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从0到1学习智能体开发0：Bash即一切

Posted on Edited on In Disqus:

shareAI-lab在github上有一系列的源码，通过研究Claude Code帮助学习智能体开发。仓库在这里。
本系列文章就是对这些源码的学习，本篇是第一篇。

v0: Bash 就是一切

终极简化：~50 行代码，1 个工具，完整的 Agent 能力。

Agent 的本质到底是什么？

v0 通过反向思考来回答——剥离一切，直到只剩下核心。

核心洞察

Unix 哲学：一切皆文件，一切皆可管道。Bash 是这个世界的入口：

你需要 Bash 命令
读文件 cat, head, grep
写文件 echo '...' > file
搜索 find, grep, rg
执行 python, npm, make
子代理 python v0_bash_agent.py "task"

最后一行是关键洞察：通过 bash 调用自身就实现了子代理。不需要 Task 工具，不需要 Agent Registry——只需要递归。

源码

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from anthropic import Anthropic
from dotenv import load_dotenv
import subprocess
import sys
import os

# 从 .env 加载环境变量（开发时更方便：不用把 key 写进代码）
# 注意：生产环境通常由部署系统注入环境变量，不一定需要 .env。
load_dotenv()

# 初始化 Anthropic 客户端。
# 约定：密钥、Base URL、模型名都通过环境变量注入，避免硬编码。
# - ANTHROPIC_API_KEY: API key
# - ANTHROPIC_BASE_URL: 可选，指向自建网关/代理（不设置则使用官方默认）
client = Anthropic(
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    base_url=os.getenv("ANTHROPIC_BASE_URL")
)
# 模型名也外置，便于切换（本脚本并不依赖某个特定模型能力）
MODEL = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")

# TOOL 的结构会原样传入 messages.create(tools=TOOL)。
# 对模型来说：“bash”就像一个可调用函数；模型输出 tool_use 块来请求执行命令，
# 我们再把执行结果以 tool_result 块的形式喂回去，形成闭环。
# Anthropic 的 tools 需要一个 input_schema（JSON Schema），用来约束工具入参结构
# 这里定义 bash 工具只接受一个字段：{"command": "..."}。
TOOL = [{
    "name": "bash",
    "description": """Execute shell command. Common patterns:
- Read: cat/head/tail, grep/find/rg/ls, wc -l
- Write: echo 'content' > file, sed -i 's/old/new/g' file
- Subagent: python v0_bash_agent.py 'task description' (spawns isolated agent, returns summary)""",
    "input_schema": {
        "type": "object",
        "properties": {"command": {"type": "string"}},
        "required": ["command"]
    }
}]

# SYSTEM提示词作为“操作手册”，核心目的是让模型更倾向于：
# - 先行动（调用 bash），后总结
# - 用 bash 完成读/写/搜索/执行
# - 复杂子任务用“递归调用自身脚本”实现“子代理”（进程隔离 = 上下文隔离）
# 这里用 f-string 把当前工作目录写进提示词，帮助模型知道“自己在哪”。
# 注意：SYSTEM 是给模型看的“行为规范/工具用法”，不是给 subprocess 执行的。
SYSTEM = f"""You are a CLI agent at {os.getcwd()}. Solve problems using bash commands.

Rules:
- Prefer tools over prose. Act first, explain briefly after.
- Read files: cat, grep, find, rg, ls, head, tail
- Write files: echo '...' > file, sed -i, or cat << 'EOF' > file
- Subagent: For complex subtasks, spawn a subagent to keep context clean:
  python v0_bash_agent.py "explore src/ and summarize the architecture"

When to use subagent:
- Task requires reading many files (isolate the exploration)
- Task is independent and self-contained
- You want to avoid polluting current conversation with intermediate details

The subagent runs in isolation and returns only its final summary."""


def chat(prompt, history=None):
    """
    用一个函数实现完整的 Agent 循环。

    这是几乎所有“编码代理/工具调用代理”共同的核心模式：
        while not done:
            response = model(messages, tools)
            if no tool calls: return
            execute tools, append results

    参数:
        prompt: 用户请求
        history: 对话历史（可变对象；在交互模式下会在多次调用间复用）

    返回:
        模型最终输出的文本结果
    """
    
    # history 维护“会话消息数组”。interactive 模式会复用同一个 history，
    # subagent 模式则每次新进程启动，新建 history，从而天然实现上下文隔离。
    #
    # history 的形状（Anthropic Messages API 约定）大致如下：
    # [
    #   {"role": "user", "content": "..." 或 [tool_result, ...]},
    #   {"role": "assistant", "content": [ {"type":"text",...}, {"type":"tool_use",...}, ... ]},
    #   ...
    # ]
    #
    # 关键点：assistant 输出 tool_use；我们必须回传 tool_result（带 tool_use_id）才能继续对话。
    
    if history is None:
        history = []

