Merge pull request #4272 from pipecat-ai/pk/llm-context-get-messages-elide-large-values

Add truncate_large_values to LLMContext.get_messages()

changelog/4272.added.md

@@ -0,0 +1 @@
+- Added `truncate_large_values` parameter to `LLMContext.get_messages()`. When `True`, returns compact deep copies of messages with binary data (base64 images, audio) replaced by short placeholders and long string values in LLM-specific messages recursively truncated. Useful for serialization, logging, and debugging tools.

src/pipecat/adapters/base_llm_adapter.py

@@ -125,16 +125,22 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
         """
         return LLMSpecificMessage(llm=self.id_for_llm_specific_messages, message=message)
 
-    def get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
+    def get_messages(
+        self, context: LLMContext, *, truncate_large_values: bool = False
+    ) -> List[LLMContextMessage]:
         """Get messages from the LLM context, including standard and LLM-specific messages.
 
         Args:
             context: The LLM context containing messages.
+            truncate_large_values: If True, return deep copies of messages with
+                large values replaced by short placeholders.
 
         Returns:
             List of messages including standard and LLM-specific messages.
         """
-        return context.get_messages(self.id_for_llm_specific_messages)
+        return context.get_messages(
+            self.id_for_llm_specific_messages, truncate_large_values=truncate_large_values
+        )
 
     def from_standard_tools(self, tools: Any) -> List[Any] | NotGiven:
         """Convert tools from standard format to provider format.

src/pipecat/adapters/services/grok_realtime_adapter.py

@@ -77,7 +77,7 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from context in a format safe for logging.
 
-        Removes or truncates sensitive data like audio content.
+        Binary data (images, audio) is replaced with short placeholders.
 
         Args:
             context: The LLM context containing messages.
@@ -85,18 +85,7 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages with sensitive data redacted.
         """
-        msgs = []
+        return self.get_messages(context, truncate_large_values=True)
-        for message in self.get_messages(context):
-            msg = copy.deepcopy(message)
-            if "content" in msg:
-                if isinstance(msg["content"], list):
-                    for item in msg["content"]:
-                        if item.get("type") == "input_audio":
-                            item["audio"] = "..."
-                        if item.get("type") == "audio":
-                            item["audio"] = "..."
-            msgs.append(msg)
-        return msgs
 
     @dataclass
     class ConvertedMessages:

src/pipecat/adapters/services/inworld_realtime_adapter.py

@@ -77,7 +77,7 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from context in a format safe for logging.
 
-        Removes or truncates sensitive data like audio content.
+        Binary data (images, audio) is replaced with short placeholders.
 
         Args:
             context: The LLM context containing messages.
@@ -85,18 +85,7 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages with sensitive data redacted.
         """
-        msgs = []
+        return self.get_messages(context, truncate_large_values=True)
-        for message in self.get_messages(context):
-            msg = copy.deepcopy(message)
-            if "content" in msg:
-                if isinstance(msg["content"], list):
-                    for item in msg["content"]:
-                        if item.get("type") == "input_audio":
-                            item["audio"] = "..."
-                        if item.get("type") == "audio":
-                            item["audio"] = "..."
-            msgs.append(msg)
-        return msgs
 
     @dataclass
     class ConvertedMessages:

src/pipecat/adapters/services/open_ai_adapter.py

@@ -6,7 +6,6 @@
 
 """OpenAI LLM adapter for Pipecat."""
 
-import copy
 from typing import Any, Dict, List, Optional, TypedDict
 
 from openai._types import NotGiven as OpenAINotGiven
@@ -119,7 +118,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
     def get_messages_for_logging(self, context: LLMContext) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about OpenAI.
 
-        Removes or truncates sensitive data like image content for safe logging.
+        Binary data (images, audio) is replaced with short placeholders.
 
