Replace max_iteration with max_steps for consistency

docs/source/en/examples/multiagents.md

@@ -120,7 +120,7 @@ Now that we have all the tools `search` and `visit_webpage`, we can use them to
 
 Which configuration to choose for this agent?
 - Web browsing is a single-timeline task that does not require parallel tool calls, so JSON tool calling works well for that. We thus choose a `JsonAgent`.
-- Also, since sometimes web search requires exploring many pages before finding the correct answer, we prefer to increase the number of `max_iterations` to 10.
+- Also, since sometimes web search requires exploring many pages before finding the correct answer, we prefer to increase the number of `max_steps` to 10.
 
 ```py
 from smolagents import (
@@ -137,7 +137,7 @@ model = HfApiModel(model_id)
 web_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), visit_webpage],
     model=model,
-    max_iterations=10,
+    max_steps=10,
 )
 ```
 

docs/source/en/examples/rag.md

@@ -137,7 +137,7 @@ _Note:_ The Inference API hosts models based on various criteria, and deployed m
 from smolagents import HfApiModel, CodeAgent
 
 agent = CodeAgent(
-    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_iterations=4, verbose=True
+    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_steps=4, verbose=True
 )
 ```
 

docs/source/en/reference/agents.md

@@ -125,6 +125,7 @@ print(model(messages))
 ### LiteLLMModel
 
 The `LiteLLMModel` leverages [LiteLLM](https://www.litellm.ai/) to support 100+ LLMs from various providers.
+You can pass kwargs upon model initialization that will then be used whenever using the model, for instance below we pass `temperature`.
 
 ```python
 from smolagents import LiteLLMModel
@@ -135,8 +136,8 @@ messages = [
   {"role": "user", "content": "No need to help, take it easy."},
 ]
 
-model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest")
+model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2)
-print(model(messages))
+print(model(messages, max_tokens=10))
 ```
 
 [[autodoc]] LiteLLMModel

docs/source/en/tutorials/building_good_agents.md

@@ -194,7 +194,7 @@ If after trying the above, you still want to change the system prompt, your new
 Then you can change the system prompt as follows:
 
 ```py
-from smolagents.prompts import CODE_SYSTEM_PROMPT, HfApiModel
+from smolagents.prompts import CODE_SYSTEM_PROMPT
 
 modified_system_prompt = CODE_SYSTEM_PROMPT + "\nHere you go!" # Change the system prompt here
 

examples/benchmark.ipynb

@@ -233,7 +233,7 @@
     "    agent = ToolCallingAgent(\n",
     "        tools=[GoogleSearchTool(), VisitWebpageTool(), PythonInterpreterTool()],\n",
     "        model=HfApiModel(model_id),\n",
-    "        max_iterations=10,\n",
+    "        max_steps=10,\n",
     "    )\n",
     "    file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n",
     "    answer_questions(eval_ds, file_name, agent, model_id, action_type)\n",
@@ -243,7 +243,7 @@
     "        tools=[GoogleSearchTool(), VisitWebpageTool()],\n",
     "        model=HfApiModel(model_id),\n",
     "        additional_authorized_imports=[\"numpy\"],\n",
-    "        max_iterations=10,\n",
+    "        max_steps=10,\n",
     "    )\n",
     "    file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n",
     "    answer_questions(eval_ds, file_name, agent, model_id, action_type)"
@@ -272,7 +272,7 @@
     "    agent = ToolCallingAgent(\n",
     "        tools=[GoogleSearchTool(), VisitWebpageTool(), PythonInterpreterTool()],\n",
     "        model=LiteLLMModel(model_id),\n",
-    "        max_iterations=10,\n",
+    "        max_steps=10,\n",
     "    )\n",
     "    file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n",
     "    answer_questions(eval_ds, file_name, agent, model_id, action_type)\n",
@@ -282,7 +282,7 @@
     "        tools=[GoogleSearchTool(), VisitWebpageTool()],\n",
     "        model=LiteLLMModel(model_id),\n",
     "        additional_authorized_imports=[\"numpy\"],\n",
-    "        max_iterations=10,\n",
+    "        max_steps=10,\n",
     "    )\n",
     "    file_name = f\"output/{model_id.replace('/', '_')}-{action_type}-26-dec-2024.jsonl\"\n",
     "    answer_questions(eval_ds, file_name, agent, model_id, action_type)"

examples/rag.py

@@ -60,7 +60,7 @@ from smolagents import HfApiModel, CodeAgent
 
 retriever_tool = RetrieverTool(docs_processed)
 agent = CodeAgent(
-    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_iterations=4, verbose=True
+    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_steps=4, verbose=True
 )
 
 agent_output = agent.run("For a transformers model training, which is slower, the forward or the backward pass?")