    # 先把用户问题追加到 history；后续工具结果也会以 user role 形式追加（见步骤 5）
    history.append({"role": "user", "content": prompt})

    while True:
        # 1) 调用模型：传入 messages（history）和 tools（这里只有 bash）
        # max_tokens 决定模型单次可输出的上限；如果任务需要很多操作，循环会多轮进行。
        # response.content 通常由若干“内容块”组成：
        # - text：模型给人的自然语言
        # - tool_use：模型请求调用某个工具（这里是 bash），并给出 input（这里是 command）
        response = client.messages.create(
            model=MODEL,
            system=SYSTEM,
            messages=history,
            tools=TOOL,
            max_tokens=8000
        )

        # 2) 将本轮 assistant 的输出写回 history（非常关键）：
        # - 既要保留自然语言 text
        # - 也要保留 tool_use 块（包含 id/name/input），这样下一步才能把 tool_result 对应回去
        #
        # 为什么要“保留 tool_use”？
        # 因为 Anthropic 要求 tool_result 通过 tool_use_id 绑定到具体的 tool_use。
        # 如果不把 tool_use 写进 history，后续就无法建立这种一一对应关系。
        content = []
        for block in response.content:
            # Anthropic SDK 的 content block 可能是不同的类型对象：
            # - text block 往往有 .text 属性
            # - tool_use block 有 .type == "tool_use" 且携带 id/name/input
            #
            # 这里用 hasattr(block, "text") 来兼容不同 SDK 实现细节。
            if hasattr(block, "text"):
                content.append({"type": "text", "text": block.text})
            elif block.type == "tool_use":
                content.append({
                    "type": "tool_use",
                    "id": block.id,
                    "name": block.name,
                    "input": block.input
                })
        history.append({"role": "assistant", "content": content})

        # 3) 结束条件：如果本轮没有工具调用（stop_reason != tool_use），就直接返回最终文本答案
        # 这里把所有 text 块拼接起来作为输出（工具块不参与输出）。
        #
        # stop_reason 常见取值含义（概念上）：
        # - "tool_use"：模型希望你执行工具，然后把结果回传
        # - 其他：模型认为本轮回答已完成（不需要再调用工具）
        #
        # 这里选择“只要不是 tool_use 就结束”，对应的就是一个最小化的 agent loop。
        if response.stop_reason != "tool_use":
            return "".join(b.text for b in response.content if hasattr(b, "text"))

        # 4) 执行每个 tool_use（bash 命令）并收集结果：
        # - tool_result 需要带 tool_use_id，才能和上一步的 tool_use 精确配对
        # - stdout+stderr 合并回传给模型，模型再决定下一步要不要继续调用工具
        results = []
        for block in response.content:
            if block.type == "tool_use":
                # 约定：bash 工具输入里只有一个字段 command
                cmd = block.input["command"]
                # 纯 UI：把即将执行的命令打印出来（黄色），便于人类观察调试
                print(f"\033[33m$ {cmd}\033[0m")  # Yellow color for commands

                try:
                    # 真正的“工具执行器”：
                    # - shell=True：让 command 以 shell 命令的方式执行（支持管道/重定向等）
                    # - capture_output=True：收集 stdout/stderr（用于打印 + 回传给模型）
                    # - timeout=300：避免卡死
                    #
                    # 安全注意：此脚本的定位是“会执行模型生成的命令的 agent”，
                    # 也意味着它拥有与你当前用户相同的本机权限；不要在不可信环境运行。
                    out = subprocess.run(
                        cmd,
                        shell=True,
                        capture_output=True,
                        text=True,
                        timeout=300,
                        cwd=os.getcwd()
                    )
                    output = out.stdout + out.stderr
                except subprocess.TimeoutExpired:
                    output = "(timeout after 300s)"

                print(output or "(empty)")
                results.append({
                    "type": "tool_result",
                    # tool_use_id 用于把“这段输出”绑定回“哪个工具调用”
                    "tool_use_id": block.id,
                    # 防止超长输出撑爆上下文/成本：只回传前 50k 字符
                    "content": output[:50000]  # Truncate very long outputs
                })

        # 5) 把工具执行结果作为 user 消息追加回 history，然后继续 while 循环
        # Anthropic 的约定：tool_result 通常放在 user role 的 content 里回传。
        #
        # 这一步相当于把“工具输出”作为新的上下文提供给模型：
        # 模型会基于这些输出决定：
        # - 是否继续调用 bash（再跑一条/多条命令）
        # - 或者直接给出最终解释/改动建议
        history.append({"role": "user", "content": results})


if __name__ == "__main__":
    if len(sys.argv) > 1:
        # Subagent mode: execute task and print result
        # This is how parent agents spawn children via bash
        #
        # 这里的 “子代理” 本质上是一个新进程：
        # - 新进程有独立的 history（从 chat() 的默认 None 开始）
        # - 执行完任务后只把最终文本 print 出来
        # 父进程把 stdout 当作“工具结果”读取，从而实现上下文隔离的分治。
        print(chat(sys.argv[1]))
    else:
        # Interactive REPL mode
        # 交互模式会复用同一个 history，让多轮对话“记住之前发生过什么”。
        history = []
        while True:
            try:
                query = input("\033[36m>> \033[0m")  # Cyan prompt
            except (EOFError, KeyboardInterrupt):
                break
            if query in ("q", "exit", ""):
                break
            print(chat(query, history))

