rootcode

rootcode.py

from langchain_moonshot import ChatMoonshot
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
import subprocess
import os
from dotenv import load_dotenv

load_dotenv()

# model definition
model = ChatOpenAI(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# create tool and bind it to agent
@tool
def execute_bash(command: str) -> str:
    """Execute a bash command and return the output."""
    user_input = input(f"Agent wants to execute: {command}\nDo you want to proceed? (y/n): ")
    if user_input.lower() != 'y':
        return "Command execution cancelled by user."
    result = subprocess.run(command, shell=True, capture_output=True, text=True)
    return result.stdout.strip()

model = model.bind_tools([execute_bash])

# create system prompt
messages = [SystemMessage(content="You are RootCode, A coding agent which helps user code locally on the machine. Use tools to work on the current project")]

# loop to get user input, tool execution and agent response
while True:
    user_input = input("You: ")
    messages.append(HumanMessage(content=user_input))
    response = model.invoke(messages)
    messages.append(response)

    while response.tool_calls:
        for tool_call in response.tool_calls:
            tool_name = tool_call["name"]
            tool_args = tool_call["args"]
            if tool_name == "execute_bash":
                tool_response = execute_bash.invoke(tool_args)
            else:
                tool_response = f"Unknown tool: {tool_name}"

            messages.append(ToolMessage(
                content=tool_response,
                name=tool_name,
                tool_call_id=tool_call["id"]
            ))
        response = model.invoke(messages)
        messages.append(response)
    
    print(f"RootCode: {response.content}")