fix(ai): add message trunction to anthropic

sentry_sdk/integrations/anthropic.py

@@ -6,6 +6,7 @@
 from sentry_sdk.ai.utils import (
     set_data_normalized,
     normalize_message_roles,
+    truncate_and_annotate_messages,
     get_start_span_function,
 )
 from sentry_sdk.consts import OP, SPANDATA, SPANSTATUS
@@ -145,12 +146,14 @@ def _set_input_data(span, kwargs, integration):
                 normalized_messages.append(message)
 
         role_normalized_messages = normalize_message_roles(normalized_messages)
-        set_data_normalized(
-            span,
-            SPANDATA.GEN_AI_REQUEST_MESSAGES,
-            role_normalized_messages,
-            unpack=False,
+        scope = sentry_sdk.get_current_scope()
+        messages_data = truncate_and_annotate_messages(
+            role_normalized_messages, span, scope
         )
+        if messages_data is not None:
+            set_data_normalized(
+                span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages_data, unpack=False
+            )
 
     set_data_normalized(
         span, SPANDATA.GEN_AI_RESPONSE_STREAMING, kwargs.get("stream", False)

tests/integrations/anthropic/test_anthropic.py

@@ -945,3 +945,52 @@ def mock_messages_create(*args, **kwargs):
     # Verify no "ai" roles remain
     roles = [msg["role"] for msg in stored_messages]
     assert "ai" not in roles
+
+
+def test_anthropic_message_truncation(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 = Anthropic(api_key="z")
+    client.messages._post = mock.Mock(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():
+        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 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) == 2
+    assert "small message 4" in str(parsed_messages[0])
+    assert "small message 5" in str(parsed_messages[1])
+    assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5