src/smolagents/agents.py

@@ -32,7 +32,7 @@ from .utils import (
     AgentParsingError,
     AgentExecutionError,
     AgentGenerationError,
-    AgentMaxIterationsError,
+    AgentMaxStepsError,
 )
 from .types import AgentAudio, AgentImage, handle_agent_output_types
 from .default_tools import FinalAnswerTool
@@ -78,7 +78,7 @@ class ActionStep(AgentStep):
     tool_call: ToolCall | None = None
     start_time: float | None = None
     end_time: float | None = None
-    iteration: int | None = None
+    step: int | None = None
     error: AgentError | None = None
     duration: float | None = None
     llm_output: str | None = None
@@ -163,7 +163,7 @@ class MultiStepAgent:
         model: Callable[[List[Dict[str, str]]], str],
         system_prompt: Optional[str] = None,
         tool_description_template: Optional[str] = None,
-        max_iterations: int = 6,
+        max_steps: int = 6,
         tool_parser: Optional[Callable] = None,
         add_base_tools: bool = False,
         verbose: bool = False,
@@ -184,7 +184,7 @@ class MultiStepAgent:
             if tool_description_template
             else DEFAULT_TOOL_DESCRIPTION_TEMPLATE
         )
-        self.max_iterations = max_iterations
+        self.max_steps = max_steps
         self.tool_parser = tool_parser
         self.grammar = grammar
         self.planning_interval = planning_interval
@@ -500,20 +500,18 @@ You have been provided with these additional arguments, that you can access usin
         Runs the agent in streaming mode, yielding steps as they are executed: should be launched only in the `run` method.
         """
         final_answer = None
-        iteration = 0
+        step = 0
-        while final_answer is None and iteration < self.max_iterations:
+        while final_answer is None and step < self.max_steps:
             step_start_time = time.time()
-            step_log = ActionStep(iteration=iteration, start_time=step_start_time)
+            step_log = ActionStep(step=step, start_time=step_start_time)
             try:
                 if (
                     self.planning_interval is not None
-                    and iteration % self.planning_interval == 0
+                    and step % self.planning_interval == 0
                 ):
-                    self.planning_step(
+                    self.planning_step(task, is_first_step=(step == 0), step=step)
-                        task, is_first_step=(iteration == 0), iteration=iteration
-                    )
                 console.print(
-                    Rule(f"[bold]Step {iteration}", characters="━", style=YELLOW_HEX)
+                    Rule(f"[bold]Step {step}", characters="━", style=YELLOW_HEX)
                 )
 
                 # Run one step!
@@ -526,12 +524,12 @@ You have been provided with these additional arguments, that you can access usin
                 self.logs.append(step_log)
                 for callback in self.step_callbacks:
                     callback(step_log)
-                iteration += 1
+                step += 1
                 yield step_log
 
-        if final_answer is None and iteration == self.max_iterations:
+        if final_answer is None and step == self.max_steps:
-            error_message = "Reached max iterations."
+            error_message = "Reached max steps."
-            final_step_log = ActionStep(error=AgentMaxIterationsError(error_message))
+            final_step_log = ActionStep(error=AgentMaxStepsError(error_message))
             self.logs.append(final_step_log)
             final_answer = self.provide_final_answer(task)
             console.print(Text(f"Final answer: {final_answer}"))
@@ -549,20 +547,18 @@ You have been provided with these additional arguments, that you can access usin
         Runs the agent in direct mode, returning outputs only at the end: should be launched only in the `run` method.
         """
         final_answer = None
-        iteration = 0
+        step = 0
-        while final_answer is None and iteration < self.max_iterations:
+        while final_answer is None and step < self.max_steps:
             step_start_time = time.time()
-            step_log = ActionStep(iteration=iteration, start_time=step_start_time)
+            step_log = ActionStep(step=step, start_time=step_start_time)
             try:
                 if (
                     self.planning_interval is not None
-                    and iteration % self.planning_interval == 0
+                    and step % self.planning_interval == 0
                 ):
-                    self.planning_step(
+                    self.planning_step(task, is_first_step=(step == 0), step=step)
-                        task, is_first_step=(iteration == 0), iteration=iteration
-                    )
                 console.print(
-                    Rule(f"[bold]Step {iteration}", characters="━", style=YELLOW_HEX)
+                    Rule(f"[bold]Step {step}", characters="━", style=YELLOW_HEX)
                 )
 
