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Python: Fix OpenAI chat client compatibility with third-party endpoints and OTel 0.4.14 (#4161)

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* Fix system message content sent as list instead of string

Some OpenAI-compatible endpoints (e.g. NVIDIA NIM) reject system messages
when content is a list of content parts. This change flattens system and
developer message content to a plain string in the Chat Completions client.

Fixes https://github.com/microsoft/agent-framework/issues/1407

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix compatibility with opentelemetry-semantic-conventions-ai 0.4.14

Version 0.4.14 removed several LLM_* attributes from SpanAttributes
(LLM_SYSTEM, LLM_REQUEST_MODEL, LLM_RESPONSE_MODEL, LLM_REQUEST_MAX_TOKENS,
LLM_REQUEST_TEMPERATURE, LLM_REQUEST_TOP_P, LLM_TOKEN_TYPE).

Move these to the OtelAttr enum with their well-known gen_ai.* string values
and update all references in observability.py and tests.

Fixes https://github.com/microsoft/agent-framework/issues/4160

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Flatten text-only message content to string for all roles

Extend the system/developer fix to all message roles. Text-only content
lists are now post-processed into plain strings, while multimodal content
(text + images/audio) remains as a list. This fixes compatibility with
OpenAI-like endpoints that cannot deserialize list content (e.g. Foundry
Local's Neutron backend).

Partially fixes https://github.com/microsoft/agent-framework/issues/4084

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix streaming text lost when usage data in same chunk

Some providers (e.g. Gemini) include both usage data and text content
in the same streaming chunk. The early return on chunk.usage caused
text and tool call parsing to be skipped entirely. Remove the early
return and process usage alongside text/tool calls.

Fixes https://github.com/microsoft/agent-framework/issues/3434

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix mypy errors in _chat_client.py

