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Author SHA1 Message Date
Evan Mattson e696dc7d52 Python: Add directory allowlist configuration for SessionsPythonTool file (#13467) 
### Motivation and Context

When `SessionsPythonTool` is used with an AI model, providing explicit
directory boundaries for file operations gives developers fine-grained
control over which parts of the filesystem the plugin can access. This
follows the pattern established by `HttpPlugin.allowed_domains`.

- Add `allowed_upload_directories` parameter to control which local
directories can be used for file uploads
- Add `allowed_download_directories` parameter for optional restrictions
on file download destinations
- Implement path canonicalization to resolve symlinks and normalize
paths

Usage:

```python
tool = SessionsPythonTool(
    credential=credential,
    allowed_upload_directories={"/app/data/uploads", "/app/user_files"},
    allowed_download_directories={"/app/data/downloads"},  # optional
)
```


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### Description

- `upload_file` now requires `allowed_upload_directories` to be
configured (deny-by-default)
- `download_file` optionally supports `allowed_download_directories`
(permissive-by-default, since it's not exposed as a kernel function)
- Both parameters accept `set[str]` or `list[str]` for convenience
- Paths are canonicalized using `os.path.realpath()` before validation

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### Contribution Checklist

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- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-01-23 09:02:05 +00:00
Dmytro Struk dc7c1c0484 Python: Updated Copilot Studio README (#13432) 
### Motivation and Context

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Renamed dependency from Copilot Studio README.

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 2025-12-23 00:28:28 +00:00
Tao Chen d84363f477 Python: Migrate to new Google GenAI SDK (#13371) 
### Motivation and Context

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Closes #12970, #13352

### Description

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1. Migrate Google AI connector to use the new Google AI SDK.
2. This PR also marks the VertexAI connector as deprecated in favor of
the Google AI connector. With this update, if users wants to connect to
a VertexAI project instead of a Google AI endpoint, they can do so with
the Google AI connector.
3. This PR also adds the ability for users to provide a custom client to
the Google AI connector (#13352).
4. This PR also marks `AzureTextCompletion` deprecated.

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 2025-11-24 21:53:57 +00:00
Nico MöllerandEvan Mattson 145ad9bfd2 Python: Upgrade Onnx Connector to use 0.9.0 (#13162) 
### Motivation and Context

Fixes : #13001 

### Description

Package version 0.9.0 introduced very helpful methods like applying chat
templates automatically from the tokenizer and also support for multi
audio & image files.

This PR adds the following functionalities:

- [x] Add Inference with multiple Images
- [x] Add Inference with multiple Audios
- [x] Chat Templates for non Multimodal Models are read via onnx 

### Samples

Text Sample with ONNX:
<img width="1728" height="90" alt="image"
src="https://github.com/user-attachments/assets/b93d9fc1-4e38-4fa9-b535-cc316e0900ed"
/>

Image Sample with ONNX :
<img width="1721" height="91" alt="image"
src="https://github.com/user-attachments/assets/47761beb-b728-4936-a6b6-fd253e041689"
/>


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---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-11-11 08:44:52 +00:00
Eduard van Valkenburg 1c11258a32 Python: added af_tool bridge for kernel_functions (#13227) 
### Motivation and Context

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Adds a new method to KernelFunction that creates a Agent Framework Tool
from that function.

Does require the user to install agent-framework-core themselves.

### Description

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These notes will help understanding how your code works. Thanks! -->

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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
 2025-10-30 15:24:16 +00:00
Taylor Rockey e73446e86e Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) 
### Motivation and Context

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  1. Why is this change required?
- The Agents SDK has been released, and the recent release changed the
namespace from `microsoft.agents` to `microsoft_agents`, which breaks
imports.
  2. What problem does it solve?
- This updates the `pyproject.toml` and copilot studio related files to
fix the imports
  3. What scenario does it contribute to?
      - Using a Copilot Studio agent as an Agent in Semantic Kernel
  4. If it fixes an open issue, please link to the issue here.
      - N/A
-->

### Description

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- Updated pyproject.toml to use the released packages for the Agents
SDK.
- Updated imports in the `copilot_studio_agent` related files to use the
new `microsoft_agents` namespace

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- [] I didn't break anyone 😄
 2025-09-23 01:01:51 +00:00
Evan Mattson 28ea2f4df8 Python: Fix AzureAIAgent readme links (#13138) 
### Motivation and Context

Current getting started with agents README links for AzureAIAgent are
broken.

