fix(ai): Keep single content input message #5345

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Merged

alexander-alderman-webb merged 66 commits into master from webb/keep-single-message

Jan 28, 2026

sentry_sdk/ai/utils.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -682,6 +682,26 @@ def truncate_messages_by_size( @@
     def truncate_and_annotate_messages(
+        messages: "Optional[List[Dict[str, Any]]]",
+        span: "Any",
+        scope: "Any",
+        max_single_message_chars: int = MAX_SINGLE_MESSAGE_CONTENT_CHARS,
+    ) -> "Optional[List[Dict[str, Any]]]":
+        if not messages:
+            return None
+        messages = redact_blob_message_parts(messages)
+        truncated_message = _truncate_single_message_content_if_present(
+            deepcopy(messages[-1]), max_chars=max_single_message_chars
+        )
+        if len(messages) > 1:
+            scope._gen_ai_original_message_count[span.span_id] = len(messages)
+        return [truncated_message]
+    def truncate_and_annotate_embedding_inputs(
         messages: "Optional[List[Dict[str, Any]]]",
         span: "Any",
         scope: "Any",
@@ Expand Down @@

sentry_sdk/integrations/litellm.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -9,6 +9,7 @@ @@
         set_data_normalized,
         truncate_and_annotate_messages,
         transform_openai_content_part,
+        truncate_and_annotate_embedding_inputs,
     )
     from sentry_sdk.consts import SPANDATA
     from sentry_sdk.integrations import DidNotEnable, Integration
@@ Expand Down Expand Up / @@ -118,7 +119,9 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None: @@
                         if isinstance(embedding_input, list)
                         else [embedding_input]
                     )
-                    messages_data = truncate_and_annotate_messages(input_list, span, scope)
+                    messages_data = truncate_and_annotate_embedding_inputs(
+                        input_list, span, scope
+                    )
                     if messages_data is not None:
                         set_data_normalized(
                             span,
@@ Expand Down @@

sentry_sdk/integrations/openai.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -11,6 +11,7 @@ @@
         set_data_normalized,
         normalize_message_roles,
         truncate_and_annotate_messages,
+        truncate_and_annotate_embedding_inputs,
     )
     from sentry_sdk.ai._openai_completions_api import (
         _is_system_instruction as _is_system_instruction_completions,
@@ Expand Down Expand Up / @@ -414,7 +415,9 @@ def _set_embeddings_input_data( @@
         ):
             normalized_messages = normalize_message_roles(messages)  # type: ignore
             scope = sentry_sdk.get_current_scope()
-            messages_data = truncate_and_annotate_messages(normalized_messages, span, scope)
+            messages_data = truncate_and_annotate_embedding_inputs(
+                normalized_messages, span, scope
+            )
             if messages_data is not None:
                 set_data_normalized(
                     span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, messages_data, unpack=False
@@ Expand Down @@

