From 168c0202d6feccac29428b4557558cf6e510a4e3 Mon Sep 17 00:00:00 2001
From: Simon Hellmayr <simon.hellmayr@sentry.io>
Date: Fri, 17 Oct 2025 11:59:36 +0200
Subject: [PATCH] fix(ai): add message truncation to langgraph

---
 sentry_sdk/integrations/langgraph.py          | 36 ++++++++----
 .../integrations/langgraph/test_langgraph.py  | 57 +++++++++++++++++++
 2 files changed, 82 insertions(+), 11 deletions(-)

diff --git a/sentry_sdk/integrations/langgraph.py b/sentry_sdk/integrations/langgraph.py
index 11aa1facf4..5bb0e0fd08 100644
--- a/sentry_sdk/integrations/langgraph.py
+++ b/sentry_sdk/integrations/langgraph.py
@@ -2,7 +2,11 @@
 from typing import Any, Callable, List, Optional
 
 import sentry_sdk
-from sentry_sdk.ai.utils import set_data_normalized, normalize_message_roles
+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
 from sentry_sdk.scope import should_send_default_pii
@@ -181,12 +185,17 @@ def new_invoke(self, *args, **kwargs):
                 input_messages = _parse_langgraph_messages(args[0])
                 if input_messages:
                     normalized_input_messages = normalize_message_roles(input_messages)
-                    set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                        normalized_input_messages,
-                        unpack=False,
+                    scope = sentry_sdk.get_current_scope()
+                    messages_data = truncate_and_annotate_messages(
+                        normalized_input_messages, span, scope
                     )
+                    if messages_data is not None:
+                        set_data_normalized(
+                            span,
+                            SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                            messages_data,
+                            unpack=False,
+                        )
 
             result = f(self, *args, **kwargs)
 
@@ -232,12 +241,17 @@ async def new_ainvoke(self, *args, **kwargs):
                 input_messages = _parse_langgraph_messages(args[0])
                 if input_messages:
                     normalized_input_messages = normalize_message_roles(input_messages)
-                    set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                        normalized_input_messages,
-                        unpack=False,
+                    scope = sentry_sdk.get_current_scope()
+                    messages_data = truncate_and_annotate_messages(
+                        normalized_input_messages, span, scope
                     )
+                    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)
 
diff --git a/tests/integrations/langgraph/test_langgraph.py b/tests/integrations/langgraph/test_langgraph.py
index 6ec6d9a96d..7cb86a5b03 100644
--- a/tests/integrations/langgraph/test_langgraph.py
+++ b/tests/integrations/langgraph/test_langgraph.py
@@ -696,3 +696,60 @@ 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_events):
+    """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,
+    )
+    events = capture_events()
+
+    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
+    assert len(events) > 0
+    tx = events[0]
+    assert tx["type"] == "transaction"
+
+    invoke_spans = [
+        span for span in tx.get("spans", []) if span.get("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["data"]
+
+    messages_data = invoke_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