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Author SHA1 Message Date
Evan Mattson 79ef93b9cf Python: Extend InMemoryCollection filter attribute blocklist (#13897) 
## Summary
- Minor defense-in-depth hardening for `InMemoryCollection` filter
parsing.
- Extends the internal dunder attribute blocklist and adds a regression
test covering the blocked set.

## Test plan
- [x] `uv run pytest tests/unit/connectors/memory/test_in_memory.py`
 2026-04-22 23:42:01 +00:00
westey 57d9adce81 Python: Add field and table name escaping for python SqlServer connector (#13893) 
### Motivation and Context

### Description

- Add field and table name escaping for python SqlServer connector

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [ ] The code builds clean without any errors or warnings
- [ ] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-04-22 21:20:10 +00:00
ふぁーandEvan Mattson eeeccb4596 Python: Respect FunctionChoiceBehavior filters in OpenAI responses agent tools (#13235) 
issue: https://github.com/microsoft/semantic-kernel/issues/13234

## Summary
- honor FunctionChoiceBehavior filters when retrieving kernel function
metadata
 
### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2026-04-08 01:24:47 +00:00
Evan MattsonMAF Dashboard BotCopilot
 		0f322bcc48 Python: Improve prompt-template msg serialize and sample usage (#13738) 
### Motivation and Context

This PR updates the Jinja2 and Handlebars prompt-template helpers to
serialize chat messages through the existing XML/message serializer
instead of assembling message XML manually. It also aligns the
prompt-template samples with the serializer-backed helper and adds
regression coverage for common message content.

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### Description

Prompt template message serialize improvements.

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

---------

Co-authored-by: MAF Dashboard Bot <[email protected]>
Co-authored-by: Copilot <[email protected]>
 2026-04-08 00:29:36 +00:00
Weiguang LiClaude Opus 4.6Evan Mattson
 		41f7b59595 Python: fix Google AI/Vertex AI crash on anyOf schema (#12442) (#13624) 
### Motivation and Context

Closes #12442

When `ChatCompletionAgent` instances are used as plugins with
`GoogleAIChatCompletion`, the application crashes with:
```
ValueError: Unknown field for Schema: anyOf
```

**Root cause**: The Semantic Kernel JSON schema builder generates
`anyOf` for Union types (e.g. `str | list[str]` on the agent's
`messages` parameter) and type-as-array for Optional types (e.g.
`["string", "null"]`). Google AI's protobuf `Schema` does not support
these constructs.

### Description

Add `sanitize_schema_for_google_ai()` to `shared_utils.py` that
recursively rewrites unsupported schema constructs:

- `anyOf` / `oneOf` with a null variant → single type + `nullable: true`
- `anyOf` / `oneOf` without null → picks the first variant
- `type` as array (e.g. `["string", "null"]`) → single type + `nullable:
true`

Applied in both
`kernel_function_metadata_to_google_ai_function_call_format()` and
`kernel_function_metadata_to_vertex_ai_function_call_format()`.

### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The existing tests pass, and I have added 12 new regression tests
- [x] I didn't break any existing functionality

> [!NOTE]
> AI-assisted contribution: Claude helped with code review, test
generation, and formatting.

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2026-03-25 05:35:40 +00:00
Giulio Leonegiulio-leonegiulio-leoneCopilot
 		83ff0a5aaa Python: fix(google-ai): skip api_key check when use_vertexai is True (#13607) 
## Summary

Fixes #13483

### Motivation and Context

When `use_vertexai=True`, users authenticate via Application Default
Credentials (ADC) and don't need an API key. However, the initialization
check for `api_key` runs unconditionally, blocking Vertex AI users with:

```
ServiceInitializationError: The API key is required when use_vertexai is False.
```

### Description

Guard the `api_key` validation with `not
google_ai_settings.use_vertexai` in all three Google AI service classes:

- `GoogleAIChatCompletion`
- `GoogleAITextCompletion`
- `GoogleAITextEmbedding`

The error message already reads *"when use_vertexai is False"*, so the
condition now matches the intent.

