Make it work

docs/source/en/reference/models.md

@@ -61,7 +61,7 @@ from smolagents import TransformersModel
 
 model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
 
-print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))
+print(model([{"role": "user", "content": [{"type": "text", "text": "Ok!"}]}], stop_sequences=["great"]))
 ```
 ```text
 >>> What a
@@ -80,9 +80,7 @@ The `HfApiModel` wraps huggingface_hub's [InferenceClient](https://huggingface.c
 from smolagents import HfApiModel
 
 messages = [
-  {"role": "user", "content": "Hello, how are you?"},
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
 ]
 
 model = HfApiModel()
@@ -102,9 +100,7 @@ You can pass kwargs upon model initialization that will then be used whenever us
 from smolagents import LiteLLMModel
 
 messages = [
-  {"role": "user", "content": "Hello, how are you?"},
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
 ]
 
 model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2, max_tokens=10)

examples/e2b_example.py

@@ -42,7 +42,7 @@ def forward(self):
 )
 
 agent.run(
-    "Return me an image of a cat. Directly use the image provided in your state.",
+    "Calculate how much is 2+2, then return me an image of a cat. Directly use the image provided in your state.",
     additional_args={"cat_image": get_cat_image()},
 )  # Asking to directly return the image from state tests that additional_args are properly sent to server.
 

src/smolagents/agents.py

@@ -58,6 +58,7 @@
 from .monitoring import Monitor
 from .prompts import (
     CODE_SYSTEM_PROMPT,
+    MANAGED_AGENT_PROMPT,
     PLAN_UPDATE_FINAL_PLAN_REDACTION,
     SYSTEM_PROMPT_FACTS,
     SYSTEM_PROMPT_FACTS_UPDATE,
@@ -179,7 +180,7 @@ def __init__(
         self.state = {}
         self.name = name
         self.description = description
-        self.managed_agent_prompt = managed_agent_prompt
+        self.managed_agent_prompt = managed_agent_prompt if managed_agent_prompt else MANAGED_AGENT_PROMPT
 
         self.managed_agents = {}
         if managed_agents is not None:
@@ -652,17 +653,13 @@ def replay(self, detailed: bool = False):
 
     def __call__(self, request, provide_run_summary=False, **kwargs):
         """Adds additional prompting for the managed agent, and runs it."""
-        full_task = self.managed_agent_prompt.format(name=self.name, task=request)
-        if self.additional_prompting:
-            full_task = full_task.replace("\n{additional_prompting}", self.additional_prompting).strip()
-        else:
-            full_task = full_task.replace("\n{additional_prompting}", "").strip()
-        output = self.agent.run(full_task, **kwargs)
+        full_task = self.managed_agent_prompt.format(name=self.name, task=request).strip()
+        output = self.run(full_task, **kwargs)
         if provide_run_summary:
             answer = f"Here is the final answer from your managed agent '{self.name}':\n"
             answer += str(output)
             answer += f"\n\nFor more detail, find below a summary of this agent's work:\nSUMMARY OF WORK FROM AGENT '{self.name}':\n"
-            for message in self.agent.write_memory_to_messages(summary_mode=True):
+            for message in self.write_memory_to_messages(summary_mode=True):
                 content = message["content"]
                 answer += "\n" + truncate_content(str(content)) + "\n---"
             answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
@@ -928,7 +925,7 @@ def step(self, memory_step: ActionStep) -> Union[None, Any]:
                 ]
             observation = "Execution logs:\n" + execution_logs
         except Exception as e:
-            if "print_outputs" in self.python_executor.state:
+            if hasattr(self.python_executor, "state") and "print_outputs" in self.python_executor.state:
                 execution_logs = self.python_executor.state["print_outputs"]
                 if len(execution_logs) > 0:
                     execution_outputs_console = [

src/smolagents/e2b_executor.py

@@ -45,9 +45,11 @@ def __init__(self, additional_imports: List[str], tools: List[Tool], logger):
                 """Please install 'e2b' extra to use E2BExecutor: `pip install "smolagents[e2b]"`"""
             )
 
+        self.logger.log("Initializing E2B executor, hold on...")
+
         self.custom_tools = {}
         self.final_answer = False
-        self.final_answer_pattern = re.compile(r"^final_answer\((.*)\)$")
+        self.final_answer_pattern = re.compile(r"final_answer\((.*?)\)")
         self.sbx = Sandbox()  # "qywp2ctmu2q7jzprcf4j")
         # TODO: validate installing agents package or not
         # print("Installing agents package on remote executor...")
@@ -90,7 +92,7 @@ def forward(self, *args, **kwargs):
         self.logger.log(tool_definition_execution.logs)
 
     def run_code_raise_errors(self, code: str):
-        if self.final_answer_pattern.match(code):
+        if self.final_answer_pattern.search(code) is not None:
             self.final_answer = True
         execution = self.sbx.run_code(
             code,
@@ -152,7 +154,9 @@ def __call__(self, code_action: str, additional_args: dict) -> Tuple[Any, Any]:
                     ]:
                         if getattr(result, attribute_name) is not None:
                             return getattr(result, attribute_name), execution_logs, self.final_answer
-            raise ValueError("No main result returned by executor!")
+            if self.final_answer:
+                raise ValueError("No main result returned by executor!")
+            return None, execution_logs, False
 
 
 __all__ = ["E2BExecutor"]

src/smolagents/prompts.py

@@ -510,7 +510,7 @@
 
 Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
 And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
-{{additional_prompting}}"""
+"""
 
 __all__ = [
     "USER_PROMPT_PLAN_UPDATE",