adding email agent
## Why are these changes needed?
This PR introduces an AI-powered email assistant that can generate
images, attach files, draft reports, and send emails to multiple
recipients or specific users based on their queries. This feature is
highly beneficial for customer management and email marketing, enhancing
automation and improving efficiency.
## Related issue number
Open #6228
## 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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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
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introduced in this PR.
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Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Closes#6265
Convert the `Message` and `Resource` dataclasses to Pydantic models in
the `llamaindex-agent` cookbook.
* Replace `dataclass` with `BaseModel` for `Message` and `Resource`
classes.
* Update imports to use `BaseModel` from `pydantic`
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
<!-- Thank you for your contribution! Please review
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pull request. -->
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## Why are these changes needed?
Add note on how to update modelinfo for new models.
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#6258
## Checks
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introduced in this PR.
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<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
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introduced in this PR.
- [x] I've made sure all auto checks have passed.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
# Azure AI Search Tool Implementation
This PR adds a new tool for Azure AI Search integration to autogen-ext,
enabling agents to search and retrieve information from Azure AI Search
indexes.
## Why Are These Changes Needed?
AutoGen currently lacks native integration with Azure AI Search, which
is a powerful enterprise search service that supports semantic, vector,
and hybrid search capabilities. This integration enables agents to:
1. Retrieve relevant information from large document collections
2. Perform semantic search with AI-powered ranking
3. Execute vector similarity search using embeddings
4. Combine text and vector approaches for optimal results
This tool complements existing retrieval capabilities and provides a
seamless way to integrate with Azure's search infrastructure.
## Features
- **Multiple Search Types**: Support for text, semantic, vector, and
hybrid search
- **Flexible Configuration**: Customizable search parameters and fields
- **Robust Error Handling**: User-friendly error messages with
actionable guidance
- **Performance Optimizations**: Configurable caching and retry
mechanisms
- **Vector Search Support**: Built-in embedding generation with
extensibility
## Usage Example
```python
from autogen_ext.tools.azure import AzureAISearchTool
from azure.core.credentials import AzureKeyCredential
from autogen import AssistantAgent, UserProxyAgent
# Create the search tool
search_tool = AzureAISearchTool.load_component({
"provider": "autogen_ext.tools.azure.AzureAISearchTool",
"config": {
"name": "DocumentSearch",
"description": "Search for information in the knowledge base",
"endpoint": "https://your-service.search.windows.net",
"index_name": "your-index",
"credential": {"api_key": "your-api-key"},
"query_type": "semantic",
"semantic_config_name": "default"
}
})
# Create an agent with the search tool
assistant = AssistantAgent(
"assistant",
llm_config={"tools": [search_tool]}
)
# Create a user proxy agent
user_proxy = UserProxyAgent(
"user_proxy",
human_input_mode="TERMINATE",
max_consecutive_auto_reply=10,
code_execution_config={"work_dir": "coding"}
)
# Start the conversation
user_proxy.initiate_chat(
assistant,
message="What information do we have about quantum computing in our knowledge base?"
)
```
## Testing
- Added unit tests for all search types (text, semantic, vector, hybrid)
- Added tests for error handling and cancellation
- All tests pass locally
## Documentation
- Added comprehensive docstrings with examples
- Included warnings about placeholder embedding implementation
- Added links to Azure AI Search documentation
## Related issue number
Closes#5419
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Resolves#5934
This PR adds ability for `AssistantAgent` to generate a
`StructuredMessage[T]` where `T` is the content type in base model.
How to use?
```python
from typing import Literal
from pydantic import BaseModel
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.ui import Console
# The response format for the agent as a Pydantic base model.
class AgentResponse(BaseModel):
thoughts: str
response: Literal["happy", "sad", "neutral"]
# Create an agent that uses the OpenAI GPT-4o model which supports structured output.
model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent(
"assistant",
model_client=model_client,
system_message="Categorize the input as happy, sad, or neutral following the JSON format.",
# Setting the output format to AgentResponse to force the agent to produce a JSON string as response.
output_content_type=AgentResponse,
)
result = await Console(agent.run_stream(task="I am happy."))
# Check the last message in the result, validate its type, and print the thoughts and response.
assert isinstance(result.messages[-1], StructuredMessage)
assert isinstance(result.messages[-1].content, AgentResponse)
print("Thought: ", result.messages[-1].content.thoughts)
print("Response: ", result.messages[-1].content.response)
await model_client.close()
```
```
---------- user ----------
I am happy.