         Args:
             context: The LLM context containing messages.
@@ -127,21 +126,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
         Returns:
             List of messages in a format ready for logging about OpenAI.
         """
-        msgs = []
+        return self.get_messages(context, truncate_large_values=True)
-        for message in self.get_messages(context):
-            msg = copy.deepcopy(message)
-            if "content" in msg:
-                if isinstance(msg["content"], list):
-                    for item in msg["content"]:
-                        if item["type"] == "image_url":
-                            if item["image_url"]["url"].startswith("data:image/"):
-                                item["image_url"]["url"] = "data:image/..."
-                        if item["type"] == "input_audio":
-                            item["input_audio"]["data"] = "..."
-            if "mime_type" in msg and msg["mime_type"].startswith("image/"):
-                msg["data"] = "..."
-            msgs.append(msg)
-        return msgs
 
     def _from_universal_context_messages(
         self,

src/pipecat/adapters/services/open_ai_realtime_adapter.py

@@ -71,7 +71,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about OpenAI Realtime.
 
-        Removes or truncates sensitive data like image content for safe logging.
+        Binary data (images, audio) is replaced with short placeholders.
 
         This is a placeholder until support for universal LLMContext machinery is added for OpenAI Realtime.
 
@@ -81,25 +81,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages in a format ready for logging about OpenAI Realtime.
         """
-        # NOTE: this is the same as in OpenAIAdapter, as that's what it was
+        return self.get_messages(context, truncate_large_values=True)
-        # prior to a refactor. Worth noting that for OpenAI Realtime
-        # specifically, not everything handled here is necessarily supported
-        # (or supported yet).
-        msgs = []
-        for message in self.get_messages(context):
-            msg = copy.deepcopy(message)
-            if "content" in msg:
-                if isinstance(msg["content"], list):
-                    for item in msg["content"]:
-                        if item["type"] == "image_url":
-                            if item["image_url"]["url"].startswith("data:image/"):
-                                item["image_url"]["url"] = "data:image/..."
-                        if item["type"] == "input_audio":
-                            item["input_audio"]["data"] = "..."
-            if "mime_type" in msg and msg["mime_type"].startswith("image/"):
-                msg["data"] = "..."
-            msgs.append(msg)
-        return msgs
 
     @dataclass
     class ConvertedMessages:

src/pipecat/adapters/services/open_ai_responses_adapter.py

@@ -6,7 +6,6 @@
 
 """OpenAI Responses API adapter for Pipecat."""
 
-import copy
 from typing import Any, Dict, List, Optional, TypedDict
 
 from openai._types import NotGiven as OpenAINotGiven
@@ -136,7 +135,7 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
     def get_messages_for_logging(self, context: LLMContext) -> List[Dict[str, Any]]:
         """Get messages from context in a format ready for logging.
 
-        Removes or truncates sensitive data like image content for safe logging.
+        Binary data (images, audio) is replaced with short placeholders.
 
         Args:
             context: The LLM context containing messages.
@@ -144,19 +143,7 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
         Returns:
             List of messages in a format ready for logging.
         """
-        msgs = []
+        return self.get_messages(context, truncate_large_values=True)
-        for message in self.get_messages(context):
-            msg = copy.deepcopy(message)
-            if "content" in msg:
-                if isinstance(msg["content"], list):
-                    for item in msg["content"]:
-                        if item.get("type") == "image_url":
-                            if item["image_url"]["url"].startswith("data:image/"):
-                                item["image_url"]["url"] = "data:image/..."
-                        if item.get("type") == "input_audio":
-                            item["input_audio"]["data"] = "..."
-            msgs.append(msg)
-        return msgs
 
     def _convert_messages_to_input(
         self, messages: List[LLMContextMessage]

src/pipecat/processors/aggregators/llm_context.py

@@ -16,6 +16,7 @@ service-specific adapter.
 
 import asyncio
 import base64
+import copy
 import io
 import wave
 from dataclasses import dataclass
@@ -198,7 +199,12 @@ class LLMContext:
         """
         return self.get_messages()
 
-    def get_messages(self, llm_specific_filter: Optional[str] = None) -> List[LLMContextMessage]:
+    def get_messages(
+        self,
+        llm_specific_filter: Optional[str] = None,
+        *,
+        truncate_large_values: bool = False,
+    ) -> List[LLMContextMessage]:
         """Get the current messages list.
 
