feat: Add generation_kwargs to run methods of Agent

haystack/components/agents/agent.py

@@ -210,9 +210,15 @@ def from_dict(cls, data: Dict[str, Any]) -> "Agent":
 
         return default_from_dict(cls, data)
 
-    def _prepare_generator_inputs(self, streaming_callback: Optional[StreamingCallbackT] = None) -> Dict[str, Any]:
+    def _prepare_generator_inputs(
+        self,
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
         """Prepare inputs for the chat generator."""
         generator_inputs: Dict[str, Any] = {"tools": self.tools}
+        if generation_kwargs is not None:
+            generator_inputs["generation_kwargs"] = generation_kwargs
         if streaming_callback is not None:
             generator_inputs["streaming_callback"] = streaming_callback
         return generator_inputs
@@ -230,7 +236,11 @@ def _create_agent_span(self) -> Any:
         )
 
     def run(
-        self, messages: List[ChatMessage], streaming_callback: Optional[StreamingCallbackT] = None, **kwargs: Any
+        self,
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs: Any
     ) -> Dict[str, Any]:
         """
         Process messages and execute tools until an exit condition is met.
@@ -239,6 +249,8 @@ def run(
             If a list of dictionaries is provided, each dictionary will be converted to a ChatMessage object.
         :param streaming_callback: A callback that will be invoked when a response is streamed from the LLM.
             The same callback can be configured to emit tool results when a tool is called.
+        :param generation_kwargs: Additional keyword arguments for LLM. These parameters will
+            override the parameters passed during component initialization.
         :param kwargs: Additional data to pass to the State schema used by the Agent.
             The keys must match the schema defined in the Agent's `state_schema`.
         :returns:
@@ -267,7 +279,7 @@ def run(
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=False
         )
-        generator_inputs = self._prepare_generator_inputs(streaming_callback=streaming_callback)
+        generator_inputs = self._prepare_generator_inputs(streaming_callback=streaming_callback, generation_kwargs=generation_kwargs)
         with self._create_agent_span() as span:
             span.set_content_tag(
                 "haystack.agent.input",
@@ -328,7 +340,11 @@ def run(
         return result
 
     async def run_async(
-        self, messages: List[ChatMessage], streaming_callback: Optional[StreamingCallbackT] = None, **kwargs: Any
+        self,
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs: Any
     ) -> Dict[str, Any]:
         """
         Asynchronously process messages and execute tools until the exit condition is met.
@@ -341,6 +357,8 @@ async def run_async(
         :param streaming_callback: An asynchronous callback that will be invoked when a response
         is streamed from the LLM. The same callback can be configured to emit tool results
         when a tool is called.
+        :param generation_kwargs: Additional keyword arguments for LLM. These parameters will
+            override the parameters passed during component initialization.
         :param kwargs: Additional data to pass to the State schema used by the Agent.
             The keys must match the schema defined in the Agent's `state_schema`.
         :returns:
@@ -369,7 +387,7 @@ async def run_async(
         streaming_callback = select_streaming_callback(
             init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True
         )
-        generator_inputs = self._prepare_generator_inputs(streaming_callback=streaming_callback)
+        generator_inputs = self._prepare_generator_inputs(streaming_callback=streaming_callback, generation_kwargs=generation_kwargs)
         with self._create_agent_span() as span:
             span.set_content_tag(
                 "haystack.agent.input",

test/components/agents/test_agent.py

@@ -746,7 +746,7 @@ def test_run(self, weather_tool):
         chat_generator = OpenAIChatGenerator(model="gpt-4o-mini")
         agent = Agent(chat_generator=chat_generator, tools=[weather_tool], max_agent_steps=3)
         agent.warm_up()
-        response = agent.run([ChatMessage.from_user("What is the weather in Berlin?")])
+        response = agent.run([ChatMessage.from_user("What is the weather in Berlin?")], generation_kwargs={"temperature": 0.0})
 
         assert isinstance(response, dict)
         assert "messages" in response