---------- assistant ----------
{
"thoughts": "The user explicitly states they are happy.",
"response": "happy"
}
Thought: The user explicitly states they are happy.
Response: happy
```
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
## Why are these changes needed?
This change addresses a compatibility issue when using Google Gemini
models with AutoGen. Specifically, Gemini returns a 400 INVALID_ARGUMENT
error when receiving a response with an empty "text" parameter.
The root cause is that Gemini does not accept empty string values (e.g.,
"") as valid inputs in the history of the conversation.
To fix this, if the content field is falsy (e.g., None, "", etc.), it is
explicitly replaced with a single whitespace (" "), which prevents the
Gemini model from rejecting the request.
- **Gemini API compatibility:** Gemini models reject empty assistant
messages (e.g., `""`), causing runtime errors. This PR ensures such
messages are safely replaced with whitespace where appropriate.
- **Avoiding regressions:** Applying the empty content workaround **only
to Gemini**, and **only to valid message types**, avoids breaking OpenAI
or other models.
- **Reducing duplication:** Previously, message transformation logic was
scattered and repeated across different message types and models.
Modularizing this pipeline removes that redundancy.
- **Improved maintainability:** With future model variants likely to
introduce more constraints, this modular structure makes it easier to
adapt transformations without writing ad-hoc code each time.
- **Testing for correctness:** The new structure is verified with tests,
ensuring the bug fix is effective and non-intrusive.
## Summary
This PR introduces a **modular transformer pipeline** for message
conversion and **fixes a Gemini-specific bug** related to empty
assistant message content.
### Key Changes
- **[Refactor]** Extracted message transformation logic into a unified
pipeline to:
- Reduce code duplication
- Improve maintainability
- Simplify debugging and extension for future model-specific logic
- **[BugFix]** Gemini models do not accept empty assistant message
content.
- Introduced `_set_empty_to_whitespace` transformer to replace empty
strings with `" "` only where needed
- Applied it **only** to `"text"` and `"thought"` message types, not to
`"tools"` to avoid serialization errors
- **Improved structure for model-specific handling**
- Transformer functions are now grouped and conditionally applied based
on message type and model family
- This design makes it easier to support future models or combinations
(e.g., Gemini + R1)
- **Test coverage added**
- Added dedicated tests to verify that empty assistant content causes
errors for Gemini
- Ensured the fix resolves the issue without affecting OpenAI models
---
## Motivation
Originally, Gemini-compatible endpoints would fail when receiving
assistant messages with empty content (`""`).
This issue required special handling without introducing brittle, ad-hoc
patches.
In addressing this, I also saw an opportunity to **modularize** the
message transformation logic across models.
This improves clarity, avoids duplication, and simplifies future
adaptations (e.g., different constraints across model families).
---
## 📘 AutoGen Modular Message Transformer: Design & Usage Guide
This document introduces the **new modular transformer system** used in
AutoGen for converting `LLMMessage` instances to SDK-specific message
formats (e.g., OpenAI-style `ChatCompletionMessageParam`).
The design improves **reusability, extensibility**, and
**maintainability** across different model families.
---
### 🚀 Overview
Instead of scattering model-specific message conversion logic across the
codebase, the new design introduces:
- Modular transformer **functions** for each message type
- Per-model **transformer maps** (e.g., for OpenAI-compatible models)
- Optional **conditional transformers** for multimodal/text hybrid
models
- Clear separation between **message adaptation logic** and
**SDK-specific builder** (e.g., `ChatCompletionUserMessageParam`)
---
### 🧱 1. Define Transform Functions
Each transformer function takes:
- `LLMMessage`: a structured AutoGen message
- `context: dict`: metadata passed through the builder pipeline
And returns:
- A dictionary of keyword arguments for the target message constructor
(e.g., `{"content": ..., "name": ..., "role": ...}`)
```python
def _set_thought_as_content_gemini(message: LLMMessage, context: Dict[str, Any]) -> Dict[str, str | None]:
assert isinstance(message, AssistantMessage)
return {"content": message.thought or " "}
```
---
### 🪢 2. Compose Transformer Pipelines
Multiple transformer functions are composed into a pipeline using
`build_transformer_func()`:
```python
base_user_transformer_funcs: List[Callable[[LLMMessage, Dict[str, Any]], Dict[str, Any]]] = [
_assert_valid_name,
_set_name,
_set_role("user"),
]
user_transformer = build_transformer_func(
funcs=base_user_transformer_funcs,
message_param_func=ChatCompletionUserMessageParam
)
```
- The `message_param_func` is the actual constructor for the target
message class (usually from the SDK).