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### Description

- Fixes #13129

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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
 2025-09-17 04:40:38 +00:00
Soumili Nandi 61d3e6ee8d Python: Introduce NvidiaChatCompletion AI Connector (#12952) 
### Motivation and Context

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Enables developers to use NVIDIA's chat models through Semantic Kernel
with structured output support.
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This change adds chat completion support with structured output to the
existing NVIDIA connector, enabling developers to use NVIDIA's chat
models through Semantic Kernel while building conversational AI
applications that require structured, validated responses. The connector
will support more model types iteratively, with future PRs introducing
VLM models and additional capabilities.

### Description

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- New Chat Completion Service:
`NvidiaChatCompletion`: Chat completion service following same structure
as other connectors
- Extends existing NVIDIA embedding connector patterns and architecture
- Custom logic for handling structured output specific to NVIDIA models
  
- Enhanced Configuration:
`NvidiaChatPromptExecutionSettings`: Chat-specific settings following
existing connector patterns
### Contribution Checklist



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- [x] I didn't break anyone 😄
 2025-09-15 23:26:38 +00:00
Tao Chen d7db35dc47 Python: Fix Azure AI Inference connector model_extras duplication (#13066) 
### Motivation and Context

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Addresses #13005 

### Description

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1. Remove the `extra_parameters` key from
`AzureAIInferenceChatPromptExecutionSettings` before calling the chat
completion API to avoid unknown parameter error.
2. Allow setting the API version on the `AzureAIInferenceXXX`
connectors.

### Contribution Checklist

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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
 2025-09-04 01:27:19 +00:00
Evan Mattson f7fdfcd9a9 Python: Add AzureAIAgent Deep Research Tool support (#13034) 
### Motivation and Context

The deep research tool was introduced for the AzureAIAgent; however, we
didn't have support yet in SK. Adding support now for using the deep
research tool for both streaming and non-streaming. Samples added as
well.

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### Description

- Add support for deep research tool.
- Add samples with notes on prerequisites to configure the sample to run
properly.
- Added support for `AZURE_AI_AGENT_DEEP_RESEARCH_MODEL` env var within
`AzureAIAgentSettings` class.
- Bumped min allowed `azure-ai-agents` package ver to 1.2.0b3.
- Closes #12978

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### Contribution Checklist

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- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
 2025-09-01 02:35:56 +00:00
Evan Mattson 86b10b95d2 Python: provide AzureAIAgent thread msg id during streaming (#13032) 
### Motivation and Context

As part of invoking an AzureAIAgent via streaming, we weren't providing
the `thread_id` via the `AgentStreamEvent.THREAD_MESSAGE_CREATED` event.

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### Description

Supply the caller with the `thread_msg_id` by sticking it into the
returned SCMC's `metadata` dictionary.
- Closes #12959

Sample output looks like:

```
Sample Output:

# User: 'Hello'
# AuthorRole.ASSISTANT: (thread message id: msg_HZ2h4Wzbj7GEcnVCjnyEuYWT) Hello! How can I assist you with 
    the menu today?
# User: 'What is the special soup?'
# AuthorRole.ASSISTANT: (thread message id: msg_TSjkJK6hHJojIkPvF6uUofHD) The special soup today is 
    Clam Chowder. Would you like to know more about it or anything else from the menu?
# User: 'How much does that cost?'
# AuthorRole.ASSISTANT: (thread message id: msg_liwTpBFrB9JpCM1oM9EXKiwq) The Clam Chowder costs $9.99. 
    Is there anything else you'd like to know?
# User: 'Thank you'
# AuthorRole.ASSISTANT: (thread message id: msg_K6lpR3gYIHethXq17T6gJcxi) You're welcome! 
    If you have any more questions or need assistance, feel free to ask. Enjoy your meal!
```

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- [X] I didn't break anyone 😄
 2025-08-30 10:05:55 +00:00
Christian GlessnerEvan MattsonEvan Mattson
 		5e50e190c4 Python: Add reasoning support for OpenAI Responses Agents (GPT-5, o4-mini, o3) (#12881) 
## Summary

Adds reasoning capabilities to Python OpenAI ResponsesAgent, bringing
parity with the C# implementation. This enables fine-grained control
over reasoning effort for O-series models (o1, o3-mini, o4-mini) and
gpt-5 that support the reasoning parameter.

