adding agent vote

crewai/agent.py

@@ -8,9 +8,10 @@ from langchain.agents import AgentExecutor
 from langchain.chat_models import ChatOpenAI as OpenAI
 from langchain.tools.render import render_text_description
 from langchain.agents.format_scratchpad import format_log_to_str
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain.agents.output_parsers import ReActSingleInputOutputParser, PydanticOutputParser
 
 from .prompts import Prompts
+from .agent.agent_vote import AgentVote
 
 class Agent(BaseModel):
 	"""Generic agent implementation."""
@@ -34,7 +35,26 @@ class Agent(BaseModel):
 		)
 	)
   
-	def execute(self, task: str) -> str:
+	def vote_agent_for_task(self, task: str) -> AgentVote:
+		"""
+		Execute a task with the agent.
+			Parameters:
+				task (str): Task to execute
+			Returns:
+				output (AgentVote): The agent voted to execute the task
+		"""
+		parser = PydanticOutputParser(pydantic_object=AgentVote)
+		prompt = Prompts.AGENT_EXECUTION_PROMPT.partial(
+			tools=render_text_description(self.tools),
+			tool_names=self.__tools_names(),
+			backstory=self.backstory,
+			role=self.role,
+			goal=self.goal,
+			format_instructions=parser.get_format_instructions()
+		)
+		return self.__function_calling(task, prompt, parser)
+
+	def execute_task(self, task: str) -> str:
 		"""
 		Execute a task with the agent.
 			Parameters:
@@ -49,16 +69,32 @@ class Agent(BaseModel):
 			role=self.role,
 			goal=self.goal,
 		)
-		return self.__run(task, prompt, self.tools)
+		return self.__execute_task(task, prompt)
 
-	def __run(self, input: str, prompt: str, tools: List[Tool]) -> str:
+	def __function_calling(self, input: str, prompt: str, parser: str) -> str:
+		inner_agent = {
+			"input": lambda x: x["input"],
+			"agent_scratchpad": lambda x: format_log_to_str(x['intermediate_steps'])
+		} | prompt | parser
+		
+		return self.__execute(inner_agent, input)
+		
+	def __execute_task(self, input: str, prompt: str) -> str:
 		chat_with_bind = self.llm.bind(stop=["\nObservation"])
-		agent = {
+		inner_agent = {
 			"input": lambda x: x["input"],
 			"agent_scratchpad": lambda x: format_log_to_str(x['intermediate_steps'])
 		} | prompt | chat_with_bind | ReActSingleInputOutputParser()
 
-		agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
+		return self.__execute(inner_agent, input)
+
+	def __execute(self, inner_agent, input):
+		agent_executor = AgentExecutor(
+			agent=inner_agent,
+			tools=self.tools,
+			verbose=True,
+			handle_parsing_errors=True
+		)
 		return agent_executor.invoke({"input": input})['output']
 
 	def __tools_names(self) -> str:

crewai/agent/agent_vote.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel, Field
+
+class AgentVote(BaseModel):
+    task: str = Field(description="Task to be executed by the agent")
+    agent_vote: str = Field(description="Agent that will execute the task")