- The pipeline is **ordered** — each function adds or overrides keys in
the builder kwargs.
---
### 🗂️ 3. Register Transformer Map
Each model family maintains a `TransformerMap`, which maps `LLMMessage`
types to transformers:
```python
__BASE_TRANSFORMER_MAP: TransformerMap = {
SystemMessage: system_transformer,
UserMessage: user_transformer,
AssistantMessage: assistant_transformer,
}
register_transformer("openai", model_name_or_family, __BASE_TRANSFORMER_MAP)
```
- `"openai"` is currently required (as only OpenAI-compatible format is
supported now).
- Registration ensures AutoGen knows how to transform each message type
for that model.
---
### 🔁 4. Conditional Transformers (Optional)
When message construction depends on runtime conditions (e.g., `"text"`
vs. `"multimodal"`), use:
```python
conditional_transformer = build_conditional_transformer_func(
funcs_map=user_transformer_funcs_claude,
message_param_func_map=user_transformer_constructors,
condition_func=user_condition,
)
```
Where:
- `funcs_map`: maps condition label → list of transformer functions
```python
user_transformer_funcs_claude = {
"text": text_transformers + [_set_empty_to_whitespace],
"multimodal": multimodal_transformers + [_set_empty_to_whitespace],
}
```
- `message_param_func_map`: maps condition label → message builder
```python
user_transformer_constructors = {
"text": ChatCompletionUserMessageParam,
"multimodal": ChatCompletionUserMessageParam,
}
```
- `condition_func`: determines which transformer to apply at runtime
```python
def user_condition(message: LLMMessage, context: Dict[str, Any]) -> str:
if isinstance(message.content, str):
return "text"
return "multimodal"
```
---
### 🧪 Example Flow
```python
llm_message = AssistantMessage(name="a", thought="let’s go")
model_family = "openai"
model_name = "claude-3-opus"
transformer = get_transformer(model_family, model_name, type(llm_message))
sdk_message = transformer(llm_message, context={})
```
---
### 🎯 Design Benefits
| Feature | Benefit |
|--------|---------|
| 🧱 Function-based modular design | Easy to compose and test |
| 🧩 Per-model registry | Clean separation across model families |
| ⚖️ Conditional support | Allows multimodal / dynamic adaptation |
| 🔄 Reuse-friendly | Shared logic (e.g., `_set_name`) is DRY |
| 📦 SDK-specific | Keeps message adaptation aligned to builder interface
|
---
### 🔮 Future Direction
- Support more SDKs and formats by introducing new message_param_func
- Global registry integration (currently `"openai"`-scoped)
- Class-based transformer variant if complexity grows
---
## Related issue number
Closes#5762
## Checks
- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ v ] I've made sure all auto checks have passed.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Rename the `ChatMessage` and `AgentEvent` base classes to `BaseChatMessage` and `BaseAgentEvent`.
Bring back the `ChatMessage` and `AgentEvent` as union of built-in concrete types to avoid breaking existing applications that depends on Pydantic serialization.
Why?
Many existing code uses containers like this:
```python
class AppMessage(BaseModel):
name: str
message: ChatMessage
# Serialization is this:
m = AppMessage(...)
m.model_dump_json()
# Fields like HandoffMessage.target will be lost because it is now treated as a base class without content or target fields.
```
The assumption on `ChatMessage` or `AgentEvent` to be a union of concrete types could be in many existing code bases. So this PR brings back the union types, while keep method type hints such as those on `on_messages` to use the `BaseChatMessage` and `BaseAgentEvent` base classes for flexibility.
Token limited model context is currently broken because it is importing
from extensions.
This fix removed the imports and updated the model context
implementation to use model client directly.
In the future, the model client's token counting should cache results
from model API to provide accurate counting.
This PR adds missing model entries for OpenAI-compatible endpoints,
including gpt-4.5-turbo, gpt-4.5-turbo-preview, and claude-3.5-sonnet.
This improves coverage and avoids potential fallback or mismatch issues
when initializing clients.
This PR refactored `AgentEvent` and `ChatMessage` union types to
abstract base classes. This allows for user-defined message types that
subclass one of the base classes to be used in AgentChat.
To support a unified interface for working with the messages, the base
classes added abstract methods for:
- Convert content to string
- Convert content to a `UserMessage` for model client
- Convert content for rendering in console.
- Dump into a dictionary
- Load and create a new instance from a dictionary
This way, all agents such as `AssistantAgent` and `SocietyOfMindAgent`
can utilize the unified interface to work with any built-in and
user-defined message type.