Fixes #12843

## Changes

### Core Implementation
- **`openai_responses_agent.py`**: Added reasoning configuration support
  - Constructor-level reasoning effort setting
  - Per-invocation reasoning effort override capability
  - Proper parameter validation and model compatibility checks
- **`responses_agent_thread_actions.py`**: Extended thread actions to
support reasoning parameters
  - Reasoning effort propagation through thread operations
  - Metadata preservation for reasoning tokens and summaries

### Sample and Tests
- **`responses_agent_reasoning.py`**: Comprehensive demonstration sample
  - Constructor vs per-invocation reasoning configuration
  - Function calling integration with reasoning
  - Reasoning comparison scenarios (low/medium/high effort)
  - Error handling and troubleshooting guidance
- **`test_openai_responses_agent_reasoning.py`**: Full unit test
coverage
  - Parameter validation tests
  - Integration scenarios with function calling
  - Edge cases and error conditions

## Features

✅ **Constructor-Level Reasoning**: Set default reasoning effort when
creating agents
✅ **Per-Invocation Override**: Override reasoning effort per request  
✅ **Priority Hierarchy**: per-invocation > constructor > model default  
✅ **Function Calling Compatible**: Works seamlessly with existing plugin
system
✅ **Azure OpenAI & OpenAI Support**: Compatible with both service
providers
✅ **Model Validation**: Automatic compatibility checks for O-series
models nad GÜT-5
✅ **Metadata Access**: Reasoning tokens and summaries available in
response metadata

## Usage Example

```python
# Constructor-level reasoning
# Constructor-level reasoning configuration
agent = AzureResponsesAgent(
    ai_model_id="gpt-5",
    client=client,
    reasoning={"effort": "low"}  # Default reasoning for all requests
)

# Per-invocation override
response = await agent.invoke(
    "Solve this complex problem step by step",
    reasoning={"effort": "high"}  
)


# Invoke with reasoning callback to capture intermediate thoughts
response = await agent.invoke(
    "Analyze this data step by step",
    reasoning={"effort": "high", "summary": "detailed"},
    on_intermediate_message=handle_reasoning_message
)

# Streaming with reasoning
async for response in agent.invoke_stream(
    "Explain quantum computing in detail",
    reasoning={"effort": "high", "summary": "detailed"},
    on_intermediate_message=handle_reasoning_message
):
    print(response.content, end="", flush=True)

---------

Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2025-08-28 23:25:26 +00:00
Dmytro Struk 18d9a8e8b2 Python: .Net: Updated encoding logic in prompt templates (#12983) 
### Motivation and Context

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Resolves: https://github.com/microsoft/semantic-kernel/issues/11821

Today, the encoding of template arguments is performed only if argument
type is `string`. In case of custom type, anonymous type or collection -
the encoding is not performed.

This PR contains changes to throw an exception in case if encoding is
enabled but complex type is used. In case of complex type, the encoding
should be performed manually according to business logic and automatic
encoding should be explicitly disabled.

This enforces stricter, but more secure template rendering rules.

**Note**: this is a breaking change for customers who use Handlebars or
Liquid template with complex type arguments. Code changes are required
when initializing template arguments:
```diff
var arguments = new KernelArguments()
{
    { "customer", new
        {
-             firstName = userInput.FirstName,
-             lastName = userInput.LastName,
+             firstName = HttpUtility.HtmlEncode(userInput.FirstName),
+             lastName = HttpUtility.HtmlEncode(userInput.LastName),
        }
    }
};

var templateFactory = new LiquidPromptTemplateFactory();
var promptTemplateConfig = new PromptTemplateConfig()
{
    TemplateFormat = "liquid"
+   InputVariables = new()
+   {
+       // We set AllowDangerouslySetContent to 'true' because each property of this argument is encoded manually. 
+       new() { Name = "customer", AllowDangerouslySetContent = true },
+   }
};

