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a9c71002d2
Comprehensive rewrite establishing the harness engineering narrative across the entire repository. README (EN/ZH/JA): added "The Model IS the Agent" manifesto with historical proof (DQN, OpenAI Five, AlphaStar, Tencent Jueyu), "What an Agent Is NOT" critique, harness engineer role definition, "Why Claude Code" as masterclass in harness design, and universe vision. Consistent framing: model = driver, harness = vehicle. docs (36 files, 3 languages): injected one-line "Harness layer" callout after the motto in every session document (s01-s12). agents (13 Python files): added harness framing comment before each module docstring. skills/agent-philosophy.md: full rewrite aligned with harness narrative.
119 lines
3.6 KiB
Markdown
119 lines
3.6 KiB
Markdown
# s01: The Agent Loop (智能体循环)
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`[ s01 ] s02 > s03 > s04 > s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"One loop & Bash is all you need"* -- 一个工具 + 一个循环 = 一个智能体。
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>
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> **Harness 层**: 循环 -- 模型与真实世界的第一道连接。
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## 问题
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语言模型能推理代码, 但碰不到真实世界 -- 不能读文件、跑测试、看报错。没有循环, 每次工具调用你都得手动把结果粘回去。你自己就是那个循环。
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## 解决方案
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```
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+--------+ +-------+ +---------+
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| User | ---> | LLM | ---> | Tool |
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| prompt | | | | execute |
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+--------+ +---+---+ +----+----+
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^ |
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| tool_result |
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+----------------+
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(loop until stop_reason != "tool_use")
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```
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一个退出条件控制整个流程。循环持续运行, 直到模型不再调用工具。
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## 工作原理
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1. 用户 prompt 作为第一条消息。
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```python
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messages.append({"role": "user", "content": query})
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```
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2. 将消息和工具定义一起发给 LLM。
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```python
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response = client.messages.create(
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model=MODEL, system=SYSTEM, messages=messages,
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tools=TOOLS, max_tokens=8000,
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)
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```
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3. 追加助手响应。检查 `stop_reason` -- 如果模型没有调用工具, 结束。
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```python
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messages.append({"role": "assistant", "content": response.content})
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if response.stop_reason != "tool_use":
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return
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```
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4. 执行每个工具调用, 收集结果, 作为 user 消息追加。回到第 2 步。
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```python
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results = []
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for block in response.content:
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if block.type == "tool_use":
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output = run_bash(block.input["command"])
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results.append({
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"type": "tool_result",
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"tool_use_id": block.id,
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"content": output,
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})
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messages.append({"role": "user", "content": results})
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```
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组装为一个完整函数:
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```python
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def agent_loop(query):
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messages = [{"role": "user", "content": query}]
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while True:
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response = client.messages.create(
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model=MODEL, system=SYSTEM, messages=messages,
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tools=TOOLS, max_tokens=8000,
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)
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messages.append({"role": "assistant", "content": response.content})
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if response.stop_reason != "tool_use":
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return
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results = []
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for block in response.content:
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if block.type == "tool_use":
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output = run_bash(block.input["command"])
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results.append({
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"type": "tool_result",
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"tool_use_id": block.id,
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"content": output,
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})
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messages.append({"role": "user", "content": results})
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```
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不到 30 行, 这就是整个智能体。后面 11 个章节都在这个循环上叠加机制 -- 循环本身始终不变。
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## 变更内容
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| 组件 | 之前 | 之后 |
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|---------------|------------|--------------------------------|
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| Agent loop | (无) | `while True` + stop_reason |
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| Tools | (无) | `bash` (单一工具) |
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| Messages | (无) | 累积式消息列表 |
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| Control flow | (无) | `stop_reason != "tool_use"` |
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## 试一试
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```sh
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cd learn-claude-code
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python agents/s01_agent_loop.py
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```
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试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
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1. `Create a file called hello.py that prints "Hello, World!"`
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2. `List all Python files in this directory`
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3. `What is the current git branch?`
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4. `Create a directory called test_output and write 3 files in it`
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