Rename shadowed variable 'args' in system/developer branch to 'sys_args'
and rename loop variable 'content' to 'msg_content' to avoid type conflict.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-02-23 10:05:36 +00:00
committed by GitHub
co-authored by Copilot
parent 75ff4f486f
commit b1c7c7c844
4 changed files with 214 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/packages/core/tests/openai/test_openai_chat_client.py
+144 -6
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@@ -642,9 +642,8 @@ def test_prepare_message_with_text_reasoning_content(openai_unit_test_env: dict[
assert len(prepared) == 1
assert "reasoning_details" in prepared[0]
assert prepared[0]["reasoning_details"] == mock_reasoning_data
# Should also have the text content
assert prepared[0]["content"][0]["type"] == "text"
assert prepared[0]["content"][0]["text"] == "The answer is 42."
# Should also have the text content (flattened to string for text-only)
assert prepared[0]["content"] == "The answer is 42."
def test_function_approval_content_is_skipped_in_preparation(openai_unit_test_env: dict[str, str]) -> None:
@@ -690,8 +689,7 @@ def test_function_approval_content_is_skipped_in_preparation(openai_unit_test_en
)
prepared_mixed = client._prepare_message_for_openai(mixed_message)
assert len(prepared_mixed) == 1 # Only text content should remain
assert prepared_mixed[0]["content"][0]["type"] == "text"
assert prepared_mixed[0]["content"][0]["text"] == "I need approval for this action."
assert prepared_mixed[0]["content"] == "I need approval for this action."
def test_usage_content_in_streaming_response(openai_unit_test_env: dict[str, str]) -> None:
@@ -730,6 +728,43 @@ def test_usage_content_in_streaming_response(openai_unit_test_env: dict[str, str
assert usage_content.usage_details["total_token_count"] == 150
def test_streaming_chunk_with_usage_and_text(openai_unit_test_env: dict[str, str]) -> None:
"""Test that text content is not lost when usage data is in the same chunk.
Some providers (e.g. Gemini) include both usage and text content in the
same streaming chunk. See https://github.com/microsoft/agent-framework/issues/3434
"""
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, Choice, ChoiceDelta
from openai.types.completion_usage import CompletionUsage
client = OpenAIChatClient()
mock_chunk = ChatCompletionChunk(
id="test-chunk",
object="chat.completion.chunk",
created=1234567890,
model="gemini-2.0-flash-lite",
choices=[
Choice(
index=0,
delta=ChoiceDelta(content="Hello world", role="assistant"),
finish_reason=None,
)
],
usage=CompletionUsage(prompt_tokens=18, completion_tokens=5, total_tokens=23),
)
update = client._parse_response_update_from_openai(mock_chunk)
# Should have BOTH text and usage content
content_types = [c.type for c in update.contents]
assert "text" in content_types, "Text content should not be lost when usage is present"
assert "usage" in content_types, "Usage content should still be present"
text_content = next(c for c in update.contents if c.type == "text")
assert text_content.text == "Hello world"
def test_parse_text_with_refusal(openai_unit_test_env: dict[str, str]) -> None:
"""Test that refusal content is parsed correctly."""
from openai.types.chat.chat_completion import ChatCompletion, Choice
@@ -814,7 +849,7 @@ def test_prepare_options_with_instructions(openai_unit_test_env: dict[str, str])
assert "messages" in prepared_options
assert len(prepared_options["messages"]) == 2
assert prepared_options["messages"][0]["role"] == "system"
assert prepared_options["messages"][0]["content"][0]["text"] == "You are a helpful assistant."
assert prepared_options["messages"][0]["content"] == "You are a helpful assistant."
def test_prepare_message_with_author_name(openai_unit_test_env: dict[str, str]) -> None:
@@ -851,6 +886,109 @@ def test_prepare_message_with_tool_result_author_name(openai_unit_test_env: dict
assert "name" not in prepared[0]
def test_prepare_system_message_content_is_string(openai_unit_test_env: dict[str, str]) -> None:
"""Test that system message content is a plain string, not a list.
Some OpenAI-compatible endpoints (e.g. NVIDIA NIM) reject system messages
with list content. See https://github.com/microsoft/agent-framework/issues/1407
"""
client = OpenAIChatClient()
message = Message(role="system", contents=[Content.from_text(text="You are a helpful assistant.")])
prepared = client._prepare_message_for_openai(message)
assert len(prepared) == 1
assert prepared[0]["role"] == "system"
assert isinstance(prepared[0]["content"], str)
assert prepared[0]["content"] == "You are a helpful assistant."
def test_prepare_developer_message_content_is_string(openai_unit_test_env: dict[str, str]) -> None:
"""Test that developer message content is a plain string, not a list."""
client = OpenAIChatClient()
message = Message(role="developer", contents=[Content.from_text(text="Follow these rules.")])
prepared = client._prepare_message_for_openai(message)
assert len(prepared) == 1
assert prepared[0]["role"] == "developer"
assert isinstance(prepared[0]["content"], str)
assert prepared[0]["content"] == "Follow these rules."
def test_prepare_system_message_multiple_text_contents_joined(openai_unit_test_env: dict[str, str]) -> None:
"""Test that system messages with multiple text contents are joined into a single string."""
client = OpenAIChatClient()
message = Message(
role="system",
contents=[
Content.from_text(text="You are a helpful assistant."),
Content.from_text(text="Be concise."),
],
)
prepared = client._prepare_message_for_openai(message)
assert len(prepared) == 1
assert prepared[0]["role"] == "system"
assert isinstance(prepared[0]["content"], str)
assert prepared[0]["content"] == "You are a helpful assistant.\nBe concise."
def test_prepare_user_message_text_content_is_string(openai_unit_test_env: dict[str, str]) -> None:
"""Test that text-only user message content is flattened to a plain string.
Some OpenAI-compatible endpoints (e.g. Foundry Local) cannot deserialize
the list format. See https://github.com/microsoft/agent-framework/issues/4084
"""
client = OpenAIChatClient()
message = Message(role="user", contents=[Content.from_text(text="Hello")])
prepared = client._prepare_message_for_openai(message)
assert len(prepared) == 1
assert prepared[0]["role"] == "user"
assert isinstance(prepared[0]["content"], str)
assert prepared[0]["content"] == "Hello"
def test_prepare_user_message_multimodal_content_remains_list(openai_unit_test_env: dict[str, str]) -> None:
"""Test that multimodal user message content remains a list."""
client = OpenAIChatClient()
message = Message(
role="user",
contents=[
Content.from_text(text="What's in this image?"),
Content.from_uri(uri="https://example.com/image.png", media_type="image/png"),
],
)
prepared = client._prepare_message_for_openai(message)
# Multimodal content must stay as list for the API
has_list_content = any(isinstance(m.get("content"), list) for m in prepared)
assert has_list_content
def test_prepare_assistant_message_text_content_is_string(openai_unit_test_env: dict[str, str]) -> None:
"""Test that text-only assistant message content is flattened to a plain string."""
client = OpenAIChatClient()
message = Message(role="assistant", contents=[Content.from_text(text="Sure, I can help.")])
prepared = client._prepare_message_for_openai(message)
assert len(prepared) == 1
assert prepared[0]["role"] == "assistant"
assert isinstance(prepared[0]["content"], str)
assert prepared[0]["content"] == "Sure, I can help."
def test_tool_choice_required_with_function_name(openai_unit_test_env: dict[str, str]) -> None:
"""Test that tool_choice with required mode and function name is correctly prepared."""
client = OpenAIChatClient()