### Before / After

```python
# Before — fails even with use_vertexai=True
GoogleAIChatCompletion(
    use_vertexai=True,
    cloud_project_id="my-project",
    cloud_region="us-central1"
)
# → ServiceInitializationError: The API key is required when use_vertexai is False.

# After — works correctly
GoogleAIChatCompletion(
    use_vertexai=True,
    cloud_project_id="my-project",
    cloud_region="us-central1"
)
# → Initializes successfully, uses ADC for authentication
```

### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The code follows the [SK coding
standards](https://github.com/microsoft/semantic-kernel/blob/main/python/README.md)

---------

Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Copilot <[email protected]>
 2026-03-24 10:01:54 +00:00
Evan MattsonMAF Dashboard BotCopilot
 		e0bd528cd9 Python: Default Dapr module allowlist to semantic_kernel prefix (#13596) 
### Motivation and Context

Follow-up to #13499. The previous PR added the `allowed_module_prefixes`
parameter but defaulted it to `None`, which meant the module restriction
was only active if developers discovered and configured it.
Secure-by-default is the right posture here — restrict first, let
developers widen as needed.

- Change `allowed_module_prefixes` default from `None` to
`("semantic_kernel.",)` across Dapr runtime step loading
- Non-SK step classes now require developers to explicitly add their
module prefix (e.g. `("semantic_kernel.", "myapp.steps.")`)
- Developers can pass `None` to opt out entirely, but the secure default
is now enforced
- The Dapr runtime code is experimental, so this is a non-breaking
change per our stability guarantees

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Please help reviewers and future users, providing the following
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<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

---------

Co-authored-by: MAF Dashboard Bot <[email protected]>
Co-authored-by: Copilot <[email protected]>
 2026-03-24 11:34:25 +09:00
Giulio Leonegiulio-leoneEvan MattsonCopilot
 		f034ed1d4c Python: fix: ChatHistoryTruncationReducer orphans TOOL role messages (#13608) 
## Summary

Fixes #12708

`ChatHistoryTruncationReducer.reduce()` can orphan `TOOL` role messages
by truncating the preceding `assistant` message that contains the
`tool_calls`, causing OpenAI to reject the history with:

```
messages with role 'tool' must be a response to a preceding message with 'tool_calls'
```

## Root Cause

`locate_safe_reduction_index()` uses
`contains_function_call_or_result()` to detect tool-related messages
during its backward scan. This function only checks `msg.items` for
`FunctionCallContent`/`FunctionResultContent` instances.

However, `TOOL` role messages can contain only text content (no
`FunctionResultContent` in `items`), which causes the backward scan to
treat them as regular messages. The truncation point then lands between
the `tool_calls` assistant message and its `TOOL` responses, orphaning
the tool results.

## Fix

Added `AuthorRole.TOOL` check to `contains_function_call_or_result()`:

```python
if msg.role == AuthorRole.TOOL:
    return True
```

This ensures **any** `TOOL` role message is recognized as part of a tool
call/result pair, regardless of whether it has `FunctionResultContent`
in its `items`.

## Test

Added
`test_locate_safe_reduction_index_tool_role_without_function_result_content`
that verifies TOOL role messages with only text content are not
separated from their tool call.

---------

Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Copilot <[email protected]>
 2026-03-24 02:00:04 +00:00
Roli BoschClaude Opus 4.6Evan Mattson
 		3c1b688dcf Python: Fix ChatHistoryTruncationReducer deleting system prompt (#13610) 
## Summary

`ChatHistoryTruncationReducer.reduce()` silently deletes
system/developer messages when truncating chat history. This is because
it calls `extract_range()`, which unconditionally filters out
system/developer messages — a function designed for the summarization
use case, not truncation.

Fixes #12612.