tests/integrations/anthropic/test_anthropic.py

-Original file line number
+Diff line change
@@ Expand Up @@
             assert span._data.get(SPANDATA.GEN_AI_USAGE_TOTAL_TOKENS) == 30
-    def test_anthropic_message_role_mapping(sentry_init, capture_events):
+    # Test messages with mixed roles including "ai" that should be mapped to "assistant"
+    @pytest.mark.parametrize(
+        "test_message,expected_role",
+        [
+            ({"role": "system", "content": "You are helpful."}, "system"),
+            ({"role": "user", "content": "Hello"}, "user"),
+            (
+                {"role": "ai", "content": "Hi there!"},
+                "assistant",
+            ),  # Should be mapped to "assistant"
+            (
+                {"role": "assistant", "content": "How can I help?"},
+                "assistant",
+            ),  # Should stay "assistant"
+        ],
+    )
+    def test_anthropic_message_role_mapping(
+        sentry_init, capture_events, test_message, expected_role
+    ):
         """Test that Anthropic integration properly maps message roles like 'ai' to 'assistant'"""
         sentry_init(
             integrations=[AnthropicIntegration(include_prompts=True)],
@@ Expand All / @@ -924,13 +942,7 @@ def mock_messages_create(*args, **kwargs): @@
         client.messages._post = mock.Mock(return_value=mock_messages_create())
-        # Test messages with mixed roles including "ai" that should be mapped to "assistant"
-        test_messages = [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "user", "content": "Hello"},
-            {"role": "ai", "content": "Hi there!"},  # Should be mapped to "assistant"
-            {"role": "assistant", "content": "How can I help?"},  # Should stay "assistant"
-        ]
+        test_messages = [test_message]
         with start_transaction(name="anthropic tx"):
             client.messages.create(
@@ Expand All / @@ -948,22 +960,7 @@ def mock_messages_create(*args, **kwargs): @@
         # Parse the stored messages
         stored_messages = json.loads(span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])
-        # Verify that "ai" role was mapped to "assistant"
-        assert len(stored_messages) == 4
-        assert stored_messages[0]["role"] == "system"
-        assert stored_messages[1]["role"] == "user"
-        assert (
-            stored_messages[2]["role"] == "assistant"
-        )  # "ai" should be mapped to "assistant"
-        assert stored_messages[3]["role"] == "assistant"  # should stay "assistant"
-        # Verify content is preserved
-        assert stored_messages[2]["content"] == "Hi there!"
-        assert stored_messages[3]["content"] == "How can I help?"
-        # Verify no "ai" roles remain
-        roles = [msg["role"] for msg in stored_messages]
-        assert "ai" not in roles
+        assert stored_messages[0]["role"] == expected_role
     def test_anthropic_message_truncation(sentry_init, capture_events):
@@ Expand Down Expand Up @@
         parsed_messages = json.loads(messages_data)
         assert isinstance(parsed_messages, list)
-        assert len(parsed_messages) == 2
-        assert "small message 4" in str(parsed_messages[0])
-        assert "small message 5" in str(parsed_messages[1])
+        assert len(parsed_messages) == 1
+        assert "small message 5" in str(parsed_messages[0])
+        assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
+    @pytest.mark.asyncio
+    async def test_anthropic_message_truncation_async(sentry_init, capture_events):
+        """Test that large messages are truncated properly in Anthropic integration."""
+        sentry_init(
+            integrations=[AnthropicIntegration(include_prompts=True)],
+            traces_sample_rate=1.0,
+            send_default_pii=True,
+        )
+        events = capture_events()
+        client = AsyncAnthropic(api_key="z")
+        client.messages._post = mock.AsyncMock(return_value=EXAMPLE_MESSAGE)
+        large_content = (
+            "This is a very long message that will exceed our size limits. " * 1000
+        )
+        messages = [
+            {"role": "user", "content": "small message 1"},
+            {"role": "assistant", "content": large_content},
+            {"role": "user", "content": large_content},
+            {"role": "assistant", "content": "small message 4"},
+            {"role": "user", "content": "small message 5"},
+        ]
+        with start_transaction():
+            await client.messages.create(max_tokens=1024, messages=messages, model="model")
+        assert len(events) > 0
+        tx = events[0]
+        assert tx["type"] == "transaction"
+        chat_spans = [
+            span for span in tx.get("spans", []) if span.get("op") == OP.GEN_AI_CHAT
+        ]
+        assert len(chat_spans) > 0
+        chat_span = chat_spans[0]
+        assert chat_span["data"][SPANDATA.GEN_AI_OPERATION_NAME] == "chat"
+        assert SPANDATA.GEN_AI_REQUEST_MESSAGES in chat_span["data"]
+        messages_data = chat_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES]
+        assert isinstance(messages_data, str)
+        parsed_messages = json.loads(messages_data)
+        assert isinstance(parsed_messages, list)
+        assert len(parsed_messages) == 1
+        assert "small message 5" in str(parsed_messages[0])
         assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
@@ Expand Down @@

tests/integrations/google_genai/test_google_genai.py

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -1497,60 +1497,6 @@ def test_generate_content_with_content_object(
  
        ]

    def test_generate_content_with_conversation_history(

        sentry_init, capture_events, mock_genai_client

    ):

        """Test generate_content with list of Content objects (conversation history)."""

        sentry_init(

            integrations=[GoogleGenAIIntegration(include_prompts=True)],

            traces_sample_rate=1.0,

            send_default_pii=True,

        )

        events = capture_events()

        mock_http_response = create_mock_http_response(EXAMPLE_API_RESPONSE_JSON)

        # Create conversation history

        contents = [

            genai_types.Content(

                role="user", parts=[genai_types.Part(text="What is the capital of France?")]

            ),

            genai_types.Content(

                role="model",

                parts=[genai_types.Part(text="The capital of France is Paris.")],

            ),

            genai_types.Content(

                role="user", parts=[genai_types.Part(text="What about Germany?")]

            ),

        ]

        with mock.patch.object(

            mock_genai_client._api_client, "request", return_value=mock_http_response

        ):

            with start_transaction(name="google_genai"):

                mock_genai_client.models.generate_content(

                    model="gemini-1.5-flash", contents=contents, config=create_test_config()

                )

        (event,) = events

        invoke_span = event["spans"][0]

        messages = json.loads(invoke_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])

        assert len(messages) == 3

        assert messages[0]["role"] == "user"

        assert messages[0]["content"] == [

            {"text": "What is the capital of France?", "type": "text"}

        ]

        assert (

            messages[1]["role"] == "assistant"

        )  # "model" should be normalized to "assistant"

        assert messages[1]["content"] == [

            {"text": "The capital of France is Paris.", "type": "text"}

        ]

        assert messages[2]["role"] == "user"

        assert messages[2]["content"] == [{"text": "What about Germany?", "type": "text"}]

    def test_generate_content_with_dict_format(

        sentry_init, capture_events, mock_genai_client

    ):