var promptTemplate = templateFactory.Create(promptTemplateConfig);
var renderedPrompt = await promptTemplate.RenderAsync(kernel, arguments); 
```

### Contribution Checklist

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- [ ] I didn't break anyone 😄
 2025-08-26 22:09:00 -07:00
Dmytro Struk 2a47ba9b7d Python: Removed usage of DefaultAzureCredential (#12964) 
### Motivation and Context

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The usage of `DefaultAzureCredential` by default is unsafe and raises
security issues. Instead, users should provide their own credential
types such as `AzureCliCredential`, `ManagedIdentityCredential` and so
on. More information here:

https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.aio?view=azure-python

In this PR:
1. Removed the usage of `DefaultAzureCredential` from authentication
logic.
2. Replaced `DefaultAzureCredential` with `AzureCliCredential` in all
examples.

**Note**: this is breaking change for Azure users, who relies on
`DefaultAzureCredential`. Users will need to update the code and pass
the credential type of their choice by using `credential` parameter when
initializing one of the Azure clients.

Before:
```python
chat_completion = AzureChatCompletion()
```

After:
```python
chat_completion = AzureChatCompletion(credential=AzureCliCredential())
```

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- [ ] I didn't break anyone 😄
 2025-08-25 18:29:13 +00:00
Evan Mattson 96bad5ceac Python: Update MCP streamable sample (#12950) 
### Motivation and Context

The MCPStreamableHttpPlugin concept sample pointing to GitHub isn't
working. Updating it to use the Microsoft Learn site MCP server instead.

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### Description

Update sample.

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 2025-08-14 15:13:10 +00:00
Tao Chen 6416c58b90 Python: Add arguments and results attributes to execute tool span (#12940) 
### Motivation and Context

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We are adding two new attributes to the execute tool span according to
this proposal to Otel Semantic Conventions:
https://github.com/open-telemetry/semantic-conventions/pull/2528

### Description

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The following two attributes are added to the `execute_tool` span:
1. `gen_ai.tool.call.arguments`
2. `gen_ai.tool.call.result`

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 2025-08-13 23:35:42 +00:00
Tao ChenandDmytro Struk 8336c320d9 Python: Add bedrock model provider parameter (#12853) 
### Motivation and Context

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Fixes #12833 

### Description

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Add an optional setting to the Bedrock AI connectors to allow users to
specify the Bedrock model providers (i.e. amazon, anthropic, etc) so
that they can use application inference profiles.


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---------

Co-authored-by: Dmytro Struk <[email protected]>
 2025-08-05 00:43:00 +00:00
Evan Mattson e5972e2430 Python: Add incomplete details reason to AzureAIAgent error message (#12796) 
### Motivation and Context

When errors occur processing a run of an AzureAIAgent, we surface the
error message but not the incomplete_details reason if it exists.

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### Description

This PR adds handling for the incomplete details reason, if it exists,
so it can be passed back via the error message.
- Closes #12752

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 2025-08-04 21:59:19 +00:00
Tao ChenandEvan Mattson b7bb2832aa Python: Input & output attributes in invoke_agent span (#12834) 
### Motivation and Context

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The OTel workgroup has been thinking about enhancing tracing for agents.
This is a proposal from our tracing team:
https://github.com/open-telemetry/semantic-conventions/pull/2528. This
PR implements part of the proposal so that when the proposal gets
merged, we can quickly follow.

### Description

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Two new attributes are added to the `invoke_agent` span, namely the
`gen_ai.agent.invocation_input` and `gen_ai.agent.invocation_output`.

<img width="747" height="583" alt="image"
src="https://github.com/user-attachments/assets/d8b81f68-915b-48ee-8f86-66a0d4f686cc"
/>


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- [x] I didn't break anyone 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-08-04 21:54:45 +00:00
Nava-7andEvan Mattson c16db27622 Python: Update README.md (#12737) 
Update links due to renamed files

### Motivation and Context

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### Description

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- [x] I didn't break anyone 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-08-04 07:22:25 +00:00
Evan Mattson 1efc7ad04e Python: Pass through arguments when creating agents from specs (#12771) 
### Motivation and Context

Currently, attempting to pass through KernelArguments through agent
creation via declarative spec isn't working properly. The arguments are
used to normalize fields, but are then not passed through fully to where
the agent is created.

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### Description

Pass arguments through, and merge them properly with the normalized
fields. This allows for underlying args to be used by the agent, like in
the case of structured outputs.
- Closes #12770

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 2025-07-24 05:47:43 +00:00
Tao Chen b723fbd5e1 Python: Add mix agent orchestration sample (#12749) 
### Motivation and Context

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We don't have a sample showing how to use a mix of agents of various
types in an orchestration. This PR adds one.
A bug is also discovered in the OpenAIResponsesAgent while creating the
sample.

### Description

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1. Add a new sample showing how to use a mix of agents in the Handoff
orchestration.
2. Fix a bug in the OpenAIResponsesAgent where the tools were not
considering the tools in the kernel instance. A new test is added too.
3. Fix a bug in the AgentActorBase where the intermediate message
callback was rejecting ChatMeesageContent type.

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 2025-07-22 16:57:05 +00:00
Eduard van Valkenburg c79f5a5c63 Python: improved MCP connect and additional samples (#12696) 
### Motivation and Context

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Adding a sample using StreamableHTTPServer towards Github.
Added a demo showing how to connect to a OAuth protected MCP server.
Also some improvements in the connect logic for MCP.


### Description

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- [x] I didn't break anyone 😄
 2025-07-22 15:14:32 +00:00
Evan Mattson 48f71b1f2e Python: Support AzureAI agent MCP tools for streaming and non-streaming invocations (#12736) 
### Motivation and Context

MCP tool support was released for the AzureAI Agent and it has yet to be
added to the SK Python AzureAIAgent. This PR adds functionality to
handle the MCP tools for both streaming and non-streaming invocations.

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### Description

Adds MCP support for the Python AzureAI Agent.
- Upgrades dependent packages to be able to use the mcp tool from the
Azure SDK.

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- [X] I didn't break anyone 😄
 2025-07-18 07:08:00 +00:00
Evan Mattson 9e3cdffc6b Python: Improve AzureAIAgent get thread msg handling. (#12535) 
### Motivation and Context

Currently, when fetching thread messages for an AzureAIAgent, it assumes
that the agent exists, so we can pull it down and get its id. This may
not always be the case - an agent could have been deleted, but the
thread still exists. We need to account for this.

When creating user messages that we add to the thread, there's no `name`
or `id` parameter allowed. We can, however, add metadata to the message.
Going forward we are adding the `agent_id` as metadata to the message,
so upon retrieval later, we can attempt to get it and return the id with
the user's message. This allows one to see which agent_id has responded
to the user's message:

