ref(langgraph): Revert input truncation

sentry_sdk/integrations/langgraph.py

@@ -5,7 +5,6 @@
 from sentry_sdk.ai.utils import (
     set_data_normalized,
     normalize_message_roles,
-    truncate_and_annotate_messages,
 )
 from sentry_sdk.consts import OP, SPANDATA
 from sentry_sdk.integrations import DidNotEnable, Integration
@@ -181,17 +180,12 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
                 input_messages = _parse_langgraph_messages(args[0])
                 if input_messages:
                     normalized_input_messages = normalize_message_roles(input_messages)
-                    scope = sentry_sdk.get_current_scope()
-                    messages_data = truncate_and_annotate_messages(
-                        normalized_input_messages, span, scope
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        normalized_input_messages,
+                        unpack=False,
                     )
-                    if messages_data is not None:
-                        set_data_normalized(
-                            span,
-                            SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                            messages_data,
-                            unpack=False,
-                        )
 
             result = f(self, *args, **kwargs)
 
@@ -234,17 +228,12 @@ async def new_ainvoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
                 input_messages = _parse_langgraph_messages(args[0])
                 if input_messages:
                     normalized_input_messages = normalize_message_roles(input_messages)
-                    scope = sentry_sdk.get_current_scope()
-                    messages_data = truncate_and_annotate_messages(
-                        normalized_input_messages, span, scope
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        normalized_input_messages,
+                        unpack=False,
                     )
-                    if messages_data is not None:
-                        set_data_normalized(
-                            span,
-                            SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                            messages_data,
-                            unpack=False,
-                        )
 
             result = await f(self, *args, **kwargs)
 

tests/integrations/langgraph/test_langgraph.py

@@ -272,8 +272,9 @@ def original_invoke(self, *args, **kwargs):
             import json
 
             request_messages = json.loads(request_messages)
-        assert len(request_messages) == 1
-        assert request_messages[0]["content"] == "Of course! How can I assist you?"
+        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?"
 
         response_text = invoke_span["attributes"][SPANDATA.GEN_AI_RESPONSE_TEXT]
         assert response_text == expected_assistant_response
@@ -1351,61 +1352,3 @@ def __init__(self, content, message_type="human"):
         # Verify no "ai" roles remain
         roles = [msg["role"] for msg in stored_messages if "role" in msg]
         assert "ai" not in roles
-
-
-def test_langgraph_message_truncation(sentry_init, capture_items):
-    """Test that large messages are truncated properly in Langgraph integration."""
-    import json
-
-    sentry_init(
-        integrations=[LanggraphIntegration(include_prompts=True)],
-        traces_sample_rate=1.0,
-        send_default_pii=True,
-    )
-    items = capture_items("transaction", "span")
-
-    large_content = (
-        "This is a very long message that will exceed our size limits. " * 1000
-    )
-    test_state = {
-        "messages": [
-            MockMessage("small message 1", name="user"),
-            MockMessage(large_content, name="assistant"),
-            MockMessage(large_content, name="user"),
-            MockMessage("small message 4", name="assistant"),
-            MockMessage("small message 5", name="user"),
-        ]
-    }
-
-    pregel = MockPregelInstance("test_graph")
-
-    def original_invoke(self, *args, **kwargs):
-        return {"messages": args[0].get("messages", [])}
-
-    with start_transaction():
-        wrapped_invoke = _wrap_pregel_invoke(original_invoke)
-        result = wrapped_invoke(pregel, test_state)
-
-    assert result is not None
-
-    spans = [item.payload for item in items if item.type == "span"]
-    invoke_spans = [
-        span
-        for span in spans
-        if span["attributes"].get("sentry.op") == OP.GEN_AI_INVOKE_AGENT
-    ]
-    assert len(invoke_spans) > 0
-
-    invoke_span = invoke_spans[0]
-    assert SPANDATA.GEN_AI_REQUEST_MESSAGES in invoke_span["attributes"]
-
-    messages_data = invoke_span["attributes"][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])
-
-    (tx,) = (item.payload for item in items if item.type == "transaction")
-    assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5