Simplify managed agents

docs/source/en/examples/multiagents.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 
 In this notebook we will make a **multi-agent web browser: an agentic system with several agents collaborating to solve problems using the web!**
 
-It will be a simple hierarchy, using a `ManagedAgent` object to wrap the managed web search agent:
+It will be a simple hierarchy:
 
 ```
               +----------------+
@@ -28,15 +28,12 @@ It will be a simple hierarchy, using a `ManagedAgent` object to wrap the managed
                        |
         _______________|______________
        |                              |
-  Code interpreter   +--------------------------------+
-       tool          |         Managed agent          |
-                     |      +------------------+      |
-                     |      | Web Search agent |      |
-                     |      +------------------+      |
-                     |         |            |         |
-                     |  Web Search tool     |         |
-                     |             Visit webpage tool |
-                     +--------------------------------+
+Code Interpreter            +------------------+
+    tool                    | Web Search agent |
+                            +------------------+
+                               |            |
+                        Web Search tool     |
+                                   Visit webpage tool
 ```
 Let's set up this system. 
 
@@ -127,7 +124,6 @@ from smolagents import (
     CodeAgent,
     ToolCallingAgent,
     HfApiModel,
-    ManagedAgent,
     DuckDuckGoSearchTool,
     LiteLLMModel,
 )
@@ -138,20 +134,14 @@ web_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), visit_webpage],
     model=model,
     max_steps=10,
-)
-```
-
-We then wrap this agent into a `ManagedAgent` that will make it callable by its manager agent.
-
-```py
-managed_web_agent = ManagedAgent(
-    agent=web_agent,
     name="search",
     description="Runs web searches for you. Give it your query as an argument.",
 )
 ```
 
-Finally we create a manager agent, and upon initialization we pass our managed agent to it in its `managed_agents` argument.
+Note that we gave this agent attributes `name` and `description`, mandatory attributes to make this agent callable by its manager agent.
+
+Then we create a manager agent, and upon initialization we pass our managed agent to it in its `managed_agents` argument.
 
 Since this agent is the one tasked with the planning and thinking, advanced reasoning will be beneficial, so a `CodeAgent` will be the best choice.
 
@@ -161,7 +151,7 @@ Also, we want to ask a question that involves the current year and does addition
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_web_agent],
+    managed_agents=[web_agent],
     additional_authorized_imports=["time", "numpy", "pandas"],
 )
 ```

docs/source/en/reference/agents.md

@@ -45,7 +45,7 @@ Both require arguments `model` and list of tools `tools` at initialization.
 
 ### ManagedAgent
 
-[[autodoc]] ManagedAgent
+_This class is deprecated since 1.8.0: now you simply need to pass attributes `name` and `description` to a normal agent to make it callable by a manager agent._
 
 ### stream_to_gradio
 

docs/source/en/tutorials/inspect_runs.md

@@ -78,27 +78,24 @@ Then you can run your agents!
 from smolagents import (
     CodeAgent,
     ToolCallingAgent,
-    ManagedAgent,
     DuckDuckGoSearchTool,
     VisitWebpageTool,
     HfApiModel,
 )
 
 model = HfApiModel()
 
-agent = ToolCallingAgent(
+search_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
     model=model,
-)
-managed_agent = ManagedAgent(
-    agent=agent,
-    name="managed_agent",
+    name="search_agent",
     description="This is an agent that can do web search.",
 )
+
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_agent],
+    managed_agents=[search_agent],
 )
 manager_agent.run(
     "If the US keeps its 2024 growth rate, how many years will it take for the GDP to double?"

docs/source/zh/reference/agents.md

@@ -47,7 +47,7 @@ Both require arguments `model` and list of tools `tools` at initialization.
 
 ### ManagedAgent
 
-[[autodoc]] ManagedAgent
+_This class is deprecated since 1.8.0: now you just need to pass name and description attributes to an agent to use it as a ManagedAgent._
 
 ### stream_to_gradio
 

examples/inspect_runs.py

@@ -7,7 +7,6 @@
     CodeAgent,
     DuckDuckGoSearchTool,
     HfApiModel,
-    ManagedAgent,
     ToolCallingAgent,
     VisitWebpageTool,
 )
@@ -23,18 +22,16 @@
 # Then we run the agentic part!
 model = HfApiModel()
 
-agent = ToolCallingAgent(
+search_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
     model=model,
-)
-managed_agent = ManagedAgent(
-    agent=agent,
-    name="managed_agent",
+    name="search_agent",
     description="This is an agent that can do web search.",
 )
+
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_agent],
+    managed_agents=[search_agent],
 )
 manager_agent.run("If the US keeps it 2024 growth rate, how many years would it take for the GDP to double?")

src/smolagents/agents.py

@@ -58,7 +58,6 @@
 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,
@@ -140,6 +139,9 @@ class MultiStepAgent:
         managed_agents (`list`, *optional*): Managed agents that the agent can call.
         step_callbacks (`list[Callable]`, *optional*): Callbacks that will be called at each step.
         planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
+        name (`str`, *optional*): Necessary for a managed agent only - the name by which this agent can be called.
+        description (`str`, *optional*): Necessary for a managed agent only - the description of this agent.
+        managed_agent_prompt (`str`, *optional*): Custom prompt for the managed agent. Defaults to None.
     """
 
     def __init__(
@@ -156,6 +158,9 @@ def __init__(
         managed_agents: Optional[List] = None,
         step_callbacks: Optional[List[Callable]] = None,
         planning_interval: Optional[int] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        managed_agent_prompt: Optional[str] = None,
     ):
         if system_prompt is None:
             system_prompt = CODE_SYSTEM_PROMPT
@@ -172,9 +177,16 @@ def __init__(
         self.grammar = grammar
         self.planning_interval = planning_interval
         self.state = {}
+        self.name = name
+        self.description = description
+        self.managed_agent_prompt = managed_agent_prompt
 
         self.managed_agents = {}
         if managed_agents is not None:
+            for managed_agent in managed_agents:
+                assert managed_agent.name and managed_agent.description, (
+                    "All managed agents need both a name and a description!"
+                )
             self.managed_agents = {agent.name: agent for agent in managed_agents}
 
         for tool in tools:
@@ -638,6 +650,26 @@ def replay(self, detailed: bool = False):
         """
         self.memory.replay(self.logger, detailed=detailed)
 