```
# Messages in the thread (asc order):

# AuthorRole.USER for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: Hello
# AuthorRole.ASSISTANT for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: Hello! How can I assist you with the menu today?
# AuthorRole.USER for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: What is the special soup?
# AuthorRole.ASSISTANT for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: The special soup today is Clam Chowder. Would you like to know more about it or anything else on the menu?
# AuthorRole.USER for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: How much does that cost?
# AuthorRole.ASSISTANT for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: The Clam Chowder costs $9.99. Would you like to order it or need information on other items?
# AuthorRole.USER for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: Thank you
# AuthorRole.ASSISTANT for name=asst_mXwZOwyJLxXGtaYKHizRH6Ip: You're welcome! If you have any more questions or need assistance with the menu, feel free to ask. Enjoy your meal!
```

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### Description

- Closes #12435

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 2025-06-19 23:52:07 +00:00
Eduard van Valkenburg 003cc4c010 Python: Feature python vector stores preb (#12271) 
### Motivation and Context

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Overhaul of the VectorStores to get it matching with dotnet and the
latest development there.

Closes: 
- #11938 
- #11598 
- #11597
- #11517 
- #10561 
- #10391 
- #9911 
- #9892 
- #10867 

### Description

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Many changes, some highlights:
- Moved from different vector fields types, to a single with a enum
first VectorStoreField()
- Renamed several items
- Made embeddinggenerrator a part of the vector field spec and do
automatic vectorization from there or from the same param on a
collection
- Adds hybrid search using the same setup
- Removed the intermediate TextSeachVectorSearch object need, by
allowing `create_search_function` directly on a collection

Will write out full changes in a migration guide in the docs.

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 2025-06-19 18:34:59 +00:00
Eduard van Valkenburg bd4168316d Python: add Foundry local sample (#12214) 
### Motivation and Context

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Sample on how to use Foundry Local with SK.

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 2025-06-19 08:18:18 +00:00
Nico MöllerChrisEvan Mattson
 		206655adbb Python: Add check for not approved and approved for Agent Sample (#12316) 
### Motivation and Context

I was having a Demo with the Agents, and found that
`step06_chat_completion_agent_group_chat.py` sample always terminated
after one iteration.

The Reviewer Agent answered "not approved", which also triggered the
`Termination Strategy`, due to the fact that the word `approved` is also
in `not approved`.
To only trigger when there is a real approval I added a check if the
answer is `approved` **and** the answer also don't include `not approve`


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---------

Co-authored-by: Chris <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2025-06-19 05:34:53 +00:00
Tao Chen 44f1253460 Python: Agent response callbacks to provide full context (#12501) 
### Motivation and Context

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Currently, the agent response callbacks in the `Orchestrations` are
triggered only when the final responses from the agents become
available. This limits developers to create solutions that can expose
the internal processing of the agents in multi agent orchestrations.

### Description

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The callbacks will now be triggered by the intermediate messages of the
agents:
1. A new sample
2. Tests

P.s. Fix a bug where the name of the intermediate messages is not
assigned.

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 2025-06-17 16:17:11 +00:00
Tao Chen 9b8f8e22c3 Python: Correct chat completion agent history truncation samples (#12474) 
### Motivation and Context

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While addressing the developer in this
[discussion](https://github.com/microsoft/semantic-kernel/discussions/12450),
I notice that there are a few changes needed to the improve the
truncation history reducer samples of the chat completion agent.

### Description

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Update the samples.


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 2025-06-13 20:11:00 +00:00
Tao Chen dfbc993d8f Python: Remove orchestration persona adaptation messages (#12446) 
### Motivation and Context

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We previously found that agents might get carried away from in long
contexts. The persona adaptation messages were designed to steer the
agents in an orchestration to respond more closely to the instructions
provided (as system message).

As we perform more testing with newer models, we believe that the
persona adaptation messages are no longer necessary. The additional
instability (because they are hardcoded messages that devs can't
override) and cost (the additional token cost which is not significant,