## Approach

Port the .NET SDK fix (PR #10344) to Python:

1. Detect the first system/developer message before truncation
2. Pass `has_system_message=True` to `locate_safe_reduction_index` so
`target_count` accounts for the preserved message (matches .NET's
`targetCount -= hasSystemMessage ? 1 : 0`)
3. Use a simple `history[truncation_index:]` slice instead of
`extract_range` (which strips system messages)
4. Prepend the system message if it was truncated away

Also adds a guard for `target_count <= 0` after the system message
adjustment to prevent `IndexError` when `target_count=1`.

## Changes

| File | Change |
|------|--------|
| `chat_history_reducer_utils.py` | Add `has_system_message` parameter,
adjust `target_count`, guard against `<= 0` |
| `chat_history_truncation_reducer.py` | Detect system message, bypass
`extract_range`, prepend if truncated |
| `test_chat_history_truncation_reducer.py` | Update 2 existing tests +
4 new tests (system, developer, no-system, target_count=1) |

## Note

The summarization reducer (`ChatHistorySummarizationReducer`) has the
same bug — it also uses `extract_range` and loses system messages during
summarization. The .NET summarization reducer preserves system messages
via `AssemblySummarizedHistory`. This should be addressed in a follow-up
PR to keep this change focused.

## Test plan

- [x] 11/11 truncation reducer tests pass (was 7, now 11)
- [x] 9/9 utils tests pass (no regressions from new parameter)
- [x] 11/11 summarization reducer tests pass (unaffected)
- [x] Reproduces and fixes the exact scenario from #12612
- [x] Edge case: `target_count=1` with system message does not crash

---------

Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2026-03-19 12:28:52 +00:00
Weiguang LiandEvan Mattson e500f727ed Python: fix prompt template engine blocking HTML tags in content (#13633) 
### Motivation and Context

Fixes #13632

When a prompt contains HTML tags such as `<p>`, `<div>`, `<b>`, etc.,
the
`ChatHistory.from_rendered_prompt()` method wraps the rendered prompt in
a
`<root>` element and parses it as XML. Any child elements whose tag
names do
not match the known SK template tags (`message`, `chat_history`) were
silently
skipped, causing their text content to be lost. This meant a prompt
like:

```
Translate: "<p>What is your name?</p>"
```

would arrive at the LLM as an empty message, because `<p>What is your
name?</p>`
was discarded during XML traversal.

### Description

In `ChatHistory.from_rendered_prompt`, when an XML child element has an
unrecognized tag (i.e. it is not `message` or `chat_history`), the
element
is now serialized back to its original text representation and appended
to
the preceding message content. The element's tail text is likewise kept
in
the same message, preserving the full original prompt text.

**Changed files:**

- `python/semantic_kernel/contents/chat_history.py` -- handle unknown
XML
  tags as literal text in `from_rendered_prompt()`
- `python/tests/unit/contents/test_chat_history.py` -- added four
regression
tests covering HTML `<p>`, multiple HTML tags, HTML surrounded by text,
  and SK tags mixed with HTML

### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The existing tests pass and new tests have been added
- [x] I didn't break any existing functionality

---------

Co-authored-by: Evan Mattson <[email protected]>
 2026-03-18 11:01:25 +00:00
Evan Mattson 3f6d72725f Python: Fix image integration tests that are now blocked due to bot policies (#13680) 
### Motivation and Context

Fix image integration tests that are now blocked due to bot policies

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### Description

Fix image integration tests that are now blocked due to bot policies

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-03-18 10:42:08 +00:00
Giulio Leonegiulio-leoneCopilot
 		cc83889787 Python: fix(python/google): preserve thought_signature in Gemini function call parts (#13609) 
### Motivation and Context

Fixes #13480

When using Gemini models (2.5 Pro / 3 Pro) with thinking enabled, the
API returns a `thought_signature` on function call `Part` objects. Per
[Google's Thought Signatures
documentation](https://ai.google.dev/gemini-api/docs/thought-signatures),
this signature **must** be included in subsequent requests. Without it,
follow-up turns with tool results fail with:

```
HTTP 400: Function call is missing a thought_signature in functionCall parts
```

The .NET connector was already fixed in #13418. This PR applies the
equivalent fix to the Python connector.

