比AgentGpt还强，一句话，生成一个可运行的工程？

    for step in STEPS[steps_config]:
        messages = step(ai, dbs)
        dbs.logs[step.__name__] = json.dumps(messages)

# Different configs of what steps to run
STEPS = {
    "default": [simple_gen, gen_entrypoint, execute_entrypoint],
    "benchmark": [simple_gen, gen_entrypoint],
    "simple": [simple_gen, gen_entrypoint, execute_entrypoint],
    "tdd": [gen_spec, gen_unit_tests, gen_code, gen_entrypoint, execute_entrypoint],
    "tdd+": [
        gen_spec,
        gen_unit_tests,
        gen_code,
        fix_code,
        gen_entrypoint,
        execute_entrypoint,
    ],
    "clarify": [clarify, gen_clarified_code, gen_entrypoint, execute_entrypoint],
    "respec": [
        gen_spec,
        respec,
        gen_unit_tests,
        gen_code,
        gen_entrypoint,
        execute_entrypoint,
    ],
    "execute_only": [execute_entrypoint],
    "use_feedback": [use_feedback],
}

def simple_gen(ai: AI, dbs: DBs):
    """Run the AI on the main prompt and save the results"""
    messages = ai.start(
        setup_sys_prompt(dbs),
        dbs.input["main_prompt"],
    )
    to_files(messages[-1]["content"], dbs.workspace)
    return messages

def gen_entrypoint(ai, dbs):
    messages = ai.start(
        system=(
            "You will get information about a codebase that is currently on disk in "
            "the current folder.\n"
            "From this you will answer with code blocks that includes all the necessary "
            "unix terminal commands to "
            "a) install dependencies "
            "b) run all necessary parts of the codebase (in parallell if necessary).\n"
            "Do not install globally. Do not use sudo.\n"
            "Do not explain the code, just give the commands.\n"
        ),
        user="Information about the codebase:\n\n" + dbs.workspace["all_output.txt"],
    )
    print()

    regex = r"```\S*\n(.+?)```"
    matches = re.finditer(regex, messages[-1]["content"], re.DOTALL)
    dbs.workspace["run.sh"] = "\n".join(match.group(1) for match in matches)
    return messages

def execute_entrypoint(ai, dbs):
    command = dbs.workspace["run.sh"]

    print("Do you want to execute this code?")
    print()
    print(command)
    print()
    print('If yes, press enter. Otherwise, type "no"')
    print()
    if input() != "":
        print("Ok, not executing the code.")
        return []
    print("Executing the code...")
    print()
    subprocess.run("bash run.sh", shell=True, cwd=dbs.workspace.path)
    return []

def setup_sys_prompt(dbs):
    return dbs.identity["generate"] + "\nUseful to know:\n" + dbs.identity["philosophy"]


def simple_gen(ai: AI, dbs: DBs):
    """Run the AI on the main prompt and save the results"""
    messages = ai.start(
        setup_sys_prompt(dbs),
        dbs.input["main_prompt"],
    )
    to_files(messages[-1]["content"], dbs.workspace)
    return messages

You will get instructions for code to write.
You will write a very long answer. Make sure that every detail of the architecture is, in the end, implemented as code.
Make sure that every detail of the architecture is, in the end, implemented as code.

Think step by step and reason yourself to the right decisions to make sure we get it right.
You will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.

Then you will output the content of each file including ALL code.
Each file must strictly follow a markdown code block format, where the following tokens must be replaced such that
FILENAME is the lowercase file name including the file extension,
LANG is the markup code block language for the code's language, and CODE is the code:

FILENAME
```LANG
CODE
```

You will start with the "entrypoint" file, then go to the ones that are imported by that file, and so on.
Please note that the code should be fully functional. No placeholders.

Follow a language and framework appropriate best practice file naming convention.
Make sure that files contain all imports, types etc. Make sure that code in different files are compatible with each other.
Ensure to implement all code, if you are unsure, write a plausible implementation.
Include module dependency or package manager dependency definition file.
Before you finish, double check that all parts of the architecture is present in the files.

##系统##
您将收到编写代码的指示。
您将编写一个非常长的答案。确保最终将架构的每个细节都实现为代码。
确保最终将架构的每个细节都实现为代码。

逐步思考并理性地做出正确决策，以确保我们正确理解。
首先，您将列出所需的核心类、函数、方法的名称，以及对它们目的的快速注释。

然后，您将输出每个文件的内容，包括所有代码。
每个文件必须严格遵循Markdown代码块格式，其中以下标记必须替换，以便：
FILENAME 是包括文件扩展名的小写文件名，
LANG 是代码语言的标记代码块语言，
CODE 是代码：

FILENAME
```LANG
CODE
```

您将从"入口点"文件开始，然后继续处理该文件导入的文件，依此类推。
请注意，代码应该是完全可用的，不要使用占位符。

遵循适合语言和框架的最佳实践文件命名约定。
确保文件包含所有导入、类型等。确保不同文件中的代码彼此兼容。
确保实现所有代码，如果不确定，请编写一个合理的实现。
包括模块依赖或包管理器依赖定义文件。
在完成之前，请仔细检查文件中是否存在架构的所有部分。

有用的信息：

您几乎总是将不同的类放在不同的文件中。
对于Python，您始终创建一个适当的requirements.txt文件。
对于NodeJS，您始终创建一个适当的package.json文件。
您总是添加一个简要描述函数定义目的的注释。
您尝试添加解释非常复杂的逻辑的注释。
您始终遵循请求语言的最佳实践，以描述代码编写为一个定义的包/项目。

Python工具首选项：
- pytest
- dataclasses

##用户##
使用html+css+javascript 些一个简单的 todo-list，将任务存储在local storage中，允许用户添加任务，删除任务，编辑任务
##助理##