but they do pollute the context which makes debugging difficult) they
introduce is not worth the benefit they provide.


Closing #12294 


> Ideally, we'd want to approach this more analytically. The lack of a
multi-agent evaluation process makes it difficult. All samples were
verified to make sure they behave the same before and after the removal
of the persona adaptation messages.

### Description

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1. Remove the persona adaptation messages.
2. Unit tests.
3. Replace `OpenAIChatCompletion` with `AzureChatCompletion` in some of
the samples.

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 2025-06-11 15:58:21 +00:00
Evan Mattson 4b23389f71 Python: Emit token usage with streaming chat completion agent. (#12416) 
### Motivation and Context

The chat completion agent was not emitting token using during streaming
invocation because we were only allowing through `response.items`. In
the case of token usage, `response.items` is [] and the usage is
contained as part of the message's `metadata` dict. This PR fixes that
bug and allows for `response.items or response.metadata.get("usage")`.
Two new samples are added to the concepts/agents/chat_completion dir to
show how one can track token use for streaming and non-streaming agent
invocation. Token usage handling is also added to the chat completion
agent integration tests.

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### Description

- Fixes a bug where we weren't emitting the streaming token usage for
the chat completion agent. Also now includes the `prompt_tokens_details`
and `completion_tokens_details` models that are returned, but not
previously handled.
- Adds new samples
- Updates integration tests to track token usage and make sure they're
non-zero.
- Closes #12411

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- [X] I didn't break anyone 😄
 2025-06-09 23:12:05 +00:00
Evan Mattson 3e31c3bdc6 Python: Remove planner-related code and samples. (#12417) 
### Motivation and Context

Per deprecation method, removing planner-related code after June 1,
2025.

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### Description

- Removing planner-related code.
- Closes #12400

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- [ ] I didn't break anyone 😄
 2025-06-09 21:35:23 +00:00
Evan Mattson e0221e58ce Python: Fix OpenAPI concept sample (#12418) 
### Motivation and Context

An OpenAPI concept sample that we have is broken. This PR fixes it to
run properly based on the current OpenAPI plugin handling.

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### Description

Fixes the broken OpenAPI sample.

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 2025-06-09 21:33:06 +00:00
Christian GlessnerandEvan Mattson 0e7556e112 Python: Add file handling support to BinaryContent for OpenAI Responses API (#12258) 
## Summary

Enhances `BinaryContent` to support file handling for OpenAI Responses
API, enabling file uploads through the responses agent while maintaining
a provider-agnostic design.

  ## Changes

  ### BinaryContent Enhancements
- **Add `can_read` property**: Indicates whether content has readable
data available
- **Add `from_file()` class method**: Creates BinaryContent instances
from file paths with automatic base64 encoding
- **Fix Unicode handling**: Prevents decode errors when processing
binary files (PDFs, images, etc.)

  ### OpenAI Responses Agent Integration
- **Add BinaryContent support**: Pattern matching case for file handling
in `responses_agent_thread_actions.py`
- **Correct OpenAI API format**: Uses proper `filename` and `file_data`
structure with data URI format
- **UUID-based filenames**: Generates appropriate filenames with
mime-type extensions
- **Provider-specific mapping**: File format conversion happens only in
OpenAI agent code

  ### Testing
- **Test coverage**: New tests for `can_read`, `from_file()`, and binary
data handling
- **Unicode error prevention**: Specific test for binary PDF-like
content
- **Base64 encoding verification**: Ensures proper data format for API
compatibility

  ## Design Principles

- **Provider-agnostic**: BinaryContent remains completely generic with
no OpenAI-specific dependencies
- **Clean separation**: OpenAI format mapping isolated to OpenAI agent
files
- **No FileContent class**: Enhances existing BinaryContent instead of
introducing new types
- **Follows existing patterns**: Similar approach to ImageContent and
TextContent handling

  ## Usage Example

  ```python
 response = await self.agent.get_response(
        messages=ChatMessageContent(
            content="Analyse PDF",
            role=AuthorRole("user"),
            items=[BinaryContent.from_file(file_path=c":/test.pdf")],
        )
    )

Co-authored-by: Evan Mattson <[email protected]>
 2025-06-09 21:15:39 +00:00
Tao Chen 2f84e5d1bf Python: Add chat completion agent code interpreter sample (#12393) 
### Motivation and Context

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Closing #10962 

### Description

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1. Add a sample to demonstrate how to create a chat completion agent
with code interpreter capabilities using the Azure Container Apps
session pool service.
2. Fix a bug that cause the session tool to throw because the upload API
doesn't return the file uploaded.
3. Modify unit tests accordingly.