子代理工作原理

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主代理
  └─ bash: python v0_bash_agent.py "分析架构"
       └─ 子代理（独立进程，全新历史）
            ├─ bash: find . -name "*.py"
            ├─ bash: cat src/main.py
            └─ 通过 stdout 返回摘要

进程隔离 = 上下文隔离

  • 子进程有自己的 history=[]
  • 父进程捕获 stdout 作为工具结果
  • 递归调用实现无限嵌套

v0 牺牲了什么

特性 v0 v3
代理类型 无 explore/code/plan
工具过滤 无 白名单
进度显示 普通 stdout 行内更新
代码复杂度 ~50 行 ~450 行

v0 证明了什么

复杂能力从简单规则中涌现：

  1. 一个工具足够 — Bash 是通往一切的入口
  2. 递归 = 层级 — 自我调用实现子代理
  3. 进程 = 隔离 — 操作系统提供上下文分离
  4. 提示词 = 约束 — 指令塑造行为

核心模式从未改变：

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while True:
    response = model(messages, tools)
    if response.stop_reason != "tool_use":
        return response.text
    results = execute(response.tool_calls)
    messages.append(results)

其他一切——待办、子代理、权限——都是围绕这个循环的精化。

带print中间结果的源码

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#!/usr/bin/env python
"""
v0_bash_agent.py - Mini Claude Code: Bash is All You Need (~50 lines core)

Core Philosophy: "Bash is All You Need"
======================================
This is the ULTIMATE simplification of a coding agent. After building v1-v3,
we ask: what is the ESSENCE of an agent?

The answer: ONE tool (bash) + ONE loop = FULL agent capability.

Why Bash is Enough:
------------------
Unix philosophy says everything is a file, everything can be piped.
Bash is the gateway to this world:

    | You need      | Bash command                           |
    |---------------|----------------------------------------|
    | Read files    | cat, head, tail, grep                  |
    | Write files   | echo '...' > file, cat << 'EOF' > file |
    | Search        | find, grep, rg, ls                     |
    | Execute       | python, npm, make, any command         |
    | **Subagent**  | python v0_bash_agent.py "task"         |

The last line is the KEY INSIGHT: calling itself via bash implements subagents!
No Task tool, no Agent Registry - just recursion through process spawning.

How Subagents Work:
------------------
    Main Agent
      |-- bash: python v0_bash_agent.py "analyze architecture"
           |-- Subagent (isolated process, fresh history)
                |-- bash: find . -name "*.py"
                |-- bash: cat src/main.py
                |-- Returns summary via stdout

Process isolation = Context isolation:
- Child process has its own history=[]
- Parent captures stdout as tool result
- Recursive calls enable unlimited nesting

Usage:
    # Interactive mode
    python v0_bash_agent.py

    # Subagent mode (called by parent agent or directly)
    python v0_bash_agent.py "explore src/ and summarize"
"""

from anthropic import Anthropic
from dotenv import load_dotenv
import subprocess
import sys
import os

# Load environment variables from .env file
load_dotenv()

# Initialize API client with credentials from environment
client = Anthropic(
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    base_url=os.getenv("ANTHROPIC_BASE_URL")
)
MODEL = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")

# The ONE tool that does everything
# Notice how the description teaches the model common patterns AND how to spawn subagents
TOOL = [{
    "name": "bash",
    "description": """Execute shell command. Common patterns:
- Read: cat/head/tail, grep/find/rg/ls, wc -l
- Write: echo 'content' > file, sed -i 's/old/new/g' file
- Subagent: python v0_bash_agent.py 'task description' (spawns isolated agent, returns summary)""",
    "input_schema": {
        "type": "object",
        "properties": {"command": {"type": "string"}},
        "required": ["command"]
    }
}]

# System prompt teaches the model HOW to use bash effectively
# Notice the subagent guidance - this is how we get hierarchical task decomposition
SYSTEM = f"""You are a CLI agent at {os.getcwd()}. Solve problems using bash commands.

Rules:
- Prefer tools over prose. Act first, explain briefly after.
- Read files: cat, grep, find, rg, ls, head, tail
- Write files: echo '...' > file, sed -i, or cat << 'EOF' > file
- Subagent: For complex subtasks, spawn a subagent to keep context clean:
  python v0_bash_agent.py "explore src/ and summarize the architecture"

When to use subagent:
- Task requires reading many files (isolate the exploration)
- Task is independent and self-contained
- You want to avoid polluting current conversation with intermediate details

The subagent runs in isolation and returns only its final summary."""


def _debug_enabled():
    return os.getenv("DEBUG_AGENT", "").strip().lower() in ("1", "true", "yes", "y", "on")


def _dbg(label, *values):
    if not _debug_enabled():
        return
    print(f"\n[debug] {label}", file=sys.stderr)
    for v in values:
        print(v, file=sys.stderr)
    print("[debug] ---", file=sys.stderr)


def chat(prompt, history=None):
    """
    The complete agent loop in ONE function.