### Description

**Response parsing** (capture `thought_signature`):
- `google_ai_chat_completion.py`: When creating `FunctionCallContent`
from response parts, capture `part.thought_signature` into
`FunctionCallContent.metadata` (both non-streaming and streaming paths)
- `vertex_ai_chat_completion.py`: Same capture via
`part_dict.get("thought_signature")` for Vertex AI

**Request building** (restore `thought_signature`):
- `google_ai/services/utils.py`: In `format_assistant_message()`, when a
`FunctionCallContent` has `thought_signature` in its metadata, construct
the `Part` directly with the `thought_signature` parameter instead of
using `Part.from_function_call()`
- `vertex_ai/services/utils.py`: In `format_assistant_message()`,
include `thought_signature` in the `Part.from_dict()` dictionary when
present

**Backward compatible**: When `thought_signature` is not present
(thinking disabled or older models), behavior is identical to before.

### Test Coverage

Added 4 new tests:
- `test_format_assistant_message_with_thought_signature` (Google AI) —
verifies round-trip preservation
- `test_format_assistant_message_without_thought_signature` (Google AI)
— verifies backward compatibility
- `test_format_assistant_message_with_thought_signature` (Vertex AI) —
verifies round-trip preservation
- `test_format_assistant_message_without_thought_signature` (Vertex AI)
— verifies backward compatibility

All 110 existing Google connector tests continue to pass.

### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] All unit tests pass, and I have added new tests
- [x] I didn't break anyone 😄

---------

Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Copilot <[email protected]>
 2026-03-13 13:06:41 +00:00
Tao ChenandEvan Mattson 5c891d1769 Python: Add support to new openai text to image model (#13651) 
### Motivation and Context

<!-- Thank you for your contribution to the semantic-kernel repo!
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Dall-e-3 and Dall-e-2 have been deprecated. The newer gpt-1-image model
returns base64 strings.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
Return url if set, otherwise return the base64 string.

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2026-03-13 08:31:46 +00:00
Eduard van Valkenburg c250b379f6 Python: Refinement of in memory vector collection filter (#13637) 
### Motivation and Context

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Fixes another way of escaping allowed commands.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
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script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-03-12 18:19:07 +00:00
Tao Chen d5c2b9d889 Python: Improves the robustness of filename handling (#13643) 
### Motivation and Context

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Improves the robustness of filename handling in the Bedrock agent
integration. Ensures that filenames sourced from external responses are
properly sanitized before use in file operations, and adds safer
defaults for file write behavior.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
1. Sanitize filenames received from Bedrock agent responses.
2. Add overwrite parameter to BinaryContent.write_to_file (defaults to
False).
3. Add tests.
4. Update samples.

### Contribution Checklist

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- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-03-12 17:02:02 +00:00
Evan Mattson 429dd1c4c0 Python: support (Azure) OpenAI realtime audio models (#13291) 
### Motivation and Context

As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.

SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.

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### Description

- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-02-28 09:01:03 +09:00
Evan Mattson b6f95e2c45 Python: Add class validation for Dapr Runtime step loading (#13499) 
### Motivation and Context

The Dapr Runtime uses string-based class names to load step classes
dynamically. This PR adds validation to ensure that only valid
KernelProcessStep subclasses can be loaded and instantiated, improving
type safety and providing better error messages when misconfigured.

The new allowed_module_prefixes parameter gives users control over which
modules are permitted for step class loading, which can be useful in
environments where stricter control is desired.