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 2025-06-06 18:13:44 +00:00
Tao Chen 3baaee2ff3 Python: streaming agent response callback in agent orchestrations (#12360) 
### Motivation and Context

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Issue: #12306 
Discussion: #12302 

Currently, we only have a **non-streaming** agent response callback for
users to interact with the agents inside an orchestration. Support for a
stream one has been requested by developers.

### Description

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Add support for a streaming agent response callback to all
orchestrations.

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 2025-06-04 23:06:58 +00:00
Tao Chen c822511f2c Python: Add special notes to samples that are still using AgentGroupchat (#12377) 
### Motivation and Context

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As we move away from `AgentGroupChat` to the new orchestrations, we want
to make sure it's clear to developers getting started with SK are aware
of the changes.

### Description

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We are including a special note to all the samples that use
`AgentGroupChat` to inform developers that this feature is no longer
maintained and they should consider using `GroupChatOrchestration`
instead.

We are keeping the samples for now so that developers can still refer to
them and we don't break any external links to these samples, but we want
to make sure they are aware of the changes and can migrate to the new
orchestrations.


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 2025-06-04 22:15:44 +00:00
Evan Mattson fb0e418a7c Python: Fix for AzureAIAgent streaming func call invocations. Other fixes/improvements for tool call content types, and logging. (#12335) 
### Motivation and Context

The AzureAIAgent streaming function call invocation is broken when
handling more than one required function call from the service. There is
nested logic to handle passing the tool calls back to the service
because it requires one to pass in a handler.

In this PR, we're now controlling everything from the main loop and are
passing back the handler as the event stream for further processing.
This does require some logic to determine if we have an initial async
context manager (based on the first call to create the run stream) or if
we're now using the handler from submitting tool calls. We can now see
that when we have multiple functions involved, we are able to get the
results as we expect:

```
Sample Output:

# User: 'What is the price of the special drink and then special food item added together?'
Function Call:> MenuPlugin-get_specials with arguments: {}
Function Result:> 
        Special Soup: Clam Chowder
        Special Salad: Cobb Salad
        Special Drink: Chai Tea
        for function: MenuPlugin-get_specials
Function Call:> MenuPlugin-get_item_price with arguments: {"menu_item": "Chai Tea"}
Function Call:> MenuPlugin-get_item_price with arguments: {"menu_item": "Clam Chowder"}
Function Result:> $9.99 for function: MenuPlugin-get_item_price
Function Result:> $9.99 for function: MenuPlugin-get_item_price
Function Call:> MathPlugin-Add with arguments: {"input":9.99,"amount":9.99}
Function Result:> 19.98 for function: MathPlugin-Add
# AuthorRole.ASSISTANT: The price of the special drink, Chai Tea, is $9.99 and the price of the special food 
    item, Clam Chowder, is $9.99. Added together, the total price is $19.98.
```

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### Description

Fix handling multiple function calls for the streaming AzureAIAgent.
- Fixes a missing `quote` from the url citation for streaming annotation
content
- Shows the correct way to retrieve Bing Grounding tool calls in the two
concept samples.
- Adds handling for tool content content for OpenAPI plugins.
- Adds logging for streaming agent invocation tool calls.
- Relaxes the handling of the `FunctionResultContent` id attribute to be
`str | None` similar to that of `FunctionCallContent`. The type hints
for `id` have always been `str | None`, but the actual type hint on the
attribute has been `str`. We don't always get a `FunctionResultContent`
id from the Azure Agent Service during streaming invocations - message
addition via the overide `__add__` method still work properly.
- Move the AzureAIAgent pkg dependencies to be installed by default,
instead of requiring the `azure` extra.
- Closes #12312
- Closes #12328
- Closes #12331
- Closes #12324

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- [X] I didn't break anyone 😄
 2025-06-03 01:11:56 +00:00
Evan Mattson 82229cecc3 Python: Improve Python README (#12289) 
### Motivation and Context

The Python README file is a bit stale, and lacking current content
related to agent framework and directly calling the AI service (not
always needing to invoke via the kernel). Additionally, there are links
to samples/notebooks that are relative and thus not resolving from pypi.

