diff --git a/pyproject.toml b/pyproject.toml
index 6d19c75..c792337 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ name = "smolagents"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "transformers>=4.0.0",
     "pytest>=8.1.0",
diff --git a/src/smolagents/agents.py b/src/smolagents/agents.py
index 9b97ce4..ce178d0 100644
--- a/src/smolagents/agents.py
+++ b/src/smolagents/agents.py
@@ -172,14 +172,17 @@ def format_prompt_with_managed_agents_descriptions(
         return prompt_template.replace(agent_descriptions_placeholder, "")
 
 
-class BaseAgent:
+class MultiStepAgent:
+    """
+    Agent class that solves the given task step by step, using the ReAct framework:
+    While the objective is not reached, the agent will perform a cycle of action (given by the LLM) and observation (obtained from the environment).
+    """
     def __init__(
         self,
         tools: Union[List[Tool], Toolbox],
         llm_engine: Optional[Callable[[List[Dict[str, str]]], str]] = None,
         system_prompt: Optional[str] = None,
         tool_description_template: Optional[str] = None,
-        additional_args: Dict = {},
         max_iterations: int = 6,
         tool_parser: Optional[Callable] = None,
         add_base_tools: bool = False,
@@ -188,6 +191,7 @@ class BaseAgent:
         managed_agents: Optional[Dict] = None,
         step_callbacks: Optional[List[Callable]] = None,
         monitor_metrics: bool = True,
+        planning_interval: Optional[int] = None,
     ):
         if llm_engine is None:
             llm_engine = HfApiEngine()
@@ -203,10 +207,10 @@ class BaseAgent:
             if tool_description_template
             else DEFAULT_TOOL_DESCRIPTION_TEMPLATE
         )
-        self.additional_args = additional_args
         self.max_iterations = max_iterations
         self.tool_parser = tool_parser
         self.grammar = grammar
+        self.planning_interval = planning_interval
 
         self.managed_agents = {}
         if managed_agents is not None:
@@ -374,38 +378,6 @@ class BaseAgent:
             )
         return rationale.strip(), action.strip()
 
-    def run(self, **kwargs):
-        """To be implemented in the child class"""
-        raise NotImplementedError
-
-
-class MultiStepAgent(BaseAgent):
-    """
-    This agent that solves the given task step by step, using the ReAct framework:
-    While the objective is not reached, the agent will perform a cycle of thinking and acting.
-    The action will be parsed from the LLM output: it consists in calls to tools from the toolbox, with arguments chosen by the LLM engine.
-    """
-
-    def __init__(
-        self,
-        tools: List[Tool],
-        llm_engine: Optional[Callable] = None,
-        system_prompt: Optional[str] = None,
-        grammar: Optional[Dict[str, str]] = None,
-        planning_interval: Optional[int] = None,
-        **kwargs,
-    ):
-        if system_prompt is None:
-            system_prompt = CODE_SYSTEM_PROMPT
-
-        super().__init__(
-            tools=tools,
-            llm_engine=llm_engine,
-            system_prompt=system_prompt,
-            grammar=grammar,
-            **kwargs,
-        )
-        self.planning_interval = planning_interval
 
     def provide_final_answer(self, task) -> str:
         """