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### Description

- Add issubclass(cls, KernelProcessStep) validation when loading step
classes from qualified names
- Add optional allowed_module_prefixes parameter for restricting which
modules can be loaded
- Centralize class loading logic in get_step_class_from_qualified_name()
utility function
- Remove duplicate _get_class_from_string methods from DaprStepInfo and
StepActor

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
 2026-02-02 10:18:19 +09:00
Roger Barreto 5b27a727a2 Python: Add file upload security controls to SessionsPythonPlugin (#13478) 
Overriding merge checks as there are unrelated Python integration test failures

### Motivation and Context

- Uploads: Require both EnableDangerousFileUploads = true AND
AllowedUploadDirectories configured
- Downloads: Permissive by default, but if AllowedDownloadDirectories is
configured, path validation
  applies
- Both use path canonicalization to prevent traversal attacks

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
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 2026-01-29 10:53:44 +00:00
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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 2026-01-23 09:02:05 +00:00
Eduard van Valkenburg 72bc02551f Python: added allowed_domains to HttpPlugin (#13462) 
### Motivation and Context

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Adds allowed_domains setup for HttpPlugin.

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 2026-01-22 12:52:56 +09:00
Eduard van Valkenburg f511c965c1 Python: inversed the filter logic for InMemory vector stores (#13457) 
### Motivation and Context

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Fixes some issues with the forbidden list approach for the in-memory
vector store filtering.

### Description

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 2026-01-19 12:22:19 +09:00
Evan Mattson adb71bc63b Python: Bump to v1.39.1 for a release (#13455) 
### Motivation and Context

Bump to v1.39.1 for a release

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### Description

Bump to v1.39.1 for a release

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 2026-01-16 07:56:08 +09:00
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
Monita e1fbafe552 Python: feat(oracle): add new Oracle connector for Semantic Kernel (#13229) 
…ync support

### Motivation and Context

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This change is required to enable Semantic Kernel users to store and
retrieve embeddings using Oracle databases. Currently, Semantic Kernel
supports vector storage for several backends, but Oracle was missing.
This connector solves that gap by providing full async support, native
VECTOR type handling, and vector index management.

### Description

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This PR introduces a new Oracle connector for Semantic Kernel with the
following features:

- Asynchronous upsert, get, delete and search operations for memory
records.

- Native Oracle VECTOR type support for storing embeddings efficiently.

- Support for HNSW and IVFFLAT vector indexes for similarity search.

- Integration with Semantic Kernel collections, enabling semantic search
and memory operations.

- Comprehensive unit tests to ensure correctness and stability.

The connector is designed to work seamlessly with existing Semantic
Kernel memory abstractions and follows the same async patterns as other
vector stores.

Integration tests have also been implemented and verified locally;
however, they are not included in this PR because the current CI
environment setup for Oracle Database support is unknown.
Once guidance is provided on Oracle DB availability in the CI pipeline,
integration tests can be enabled and added in a follow-up PR.

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 2025-11-26 20:08:16 +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
Tao Chen b2cceb5985 .Net: Update OTel GenAI operation names (#13334) 
### Motivation and Context

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OTel GenAI operation names have the following updates:
1. `chat.completion` to `chat`
2. `text.completion` to `text_completion`

### Description

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This PR updates those operation names

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 2025-11-05 05:07:34 +00:00
Evan Mattson 05d659d6e9 Python: Fix non-string KernelArguments being converted to strings in prompt template function calls (#13292) 
### Motivation and Context

Fixes #13199 - Non-string kernel arguments are now preserved when passed
to functions in prompt templates. Adds unit tests.

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 2025-10-30 06:11:35 +00:00
Tao ChenCopilotmoonbox3
 		1f8fea2b49 Python: Fix ai connector integration tests (#13311) 
### Motivation and Context

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Vertex AI and Bedrock integration tests have been failing.


### Description

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1. This PR fixes the vertex ai integration tests by upgrading the Google
AI SDK version.
2. This PR fixes the Bedrock integration tests by upgrading the models.


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---------

Co-authored-by: Copilot <[email protected]>
Co-authored-by: moonbox3 <[email protected]>
 2025-10-30 04:22:14 +00:00
Taylor Rockey e73446e86e Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) 
### Motivation and Context

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- 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
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
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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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 2025-09-15 23:26:38 +00:00
Evan Mattson d8af1ed633 Python: Safely clone plugins (#13109) 
### Motivation and Context

Currently, we attempt to deep copy plugins, no matter what the plugin
instance holds. This will cause issues when plugins contain unpickleable
items like an async generator.