This PR also introduces a new message type, `StructuredMessage` for
AgentChat (Resolves#5131), which is a generic type that requires a
user-specified content type.
You can create a `StructuredMessage` as follow:
```python
class MessageType(BaseModel):
data: str
references: List[str]
message = StructuredMessage[MessageType](content=MessageType(data="data", references=["a", "b"]), source="user")
# message.content is of type `MessageType`.
```
This PR addresses the receving side of this message type. To produce
this message type from `AssistantAgent`, the work continue in #5934.
Added unit tests to verify this message type works with agents and
teams.
<!-- Thank you for your contribution! Please review
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pull request. -->
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## Why are these changes needed?
- Adds tracing docs page for AgentChat with Jaeger example
- [x] Runtime tracing: Example code where tracing is done with the
SingleThreaded Runtime, logging all events
- [x] Custom event tracing: Example code logging messages returned from
`team.run_stream()`
- [ ] LLM span tracing .. depends on
https://github.com/microsoft/autogen/issues/5895
- [ ] [TBD] Distributed tracing
See
[tracing.ipynb](bdb6ac5315/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb)
here
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
#5992
## Open Questions
@ekzu
- What is the recommended way to directly log custom events like
LLMCallEvents and ToolCallEvents? LogEventhandlers in user code that
become traced spans?
- Currenltly tool calls and their args are already logged (not sure
where this is done), but LLM call events are not. Should we include
samples on this?
## 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>
## Why are these changes needed?
fix the accessibility issue that screen reader doesn't announce the
theme when it changes
## Related issue number
#5631 (13) (31) (59)
---------
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. -->
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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. -->
If user tab to a code block copy button then hit enter, screen reader
doesn't announce "Copied". This PR fixed this bug.
## Related issue number
#5631 (8)
<!-- For example: "Closes #1234" -->
## Checks
- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.
## Why are these changes needed?
Fixes (53) on screen reader issues. A special thanks to @sjay8 for
starting the work on this task
## Related issue number
https://github.com/microsoft/autogen/issues/5631
<!-- Thank you for your contribution! Please review
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pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
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assign them to your PR. -->
## Why are these changes needed?
Fixes Screen Reader issue (58)
## Related issue number
https://github.com/microsoft/autogen/issues/5631
## 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>
Resolves#5982
This PR adds support for `json_schema` as a `response_format` type in
`OpenAIChatCompletionClient`. This is necessary because it allows the
client to be serialized along with the schema. If user use
`response_format=SomeBaseModel`, the client cannot be serialized.
Usage:
```python
# Structured output response, with a pre-defined JSON schema.
OpenAIChatCompletionClient(...,
response_format = {
"type": "json_schema",
"json_schema": {
"name": "name of the schema, must be an identifier.",
"description": "description for the model.",
# You can convert a Pydantic (v2) model to JSON schema
# using the `model_json_schema()` method.
"schema": "<the JSON schema itself>",
# Whether to enable strict schema adherence when
# generating the output. If set to true, the model will
# always follow the exact schema defined in the
# `schema` field. Only a subset of JSON Schema is
# supported when `strict` is `true`.
# To learn more, read
# https://platform.openai.com/docs/guides/structured-outputs.
"strict": False, # or True
},
},
)
````
## Summary of Changes
- Added 'candidate_func' to 'SelectorGroupChat' to narrow-down the pool
of candidate speakers.
- Introduced a test in tests/test_group_chat_endpoint.py to validate its
functionality.
- Updated the selector group chat user guide with an example
demonstrating 'candidate_func'.
## Why are these changes needed?
- These changes adds a new parameter `candidate_func` to
`SelectorGroupChat` that helps user narrow-down the set of agents for
speaker selection, allowing users to automatically select next speaker
from a smaller pool of agents.
## Related issue number
Closes#5828
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Signed-off-by: Abhijeetsingh Meena <abhijeet040403@gmail.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
<!-- 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?
https://github.com/user-attachments/assets/b649053b-c377-40c7-aa51-ee64af766fc2
<img width="100%" alt="image"
src="https://github.com/user-attachments/assets/03ba1df5-c9a2-4734-b6a2-0eb97ec0b0e0"
/>
## Authentication
This PR implements an experimental authentication feature to enable
personalized experiences (multiple users). Currently, only GitHub
authentication is supported. You can extend the base authentication
class to add support for other authentication methods.
By default authenticatio is disabled and only enabled when you pass in
the `--auth-config` argument when running the application.
