fix(azure): sanitize messages and handle temperature

nanobot/providers/azure_openai_provider.py

@@ -11,6 +11,8 @@ import json_repair
 
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
 
+_AZURE_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name"})
+
 
 class AzureOpenAIProvider(LLMProvider):
     """
@@ -67,19 +69,38 @@ class AzureOpenAIProvider(LLMProvider):
             "x-session-affinity": uuid.uuid4().hex,  # For cache locality
         }
 
+    @staticmethod
+    def _supports_temperature(
+        deployment_name: str,
+        reasoning_effort: str | None = None,
+    ) -> bool:
+        """Return True when temperature is likely supported for this deployment."""
+        if reasoning_effort:
+            return False
+        name = deployment_name.lower()
+        return not any(token in name for token in ("gpt-5", "o1", "o3", "o4"))
+
     def _prepare_request_payload(
         self,
+        deployment_name: str,
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         max_tokens: int = 4096,
+        temperature: float = 0.7,
         reasoning_effort: str | None = None,
     ) -> dict[str, Any]:
         """Prepare the request payload with Azure OpenAI 2024-10-21 compliance."""
         payload: dict[str, Any] = {
-            "messages": self._sanitize_empty_content(messages),
+            "messages": self._sanitize_request_messages(
+                self._sanitize_empty_content(messages),
+                _AZURE_MSG_KEYS,
+            ),
             "max_completion_tokens": max(1, max_tokens),  # Azure API 2024-10-21 uses max_completion_tokens
         }
 
+        if self._supports_temperature(deployment_name, reasoning_effort):
+            payload["temperature"] = temperature
+
         if reasoning_effort:
             payload["reasoning_effort"] = reasoning_effort
 
@@ -116,7 +137,7 @@ class AzureOpenAIProvider(LLMProvider):
         url = self._build_chat_url(deployment_name)
         headers = self._build_headers()
         payload = self._prepare_request_payload(
-            messages, tools, max_tokens, reasoning_effort
+            deployment_name, messages, tools, max_tokens, temperature, reasoning_effort
         )
 
         try:

nanobot/providers/base.py

@@ -87,6 +87,20 @@ class LLMProvider(ABC):
             result.append(msg)
         return result
 
+    @staticmethod
+    def _sanitize_request_messages(
+        messages: list[dict[str, Any]],
+        allowed_keys: frozenset[str],
+    ) -> list[dict[str, Any]]:
+        """Keep only provider-safe message keys and normalize assistant content."""
+        sanitized = []
+        for msg in messages:
+            clean = {k: v for k, v in msg.items() if k in allowed_keys}
+            if clean.get("role") == "assistant" and "content" not in clean:
+                clean["content"] = None
+            sanitized.append(clean)
+        return sanitized
+
     @abstractmethod
     async def chat(
         self,

nanobot/providers/litellm_provider.py

@@ -180,7 +180,7 @@ class LiteLLMProvider(LLMProvider):
     def _sanitize_messages(messages: list[dict[str, Any]], extra_keys: frozenset[str] = frozenset()) -> list[dict[str, Any]]:
         """Strip non-standard keys and ensure assistant messages have a content key."""
         allowed = _ALLOWED_MSG_KEYS | extra_keys
-        sanitized = []
+        sanitized = LLMProvider._sanitize_request_messages(messages, allowed)
         id_map: dict[str, str] = {}
 
         def map_id(value: Any) -> Any:
@@ -188,12 +188,7 @@ class LiteLLMProvider(LLMProvider):
                 return value
             return id_map.setdefault(value, LiteLLMProvider._normalize_tool_call_id(value))
 
-        for msg in messages:
+        for clean in sanitized:
-            clean = {k: v for k, v in msg.items() if k in allowed}
-            # Strict providers require "content" even when assistant only has tool_calls
-            if clean.get("role") == "assistant" and "content" not in clean:
-                clean["content"] = None
-
             # Keep assistant tool_calls[].id and tool tool_call_id in sync after
             # shortening, otherwise strict providers reject the broken linkage.
             if isinstance(clean.get("tool_calls"), list):
@@ -209,7 +204,6 @@ class LiteLLMProvider(LLMProvider):
 
             if "tool_call_id" in clean and clean["tool_call_id"]:
                 clean["tool_call_id"] = map_id(clean["tool_call_id"])
-            sanitized.append(clean)
         return sanitized
 
     async def chat(

tests/test_azure_openai_provider.py

@@ -1,9 +1,9 @@
 """Test Azure OpenAI provider implementation (updated for model-based deployment names)."""
 
-import asyncio
-import pytest
 from unittest.mock import AsyncMock, Mock, patch
 
+import pytest
+
 from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
 from nanobot.providers.base import LLMResponse
 
@@ -89,22 +89,65 @@ def test_prepare_request_payload():
     )
     
     messages = [{"role": "user", "content": "Hello"}]
-    payload = provider._prepare_request_payload(messages, max_tokens=1500)
+    payload = provider._prepare_request_payload("gpt-4o", messages, max_tokens=1500, temperature=0.8)
     
     assert payload["messages"] == messages
     assert payload["max_completion_tokens"] == 1500  # Azure API 2024-10-21 uses max_completion_tokens
-    assert "temperature" not in payload  # Temperature not included in payload
+    assert payload["temperature"] == 0.8
     assert "tools" not in payload
     
     # Test with tools
     tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
-    payload_with_tools = provider._prepare_request_payload(messages, tools=tools)
+    payload_with_tools = provider._prepare_request_payload("gpt-4o", messages, tools=tools)
     assert payload_with_tools["tools"] == tools
     assert payload_with_tools["tool_choice"] == "auto"
     
     # Test with reasoning_effort
-    payload_with_reasoning = provider._prepare_request_payload(messages, reasoning_effort="medium")
+    payload_with_reasoning = provider._prepare_request_payload(
+        "gpt-5-chat", messages, reasoning_effort="medium"
+    )
     assert payload_with_reasoning["reasoning_effort"] == "medium"
+    assert "temperature" not in payload_with_reasoning
+
+
+def test_prepare_request_payload_sanitizes_messages():
+    """Test Azure payload strips non-standard message keys before sending."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+
+    messages = [
+        {
+            "role": "assistant",
+            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
+            "reasoning_content": "hidden chain-of-thought",
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "x",
+            "content": "ok",
+            "extra_field": "should be removed",
+        },
+    ]
+
+    payload = provider._prepare_request_payload("gpt-4o", messages)
+
+    assert payload["messages"] == [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "x",
+            "content": "ok",
+        },
+    ]
 
 
 @pytest.mark.asyncio
@@ -349,7 +392,7 @@ if __name__ == "__main__":
     
     # Test payload preparation
     messages = [{"role": "user", "content": "Test"}]
-    payload = provider._prepare_request_payload(messages, max_tokens=1000)
+    payload = provider._prepare_request_payload("gpt-4o-deployment", messages, max_tokens=1000)
     assert payload["max_completion_tokens"] == 1000  # Azure 2024-10-21 format
     print("✅ Payload preparation works correctly")