    This is the core pattern that ALL coding agents share:
        while not done:
            response = model(messages, tools)
            if no tool calls: return
            execute tools, append results

    Args:
        prompt: User's request
        history: Conversation history (mutable, shared across calls in interactive mode)

    Returns:
        Final text response from the model
    """
    if history is None:
        history = []

    history.append({"role": "user", "content": prompt})
    _dbg("history (after user append)", history)

    while True:
        # 1. Call the model with tools
        response = client.messages.create(
            model=MODEL,
            system=SYSTEM,
            messages=history,
            tools=TOOL,
            max_tokens=8000
        )
        _dbg("response.stop_reason", getattr(response, "stop_reason", None))
        _dbg("response.content", getattr(response, "content", None))

        # 2. Build assistant message content (preserve both text and tool_use blocks)
        content = []
        for block in response.content:
            if hasattr(block, "text"):
                content.append({"type": "text", "text": block.text})
            elif block.type == "tool_use":
                content.append({
                    "type": "tool_use",
                    "id": block.id,
                    "name": block.name,
                    "input": block.input
                })
        history.append({"role": "assistant", "content": content})
        _dbg("history (after assistant append)", history)

        # 3. If model didn't call tools, we're done
        if response.stop_reason != "tool_use":
            final_text = "".join(b.text for b in response.content if hasattr(b, "text"))
            _dbg("final_text", final_text)
            return final_text

        # 4. Execute each tool call and collect results
        results = []
        for block in response.content:
            if block.type == "tool_use":
                cmd = block.input["command"]
                _dbg("tool_use", {"id": block.id, "name": block.name, "input": block.input})
                print(f"\033[33m$ {cmd}\033[0m")  # Yellow color for commands

                try:
                    out = subprocess.run(
                        cmd,
                        shell=True,
                        capture_output=True,
                        text=True,
                        timeout=300,
                        cwd=os.getcwd()
                    )
                    stdout = out.stdout or ""
                    stderr = out.stderr or ""
                    output = stdout + stderr
                    _dbg("tool_result", output)
                except subprocess.TimeoutExpired:
                    output = "(timeout after 300s)"
                    _dbg("tool_result.timeout", output)

                print(output or "(empty)")
                results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": output[:50000]  # Truncate very long outputs
                })
                _dbg("results (after append)", results)

        # 5. Append results and continue the loop
        history.append({"role": "user", "content": results})
        _dbg("history (after tool results append)", history)


if __name__ == "__main__":
    if len(sys.argv) > 1:
        # Subagent mode: execute task and print result
        # This is how parent agents spawn children via bash
        print(chat(sys.argv[1]))
    else:
        # Interactive REPL mode
        history = []
        while True:
            try:
                query = input("\033[36m>> \033[0m")  # Cyan prompt
            except (EOFError, KeyboardInterrupt):
                break
            if query in ("q", "exit", ""):
                break
            print(chat(query, history))

观察运行结果

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>> who are you

[debug] history (after user append)
[{'role': 'user', 'content': 'who are you'}]
[debug] ---

[debug] response.stop_reason
tool_use
[debug] ---

[debug] response.content
[TextBlock(citations=None, text="I am a CLI agent designed to help you solve problems using bash commands. I'm currently operating in the directory `/Users/qixinbo/Projects/learn-claude-code`.\n\nLet me show you where we are:", type='text'), ToolUseBlock(id='toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', input={'command': 'pwd && ls -la'}, name='bash', type='tool_use')]
[debug] ---

[debug] history (after assistant append)
[{'role': 'user', 'content': 'who are you'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': "I am a CLI agent designed to help you solve problems using bash commands. I'm currently operating in the directory `/Users/qixinbo/Projects/learn-claude-code`.\n\nLet me show you where we are:"}, {'type': 'tool_use', 'id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'name': 'bash', 'input': {'command': 'pwd && ls -la'}}]}]
[debug] ---

[debug] tool_use
{'id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'name': 'bash', 'input': {'command': 'pwd && ls -la'}}
[debug] ---
$ pwd && ls -la

[debug] tool_result
/Users/qixinbo/Projects/learn-claude-code
total 272
drwxr-xr-x  21 qixinbo  staff    672  1 18 13:39 .
drwxr-xr-x  28 qixinbo  staff    896  1 18 10:54 ..
-rw-r--r--   1 qixinbo  staff    147  1 18 11:13 .env
-rw-r--r--   1 qixinbo  staff    131  1 18 10:54 .env.example
drwxr-xr-x  12 qixinbo  staff    384  1 18 13:28 .git
-rw-r--r--   1 qixinbo  staff   4688  1 18 10:54 .gitignore
-rw-r--r--@  1 qixinbo  staff   1068  1 18 10:54 LICENSE
-rw-r--r--   1 qixinbo  staff   6607  1 18 10:54 README.md
-rw-r--r--   1 qixinbo  staff   6312  1 18 10:54 README_zh.md
drwxr-xr-x@  7 qixinbo  staff    224  1 18 13:39 __pycache__
drwxr-xr-x   8 qixinbo  staff    256  1 18 10:54 articles
-rw-r--r--   1 qixinbo  staff     12  1 18 13:39 debug.txt
drwxr-xr-x  12 qixinbo  staff    384  1 18 10:54 docs
-rw-r--r--@  1 qixinbo  staff     27  1 18 10:54 requirements.txt
drwxr-xr-x   6 qixinbo  staff    192  1 18 10:54 skills
-rw-r--r--@  1 qixinbo  staff   7871  1 18 13:39 v0_bash_agent.py
-rw-r--r--   1 qixinbo  staff   1881  1 18 10:54 v0_bash_agent_mini.py
-rw-r--r--   1 qixinbo  staff  13509  1 18 13:28 v1_basic_agent.py
-rw-r--r--   1 qixinbo  staff  16967  1 18 13:28 v2_todo_agent.py
-rw-r--r--   1 qixinbo  staff  19775  1 18 13:28 v3_subagent.py
-rw-r--r--   1 qixinbo  staff  24461  1 18 13:28 v4_skills_agent.py

