Feat/preserve thought signature for gemini3

src/strands/models/gemini.py

@@ -65,6 +65,10 @@ def __init__(
 
         self.client_args = client_args or {}
 
+        # Store the last thought_signature from Gemini responses for multi-turn conversations
+        # See: https://ai.google.dev/gemini-api/docs/thought-signatures
+        self.last_thought_signature: Optional[bytes] = None
+
     @override
     def update_config(self, **model_config: Unpack[GeminiConfig]) -> None:  # type: ignore[override]
         """Update the Gemini model configuration with the provided arguments.
@@ -141,12 +145,15 @@ def _format_request_content_part(self, content: ContentBlock) -> genai.types.Par
             )
 
         if "toolUse" in content:
+            # Use the last thought_signature stored from previous Gemini responses
+            # This is required for Gemini models that use thought signatures in multi-turn conversations
             return genai.types.Part(
                 function_call=genai.types.FunctionCall(
                     args=content["toolUse"]["input"],
                     id=content["toolUse"]["toolUseId"],
                     name=content["toolUse"]["name"],
                 ),
+                thought_signature=self.last_thought_signature,
             )
 
         raise TypeError(f"content_type=<{next(iter(content))}> | unsupported type")
@@ -170,7 +177,7 @@ def _format_request_content(self, messages: Messages) -> list[genai.types.Conten
             for message in messages
         ]
 
-    def _format_request_tools(self, tool_specs: Optional[list[ToolSpec]]) -> list[genai.types.Tool | Any]:
+    def _format_request_tools(self, tool_specs: Optional[list[ToolSpec]]) -> Optional[list[genai.types.Tool | Any]]:
         """Format tool specs into Gemini tools.
 
         - Docs: https://googleapis.github.io/python-genai/genai.html#genai.types.Tool
@@ -179,8 +186,11 @@ def _format_request_tools(self, tool_specs: Optional[list[ToolSpec]]) -> list[ge
             tool_specs: List of tool specifications to make available to the model.
 
         Return:
-            Gemini tool list.
+            Gemini tool list, or None if no tools are provided.
         """
+        if not tool_specs:
+            return None
+
         return [
             genai.types.Tool(
                 function_declarations=[
@@ -189,7 +199,7 @@ def _format_request_tools(self, tool_specs: Optional[list[ToolSpec]]) -> list[ge
                         name=tool_spec["name"],
                         parameters_json_schema=tool_spec["inputSchema"]["json"],
                     )
-                    for tool_spec in tool_specs or []
+                    for tool_spec in tool_specs
                 ],
             ),
         ]
@@ -212,9 +222,19 @@ def _format_request_config(
         Returns:
             Gemini request config.
         """
+        # Disable thinking text output when tools are present
+        # Note: Setting include_thoughts=False prevents thinking text in responses but
+        # Gemini still returns thought_signature for function calls. As of Strands v1.18+,
+        # the framework properly preserves this field through the message history.
+        # See: https://ai.google.dev/gemini-api/docs/thought-signatures
+        thinking_config = None
+        if tool_specs:
+            thinking_config = genai.types.ThinkingConfig(include_thoughts=False)
+
         return genai.types.GenerateContentConfig(
             system_instruction=system_prompt,
             tools=self._format_request_tools(tool_specs),
+            thinking_config=thinking_config,
             **(params or {}),
         )
 
@@ -268,14 +288,14 @@ def _format_chunk(self, event: dict[str, Any]) -> StreamEvent:
                         #       that name be set in the equivalent FunctionResponse type. Consequently, we assign
                         #       function name to toolUseId in our tool use block. And another reason, function_call is
                         #       not guaranteed to have id populated.
+                        tool_use: dict[str, Any] = {
+                            "name": event["data"].function_call.name,
+                            "toolUseId": event["data"].function_call.name,
+                        }
+
                         return {
                             "contentBlockStart": {
-                                "start": {
-                                    "toolUse": {
-                                        "name": event["data"].function_call.name,
-                                        "toolUseId": event["data"].function_call.name,
-                                    },
-                                },
+                                "start": {"toolUse": cast(Any, tool_use)},
                             },
                         }
 
