에이전트 SDK 삼국지

# allowed_tools=[] 로 툴 접근을 명시적으로 비워야 진짜 챗봇이 된다.
# 기본값은 Claude Code의 전체 툴셋을 그대로 물려받기 때문이다.
import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage

async def main():
    async for message in query(
        prompt="What is 2+2?",
        options=ClaudeAgentOptions(
            allowed_tools=[],
            system_prompt="You are a helpful chatbot. Be conversational and warm.",
        ),
    ):
        if isinstance(message, ResultMessage):
            print(message.result)

asyncio.run(main())

# 순수 챗봇이라면 create_agent()조차 필요 없다.
# LCEL로 prompt | model 파이프만 조립하면 끝 — 툴 호출 루프 자체가 없다.
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate

model = ChatAnthropic(model="claude-sonnet-4-20250514")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful chatbot. Be conversational and warm."),
    ("human", "{input}"),
])

chain = prompt | model
print(chain.invoke({"input": "What is 2+2?"}).content)

import { query } from "@anthropic-ai/claude-agent-sdk";

for await (const message of query({
  prompt: "Find and fix all failing tests in the auth module",
  options: {
    allowedTools: ["Read", "Edit", "Bash", "Glob", "Grep"],
    permissionMode: "acceptEdits",   // 파일 편집은 자동 승인
    maxTurns: 50,
    hooks: {
      PreToolUse: async (event) => {
        if (event.toolName === "Bash" && event.input.command?.includes("rm -rf")) {
          return { allow: false, reason: "Destructive command blocked" };
        }
        return { allow: true };
      },
    },
    agents: {
      "code-reviewer": {
        description: "Security and quality reviewer",
        prompt: "You are a code review expert...",
        tools: ["Read", "Grep", "Glob"],
        model: "opus",
      },
    },
  },
})) {
  if (message.type === "result") {
    console.log(`Cost: $${message.total_cost_usd}, Turns: ${message.num_turns}`);
  }
}

from langchain.agents import create_agent
from langchain.tools import tool
from langgraph.checkpoint.memory import InMemorySaver
import subprocess

# 파일시스템 · 셸 툴은 LangChain이 대신 만들어주지 않는다 — 직접 정의한다.
@tool
def read_file(path: str) -> str:
    """Read a file's contents."""
    return open(path).read()

@tool
def run_shell(command: str) -> str:
    """Run a shell command and return its output."""
    return subprocess.run(command, shell=True, capture_output=True, text=True).stdout

agent = create_agent(
    model="anthropic:claude-sonnet-4-20250514",
    tools=[read_file, run_shell],
    system_prompt="You are a senior developer. Follow best practices.",
    checkpointer=InMemorySaver(),   # thread_id 단위로 대화 상태 재개
)

config = {"configurable": {"thread_id": "fix-auth-tests"}}
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Find and fix all failing tests in the auth module"}]},
    config=config,
)