Seems to fix the vetting stage

src/cleveragents/agents/llm.py

@@ -425,10 +425,21 @@ class LLMAgent(AgentWithMemory):
         )
         conversation_messages = [m for m in messages if m.get("role") != "system"]
 
-        contents: List[Dict[str, Any]] = [
-            {"role": msg["role"], "parts": [{"text": msg["content"]}]}
-            for msg in conversation_messages
-        ]
+        # Map roles to Google Gemini's expected format
+        role_mapping = {
+            "user": "user",
+            "assistant": "model",
+            "model": "model"  # In case it's already mapped
+        }
+
+        contents: List[Dict[str, Any]] = []
+        for msg in conversation_messages:
+            original_role = msg.get("role", "user")
+            gemini_role = role_mapping.get(original_role, "user")
+            contents.append({
+                "role": gemini_role, 
+                "parts": [{"text": msg["content"]}]
+            })
 
         data: Dict[str, Any] = {
             "contents": contents,