                 # Run one step!
@@ -577,11 +573,11 @@ You have been provided with these additional arguments, that you can access usin
                 self.logs.append(step_log)
                 for callback in self.step_callbacks:
                     callback(step_log)
-                iteration += 1
+                step += 1
 
-        if final_answer is None and iteration == self.max_iterations:
+        if final_answer is None and step == self.max_steps:
-            error_message = "Reached max iterations."
+            error_message = "Reached max steps."
-            final_step_log = ActionStep(error=AgentMaxIterationsError(error_message))
+            final_step_log = ActionStep(error=AgentMaxStepsError(error_message))
             self.logs.append(final_step_log)
             final_answer = self.provide_final_answer(task)
             console.print(Text(f"Final answer: {final_answer}"))
@@ -592,14 +588,14 @@ You have been provided with these additional arguments, that you can access usin
 
         return handle_agent_output_types(final_answer)
 
-    def planning_step(self, task, is_first_step: bool, iteration: int):
+    def planning_step(self, task, is_first_step: bool, step: int):
         """
         Used periodically by the agent to plan the next steps to reach the objective.
 
         Args:
             task (`str`): The task to perform
             is_first_step (`bool`): If this step is not the first one, the plan should be an update over a previous plan.
-            iteration (`int`): The number of the current step, used as an indication for the LLM.
+            step (`int`): The number of the current step, used as an indication for the LLM.
         """
         if is_first_step:
             message_prompt_facts = {
@@ -687,7 +683,7 @@ Now begin!""",
                         show_agents_descriptions(self.managed_agents)
                     ),
                     facts_update=facts_update,
-                    remaining_steps=(self.max_iterations - iteration),
+                    remaining_steps=(self.max_steps - step),
                 ),
             }
             plan_update = self.model(

src/smolagents/models.py

@@ -282,11 +282,11 @@ class HfApiModel(Model):
 class TransformersModel(Model):
     """This engine initializes a model and tokenizer from the given `model_id`.
 
-        Parameters:
+    Parameters:
-            model_id (`str`, *optional*, defaults to `"HuggingFaceTB/SmolLM2-1.7B-Instruct"`):
+        model_id (`str`, *optional*, defaults to `"HuggingFaceTB/SmolLM2-1.7B-Instruct"`):
-                The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
+            The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
-            device (`str`, optional, defaults to `"cuda"` if available, else `"cpu"`.): 
+        device (`str`, optional, defaults to `"cuda"` if available, else `"cpu"`.):
-                The device to load the model on (`"cpu"` or `"cuda"`). 
+            The device to load the model on (`"cpu"` or `"cuda"`).
     """
 
     def __init__(self, model_id: Optional[str] = None, device: Optional[str] = None):
@@ -310,7 +310,9 @@ class TransformersModel(Model):
                 f"Failed to load tokenizer and model for {model_id=}: {e}. Loading default tokenizer and model instead from {model_id=}."
             )
             self.tokenizer = AutoTokenizer.from_pretrained(default_model_id)
-            self.model = AutoModelForCausalLM.from_pretrained(default_model_id).to(self.device)
+            self.model = AutoModelForCausalLM.from_pretrained(default_model_id).to(
+                self.device
+            )
 
     def make_stopping_criteria(self, stop_sequences: List[str]) -> StoppingCriteriaList:
         class StopOnStrings(StoppingCriteria):
@@ -424,7 +426,7 @@ class LiteLLMModel(Model):
         model_id="anthropic/claude-3-5-sonnet-20240620",
         api_base=None,
         api_key=None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.model_id = model_id

src/smolagents/tools.py

@@ -575,7 +575,6 @@ class Tool:
         from gradio_client import Client, handle_file
 
         class SpaceToolWrapper(Tool):
-            
             skip_forward_signature_validation = True
 
             def __init__(
@@ -907,12 +906,17 @@ class ToolCollection:
     ```
     """
 
-    def __init__(self, collection_slug: str, token: Optional[str] = None, trust_remote_code=False):
+    def __init__(
+        self, collection_slug: str, token: Optional[str] = None, trust_remote_code=False
+    ):
         self._collection = get_collection(collection_slug, token=token)
         self._hub_repo_ids = {
             item.item_id for item in self._collection.items if item.item_type == "space"
         }
-        self.tools = {Tool.from_hub(repo_id,token,trust_remote_code) for repo_id in self._hub_repo_ids}
+        self.tools = {
+            Tool.from_hub(repo_id, token, trust_remote_code)
+            for repo_id in self._hub_repo_ids
+        }
 
 
 def tool(tool_function: Callable) -> Tool:

src/smolagents/utils.py

@@ -31,7 +31,7 @@ def is_pygments_available():
     return _pygments_available
 
 
-console = Console()
+console = Console(width=200)
 
