feat(gemini): Add support for Gemini internal tools

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

@@ -217,7 +217,9 @@ def __init__(
         max_guardrail_retries: int = 3,
         handoffs: Optional[List[Union['Agent', 'Handoff']]] = None,
         base_url: Optional[str] = None,
-        api_key: Optional[str] = None
+        api_key: Optional[str] = None,
+        gemini_google_search: bool = False,
+        gemini_code_execution: bool = False,
     ):
         """Initialize an Agent instance.
 
@@ -308,6 +310,10 @@ def __init__(
                 If provided, automatically creates a custom LLM instance. Defaults to None.
             api_key (Optional[str], optional): API key for LLM provider. If not provided,
                 falls back to environment variables. Defaults to None.
+            gemini_google_search (bool, optional): Enable Google Search grounding for Gemini models.
+                Defaults to False.
+            gemini_code_execution (bool, optional): Enable code execution for Gemini models.
+                Defaults to False.
 
         Raises:
             ValueError: If all of name, role, goal, backstory, and instructions are None.
@@ -479,6 +485,10 @@ def __init__(
         self.handoffs = handoffs if handoffs else []
         self._process_handoffs()
 
+        # Gemini-specific tools
+        self.gemini_google_search = gemini_google_search
+        self.gemini_code_execution = gemini_code_execution
+
         # Check if knowledge parameter has any values
         if not knowledge:
             self.knowledge = None
@@ -588,6 +598,19 @@ def _setup_guardrail(self):
         else:
             raise ValueError("Agent guardrail must be either a callable or a string description")
 
+    def _prepare_gemini_tools(self):
+        """Prepare Gemini internal tools based on configuration.
+
+        Returns:
+            List of Gemini internal tool dictionaries
+        """
+        gemini_tools = []
+        if self.gemini_google_search:
+            gemini_tools.append({"google_search_retrieval": {}})
+        if self.gemini_code_execution:
+            gemini_tools.append({"code_execution": {}})
+        return gemini_tools
+
     def _process_handoffs(self):
         """Process handoffs and convert them to tools that can be used by the agent."""
         if not self.handoffs:
@@ -822,6 +845,7 @@ def _format_tools_for_completion(self, tools=None):
         - Callable functions
         - String function names
         - Objects with to_openai_tool() method
+        - Gemini internal tools (google_search_retrieval, code_execution)
 
         Args:
             tools: List of tools in various formats or None to use self.tools
@@ -837,8 +861,12 @@ def _format_tools_for_completion(self, tools=None):
 
         formatted_tools = []
         for tool in tools:
+            # Handle Gemini-specific internal tools
+            if isinstance(tool, dict) and ("google_search_retrieval" in tool or "code_execution" in tool):
+                formatted_tools.append(tool)
+                continue
             # Handle pre-formatted OpenAI tools
-            if isinstance(tool, dict) and tool.get('type') == 'function':
+            elif isinstance(tool, dict) and tool.get('type') == 'function':
                 # Validate nested dictionary structure before accessing
                 if 'function' in tool and isinstance(tool['function'], dict) and 'name' in tool['function']:
                     formatted_tools.append(tool)
@@ -1199,6 +1227,13 @@ def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pyd
                                 tool_param = [openai_tool]
                             logging.debug(f"Converted MCP tool: {tool_param}")
 
+                # Add Gemini-specific tools if enabled
+                gemini_tools = self._prepare_gemini_tools()
+                if gemini_tools:
+                    if tool_param is None:
+                        tool_param = []
+                    tool_param.extend(gemini_tools)
+
                 # Store chat history length for potential rollback
                 chat_history_length = len(self.chat_history)
 
@@ -1264,6 +1299,17 @@ def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pyd
                 display_error(f"Error in LLM chat: {e}")
                 return None
         else:
+            # Add Gemini-specific tools if enabled (for non-custom LLM path)
+            if tools is None or (isinstance(tools, list) and len(tools) == 0):
+                # Check if we need to add Gemini tools
+                gemini_tools = self._prepare_gemini_tools()
+                if gemini_tools:
+                    if tools is None:
+                        tools = gemini_tools
+                    else:
+                        tools = list(tools)  # Make a copy
+                        tools.extend(gemini_tools)
+
             # Use the new _build_messages helper method
             messages, original_prompt = self._build_messages(prompt, temperature, output_json, output_pydantic)
 
@@ -1517,6 +1563,13 @@ async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None
                     prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
 
             if self._using_custom_llm:
+                # Add Gemini-specific tools if enabled
+                gemini_tools = self._prepare_gemini_tools()
+                if gemini_tools:
+                    if tools is None:
+                        tools = []
+                    tools.extend(gemini_tools)
+
                 # Store chat history length for potential rollback
                 chat_history_length = len(self.chat_history)
 
@@ -1580,6 +1633,17 @@ async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None
                     return None
 
             # For OpenAI client
+            # Add Gemini-specific tools if enabled (for non-custom LLM path)
+            if tools is None or (isinstance(tools, list) and len(tools) == 0):
+                # Check if we need to add Gemini tools
+                gemini_tools = self._prepare_gemini_tools()
+                if gemini_tools:
+                    if tools is None:
+                        tools = gemini_tools
+                    else:
+                        tools = list(tools)  # Make a copy
+                        tools.extend(gemini_tools)
+
             # Use the new _build_messages helper method
             messages, original_prompt = self._build_messages(prompt, temperature, output_json, output_pydantic)
 

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

@@ -519,6 +519,10 @@ def _format_tools_for_litellm(self, tools: Optional[List[Any]]) -> Optional[List
 
         formatted_tools = []
         for tool in tools:
+            # Check for Gemini-specific tools
+            if isinstance(tool, dict) and ("google_search_retrieval" in tool or "code_execution" in tool):
+                formatted_tools.append(tool)
+                continue
             # Check if the tool is already in OpenAI format (e.g. from MCP.to_openai_tool())
             if isinstance(tool, dict) and 'type' in tool and tool['type'] == 'function':
                 # Validate nested dictionary structure before accessing