fix(zai): flatten list-format content in tool/assistant messages before sending to GLM

GLM's Jinja chat template checks ``m.content is string`` and silently
drops list-format content (e.g. [{"type": "text", "text": "..."}]),
causing tool results to be lost and the model to respond as if the tool
returned no data.

Add ZAIChatConfig._transform_messages() that normalises list-format
content in tool and assistant messages to plain strings before delegating
to the parent OpenAIGPTConfig transformer. User-facing content in user
messages is not affected.

Fixes #25868

litellm/llms/zai/chat/transformation.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple
+from typing import Any, Coroutine, List, Optional, Tuple, Union
 
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
@@ -8,6 +8,30 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig
 ZAI_API_BASE = "https://api.z.ai/api/paas/v4"
 
 
+def _flatten_content_parts(content: Any) -> Any:
+    """Flatten OpenAI multi-part content to a plain string.
+
+    The OpenAI spec allows tool/assistant message content as either a plain
+    string or a list of content parts (e.g. [{"type": "text", "text": "..."}]).
+    GLM's chat template checks ``m.content is string`` and silently drops
+    list-format content (same root cause as vllm-project/vllm#39614).
+    This helper normalises both forms to a plain string.
+    """
+    if isinstance(content, str) or content is None:
+        return content
+    if isinstance(content, list):
+        parts = []
+        for part in content:
+            if isinstance(part, dict):
+                text = part.get("text")
+                if text:
+                    parts.append(text)
+            elif isinstance(part, str):
+                parts.append(part)
+        return "\n".join(parts) if parts else ""
+    return content
+
+
 class ZAIChatConfig(OpenAIGPTConfig):
     @property
     def custom_llm_provider(self) -> Optional[str]:
@@ -56,3 +80,21 @@ class ZAIChatConfig(OpenAIGPTConfig):
             pass
 
         return base_params
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: bool = False
+    ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]:
+        """Flatten list-format content in tool and assistant messages before sending to ZAI.
+
+        GLM's chat template checks ``m.content is string`` and silently drops list-format
+        content (e.g. [{"type": "text", "text": "..."}]).  This ensures tool results and
+        assistant messages always reach the model as plain strings.
+
+        Issue: https://github.com/BerriAI/litellm/issues/25868
+        """
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
+            if role in ("tool", "assistant") and isinstance(content, list):
+                message["content"] = _flatten_content_parts(content)  # type: ignore
+        return super()._transform_messages(messages=messages, model=model, is_async=is_async)  # type: ignore

tests/test_litellm/llms/zai/test_zai_provider.py

@@ -4,6 +4,7 @@ Tests for Z.AI (Zhipu AI) provider - GLM models
 
 import json
 import math
+from typing import cast
 
 import pytest
 import respx
@@ -179,3 +180,116 @@ def test_zai_sync_completion(respx_mock, zai_response, monkeypatch):
 
     assert response.choices[0].message.content == "Hello! How can I help you today?"
     assert response.usage.total_tokens == 25
+
+
+class TestZAIMessageTransformation:
+    """Tests for ZAI message content flattening.
+
+    Issue: https://github.com/BerriAI/litellm/issues/25868
+    GLM's Jinja chat template checks ``m.content is string`` and silently drops
+    list-format content. ZAIChatConfig._transform_messages must flatten these
+    before forwarding to z.ai.
+    """
+
+    def test_flatten_tool_message_content_list(self):
+        """Tool message with list-format content is flattened to a plain string."""
+        from litellm.llms.zai.chat.transformation import ZAIChatConfig
+
+        config = ZAIChatConfig()
+        messages = cast(
+            list,
+            [
+                {"role": "user", "content": "What is the temperature in Tokyo?"},
+                {
+                    "role": "assistant",
+                    "content": "Let me check.",
+                    "tool_calls": [
+                        {
+                            "id": "call_1",
+                            "type": "function",
+                            "function": {"name": "get_temp", "arguments": '{"city": "Tokyo"}'},
+                        }
+                    ],
+                },
+                {
+                    "role": "tool",
+                    "tool_call_id": "call_1",
+                    "content": [{"type": "text", "text": "22.5\u00b0C, partly cloudy."}],
+                },
+            ],
+        )
+
+        result = config._transform_messages(messages=messages, model="glm-5.1")
+
+        tool_msg = result[2]
+        assert isinstance(tool_msg["content"], str), (
+            f"Expected str content, got {type(tool_msg['content'])}"
+        )
+        assert tool_msg["content"] == "22.5\u00b0C, partly cloudy."
+
+    def test_flatten_assistant_message_content_list(self):
+        """Assistant message with list-format content is flattened to a plain string."""
+        from litellm.llms.zai.chat.transformation import ZAIChatConfig
+
+        config = ZAIChatConfig()
+        messages = cast(
+            list,
+            [
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": "Let me think about this."}],
+                },
+            ],
+        )
+
+        result = config._transform_messages(messages=messages, model="glm-5.1")
+
+        assert result[0]["content"] == "Let me think about this."
+
+    def test_string_content_passes_through_unchanged(self):
+        """String content is not modified by the flattening step."""
+        from litellm.llms.zai.chat.transformation import ZAIChatConfig
+
+        config = ZAIChatConfig()
+        messages = cast(
+            list,
+            [
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Hi there!"},
+                {"role": "tool", "tool_call_id": "c1", "content": "Result data"},
+            ],
+        )
+
+        result = config._transform_messages(messages=messages, model="glm-5.1")
+
+        assert result[0]["content"] == "Hello"
+        assert result[1]["content"] == "Hi there!"
+        assert result[2]["content"] == "Result data"
+
+    def test_flatten_content_parts_helper_multipart(self):
+        """Multiple text parts are joined with newline."""
+        from litellm.llms.zai.chat.transformation import _flatten_content_parts
+
+        content = [
+            {"type": "text", "text": "Line 1"},
+            {"type": "text", "text": "Line 2"},
+        ]
+        assert _flatten_content_parts(content) == "Line 1\nLine 2"
+
+    def test_flatten_content_parts_helper_empty_list(self):
+        """Empty list returns empty string."""
+        from litellm.llms.zai.chat.transformation import _flatten_content_parts
+
+        assert _flatten_content_parts([]) == ""
+
+    def test_flatten_content_parts_helper_string_passthrough(self):
+        """Plain string passes through unchanged."""
+        from litellm.llms.zai.chat.transformation import _flatten_content_parts
+
+        assert _flatten_content_parts("already a string") == "already a string"
+
+    def test_flatten_content_parts_helper_none(self):
+        """None passes through unchanged."""
+        from litellm.llms.zai.chat.transformation import _flatten_content_parts
+
+        assert _flatten_content_parts(None) is None