fix: resolve OpenRouter XML tool call routing issue

- Remove tools parameter for XML format models to avoid provider routing errors
- Add tool descriptions and XML format instructions to system prompt
- Maintain backward compatibility with existing functionality
- Fix issue where OpenRouter couldn''t find providers supporting tools for Qwen models

Co-authored-by: Mervin Praison <MervinPraison@users.noreply.github.com>

Author: github-actions[bot]

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -722,6 +722,19 @@ def _build_messages(self, prompt, system_prompt=None, chat_history=None, output_
                 if schema_model and hasattr(schema_model, 'model_json_schema'):
                     system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(schema_model.model_json_schema())}"
 
+            # For XML format models, add tool descriptions to system prompt
+            if tools and self._supports_xml_tool_format():
+                system_prompt += "\n\nYou have access to the following tools:"
+                for tool in tools:
+                    if isinstance(tool, dict) and 'function' in tool:
+                        func = tool['function']
+                        name = func.get('name', 'unknown')
+                        description = func.get('description', 'No description available')
+                        system_prompt += f"\n- {name}: {description}"
+                
+                system_prompt += "\n\nWhen you need to use a tool, wrap your tool call in XML tags like this:"
+                system_prompt += "\n<tool_call>\n{\"name\": \"tool_name\", \"arguments\": {\"param\": \"value\"}}\n</tool_call>"
+            
             # Skip system messages for legacy o1 models as they don't support them
             if not self._needs_system_message_skip():
                 messages.append({"role": "system", "content": system_prompt})
@@ -3155,19 +3168,28 @@ def _build_completion_params(self, **override_params) -> Dict[str, Any]:
 
                     logging.debug(f"Using Gemini native structured output with schema: {json.dumps(schema, indent=2)}")
 
-        # Add tool_choice="auto" when tools are provided (unless already specified)
-        if 'tools' in params and params['tools'] and 'tool_choice' not in params:
-            # For Gemini models, use tool_choice to encourage tool usage
-            if self._is_gemini_model():
-                try:
-                    import litellm
-                    # Check if model supports function calling before setting tool_choice
-                    if litellm.supports_function_calling(model=self.model):
-                        params['tool_choice'] = 'auto'
-                except Exception as e:
-                    # If check fails, still set tool_choice for known Gemini models
-                    logging.debug(f"Could not verify function calling support: {e}. Setting tool_choice anyway.")
-                    params['tool_choice'] = 'auto'
+        # Handle XML format models (like Qwen) differently for tool calls
+        if 'tools' in params and params['tools']:
+            if self._supports_xml_tool_format():
+                # For XML format models, remove tools parameter to avoid OpenRouter routing issues
+                # Tools will be described in the system prompt instead
+                logging.debug("Removing tools parameter for XML format model to avoid provider routing issues")
+                params.pop('tools', None)
+                params.pop('tool_choice', None)
+            else:
+                # Add tool_choice="auto" when tools are provided (unless already specified)
+                if 'tool_choice' not in params:
+                    # For Gemini models, use tool_choice to encourage tool usage
+                    if self._is_gemini_model():
+                        try:
+                            import litellm
+                            # Check if model supports function calling before setting tool_choice
+                            if litellm.supports_function_calling(model=self.model):
+                                params['tool_choice'] = 'auto'
+                        except Exception as e:
+                            # If check fails, still set tool_choice for known Gemini models
+                            logging.debug(f"Could not verify function calling support: {e}. Setting tool_choice anyway.")
+                            params['tool_choice'] = 'auto'
 
         return params
 

test_openrouter_xml_fix.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+"""Test script for OpenRouter XML tool call fix"""
+
+import os
+import sys
+
+# Add the source directory to the path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+from praisonaiagents import Agent
+
+def get_weather(city: str) -> str:
+    """Get weather information for a city"""
+    return f"The weather in {city} is sunny with 22°C"
+
+def main():
+    print("Testing OpenRouter XML tool call fix...")
+    
+    # Test with auto-detection (should detect Qwen as XML format)
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        llm="openrouter/qwen/qwen-2.5-7b-instruct",
+        tools=[get_weather],
+        verbose=True
+    )
+    
+    print("Created agent with Qwen model...")
+    
+    # Get the LLM instance directly from the agent
+    llm_instance = agent.llm_instance  # This should be the LLM object
+    print(f"XML tool format supported: {llm_instance._supports_xml_tool_format()}")
+    
+    # Test the tool call without actually making API request
+    # We'll just verify the parameters are built correctly
+    test_tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get weather information for a city",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "city": {
+                            "type": "string",
+                            "description": "The city name"
+                        }
+                    },
+                    "required": ["city"]
+                }
+            }
+        }
+    ]
+    
+    # Test _build_completion_params
+    params = llm_instance._build_completion_params(
+        messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
+        tools=test_tools,
+        temperature=0.2
+    )
+    
+    print("\n=== Completion Parameters ===")
+    print(f"Model: {params.get('model')}")
+    print(f"Tools included: {'tools' in params}")
+    print(f"Tool choice included: {'tool_choice' in params}")
+    
+    # Test _build_messages
+    messages, original = llm_instance._build_messages(
+        prompt="What's the weather in Tokyo?",
+        system_prompt="You are a helpful assistant",
+        tools=test_tools
+    )
+    
+    print("\n=== System Message ===")
+    for msg in messages:
+        if msg['role'] == 'system':
+            print(msg['content'])
+            break
+    
+    print("\n✅ Test completed successfully!")
+    print("Key improvements:")
+    print("- Tools parameter is removed for XML format models")
+    print("- Tool descriptions are added to system prompt")
+    print("- XML tool call format instructions are included")
+
+if __name__ == "__main__":
+    main()