 BASE_BUILTIN_MODULES = [
     "collections",
@@ -69,7 +69,7 @@ class AgentExecutionError(AgentError):
     pass
 
 
-class AgentMaxIterationsError(AgentError):
+class AgentMaxStepsError(AgentError):
     """Exception raised for errors in execution in the agent"""
 
     pass

tests/test_agents.py

@@ -22,7 +22,7 @@ from pathlib import Path
 
 from smolagents.types import AgentText, AgentImage
 from smolagents.agents import (
-    AgentMaxIterationsError,
+    AgentMaxStepsError,
     ManagedAgent,
     CodeAgent,
     ToolCallingAgent,
@@ -279,15 +279,15 @@ class AgentTests(unittest.TestCase):
     def test_setup_agent_with_empty_toolbox(self):
         ToolCallingAgent(model=FakeToolCallModel(), tools=[])
 
-    def test_fails_max_iterations(self):
+    def test_fails_max_steps(self):
         agent = CodeAgent(
             tools=[PythonInterpreterTool()],
             model=fake_code_model_no_return,  # use this callable because it never ends
-            max_iterations=5,
+            max_steps=5,
         )
         agent.run("What is 2 multiplied by 3.6452?")
         assert len(agent.logs) == 8
-        assert type(agent.logs[-1].error) is AgentMaxIterationsError
+        assert type(agent.logs[-1].error) is AgentMaxStepsError
 
     def test_init_agent_with_different_toolsets(self):
         toolset_1 = []
@@ -325,7 +325,7 @@ class AgentTests(unittest.TestCase):
         agent = CodeAgent(
             tools=[],
             model=fake_code_functiondef,
-            max_iterations=2,
+            max_steps=2,
             additional_authorized_imports=["numpy"],
         )
         res = agent.run("ok")

tests/test_monitoring.py

@@ -41,7 +41,7 @@ final_answer('This is the final answer.')
         agent = CodeAgent(
             tools=[],
             model=FakeLLMModel(),
-            max_iterations=1,
+            max_steps=1,
         )
 
         agent.run("Fake task")
@@ -61,7 +61,7 @@ final_answer('This is the final answer.')
         agent = ToolCallingAgent(
             tools=[],
             model=FakeLLMModel(),
-            max_iterations=1,
+            max_steps=1,
         )
 
         agent.run("Fake task")
@@ -69,7 +69,7 @@ final_answer('This is the final answer.')
         self.assertEqual(agent.monitor.total_input_token_count, 10)
         self.assertEqual(agent.monitor.total_output_token_count, 20)
 
-    def test_code_agent_metrics_max_iterations(self):
+    def test_code_agent_metrics_max_steps(self):
         class FakeLLMModel:
             def __init__(self):
                 self.last_input_token_count = 10
@@ -81,7 +81,7 @@ final_answer('This is the final answer.')
         agent = CodeAgent(
             tools=[],
             model=FakeLLMModel(),
-            max_iterations=1,
+            max_steps=1,
         )
 
         agent.run("Fake task")
@@ -103,7 +103,7 @@ final_answer('This is the final answer.')
         agent = CodeAgent(
             tools=[],
             model=FakeLLMModel(),
-            max_iterations=1,
+            max_steps=1,
         )
         agent.run("Fake task")
 
@@ -123,7 +123,7 @@ final_answer('This is the final answer.')
         agent = CodeAgent(
             tools=[],
             model=dummy_model,
-            max_iterations=1,
+            max_steps=1,
         )
 
         # Use stream_to_gradio to capture the output
@@ -145,7 +145,7 @@ final_answer('This is the final answer.')
         agent = ToolCallingAgent(
             tools=[],
             model=FakeLLM(),
-            max_iterations=1,
+            max_steps=1,
         )
 
         # Use stream_to_gradio to capture the output
@@ -172,7 +172,7 @@ final_answer('This is the final answer.')
         agent = CodeAgent(
             tools=[],
             model=dummy_model,
-            max_iterations=1,
+            max_steps=1,
         )
 
         # Use stream_to_gradio to capture the output