need to
add a test harness around the implementation.
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## Why are these changes needed?
FunctionTool supports passing in a partial
## Related issue number
Closes#5151
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introduced in this PR.
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Resolves#3983
* introduce `model_client_stream` parameter in `AssistantAgent` to
enable token-level streaming output.
* introduce `ModelClientStreamingChunkEvent` as a type of `AgentEvent`
to pass the streaming chunks to the application via `run_stream` and
`on_messages_stream`. Although this will not affect the inner messages
list in the final `Response` or `TaskResult`.
* handle this new message type in `Console`.
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## Why are these changes needed?
It is currently hard to add a description to a component (defaults to
None also) .. you have to call super.dump() modify and return. This PR
makes the experience better.
- allows you specify `component_description` and `component_label` as an
optional class var. label is an optional human readable name for the the
component.
- will use component_description if provided int he description field
when dumped if there is no description, will use the first line of class
docstring. Takes advantage of all the good practices we have in writing
good docstrings. label defaults to component type.
For example
```python
model_client=OpenAIChatCompletionClient( model="gpt-4o-2024-08-06" )
config = model_client.dump_component()
print(config.model_dump_json())
```
Note the description field below is no longer None and there is a label
```python
{
"provider": "autogen_ext.models.openai.OpenAIChatCompletionClient",
"component_type": "model",
"version": 1,
"component_version": 1,
"description": "Chat completion client for OpenAI hosted models.",
"label": "OpenAIChatCompletionClient",
"config": { "model": "gpt-4o-2024-08-06" }
}
```
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
None, felt faster to fix.
## Checks
- [x] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation 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.
Partially resolves: #5118
Once the extension page is ready, update the tutorial pages to reduce
duplication.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.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?
Make AssistantAgent and Handoff use BaseTool.
This ensures that they can be made declarative/serialized
<!-- 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://microsoft.github.io/autogen/docs/Contribute#documentation 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.
* Rebase to latest main branch
* Moved _azure module to azure
* Validate extra_create_args in and json response
* Added Support for Github Models
* Added normalize_name and assert_valid name
* Added Tests for AzureAIChatCompletionClient
* WIP: Azure AI Client
* Added: object-level usage data
* Added: doc string
* Added: check existing response_format value
* Added: _validate_config and _create_client
* lint
* merge dependencies
* add tests for img and function calling
* support actual tests through env vars
* address mypy errors
* doc example fix
* fmt
* fix doc fmt
* Update python/packages/autogen-ext/src/autogen_ext/models/azure/_azure_ai_client.py
---------
Co-authored-by: Rohan Thacker <thackerrohan4@gmail.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
* initial pass on making group chats declarative
* update group chat tests
* update impl to include participant serialization for all teams
* v1 making soc declarative
* update memory test
* update chatagent and team base classes
* update serialization doc notebook
* fomating updates
* vi1 for declarative tools
* make functtools declarative
* add tests
* update imports
* update formatting
* move tests, format fixes
* format updates
* update test
* add user warning to _from_config
* add warning on load_component to docs
---------
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
Update _magentic_one_orchestrator.py
In a Magentic Group Settting, if one of the Assitant Agents uses a tool it gives the following error, note this is under a FALSE reflect_on_tool variable.
Making it true, wont happen, though more tokens will be consumed and it will have a worst output and the philosophy of a tool as an answer is not followed...
Eric Zhu