[debug] ---
/Users/qixinbo/Projects/learn-claude-code
total 272
drwxr-xr-x  21 qixinbo  staff    672  1 18 13:39 .
drwxr-xr-x  28 qixinbo  staff    896  1 18 10:54 ..
-rw-r--r--   1 qixinbo  staff    147  1 18 11:13 .env
-rw-r--r--   1 qixinbo  staff    131  1 18 10:54 .env.example
drwxr-xr-x  12 qixinbo  staff    384  1 18 13:28 .git
-rw-r--r--   1 qixinbo  staff   4688  1 18 10:54 .gitignore
-rw-r--r--@  1 qixinbo  staff   1068  1 18 10:54 LICENSE
-rw-r--r--   1 qixinbo  staff   6607  1 18 10:54 README.md
-rw-r--r--   1 qixinbo  staff   6312  1 18 10:54 README_zh.md
drwxr-xr-x@  7 qixinbo  staff    224  1 18 13:39 __pycache__
drwxr-xr-x   8 qixinbo  staff    256  1 18 10:54 articles
-rw-r--r--   1 qixinbo  staff     12  1 18 13:39 debug.txt
drwxr-xr-x  12 qixinbo  staff    384  1 18 10:54 docs
-rw-r--r--@  1 qixinbo  staff     27  1 18 10:54 requirements.txt
drwxr-xr-x   6 qixinbo  staff    192  1 18 10:54 skills
-rw-r--r--@  1 qixinbo  staff   7871  1 18 13:39 v0_bash_agent.py
-rw-r--r--   1 qixinbo  staff   1881  1 18 10:54 v0_bash_agent_mini.py
-rw-r--r--   1 qixinbo  staff  13509  1 18 13:28 v1_basic_agent.py
-rw-r--r--   1 qixinbo  staff  16967  1 18 13:28 v2_todo_agent.py
-rw-r--r--   1 qixinbo  staff  19775  1 18 13:28 v3_subagent.py
-rw-r--r--   1 qixinbo  staff  24461  1 18 13:28 v4_skills_agent.py


[debug] results (after append)
[{'type': 'tool_result', 'tool_use_id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'content': '/Users/qixinbo/Projects/learn-claude-code\ntotal 272\ndrwxr-xr-x  21 qixinbo  staff    672  1 18 13:39 .\ndrwxr-xr-x  28 qixinbo  staff    896  1 18 10:54 ..\n-rw-r--r--   1 qixinbo  staff    147  1 18 11:13 .env\n-rw-r--r--   1 qixinbo  staff    131  1 18 10:54 .env.example\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 13:28 .git\n-rw-r--r--   1 qixinbo  staff   4688  1 18 10:54 .gitignore\n-rw-r--r--@  1 qixinbo  staff   1068  1 18 10:54 LICENSE\n-rw-r--r--   1 qixinbo  staff   6607  1 18 10:54 README.md\n-rw-r--r--   1 qixinbo  staff   6312  1 18 10:54 README_zh.md\ndrwxr-xr-x@  7 qixinbo  staff    224  1 18 13:39 __pycache__\ndrwxr-xr-x   8 qixinbo  staff    256  1 18 10:54 articles\n-rw-r--r--   1 qixinbo  staff     12  1 18 13:39 debug.txt\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 10:54 docs\n-rw-r--r--@  1 qixinbo  staff     27  1 18 10:54 requirements.txt\ndrwxr-xr-x   6 qixinbo  staff    192  1 18 10:54 skills\n-rw-r--r--@  1 qixinbo  staff   7871  1 18 13:39 v0_bash_agent.py\n-rw-r--r--   1 qixinbo  staff   1881  1 18 10:54 v0_bash_agent_mini.py\n-rw-r--r--   1 qixinbo  staff  13509  1 18 13:28 v1_basic_agent.py\n-rw-r--r--   1 qixinbo  staff  16967  1 18 13:28 v2_todo_agent.py\n-rw-r--r--   1 qixinbo  staff  19775  1 18 13:28 v3_subagent.py\n-rw-r--r--   1 qixinbo  staff  24461  1 18 13:28 v4_skills_agent.py\n'}]
[debug] ---