         Args:
@@ -207,22 +213,110 @@ class LLMContext:
                 messages. If messages end up being filtered, an error will be
                 logged; this is intended to catch accidental use of
                 incompatible LLM-specific messages.
+            truncate_large_values: If True, return deep copies of messages with
+                large values shortened. For standard messages, known binary
+                data (base64-encoded images, audio) is replaced with short
+                placeholders. For LLM-specific messages, long string values
+                are truncated.
 
         Returns:
             List of conversation messages.
         """
         if llm_specific_filter is None:
-            return self._messages
+            messages = self._messages
-        filtered_messages = [
+        else:
-            msg
+            messages = [
-            for msg in self._messages
+                msg
-            if not isinstance(msg, LLMSpecificMessage) or msg.llm == llm_specific_filter
+                for msg in self._messages
-        ]
+                if not isinstance(msg, LLMSpecificMessage) or msg.llm == llm_specific_filter
-        if len(filtered_messages) < len(self._messages):
+            ]
-            logger.error(
+            if len(messages) < len(self._messages):
-                f"Attempted to use incompatible LLMSpecificMessages with LLM '{llm_specific_filter}'."
+                logger.error(
-            )
+                    f"Attempted to use incompatible LLMSpecificMessages with LLM '{llm_specific_filter}'."
-        return filtered_messages
+                )
+
+        if truncate_large_values:
+            messages = LLMContext._truncate_large_values_from_messages(messages)
+
+        return messages
+
+    @staticmethod
+    def _truncate_large_values_from_messages(
+        messages: List[LLMContextMessage],
+    ) -> List[LLMContextMessage]:
+        """Return deep copies of messages with large values replaced by placeholders.
+
+        For standard (universal-format) messages, the following known binary
+        patterns are replaced with short placeholders:
+
+        - ``image_url`` items with ``data:image/...`` base64 URLs
+        - ``input_audio`` items with ``input_audio.data`` or ``audio`` fields
+        - ``audio`` items with an ``audio`` field
+        - Top-level messages with a ``mime_type`` starting with ``image/``
+
+        For ``LLMSpecificMessage`` instances, long string values are truncated
+        since the internal structure is provider-specific.
+        """
+        result = []
+        for message in messages:
+            if isinstance(message, LLMSpecificMessage):
+                msg_copy = copy.deepcopy(message)
+                msg_copy.message = LLMContext._truncate_long_strings(msg_copy.message)
+                result.append(msg_copy)
+                continue
+
+            msg = copy.deepcopy(message)
+            content = msg.get("content")
+            if isinstance(content, list):
+                for item in content:
+                    item_type = item.get("type")
+                    if item_type == "image_url":
+                        url = item.get("image_url", {}).get("url", "")
+                        if url.startswith("data:image/"):
+                            item["image_url"]["url"] = "data:image/..."
+                    elif item_type == "input_audio":
+                        if "input_audio" in item:
+                            item["input_audio"]["data"] = "..."
+                        if "audio" in item:
+                            item["audio"] = "..."
+                    elif item_type == "audio":
+                        if "audio" in item:
+                            item["audio"] = "..."
+
+            if msg.get("mime_type", "").startswith("image/"):
+                msg["data"] = "..."
+
+            result.append(msg)
+        return result
+
+    @staticmethod
+    def _truncate_long_strings(value: Any, *, max_length: int = 100) -> Any:
+        """Recursively truncate long strings in a nested structure.
+
+        Preserves the structure of dicts and lists while truncating any string
+        values that exceed ``max_length``.
+
+        Args:
+            value: The value to process (dict, list, str, or other).
+            max_length: Strings longer than this are truncated.
+
+        Returns:
+            A copy of the structure with long strings truncated.
+        """
+        if isinstance(value, str):
+            if len(value) > max_length:
+                return f"{value[:max_length]}...({len(value)} chars)"
+            return value
+        elif isinstance(value, dict):
+            return {
+                k: LLMContext._truncate_long_strings(v, max_length=max_length)
+                for k, v in value.items()
+            }
+        elif isinstance(value, list):
+            return [
+                LLMContext._truncate_long_strings(item, max_length=max_length) for item in value
+            ]
+        return value
 
