Agent

参考：

• Build a Coding Agent from Scratch: The Complete Python Tutorial - Sid Bharath

1. system_prompt

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   SYSTEM_PROMPT = """You are a helpful coding agent that assists with programming tasks and file operations. When responding to requests: 1. Analyze what the user needs 2. Use the minimum number of tools necessary to accomplish the task 3. After using tools, provide a concise summary of what was done IMPORTANT: Once you've completed the requested task, STOP and provide your final response. Do not continue creating additional files or performing extra actions unless specifically asked. Examples of good behavior: - User: "Create a file that adds numbers" → Create ONE file, then summarize - User: "Create files for add and subtract" → Create ONLY those two files, then summarize - User: "Create math operation files" → Ask for clarification on which operations, or create a reasonable set and stop After receiving tool results: - If the task is complete, provide a final summary - Only continue with more tools if the original request is not yet fulfilled - Do not interpret successful tool execution as a request to do more Be concise and efficient. Complete the requested task and stop."""

Reason –> Action –> Observe

• Reason: 是否/使用哪些工具
• Action: 调用工具
• Observe: 观察工具调用结果

两种调用方式

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def auto_tool_use(self, user: str, max_tool_auto_call: int):

    n = 0
    while n < max_tool_auto_call:
        # 1) 整理 messages
        messages = self.messages[max_history_count:]
        # 2) llm 调用 :::: Reason
        ret = self.llm.invode(user)

        n += 1
        # 3) 判断是否需要 tool 调用
        if "tool_use" not in ret:
            msg = ret.content
            messages.append({"role": "user", "content": user})
            messages.append({"role": "assistant", "content": msg})
            break

        # 4) 执行 tool 调用 :::: Action
        tool_calls = ret.tool_calls
        tool_results: list[dict] = self._execute__tools(tool_calls)
        # 5) 保存 tool 调用结果 :::: Observe
        self.messages.extend(
            [
                {
                    "role": "user",
                    # 注意：这里content不再是str, 而是一个 list[dict]
                    "content": [
                        {
                            "type": "tool_result",
                            "tool_use_id": t["tool_use_id"],
                            "content": t["content"],
                        }
                        for t in tool_results
                    ],
                }
            ]
        )
        # 6) 下一轮 Reasoning --> Action --> Oberve
        #    根据 tool 调用的结果判断是否已经结果问题，如果没有结果，LLM 根据需要再次触发 tool 调用
    return messages

参考：

• A Guide to Context Engineering: Setting Agents Up for Success - Sid Bharath

Claude Code 包括了三层 context (three-layout context)

1. 第一层： Cluade.md

   • persistent layer

     • 长期记忆
   • 在初始化一个项目任务时，Cluade 会先扫描项目的重要文件（requirements.txt, README,项目结构等等），然后创建 Cluade.md

   • 包括内容：

     • projtect convention 项目风格
     • commit 规范
     • architecture decisions
     • coding standards
     • 已经你个人规定的其他要求

2. 第二层： dynamic layer

   • 根据用户的 query 命令，去获取项目中的相关内容来增强 context

   • 比如：用户提取修复 auth bug, 它会

     1. 查找和认证相关的文件和代码，理解代码逻辑，查找相关 codebase,比如 middleware, config, tests
     2. 扩充 context
     3. 制定修复 bug 的方案
     4. 让用户确定是否采用

3. 第三层：conversation

   • 对话本身

     1. user query
     2. tools used
     3. content retrieved