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### Description

- Closes #12819

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 2025-09-12 00:25:07 +00:00
Evan Mattson 22ff5f844f Python: Fix responses agent kernel args bug (#13056) 
### Motivation and Context

The `arguments: KernelArguments` are not getting handled properly for
the Responses Agent invoke. Use the provided `arguments` instead of
trying to get them from the kwargs.

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### Description

- Closes #13053
- Adds unit test coverage. 

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 2025-09-04 01:27:27 +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.

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 2025-09-04 01:27:19 +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())
```

### Contribution Checklist

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- [ ] I didn't break anyone 😄
 2025-08-25 18:29:13 +00:00
AllaouaAllaoua BenchikhEvan MattsonEvan Mattson
 		45fe60545c Python: Fix issue with orphaned tool/tool_calls (#12923) 
### Motivation and Context

Tool/tool_calls are getting orphaned.

Issue : https://github.com/microsoft/semantic-kernel/issues/12708

How to reproduce :

```python
from semantic_kernel.contents import (
    AuthorRole,
    ChatMessageContent,
    FunctionCallContent,
    FunctionResultContent,
    ChatHistoryTruncationReducer,
)

messages = [
    ChatMessageContent(role=AuthorRole.SYSTEM, content="sys"),
    ChatMessageContent(role=AuthorRole.USER, content="user A"),
    ChatMessageContent(
        role=AuthorRole.ASSISTANT,
        items=[
            FunctionCallContent(
                id="123", function_name="search", plugin_name="plugin", arguments={"q": "x"}
            )
        ],
    ),
    ChatMessageContent(
        role=AuthorRole.TOOL,
        items=[
            FunctionResultContent(
                id="123", function_name="search", plugin_name="plugin", result="RESULT"
            )
        ],
    ),
    ChatMessageContent(role=AuthorRole.USER, content="user B"),
    ChatMessageContent(role=AuthorRole.ASSISTANT, content="done"),
]

reducer = ChatHistoryTruncationReducer(target_count=3, threshold_count=0)
reducer.messages = messages

print("Before reduce:", [m.role for m in reducer.messages])
await reducer.reduce()
print("After reduce:", [m.role for m in reducer.messages])