    @@ -1720,17 +1666,12 @@ def test_generate_content_with_function_response(
  
        invoke_span = event["spans"][0]

        messages = json.loads(invoke_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])

        assert len(messages) == 2

        assert len(messages) == 1

        # First message is user message

        assert messages[0]["role"] == "user"

        assert messages[0]["content"] == [

            {"text": "What's the weather in Paris?", "type": "text"}

        ]

        # Second message is tool message

        assert messages[1]["role"] == "tool"

        assert messages[1]["content"]["toolCallId"] == "call_123"

        assert messages[1]["content"]["toolName"] == "get_weather"

        assert messages[1]["content"]["output"] == '"Sunny, 72F"'

        assert messages[0]["role"] == "tool"

        assert messages[0]["content"]["toolCallId"] == "call_123"

        assert messages[0]["content"]["toolName"] == "get_weather"

        assert messages[0]["content"]["output"] == '"Sunny, 72F"'

    def test_generate_content_with_mixed_string_and_content(

    @@ -1771,18 +1712,10 @@ def test_generate_content_with_mixed_string_and_content(
  
        invoke_span = event["spans"][0]

        messages = json.loads(invoke_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])

        assert len(messages) == 3

        # String becomes user message

        assert messages[0]["role"] == "user"

        assert messages[0]["content"] == "Hello, this is a string message"

        # Model role normalized to assistant

        assert messages[1]["role"] == "assistant"

        assert messages[1]["content"] == [

            {"text": "Hi! How can I help you?", "type": "text"}

        ]

        assert len(messages) == 1

        # User message

        assert messages[2]["role"] == "user"

        assert messages[2]["content"] == [{"text": "Tell me a joke", "type": "text"}]

        assert messages[0]["role"] == "user"

        assert messages[0]["content"] == [{"text": "Tell me a joke", "type": "text"}]

    def test_generate_content_with_part_object_directly(

    @@ -1850,13 +1783,9 @@ def test_generate_content_with_list_of_dicts(
  
        invoke_span = event["spans"][0]

        messages = json.loads(invoke_span["data"][SPANDATA.GEN_AI_REQUEST_MESSAGES])

        assert len(messages) == 3

        assert len(messages) == 1

        assert messages[0]["role"] == "user"

        assert messages[0]["content"] == [{"text": "First user message", "type": "text"}]

        assert messages[1]["role"] == "assistant"

        assert messages[1]["content"] == [{"text": "First model response", "type": "text"}]

        assert messages[2]["role"] == "user"

        assert messages[2]["content"] == [{"text": "Second user message", "type": "text"}]

        assert messages[0]["content"] == [{"text": "Second user message", "type": "text"}]

    def test_generate_content_with_dict_inline_data(

tests/integrations/langchain/test_langchain.py

-Original file line number
+Diff line change
@@ Expand Up @@
         parsed_messages = json.loads(messages_data)
         assert isinstance(parsed_messages, list)
-        assert len(parsed_messages) == 2
-        assert "small message 4" in str(parsed_messages[0])
-        assert "small message 5" in str(parsed_messages[1])
+        assert len(parsed_messages) == 1
+        assert "small message 5" in str(parsed_messages[0])
         assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
@@ Expand Down @@

tests/integrations/langgraph/test_langgraph.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -270,9 +270,8 @@ def original_invoke(self, *args, **kwargs): @@
                 import json
                 request_messages = json.loads(request_messages)
-            assert len(request_messages) == 2
-            assert request_messages[0]["content"] == "Hello, can you help me?"
-            assert request_messages[1]["content"] == "Of course! How can I assist you?"
+            assert len(request_messages) == 1
+            assert request_messages[0]["content"] == "Of course! How can I assist you?"
             response_text = invoke_span["data"][SPANDATA.GEN_AI_RESPONSE_TEXT]
             assert response_text == expected_assistant_response
@@ Expand Down Expand Up / @@ -1383,7 +1382,6 @@ def original_invoke(self, *args, **kwargs): @@
         parsed_messages = json.loads(messages_data)
         assert isinstance(parsed_messages, list)
-        assert len(parsed_messages) == 2
-        assert "small message 4" in str(parsed_messages[0])
-        assert "small message 5" in str(parsed_messages[1])
+        assert len(parsed_messages) == 1
+        assert "small message 5" in str(parsed_messages[0])
         assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5

tests/integrations/litellm/test_litellm.py

-Original file line number
+Diff line change
@@ Expand Up @@
         parsed_messages = json.loads(messages_data)
         assert isinstance(parsed_messages, list)
-        assert len(parsed_messages) == 2
-        assert "small message 4" in str(parsed_messages[0])
-        assert "small message 5" in str(parsed_messages[1])
+        assert len(parsed_messages) == 1
+        assert "small message 5" in str(parsed_messages[0])
         assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
@@ Expand Down @@

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fix(ai): Keep single content input message #5345

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