[debug] history (after tool results append)
[{'role': 'user', 'content': 'who are you'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': "I am a CLI agent designed to help you solve problems using bash commands. I'm currently operating in the directory `/Users/qixinbo/Projects/learn-claude-code`.\n\nLet me show you where we are:"}, {'type': 'tool_use', 'id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'name': 'bash', 'input': {'command': 'pwd && ls -la'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'content': '/Users/qixinbo/Projects/learn-claude-code\ntotal 272\ndrwxr-xr-x  21 qixinbo  staff    672  1 18 13:39 .\ndrwxr-xr-x  28 qixinbo  staff    896  1 18 10:54 ..\n-rw-r--r--   1 qixinbo  staff    147  1 18 11:13 .env\n-rw-r--r--   1 qixinbo  staff    131  1 18 10:54 .env.example\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 13:28 .git\n-rw-r--r--   1 qixinbo  staff   4688  1 18 10:54 .gitignore\n-rw-r--r--@  1 qixinbo  staff   1068  1 18 10:54 LICENSE\n-rw-r--r--   1 qixinbo  staff   6607  1 18 10:54 README.md\n-rw-r--r--   1 qixinbo  staff   6312  1 18 10:54 README_zh.md\ndrwxr-xr-x@  7 qixinbo  staff    224  1 18 13:39 __pycache__\ndrwxr-xr-x   8 qixinbo  staff    256  1 18 10:54 articles\n-rw-r--r--   1 qixinbo  staff     12  1 18 13:39 debug.txt\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 10:54 docs\n-rw-r--r--@  1 qixinbo  staff     27  1 18 10:54 requirements.txt\ndrwxr-xr-x   6 qixinbo  staff    192  1 18 10:54 skills\n-rw-r--r--@  1 qixinbo  staff   7871  1 18 13:39 v0_bash_agent.py\n-rw-r--r--   1 qixinbo  staff   1881  1 18 10:54 v0_bash_agent_mini.py\n-rw-r--r--   1 qixinbo  staff  13509  1 18 13:28 v1_basic_agent.py\n-rw-r--r--   1 qixinbo  staff  16967  1 18 13:28 v2_todo_agent.py\n-rw-r--r--   1 qixinbo  staff  19775  1 18 13:28 v3_subagent.py\n-rw-r--r--   1 qixinbo  staff  24461  1 18 13:28 v4_skills_agent.py\n'}]}]
[debug] ---

[debug] response.stop_reason
end_turn
[debug] ---

[debug] response.content
[TextBlock(citations=None, text="I'm a bash-focused assistant that can:\n\n1. **Execute shell commands** - read files, write files, navigate directories, run programs\n2. **Help with file operations** - create, modify, search through files and directories  \n3. **Spawn subagents** - for complex tasks, I can create isolated agents to handle subtasks and return summaries\n4. **Work with code projects** - as you can see, this appears to be a project about learning Claude code with various agent implementations\n\nI prefer to act with tools rather than just explain things. What would you like me to help you with in this project directory?", type='text')]
[debug] ---

[debug] history (after assistant append)
[{'role': 'user', 'content': 'who are you'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': "I am a CLI agent designed to help you solve problems using bash commands. I'm currently operating in the directory `/Users/qixinbo/Projects/learn-claude-code`.\n\nLet me show you where we are:"}, {'type': 'tool_use', 'id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'name': 'bash', 'input': {'command': 'pwd && ls -la'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01R4E1Pj7q8BrwSqA2gJoeDJ', 'content': '/Users/qixinbo/Projects/learn-claude-code\ntotal 272\ndrwxr-xr-x  21 qixinbo  staff    672  1 18 13:39 .\ndrwxr-xr-x  28 qixinbo  staff    896  1 18 10:54 ..\n-rw-r--r--   1 qixinbo  staff    147  1 18 11:13 .env\n-rw-r--r--   1 qixinbo  staff    131  1 18 10:54 .env.example\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 13:28 .git\n-rw-r--r--   1 qixinbo  staff   4688  1 18 10:54 .gitignore\n-rw-r--r--@  1 qixinbo  staff   1068  1 18 10:54 LICENSE\n-rw-r--r--   1 qixinbo  staff   6607  1 18 10:54 README.md\n-rw-r--r--   1 qixinbo  staff   6312  1 18 10:54 README_zh.md\ndrwxr-xr-x@  7 qixinbo  staff    224  1 18 13:39 __pycache__\ndrwxr-xr-x   8 qixinbo  staff    256  1 18 10:54 articles\n-rw-r--r--   1 qixinbo  staff     12  1 18 13:39 debug.txt\ndrwxr-xr-x  12 qixinbo  staff    384  1 18 10:54 docs\n-rw-r--r--@  1 qixinbo  staff     27  1 18 10:54 requirements.txt\ndrwxr-xr-x   6 qixinbo  staff    192  1 18 10:54 skills\n-rw-r--r--@  1 qixinbo  staff   7871  1 18 13:39 v0_bash_agent.py\n-rw-r--r--   1 qixinbo  staff   1881  1 18 10:54 v0_bash_agent_mini.py\n-rw-r--r--   1 qixinbo  staff  13509  1 18 13:28 v1_basic_agent.py\n-rw-r--r--   1 qixinbo  staff  16967  1 18 13:28 v2_todo_agent.py\n-rw-r--r--   1 qixinbo  staff  19775  1 18 13:28 v3_subagent.py\n-rw-r--r--   1 qixinbo  staff  24461  1 18 13:28 v4_skills_agent.py\n'}]}, {'role': 'assistant', 'content': [{'type': 'text', 'text': "I'm a bash-focused assistant that can:\n\n1. **Execute shell commands** - read files, write files, navigate directories, run programs\n2. **Help with file operations** - create, modify, search through files and directories  \n3. **Spawn subagents** - for complex tasks, I can create isolated agents to handle subtasks and return summaries\n4. **Work with code projects** - as you can see, this appears to be a project about learning Claude code with various agent implementations\n\nI prefer to act with tools rather than just explain things. What would you like me to help you with in this project directory?"}]}]
[debug] ---