     @property
     def tools(self) -> ToolsSchema | NotGiven:

tests/test_llm_context.py

@@ -0,0 +1,346 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Unit tests for LLMContext core functionality."""
+
+import unittest
+
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMSpecificMessage,
+)
+
+
+class TestGetMessagesTruncateLargeValues(unittest.TestCase):
+    """Tests for LLMContext.get_messages(truncate_large_values=True)."""
+
+    # -- Standard messages: binary elision -----------------------------------
+
+    def test_default_preserves_all_data(self):
+        """truncate_large_values defaults to False, preserving all data."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe this image"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRg=="},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages()
+
+        self.assertEqual(
+            result[0]["content"][1]["image_url"]["url"],
+            "data:image/jpeg;base64,/9j/4AAQSkZJRg==",
+        )
+
+    def test_elides_base64_image_url(self):
+        """Base64 data:image/ URLs are replaced with a placeholder."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe this image"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRg=="},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"][0]["text"], "Describe this image")
+        self.assertEqual(result[0]["content"][1]["image_url"]["url"], "data:image/...")
+
+    def test_preserves_http_image_url(self):
+        """HTTP image URLs are not elided (they aren't binary data)."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "https://example.com/image.jpg"},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(
+            result[0]["content"][0]["image_url"]["url"],
+            "https://example.com/image.jpg",
+        )
+
+    def test_elides_input_audio_data(self):
+        """input_audio items have their data field elided."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Audio follows"},
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": "UklGRiQA" * 1000, "format": "wav"},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"][1]["input_audio"]["data"], "...")
+        self.assertEqual(result[0]["content"][1]["input_audio"]["format"], "wav")
+
+    def test_elides_audio_field(self):
+        """Items with an 'audio' field are elided (used by some realtime adapters)."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "input_audio", "audio": "UklGRiQA" * 1000},
+                    {"type": "audio", "audio": "UklGRiQA" * 1000},
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"][0]["audio"], "...")
+        self.assertEqual(result[0]["content"][1]["audio"], "...")
+
+    def test_elides_top_level_mime_type_image(self):
+        """Messages with top-level mime_type image/ have their data elided."""
+        messages = [
+            {
+                "role": "user",
+                "mime_type": "image/png",
+                "data": "iVBORw0KGgoAAAANSU" * 1000,
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["data"], "...")
+        self.assertEqual(result[0]["mime_type"], "image/png")
+
+    def test_mixed_content_elides_only_binary(self):
+        """In a message with text, image, and audio, only binary parts are elided."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Here is an image and audio"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/png;base64,iVBORw=="},
+                    },
+                    {
+                        "type": "input_audio",
+                        "input_audio": {"data": "UklGRiQA", "format": "wav"},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"][0]["text"], "Here is an image and audio")
+        self.assertEqual(result[0]["content"][1]["image_url"]["url"], "data:image/...")
+        self.assertEqual(result[0]["content"][2]["input_audio"]["data"], "...")
+
+    def test_text_only_messages_unchanged(self):
+        """Plain text messages are completely unaffected."""
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result, messages)
+
+    def test_does_not_mutate_original(self):
+        """Returns copies; originals are untouched."""
+        original_url = "data:image/jpeg;base64,/9j/4AAQSkZJRg=="
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": original_url},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        _ = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(
+            context.get_messages()[0]["content"][0]["image_url"]["url"],
+            original_url,
+        )
+
+    def test_multiple_images_all_elided(self):
+        """Multiple image_url items in the same message are all elided."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/jpeg;base64,AAAA"},
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "data:image/png;base64,BBBB"},
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "https://example.com/photo.jpg"},
+                    },
+                ],
+            }
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"][0]["image_url"]["url"], "data:image/...")
