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- Remove all reverse-engineered Claude Code source code - Replace with 100% original educational content from mini-claude-code - Add clear disclaimer: independent project, not affiliated with Anthropic - 5 progressive agent implementations (v0-v4, ~1100 lines total) - Include agent-builder skill for teaching agent construction - Bilingual documentation (EN + ZH) This repository now focuses purely on teaching how modern AI agents work through original, from-scratch implementations. Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
118 lines
3.6 KiB
Markdown
118 lines
3.6 KiB
Markdown
# v0: Bash 就是一切
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**终极简化：~50 行代码，1 个工具，完整的 Agent 能力。**
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在构建 v1、v2、v3 之后，一个问题浮现：Agent 的*本质*到底是什么？
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v0 通过反向思考来回答——剥离一切，直到只剩下核心。
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## 核心洞察
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Unix 哲学：一切皆文件，一切皆可管道。Bash 是这个世界的入口：
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| 你需要 | Bash 命令 |
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|--------|-----------|
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| 读文件 | `cat`, `head`, `grep` |
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| 写文件 | `echo '...' > file` |
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| 搜索 | `find`, `grep`, `rg` |
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| 执行 | `python`, `npm`, `make` |
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| **子代理** | `python v0_bash_agent.py "task"` |
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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。
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## 完整代码
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```python
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#!/usr/bin/env python
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from anthropic import Anthropic
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import subprocess, sys, os
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client = Anthropic(api_key="your-key", base_url="...")
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TOOL = [{
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"name": "bash",
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"description": """执行 shell 命令。模式：
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- 读取: cat/grep/find/ls
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- 写入: echo '...' > file
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- 子代理: python v0_bash_agent.py 'task description'""",
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"input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}
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}]
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SYSTEM = f"CLI agent at {os.getcwd()}. Use bash. Spawn subagent for complex tasks."
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def chat(prompt, history=[]):
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history.append({"role": "user", "content": prompt})
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while True:
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r = client.messages.create(model="...", system=SYSTEM, messages=history, tools=TOOL, max_tokens=8000)
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history.append({"role": "assistant", "content": r.content})
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if r.stop_reason != "tool_use":
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return "".join(b.text for b in r.content if hasattr(b, "text"))
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results = []
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for b in r.content:
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if b.type == "tool_use":
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out = subprocess.run(b.input["command"], shell=True, capture_output=True, text=True, timeout=300)
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results.append({"type": "tool_result", "tool_use_id": b.id, "content": out.stdout + out.stderr})
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history.append({"role": "user", "content": results})
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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print(chat(sys.argv[1])) # 子代理模式
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else:
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h = []
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while (q := input(">> ")) not in ("q", ""):
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print(chat(q, h))
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```
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这就是整个 Agent。~50 行。
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## 子代理工作原理
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```
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主代理
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└─ bash: python v0_bash_agent.py "分析架构"
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└─ 子代理（独立进程，全新历史）
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├─ bash: find . -name "*.py"
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├─ bash: cat src/main.py
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└─ 通过 stdout 返回摘要
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```
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**进程隔离 = 上下文隔离**
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- 子进程有自己的 `history=[]`
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- 父进程捕获 stdout 作为工具结果
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- 递归调用实现无限嵌套
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## v0 牺牲了什么
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| 特性 | v0 | v3 |
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|------|----|----|
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| 代理类型 | 无 | explore/code/plan |
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| 工具过滤 | 无 | 白名单 |
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| 进度显示 | 普通 stdout | 行内更新 |
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| 代码复杂度 | ~50 行 | ~450 行 |
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## v0 证明了什么
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**复杂能力从简单规则中涌现：**
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1. **一个工具足够** — Bash 是通往一切的入口
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2. **递归 = 层级** — 自我调用实现子代理
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3. **进程 = 隔离** — 操作系统提供上下文分离
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4. **提示词 = 约束** — 指令塑造行为
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核心模式从未改变：
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```python
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while True:
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response = model(messages, tools)
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if response.stop_reason != "tool_use":
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return response.text
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results = execute(response.tool_calls)
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messages.append(results)
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```
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其他一切——待办、子代理、权限——都是围绕这个循环的精化。
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---
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**Bash 就是一切。**
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[← 返回 README](../README_zh.md)
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