[debug] final_text
I'm a bash-focused assistant that can:

1. **Execute shell commands** - read files, write files, navigate directories, run programs
2. **Help with file operations** - create, modify, search through files and directories
3. **Spawn subagents** - for complex tasks, I can create isolated agents to handle subtasks and return summaries
4. **Work with code projects** - as you can see, this appears to be a project about learning Claude code with various agent implementations

I prefer to act with tools rather than just explain things. What would you like me to help you with in this project directory?
[debug] ---
I'm a bash-focused assistant that can:

1. **Execute shell commands** - read files, write files, navigate directories, run programs
2. **Help with file operations** - create, modify, search through files and directories
3. **Spawn subagents** - for complex tasks, I can create isolated agents to handle subtasks and return summaries
4. **Work with code projects** - as you can see, this appears to be a project about learning Claude code with various agent implementations

I prefer to act with tools rather than just explain things. What would you like me to help you with in this project directory?

第二次：

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>> 查一下有多个py文件在该文件夹

[debug] history (after user append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}]
[debug] ---

[debug] response.stop_reason
tool_use
[debug] ---

[debug] response.content
[TextBlock(citations=None, text='我来查看一下当前文件夹中有多少个Python文件。', type='text'), ToolUseBlock(id='toolu_0156QhdnZRPGSaYqvumccv1a', input={'command': 'find . -name "*.py" -type f'}, name='bash', type='tool_use')]
[debug] ---

[debug] history (after assistant append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '我来查看一下当前文件夹中有多少个Python文件。'}, {'type': 'tool_use', 'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}]}]
[debug] ---

[debug] tool_use
{'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}
[debug] ---
$ find . -name "*.py" -type f

[debug] tool_result
./v0_bash_agent_mini.py
./v1_basic_agent.py
./v4_skills_agent.py
./v3_subagent.py
./v2_todo_agent.py
./v0_bash_agent.py
./skills/agent-builder/references/subagent-pattern.py
./skills/agent-builder/references/tool-templates.py
./skills/agent-builder/references/minimal-agent.py
./skills/agent-builder/scripts/init_agent.py

[debug] ---
./v0_bash_agent_mini.py
./v1_basic_agent.py
./v4_skills_agent.py
./v3_subagent.py
./v2_todo_agent.py
./v0_bash_agent.py
./skills/agent-builder/references/subagent-pattern.py
./skills/agent-builder/references/tool-templates.py
./skills/agent-builder/references/minimal-agent.py
./skills/agent-builder/scripts/init_agent.py


[debug] results (after append)
[{'type': 'tool_result', 'tool_use_id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'content': './v0_bash_agent_mini.py\n./v1_basic_agent.py\n./v4_skills_agent.py\n./v3_subagent.py\n./v2_todo_agent.py\n./v0_bash_agent.py\n./skills/agent-builder/references/subagent-pattern.py\n./skills/agent-builder/references/tool-templates.py\n./skills/agent-builder/references/minimal-agent.py\n./skills/agent-builder/scripts/init_agent.py\n'}]
[debug] ---

[debug] history (after tool results append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '我来查看一下当前文件夹中有多少个Python文件。'}, {'type': 'tool_use', 'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'content': './v0_bash_agent_mini.py\n./v1_basic_agent.py\n./v4_skills_agent.py\n./v3_subagent.py\n./v2_todo_agent.py\n./v0_bash_agent.py\n./skills/agent-builder/references/subagent-pattern.py\n./skills/agent-builder/references/tool-templates.py\n./skills/agent-builder/references/minimal-agent.py\n./skills/agent-builder/scripts/init_agent.py\n'}]}]
[debug] ---

[debug] response.stop_reason
tool_use
[debug] ---

[debug] response.content
[ToolUseBlock(id='toolu_01ChrTdDzP34cMu6a2Dn8hmK', input={'command': 'find . -name "*.py" -type f | wc -l'}, name='bash', type='tool_use')]
[debug] ---

