first stab at early concepts

crewai/agent.py

@@ -0,0 +1,48 @@
+"""Generic agent."""
+
+from typing import List, Any
+from pydantic import BaseModel, Field
+
+from langchain.tools import Tool
+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 .prompts import AGENT_EXECUTION_PROMPT
+
+class Agent(BaseModel):
+	role: str = Field(description="Role of the agent")
+	goal: str = Field(description="Objective of the agent")
+	backstory: str = Field(description="Backstory of the agent")
+	tools: List[Tool] = Field(description="Tools at agents disposal")
+	llm: str = Field(description="LLM of the agent", default=OpenAI(
+		temperature=0.7,
+		model="gpt-4",
+		verbose=True
+	))
+  
+	def execute(self, task: str) -> str:
+		prompt = AGENT_EXECUTION_PROMPT.partial(
+			tools=render_text_description(self.tools),
+			tool_names=self.__tools_names(),
+			backstory=self.backstory,
+			role=self.role,
+			goal=self.goal,
+		)
+		return self.__run(task, prompt, self.tools)
+
+	def __run(self, input: str, prompt: str, tools: List[Tool]) -> str:
+		chat_with_bind = self.llm.bind(stop=["\nObservation"])
+		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 agent_executor.invoke({"input": input})['output']
+
+	def __tools_names(self) -> str:
+		return ", ".join([t.name for t in self.tools])
+