The [`ALL_SETTINGS.md`
guide](https://github.com/microsoft/semantic-kernel/blob/main/python/samples/concepts/setup/ALL_SETTINGS.md#semantic-kernel-settings)
is also missing the Agent Framework settings used. Adding those in this
PR.

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### Description

Improve the README to be more relevant.
- Closes #12286

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 2025-05-28 23:34:20 +00:00
Evan Mattson ff1997047d Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247) 
### Motivation and Context

This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.

To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.

In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.

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### Description

Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979

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 2025-05-26 05:02:59 +00:00
Tao Chen 7243188e92 Python: Use BaseModel instead of KernelBaseModel in samples (#12224) 
### Motivation and Context

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It's unnecessary to use `KernelBaseModel` in the samples.

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Switch to `BaseModel`.


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- [x] I didn't break anyone 😄
 2025-05-22 04:37:21 +00:00
Evan Mattson c084b067de Python: Support structured outputs with Azure AI inference chat completion. (#12183) 
### Motivation and Context

The Azure AI inference SDK released support for structured outputs for
chat completions (for models that support it). We had not brought this
into SK. This PR adds functionality and the ability to run the
structured output samples that exist today.

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### Description

Adds functionality to handle structured outputs for the Azure AI
Inference Chat Completion calls.
- Fixes a bug within AzureAIInferenceChatCompletion where the wrong
isinstance check type is used. This prevented adding tool calls during
streaming invocation. Also properly add the `ai_model_id` from the
connector during message construction during streaming invocation,
- Adds unit tests for AzureAIInferenceChatCompletion structured outputs
- Closes #11952

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- [X] I didn't break anyone 😄
 2025-05-21 09:29:03 +00:00
Evan MattsonandTao Chen c64e51c40e Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084) 
### Motivation and Context

The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.

- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.

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### Description

Update the AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177 

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---------

Co-authored-by: Tao Chen <[email protected]>
 2025-05-19 23:43:40 +00:00
Eduard van Valkenburg 03f0aba633 Python: added WebRTC support for Azure OpenAI Realtime (#12078) 
### Motivation and Context

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Added support for WebRTC for Azure OpenAI Realtime models.

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 2025-05-19 18:24:34 +00:00
Eduard van ValkenburgandEvan Mattson 0634c30241 Python: marked planners deprecated and removed all related items (#12058) 
### Motivation and Context

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In line with our dotnet, marking all planners as deprecated, and
removing tests and samples using them.

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---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-05-19 02:47:36 +00:00
Tao ChenandEvan Mattson d67f568d99 Python: Multi-agent orchestration: Magentic (#12104) 
### Motivation and Context

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PR for Magentic multi-agent orchestration.
ADR:
https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0071-multi-agent-orchestration.md
Initial PR: https://github.com/microsoft/semantic-kernel/pull/11993

### Description

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1. Implementations for the magentic orchestrations
2. Unit tests and samples

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Co-authored-by: Evan Mattson <[email protected]>
 2025-05-16 21:53:43 +00:00
Evan Mattson d7c2489a24 Python: Add MultiAgent Orchestration to README. (#12073) 
### Motivation and Context

The new multi-agent orchestration samples are missing from the getting
started with agents README. Add them in with their descriptions. Also,
remove two unused intermediate message callback lists from two samples
that are no longer used.

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### Description

Add samples to README. Clean up some samples.

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 2025-05-15 17:14:30 +00:00
Evan Mattson 50cf782645 Python: Remove Copilot Studio Direct link from Pyproject.toml (#12068) 
### Motivation and Context

The pyproject.toml currently references Microsoft Copilot Studio
packages that are only on test PyPI, and not yet available on the Public
PyPI repo. This breaks the ability to push the public SK package to
PyPI.

We will, until public PyPI packages are available, remove the extra from
our pyproject.toml and place explicit package install instructions in
the Copilot Studio Agent README.

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### Description

Explicit instructions on how to install the necessary packages to use
the CopilotStudioAgent while the packages are on Test PyPI.

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 2025-05-15 00:50:48 +00:00
Tao Chen ee962943d6 Python: Multi-agent orchestration: Handoff (#12046) 
### Motivation and Context

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PR for Handoff multi-agent orchestration.
ADR:
https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0071-multi-agent-orchestration.md
Initial PR: https://github.com/microsoft/semantic-kernel/pull/11993

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This PR includes the following:

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2. Unit tests and samples

Follow up PRs will add the following:

- Magentic One

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 2025-05-14 22:50:56 +00:00