[debug] history (after assistant append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '我来查看一下当前文件夹中有多少个Python文件。'}, {'type': 'tool_use', 'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'content': './v0_bash_agent_mini.py\n./v1_basic_agent.py\n./v4_skills_agent.py\n./v3_subagent.py\n./v2_todo_agent.py\n./v0_bash_agent.py\n./skills/agent-builder/references/subagent-pattern.py\n./skills/agent-builder/references/tool-templates.py\n./skills/agent-builder/references/minimal-agent.py\n./skills/agent-builder/scripts/init_agent.py\n'}]}, {'role': 'assistant', 'content': [{'type': 'tool_use', 'id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f | wc -l'}}]}]
[debug] ---

[debug] tool_use
{'id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f | wc -l'}}
[debug] ---
$ find . -name "*.py" -type f | wc -l

[debug] tool_result
      10

[debug] ---
      10


[debug] results (after append)
[{'type': 'tool_result', 'tool_use_id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'content': '      10\n'}]
[debug] ---

[debug] history (after tool results append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '我来查看一下当前文件夹中有多少个Python文件。'}, {'type': 'tool_use', 'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'content': './v0_bash_agent_mini.py\n./v1_basic_agent.py\n./v4_skills_agent.py\n./v3_subagent.py\n./v2_todo_agent.py\n./v0_bash_agent.py\n./skills/agent-builder/references/subagent-pattern.py\n./skills/agent-builder/references/tool-templates.py\n./skills/agent-builder/references/minimal-agent.py\n./skills/agent-builder/scripts/init_agent.py\n'}]}, {'role': 'assistant', 'content': [{'type': 'tool_use', 'id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f | wc -l'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'content': '      10\n'}]}]
[debug] ---

[debug] response.stop_reason
end_turn
[debug] ---

[debug] response.content
[TextBlock(citations=None, text='在当前文件夹中总共有 **10个** Python文件：\n\n**根目录下的文件：**\n1. v0_bash_agent_mini.py\n2. v1_basic_agent.py\n3. v4_skills_agent.py\n4. v3_subagent.py\n5. v2_todo_agent.py\n6. v0_bash_agent.py\n\n**子目录下的文件：**\n7. skills/agent-builder/references/subagent-pattern.py\n8. skills/agent-builder/references/tool-templates.py\n9. skills/agent-builder/references/minimal-agent.py\n10. skills/agent-builder/scripts/init_agent.py', type='text')]
[debug] ---

[debug] history (after assistant append)
[{'role': 'user', 'content': '查一下有多个py文件在该文件夹'}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '我来查看一下当前文件夹中有多少个Python文件。'}, {'type': 'tool_use', 'id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_0156QhdnZRPGSaYqvumccv1a', 'content': './v0_bash_agent_mini.py\n./v1_basic_agent.py\n./v4_skills_agent.py\n./v3_subagent.py\n./v2_todo_agent.py\n./v0_bash_agent.py\n./skills/agent-builder/references/subagent-pattern.py\n./skills/agent-builder/references/tool-templates.py\n./skills/agent-builder/references/minimal-agent.py\n./skills/agent-builder/scripts/init_agent.py\n'}]}, {'role': 'assistant', 'content': [{'type': 'tool_use', 'id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'name': 'bash', 'input': {'command': 'find . -name "*.py" -type f | wc -l'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01ChrTdDzP34cMu6a2Dn8hmK', 'content': '      10\n'}]}, {'role': 'assistant', 'content': [{'type': 'text', 'text': '在当前文件夹中总共有 **10个** Python文件：\n\n**根目录下的文件：**\n1. v0_bash_agent_mini.py\n2. v1_basic_agent.py\n3. v4_skills_agent.py\n4. v3_subagent.py\n5. v2_todo_agent.py\n6. v0_bash_agent.py\n\n**子目录下的文件：**\n7. skills/agent-builder/references/subagent-pattern.py\n8. skills/agent-builder/references/tool-templates.py\n9. skills/agent-builder/references/minimal-agent.py\n10. skills/agent-builder/scripts/init_agent.py'}]}]
[debug] ---

[debug] final_text
在当前文件夹中总共有 **10个** Python文件：

**根目录下的文件：**
1. v0_bash_agent_mini.py
2. v1_basic_agent.py
3. v4_skills_agent.py
4. v3_subagent.py
5. v2_todo_agent.py
6. v0_bash_agent.py

**子目录下的文件：**
7. skills/agent-builder/references/subagent-pattern.py
8. skills/agent-builder/references/tool-templates.py
9. skills/agent-builder/references/minimal-agent.py
10. skills/agent-builder/scripts/init_agent.py
[debug] ---
在当前文件夹中总共有 **10个** Python文件：

**根目录下的文件：**
1. v0_bash_agent_mini.py
2. v1_basic_agent.py
3. v4_skills_agent.py
4. v3_subagent.py
5. v2_todo_agent.py
6. v0_bash_agent.py

**子目录下的文件：**
7. skills/agent-builder/references/subagent-pattern.py
8. skills/agent-builder/references/tool-templates.py
9. skills/agent-builder/references/minimal-agent.py
10. skills/agent-builder/scripts/init_agent.py