### Enable GitHub Authentication
To enable GitHub authentication, create a `auth.yaml` file in your app
directory:
```yaml
type: github
jwt_secret: "your-secret-key"
token_expiry_minutes: 60
github:
client_id: "your-github-client-id"
client_secret: "your-github-client-secret"
callback_url: "http://localhost:8081/api/auth/callback"
scopes: ["user:email"]
```
Please see the documentation on [GitHub
OAuth](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/authenticating-to-the-rest-api-with-an-oauth-app)
for more details on obtaining the `client_id` and `client_secret`.
To pass in this configuration you can use the `--auth-config` argument
when running the application:
```bash
autogenstudio ui --auth-config /path/to/auth.yaml
```
Or set the environment variable:
```bash
export AUTOGENSTUDIO_AUTH_CONFIG="/path/to/auth.yaml"
```
```{note}
- Authentication is currently experimental and may change in future releases
- User data is stored in your configured database
- When enabled, all API endpoints require authentication except for the authentication endpoints
- WebSocket connections require the token to be passed as a query parameter (`?token=your-jwt-token`)
```
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#4350
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---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
<!-- Thank you for your contribution! Please review
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## Why are these changes needed?
<img width="1151" alt="image"
src="https://github.com/user-attachments/assets/98bc91ee-749c-4831-b36f-10322979883b"
/>
- Update migration guide to cover teachability/rag agents (mention how
similar functionality can be accomplished with AssistantAgent + Memory)
- Update memory docs to explicitly add a text chunking example and a rag
agent
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5772Closes#4742
## Checks
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introduced in this PR.
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## Why are these changes needed?
This reverts the base image in AutoGen Studio Dockerfile to `FROM
python:3.10-slim`. This fixes the Docker image build failure due to
conflicting UID with Dev Container's `vscode` user.
## Related issue number
Fixes#5929
## Checks
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## Why are these changes needed?
The [agentchat teams
docs](https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/tutorial/teams.html)
page did not list out the teams currently supported. This is confusing
for readers/uisers as they have to search around to discover that
selector groupchat, swarm and magentic one are available.
This PR adds a list of supported teams to the top of the teams page and
links to the relevant tutorials.
<!-- 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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Fixed a typo, chroma_user_memory instead of user_memory
## Why are these changes needed?
There's a confusing typo in the documentation.
## Related issue number
None
## Checks
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introduced in this PR.
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Co-authored-by: Victor Dibia <victordibia@microsoft.com>
These changes allow for 2 important use-cases:
1. Add a span for tool calls which will enable tracing of all tool calls
in agent_chat
2. Allow runtimes to pick up global `tracer_providers` if they are
available. This is very helpful because it allows for nested teams/agent
to all use the same tracer.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
These changes are needed because there is currently no way to get
logging information about Streaming LLM requests/responses.
I decided to put the StreamStart event AFTER the first chunk so there
aren't false positives about connections/auth.
Closes#5730
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
This pull request introduces the integration of the `llama-cpp` library
into the `autogen-ext` package, with significant changes to the project
dependencies and the implementation of a new chat completion client. The
most important changes include updating the project dependencies, adding
a new module for the `LlamaCppChatCompletionClient`, and implementing
the client with various functionalities.
### Project Dependencies:
*
[`python/packages/autogen-ext/pyproject.toml`](diffhunk://#diff-095119d4420ff09059557bd25681211d1772c2be0fbe0ff2d551a3726eff1b4bR34-R38):
Added `llama-cpp-python` as a new dependency under the `llama-cpp`
section.
### New Module:
*
[`python/packages/autogen-ext/src/autogen_ext/models/llama_cpp/__init__.py`](diffhunk://#diff-42ae3ba17d51ca917634c4ea3c5969cf930297c288a783f8d9c126f2accef71dR1-R8):
Introduced the `LlamaCppChatCompletionClient` class and handled import
errors with a descriptive message for missing dependencies.
### Implementation of `LlamaCppChatCompletionClient`:
*
`python/packages/autogen-ext/src/autogen_ext/models/llama_cpp/_llama_cpp_completion_client.py`:
- Added the `LlamaCppChatCompletionClient` class with methods to
initialize the client, create chat completions, detect and execute
tools, and handle streaming responses.
- Included detailed logging for debugging purposes and implemented
methods to count tokens, track usage, and provide model information.…d
chat capabilities
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
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https://microsoft.github.io/autogen/. See
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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: aribornstein <x@x.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
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## Why are these changes needed?
Add anthropic docs
- Add api docs
- Add sample code + usage in agent chat user guide
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5856
## Checks
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Yash Malik