Hello-Agents 深度评测

import openai

client = openai.OpenAI()

SYSTEM_PROMPT = """你是一个能使用工具的助手。
思考过程：
1. Thought: 分析用户问题，决定下一步
2. Action: 调用合适的工具
3. Observation: 观察工具返回结果
4. 循环直到得出最终答案
"""

def react_loop(question, max_steps=5):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": question}
    ]

    for step in range(max_steps):
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=available_tools
        )

        msg = response.choices[0].message

        if msg.content:  # Agent 认为可以给出最终答案
            return msg.content

        if msg.tool_calls:  # Agent 决定调用工具
            for tool_call in msg.tool_calls:
                result = execute_tool(tool_call)
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": str(result)
                })
            messages.append(msg)

    return "达到最大步数限制，无法完成"

class Agent:
    def __init__(self, name, system_prompt, tools=None):
        self.name = name
        self.system_prompt = system_prompt
        self.tools = tools or []
        self.memory = []

    def think(self, user_input):
        # 将记忆 + 输入构建为完整上下文
        context = self._build_context(user_input)
        response = call_llm(context, tools=self.tools)
        self.memory.append(response)
        return response

    def _build_context(self, user_input):
        # 上下文工程：截取相关记忆片段
        return format_context(
            self.system_prompt, self.memory, user_input
        )