- Added the support Azure AI Agent. The new agent is named AzureAIAgent.
- The agent supports Bing search, file search, and Azure search tools.
- Added a Jupiter notebook to demonstrate the usage of the AzureAIAgent.
## What's missing?
- AzureAIAgent support only text message responses
- Parallel execution for the custom functions.
## Related issue number
[5545](https://github.com/microsoft/autogen/issues/5545#event-16626859772)
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
<!-- 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?
This is an initial exploration of what could be a solution for #6214 .
It implements a simple text canvas using difflib and also a memory
component and a tool component for interacting with the canvas. Still in
early testing but would love feedback on the design.
## Related issue number
<!-- For example: "Closes #1234" -->
## Checks
- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.
---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
This PR introduces a safer and more controllable execution environment
for LLM code execution in version 0.4 by enabling the use of Jupyter
inside a container. This enhancement addresses security concerns and
provides a more robust execution context. In particular, it allows:
Isolation of code execution via containerized Jupyter environments.
Persistent memory of variables and their values throughout the
conversation.
Memory of code execution results to support more advanced reasoning and
follow-up tasks.
These improvements help build a more interactive and stateful LLM-agent
programming experience, especially for iterative code generation and
debugging scenarios.
## Related issue number
Open #6153
## Checks
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Resolves#6232, #6198
This PR introduces an optional parameter `session` to `mcp_server_tools`
to support reuse of the same session.
```python
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, create_mcp_server_session, mcp_server_tools
async def main() -> None:
model_client = OpenAIChatCompletionClient(model="gpt-4o", parallel_tool_calls=False) # type: ignore
params = StdioServerParams(
command="npx",
args=["@playwright/mcp@latest"],
read_timeout_seconds=60,
)
async with create_mcp_server_session(params) as session:
await session.initialize()
tools = await mcp_server_tools(server_params=params, session=session)
print(f"Tools: {[tool.name for tool in tools]}")
agent = AssistantAgent(
name="Assistant",
model_client=model_client,
tools=tools, # type: ignore
)
termination = TextMentionTermination("TERMINATE")
team = RoundRobinGroupChat([agent], termination_condition=termination)
await Console(
team.run_stream(
task="Go to https://ekzhu.com/, visit the first link in the page, then tell me about the linked page."
)
)
asyncio.run(main())
```
Based on discussion in this thread: #6284, we will consider
serialization and deserialization of MCP server tools when used in this
manner in a separate issue.
This PR also replaces the `json_schema_to_pydantic` dependency with
built-in utils.
## Description
This PR pins opentelemetry-proto version to >=1.28.0, which uses
protobuf > 5.0, < 6.0 to generate protobuf files.
## Related issue number
Closes#6304
<!-- Thank you for your contribution! Please review
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?
`IncludeEnum` was removed in ChromaDB when it was updated to `1.0.0`.
This caused issues when using `ChromaDBVectorMemory`. This PR fixes
those issues
## Related issue number
Closes#6241
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
# 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>
## Why are these changes needed?
This PR fixes a `TypeError: Cannot instantiate typing.Union` that occurs
when using the `MultimodalWebSurfer_agent` with Anthropic models. The
error was caused by the incorrect usage of `typing.Union` as a class
constructor instead of a type hint within the `_anthropic_client.py`
file. The code was attempting to instantiate `typing.Union`, which is
not allowed. The fix correctly uses `typing.Union` within type hints,
and uses the correct `Base64ImageSourceParam` type. It also updates the
`pyproject.toml` dependency.
## Related issue number
Closes#6035
## 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.
- [v] I've made sure all auto checks have passed.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.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
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->
## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
## Related issue number
<!-- For example: "Closes #1234" -->
## 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.
---------
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>
_(EXPERIMENTAL, RESEARCH IN PROGRESS)_
In 2023 AutoGen introduced [Teachable
Agents](https://microsoft.github.io/autogen/0.2/blog/2023/10/26/TeachableAgent/)
that users could teach new facts, preferences and skills. But teachable
agents were limited in several ways: They could only be
`ConversableAgent` subclasses, they couldn't learn a new skill unless
the user stated (in a single turn) both the task and how to solve it,
and they couldn't learn on their own. **Task-Centric Memory** overcomes
these limitations, allowing users to teach arbitrary agents (or teams)
more flexibly and reliably, and enabling agents to learn from their own
trial-and-error experiences.
This PR is large and complex. All of the files are new, and most of the
added components depend on the others to run at all. But the review
process can be accelerated if approached in the following order.
1. Start with the [Task-Centric Memory
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/packages/autogen-ext/src/autogen_ext/task_centric_memory).
1. Install the memory extension locally, since it won't be in pypi until
it's merged. In the `agentic_memory` branch, and the `python/packages`
directory:
- `pip install -e autogen-agentchat`
- `pip install -e autogen-ext[openai]`
- `pip install -e autogen-ext[task-centric-memory]`
2. Run the Quickstart sample code, then immediately open the
`./pagelogs/quick/0 Call Tree.html` file in a browser to view the work
in progress.
3. Click through the web page links to see the details.
2. Continue through the rest of the main README to get a high-level
overview of the architecture.
3. Read through the [code samples
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/samples/task_centric_memory),
running each of the 4 code samples while viewing their page logs.
4. Skim through the 4 code samples, along with their corresponding yaml
config files:
1. `chat_with_teachable_agent.py`
2. `eval_retrieval.py`
3. `eval_teachability.py`
4. `eval_learning_from_demonstration.py`
5. `eval_self_teaching.py`
6. Read `task_centric_memory_controller.py`, referring back to the
previously generated page logs as needed. This is the most important and
complex file in the PR.
7. Read the remaining core files.
1. `_task_centric_memory_bank.py`
2. `_string_similarity_map.py`
3. `_prompter.py`
8. Read the supporting files in the utils dir.
1. `teachability.py`
2. `apprentice.py`
3. `grader.py`
4. `page_logger.py`
5. `_functions.py`
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->
## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
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.
@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>
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>
* 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>
* use caching to run tests and report coverage
* fix test step dep name
* try to fix cov fname
* add working dir to mv step
* update artifact download
* fmt
* reduce concurrency on ext test
---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
* Add ChatCompletionCache along with AbstractStore for caching completions
* Addressing comments
* Improve interface for cachestore
* Improve documentation & revert protocol
* Make cache store typed, and improve docs
* remove unnecessary casts
Eric Zhu
Abdo Talema
Leonardo Pinheiro
masquerlin
cheng-tan
Sungjun.Kim
Macon Pegram
Jay Prakash Thakur
EeS
afourney
PythicCoder
Ricky Loynd
Victor Dibia
rylativity
Eitan Yarmush
Richárd Gyikó
Jack Gerrits
Mohammad Mazraeh
Gerardo Moreno
Leon De Andrade
Sachin Joglekar