# After reduce: [<AuthorRole.TOOL: 'tool'>, <AuthorRole.USER: 'user'>, <AuthorRole.ASSISTANT: 'assistant'>]
```

### Description

Move backward as long as we are in a tool/tool_call island when reducing
the messages.

---------

Co-authored-by: Allaoua Benchikh <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2025-08-14 00:23:04 +00:00
Evan Mattson 40734345dc Python: Fix the Responses agent msg chaining with reasoning models (#12907) 
### Motivation and Context

When running a reasoning model with the OpenAI responses agent, it was
producing 400s due to the way we were either including or not including
FCC related to tool calls. This PR fixes it for both the default
(store_enabled=True) and the case where uses don't want to use OpenAI's
previous response id as a pseudo-thread (store_enabled=False).

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### Description

- Closes #12843

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### Contribution Checklist

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- [X] I didn't break anyone 😄
 2025-08-13 10:35:38 +00:00
Mehdi MoshtaghiandEvan Mattson 73ad6e8923 Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803) 
### Description

This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.

-   Fixes: #12802

### Motivation and Context

When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.

Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.

### Description of the change

This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:

1.  It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.

This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.

### How has this been tested?

The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.

### Contribution Checklist

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- [X] The code builds clean without any errors or warnings
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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 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-08-07 22:48:43 +00:00
Evan Mattson 91392a77e0 Python: Fix ResponsesAgent image duplication in chat history preparation (#12859) 
### Motivation and Context

The ResponsesAgent is duplicating messages in chat history preparation
when messages contained multiple content items (text + images). The
`response_inputs.append()` call is inside the content processing loop,
causing each message to be added once per content item.

The fix:
- Moved `response_inputs.append()` outside the content processing loop
- Added unit test to verify multiple images don't get duplicated

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### Description

- Fixed loop placement in `_prepare_chat_history_for_request`
- Added test `test_prepare_chat_history_multiple_images_no_duplication`
- Fixes #12848

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### 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 😄
 2025-08-05 14:58:35 +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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These notes will help understanding how your code works. Thanks! -->
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.


### 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 😄

---------

Co-authored-by: Dmytro Struk <[email protected]>
 2025-08-05 00:43:00 +00:00
Evan Mattson 1198b573d1 Python: Add ability to specify encoding when adding a plugin. Add tests. (#12797) 
### Motivation and Context

Right now when adding a plugin, we don't allow one to specify the type
of encoding used.

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### Description

Allow configuration for the type of encoding used.
- Adds unit tests
- Closes #12440

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raises no violations
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- [X] I didn't break anyone 😄
 2025-08-04 22:00:30 +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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These notes will help understanding how your code works. Thanks! -->
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"
/>


### Contribution Checklist

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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
Evan Mattson 30391ade09 Python: AzureAI Agent int test update to pass (#12849) 
### Motivation and Context

The AzureAI Agent code interpreter is failing when asking it to use the
code interpreter to create bar charts. Updating the unit test so that it
uses the code interpreter and doesn't fail/block.

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### Description

Update integration test while trying to understand the root cause of the
failure.

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- [X] I didn't break anyone 😄
 2025-08-04 15:51:41 +09:00
Evan Mattson 5332c2e330 Python: Require tool_call_id parameter for string-based tool messages in ChatHistory (#12753) 
### Motivation and Context

The `add_tool_message` method of `ChatHistory` accepts string content
without requiring a `tool_call_id` parameter. This design is flawed from
an API design perspective. It created tool messages that:

1. Violated chat completion protocols: most LLM APIs (OpenAI, etc.)
require tool messages to have a `tool_call_id` that references a
previous function call
2. Broke conversation flow: tool messages should always be responses to
specific tool calls, maintaining the call-response relationship
3. Created malformed message sequences: messages without proper tool
call IDs would fail when sent to language models

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### Description

This PR modifies the `add_tool_message` string overload to require a
`tool_call_id` parameter, which makes sure we have proper tool call
protocol compliance.

1. The string overload now validates that `tool_call_id` is provided and
raises a clear error if missing
2. Tool messages now create `FunctionResultContent` objects with both
`id` and `call_id` set to the provided `tool_call_id`
3. Added support for an optional `function_name` parameter for better
bookkeeping
4. Provides informative error messages explaining why `tool_call_id` is
required
- Closes #12744 

Before:

```python
# This would create an invalid tool message without tool_call_id
# when the `.to_dict()` method is called on `ChatMessageContent`
chat_history.add_tool_message("Function result")  # Creates broken message and losing pairing to the corresponding `FunctionCallContent`.
```

After: 

```python
# Now requires tool_call_id for proper protocol compliance
chat_history.add_tool_message("Function result")  # raises clear error due to missing `tool_call_id`
chat_history.add_tool_message("Function result", tool_call_id="call_123")  # works correctly

# Optional function name for better bookkeeping
chat_history.add_tool_message("Function result", tool_call_id="call_123", function_name="get_weather") # works as well
```

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### Contribution Checklist

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- [X] I didn't break anyone 😄
 2025-07-23 04:07:20 +00:00
Evan Mattson d4f6703cf3 Python: fix Bing grounding & custom search content generation (#12760) 
### Motivation and Context

The same `agent_content_generation` method is used to handle both
`bing_grounding` and `bing_custom_search` tool calls; however, the
method expected that the dicts on the underlying types were named the
same - which they aren't. They have their respective names, as shown
above.

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### Description

Fix Bing content generation based on the instance we're dealing with.
- Add unit tests.
- Closes #12746

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raises no violations
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- [X] I didn't break anyone 😄
 2025-07-23 03:17:29 +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

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
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.

### 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 😄
 2025-07-22 16:57:05 +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