标题：Show HN: 使用 Code 实现 Claude 代码功能

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codesys| codesys
examples| examples
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README.md| README.md
advanced.py| advanced.py
example.py| example.py
plan_and_execute.py| plan_and_execute.py
pyproject.toml| pyproject.toml
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codesys SDK

一个用于与 Claude CLI 工具交互的 Python SDK。

Installation

pip install codesys

Requirements

Quick Start

from codesys import Agent
# 使用一个工作目录初始化
agent = Agent(working_dir="/Users/seansullivan/lmsys-sdk/")
# 运行 Claude 并使用 prompt，自动打印流式输出
lines = agent.run("""/init""", stream=True)

Practical Use:

我发现使用这个 SDK 最有效的方法是模仿我实际使用 Claude 代码的工作流程，我发现这种方法非常有效。 工作流程很简单：通过探索代码库来计划任务，然后实现该计划。

#!/usr/bin/env python3
import argparse
import os
from codesys import Agent

# 硬编码的默认值 - 如果需要，可以直接在代码中修改这些值
DEFAULT_WORKING_DIR = os.getcwd() # 默认使用当前工作目录

DEFAULT_USER_MESSAGE = "Your default task message here" # 替换成你的默认消息


def generate_plan(working_dir, user_message):
  """根据用户消息在 plan.md 文件中生成一个计划。"""
  prompt = f'''
给定以下任务，在 plan.md 文件中生成一个计划:
<task>
{user_message}
</task>
鉴于这个任务，探索代码库，并创建一个用于实现该任务的计划，保存到 plan.md 文件中。 ultrathink
'''
  Agent(working_dir=working_dir).run(prompt, stream=True)


def execute_plan(working_dir):
  """执行 plan.md 中制定的计划。"""
  prompt = '''
实现 plan.md 文件中制定的任务: ultrathink
'''
  Agent(working_dir=working_dir).run(prompt, stream=True)


def main():
  parser = argparse.ArgumentParser(description='根据任务生成并执行计划。')
  parser.add_argument('--working-dir', '-w', help='Agent 的工作目录')
  parser.add_argument('--message', '-m', help='用于生成计划的任务消息')
  args = parser.parse_args()
  # 如果提供了命令行参数，则使用命令行参数，否则使用硬编码的默认值
  working_dir = args.working_dir if args.working_dir else DEFAULT_WORKING_DIR
  user_message = args.message if args.message else DEFAULT_USER_MESSAGE
  print(f"Working directory: {working_dir}")
  print(f"Generating plan for task: {user_message}")
  generate_plan(working_dir, user_message)
  print("Executing plan from plan.md")
  execute_plan(working_dir)
if __name__ == "__main__":
  main()

Features

API Reference

Agent Class

Agent(working_dir=None, allowed_tools=None)

Parameters:

Methods

run

run(prompt, stream=False, output_format=None, additional_args=None, auto_print=True)

使用指定的 prompt 运行 Claude。 Parameters:

Returns:

run_with_tools

run_with_tools(prompt, tools, stream=False, auto_print=True)

使用特定的允许工具运行 Claude。 Parameters:

Returns:

Example: Automatic Streaming

from codesys import Agent
agent = Agent()
# 这将逐行自动打印输出
lines = agent.run("Generate a short story", stream=True)

Example: Manual Streaming with JSON parsing

from codesys import Agent
import json
agent = Agent()
process = agent.run("Generate a short story", stream=True, output_format="stream-json", auto_print=False)
for line in process.stdout:
  if line.strip():
    try:
      data = json.loads(line)
      print(data.get("content", ""))
    except json.JSONDecodeError:
      print(f"Error parsing JSON: {line}")

Examples

from codesys import Agent
# 使用一个工作目录初始化
agent = Agent(working_dir="/Users/seansullivan/lmsys-sdk/")
# 运行 Claude 并使用 prompt，自动打印流式输出
lines = agent.run("create another example of example1_custom_tools.py which shows how to use read only tools. note the source code of the sdk in codesys/agent.py", stream=True)

"""
Example 1: Customizing tools during initialization
This example demonstrates how to initialize an Agent with only specific tools.
"""
from codesys import Agent
# Initialize with only specific tools
restricted_agent = Agent(
  working_dir="./",
  allowed_tools=["Edit", "Write", "View"] # Only allow editing, writing files and viewing
) # Implementation in agent.py lines 19-39
print(f"Agent initialized with tools: {restricted_agent.allowed_tools}")

from codesys import Agent
# Initialize with default tools
agent = Agent(working_dir="./") # Implementation in agent.py lines 19-39
print(f"Default tools: {agent.allowed_tools}")
# Run with only specific tools for one operation
bash_only_response = agent.run_with_tools(
  prompt="List files in the current directory",
  tools=["Bash"], # Only allow Bash for this specific run
  stream=False
) # Implementation in agent.py lines 132-155
print(f"Tools after run_with_tools: {agent.allowed_tools} # Original tools are restored")

"""
Example 3: Manual handling of streaming output
This example demonstrates how to manually handle streaming output from the agent.
"""
from codesys import Agent
import json
import time
# Initialize an agent
agent = Agent(working_dir="./")
# Get a process for streaming manually
process = agent.run(
  prompt="Explain what an LLM Agent is in 3 sentences",
  stream=True,
  auto_print=False # Don't auto-print, we'll handle the output manually
) # Implementation in agent.py lines 41-96 (stream=True, auto_print=False path)
print("Streaming output manually, processing each line:")
for i, line in enumerate(process.stdout):
  # Parse the JSON line
  try:
    data = json.loads(line)
    # Do something with each piece of output
    print(f"Line {i+1}: {data.get('content', '')}")
  except json.JSONDecodeError:
    print(f"Raw line: {line}")
  # Simulate processing time
  time.sleep(0.1)
  # Compare with agent.py lines 98-116 (auto-handling of streaming)

"""
Example 4: Using output formats and additional arguments
This example demonstrates how to use different output formats and pass additional arguments.
"""
from codesys import Agent
# Initialize an agent
agent = Agent(working_dir="./")
# Run with custom output format and additional arguments
response = agent.run(
  prompt="What can you tell me about this codebase?",
  output_format="json", # Request JSON output
  additional_args={
    "temperature": 0.7,   # Set temperature
    "max-tokens": 500,   # Limit output tokens
    "silent": True     # Suppress progress output
  }
) # Implementation in agent.py lines 41-70 (output_format handling), 74-80 (additional_args)
print(f"Response type: {type(response)}")
print("First 100 characters of response:", response[:100] if isinstance(response, str) else "Not a string")

License

MIT

CodeSYS

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