@@ -373,15 +393,28 @@ async def stream(
             yield self._format_chunk({"chunk_type": "content_start", "data_type": "text"})
 
             tool_used = False
+
             async for event in response:
                 candidates = event.candidates
                 candidate = candidates[0] if candidates else None
                 content = candidate.content if candidate else None
                 parts = content.parts if content and content.parts else []
 
                 for part in parts:
+                    # Capture thought_signature and store it for use in subsequent requests
+                    # According to Gemini docs, thought_signature can be on any part
+                    # See: https://ai.google.dev/gemini-api/docs/thought-signatures
+                    if hasattr(part, "thought_signature") and part.thought_signature:
+                        self.last_thought_signature = part.thought_signature
+
                     if part.function_call:
-                        yield self._format_chunk({"chunk_type": "content_start", "data_type": "tool", "data": part})
+                        yield self._format_chunk(
+                            {
+                                "chunk_type": "content_start",
+                                "data_type": "tool",
+                                "data": part,
+                            }
+                        )
                         yield self._format_chunk({"chunk_type": "content_delta", "data_type": "tool", "data": part})
                         yield self._format_chunk({"chunk_type": "content_stop", "data_type": "tool", "data": part})
                         tool_used = True

tests/strands/models/test_gemini.py

@@ -84,7 +84,7 @@ async def test_stream_request_default(gemini_client, model, messages, model_id):
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {"tools": [{"function_declarations": []}]},
+        "config": {},
         "contents": [{"parts": [{"text": "test"}], "role": "user"}],
         "model": model_id,
     }
@@ -99,7 +99,6 @@ async def test_stream_request_with_params(gemini_client, model, messages, model_
 
     exp_request = {
         "config": {
-            "tools": [{"function_declarations": []}],
             "temperature": 1,
         },
         "contents": [{"parts": [{"text": "test"}], "role": "user"}],
@@ -113,7 +112,7 @@ async def test_stream_request_with_system_prompt(gemini_client, model, messages,
     await anext(model.stream(messages, system_prompt=system_prompt))
 
     exp_request = {
-        "config": {"system_instruction": system_prompt, "tools": [{"function_declarations": []}]},
+        "config": {"system_instruction": system_prompt},
         "contents": [{"parts": [{"text": "test"}], "role": "user"}],
         "model": model_id,
     }
@@ -146,9 +145,7 @@ async def test_stream_request_with_document(content, formatted_part, gemini_clie
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [{"parts": [formatted_part], "role": "user"}],
         "model": model_id,
     }
@@ -173,9 +170,7 @@ async def test_stream_request_with_image(gemini_client, model, model_id):
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [
             {
                 "parts": [
@@ -214,9 +209,7 @@ async def test_stream_request_with_reasoning(gemini_client, model, model_id):
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [
             {
                 "parts": [
@@ -251,6 +244,7 @@ async def test_stream_request_with_tool_spec(gemini_client, model, model_id, too
                     ],
                 },
             ],
+            "thinking_config": {"include_thoughts": False},
         },
         "contents": [],
         "model": model_id,
@@ -277,9 +271,7 @@ async def test_stream_request_with_tool_use(gemini_client, model, model_id):
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [
             {
                 "parts": [
@@ -327,9 +319,7 @@ async def test_stream_request_with_tool_results(gemini_client, model, model_id):
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [
             {
                 "parts": [
@@ -371,9 +361,7 @@ async def test_stream_request_with_empty_content(gemini_client, model, model_id)
     await anext(model.stream(messages))
 
     exp_request = {
-        "config": {
-            "tools": [{"function_declarations": []}],
-        },
+        "config": {},
         "contents": [{"parts": [], "role": "user"}],
         "model": model_id,
     }
@@ -614,7 +602,6 @@ async def test_structured_output(gemini_client, model, messages, model_id, weath
 
     exp_request = {
         "config": {
-            "tools": [{"function_declarations": []}],
             "response_mime_type": "application/json",
             "response_schema": weather_output.model_json_schema(),
         },