+    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)
+        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):
+                content = message["content"]
+                answer += "\n" + truncate_content(str(content)) + "\n---"
+            answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
+            return answer
+        else:
+            return output
+
 
 class ToolCallingAgent(MultiStepAgent):
     """
@@ -928,59 +960,4 @@ def step(self, memory_step: ActionStep) -> Union[None, Any]:
         return output if is_final_answer else None
 
 
-class ManagedAgent:
-    """
-    ManagedAgent class that manages an agent and provides additional prompting and run summaries.
-
-    Args:
-        agent (`object`): The agent to be managed.
-        name (`str`): The name of the managed agent.
-        description (`str`): A description of the managed agent.
-        additional_prompting (`Optional[str]`, *optional*): Additional prompting for the managed agent. Defaults to None.
-        provide_run_summary (`bool`, *optional*): Whether to provide a run summary after the agent completes its task. Defaults to False.
-        managed_agent_prompt (`Optional[str]`, *optional*): Custom prompt for the managed agent. Defaults to None.
-
-    """
-
-    def __init__(
-        self,
-        agent,
-        name,
-        description,
-        additional_prompting: Optional[str] = None,
-        provide_run_summary: bool = False,
-        managed_agent_prompt: Optional[str] = None,
-    ):
-        self.agent = agent
-        self.name = name
-        self.description = description
-        self.additional_prompting = additional_prompting
-        self.provide_run_summary = provide_run_summary
-        self.managed_agent_prompt = managed_agent_prompt if managed_agent_prompt else MANAGED_AGENT_PROMPT
-
-    def write_full_task(self, task):
-        """Adds additional prompting for the managed agent, like 'add more detail in your answer'."""
-        full_task = self.managed_agent_prompt.format(name=self.name, task=task)
-        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()
-        return full_task
-
-    def __call__(self, request, **kwargs):
-        full_task = self.write_full_task(request)
-        output = self.agent.run(full_task, **kwargs)
-        if self.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):
-                content = message["content"]
-                answer += "\n" + truncate_content(str(content)) + "\n---"
-            answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
-            return answer
-        else:
-            return output
-
-
-__all__ = ["ManagedAgent", "MultiStepAgent", "CodeAgent", "ToolCallingAgent", "AgentMemory"]
+__all__ = ["MultiStepAgent", "CodeAgent", "ToolCallingAgent", "AgentMemory"]

tests/test_agents.py

@@ -25,7 +25,6 @@
 from smolagents.agents import (
     AgentMaxStepsError,
     CodeAgent,
-    ManagedAgent,
     MultiStepAgent,
     ToolCall,
     ToolCallingAgent,
@@ -465,22 +464,20 @@ def test_function_persistence_across_steps(self):
         assert res[0] == 0.5
 
     def test_init_managed_agent(self):
-        agent = CodeAgent(tools=[], model=fake_code_functiondef)
-        managed_agent = ManagedAgent(agent, name="managed_agent", description="Empty")
-        assert managed_agent.name == "managed_agent"
-        assert managed_agent.description == "Empty"
+        agent = CodeAgent(tools=[], model=fake_code_functiondef, name="managed_agent", description="Empty")
+        assert agent.name == "managed_agent"
+        assert agent.description == "Empty"
 
     def test_agent_description_gets_correctly_inserted_in_system_prompt(self):
-        agent = CodeAgent(tools=[], model=fake_code_functiondef)
-        managed_agent = ManagedAgent(agent, name="managed_agent", description="Empty")
+        managed_agent = CodeAgent(tools=[], model=fake_code_functiondef, name="managed_agent", description="Empty")
         manager_agent = CodeAgent(
             tools=[],
             model=fake_code_functiondef,
             managed_agents=[managed_agent],
         )
-        assert "You can also give requests to team members." not in agent.system_prompt
+        assert "You can also give requests to team members." not in managed_agent.system_prompt
         print("ok1")
-        assert "{{managed_agents_descriptions}}" not in agent.system_prompt
+        assert "{{managed_agents_descriptions}}" not in managed_agent.system_prompt
         assert "You can also give requests to team members." in manager_agent.system_prompt
 
     def test_code_agent_missing_import_triggers_advice_in_error_log(self):
@@ -587,18 +584,14 @@ def __call__(
             tools=[],
             model=managed_model,
             max_steps=10,
-        )
-
-        managed_web_agent = ManagedAgent(
-            agent=web_agent,
             name="search_agent",
             description="Runs web searches for you. Give it your request as an argument. Make the request as detailed as needed, you can ask for thorough reports",
         )
 
         manager_code_agent = CodeAgent(
             tools=[],
             model=manager_model,
-            managed_agents=[managed_web_agent],
+            managed_agents=[web_agent],
             additional_authorized_imports=["time", "numpy", "pandas"],
         )
 
@@ -608,7 +601,7 @@ def __call__(
         manager_toolcalling_agent = ToolCallingAgent(
             tools=[],
             model=manager_model,
-            managed_agents=[managed_web_agent],
+            managed_agents=[web_agent],
         )
 
         report = manager_toolcalling_agent.run("Fake question.")