+        self.assertEqual(result[0]["content"][1]["image_url"]["url"], "data:image/...")
+        self.assertEqual(
+            result[0]["content"][2]["image_url"]["url"],
+            "https://example.com/photo.jpg",
+        )
+
+    def test_works_with_llm_specific_filter(self):
+        """truncate_large_values works together with llm_specific_filter."""
+        adapter = OpenAILLMAdapter()
+        std_msg = {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQ"},
+                },
+            ],
+        }
+        specific_msg = adapter.create_llm_specific_message(
+            {"role": "assistant", "content": "response"}
+        )
+        context = LLMContext(messages=[std_msg, specific_msg])
+
+        result = context.get_messages("openai", truncate_large_values=True)
+
+        self.assertEqual(len(result), 2)
+        self.assertEqual(result[0]["content"][0]["image_url"]["url"], "data:image/...")
+
+    def test_string_content_with_no_binary(self):
+        """Messages with string content (not list) pass through fine."""
+        messages = [
+            {"role": "user", "content": "Just a string"},
+        ]
+        context = LLMContext(messages=messages)
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(result[0]["content"], "Just a string")
+
+    # -- LLMSpecificMessage: long-string truncation --------------------------
+
+    def test_llm_specific_short_values_preserved(self):
+        """Short string values in LLMSpecificMessage are kept as-is."""
+        inner = {"type": "thought", "text": "brief thought"}
+        specific_msg = LLMSpecificMessage(llm="anthropic", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        result = context.get_messages(truncate_large_values=True)
+
+        self.assertIsInstance(result[0], LLMSpecificMessage)
+        self.assertEqual(result[0].message["type"], "thought")
+        self.assertEqual(result[0].message["text"], "brief thought")
+
+    def test_llm_specific_long_string_truncated(self):
+        """Long string values in LLMSpecificMessage are truncated."""
+        long_signature = "a" * 500
+        inner = {"type": "thought", "text": "short", "signature": long_signature}
+        specific_msg = LLMSpecificMessage(llm="anthropic", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        result = context.get_messages(truncate_large_values=True)
+
+        msg = result[0].message
+        self.assertEqual(msg["type"], "thought")
+        self.assertEqual(msg["text"], "short")
+        # Signature should be truncated
+        self.assertIn("...", msg["signature"])
+        self.assertIn("500 chars", msg["signature"])
+        self.assertTrue(len(msg["signature"]) < len(long_signature))
+
+    def test_llm_specific_nested_dict_truncated(self):
+        """Long strings nested in dicts within LLMSpecificMessage are truncated."""
+        inner = {
+            "type": "thought_signature",
+            "signature": "x" * 200,
+            "bookmark": {"text": "y" * 200},
+        }
+        specific_msg = LLMSpecificMessage(llm="google", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        result = context.get_messages(truncate_large_values=True)
+
+        msg = result[0].message
+        self.assertEqual(msg["type"], "thought_signature")
+        self.assertIn("...", msg["signature"])
+        self.assertIn("...", msg["bookmark"]["text"])
+
+    def test_llm_specific_list_values_truncated(self):
+        """Long strings inside lists within LLMSpecificMessage are truncated."""
+        inner = {"items": ["short", "a" * 200]}
+        specific_msg = LLMSpecificMessage(llm="test", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        result = context.get_messages(truncate_large_values=True)
+
+        msg = result[0].message
+        self.assertEqual(msg["items"][0], "short")
+        self.assertIn("...", msg["items"][1])
+
+    def test_llm_specific_non_string_values_preserved(self):
+        """Non-string values (ints, bools, None) in LLMSpecificMessage are untouched."""
+        inner = {"type": "test", "count": 42, "active": True, "extra": None}
+        specific_msg = LLMSpecificMessage(llm="test", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        result = context.get_messages(truncate_large_values=True)
+
+        msg = result[0].message
+        self.assertEqual(msg["count"], 42)
+        self.assertEqual(msg["active"], True)
+        self.assertIsNone(msg["extra"])
+
+    def test_llm_specific_does_not_mutate_original(self):
+        """Truncation returns a copy; original LLMSpecificMessage is untouched."""
+        long_sig = "a" * 500
+        inner = {"signature": long_sig}
+        specific_msg = LLMSpecificMessage(llm="anthropic", message=inner)
+        context = LLMContext(messages=[specific_msg])
+
+        _ = context.get_messages(truncate_large_values=True)
+
+        self.assertEqual(specific_msg.message["signature"], long_sig)
+
+
+if __name__ == "__main__":
+    unittest.main()