在自动化办公日益重要的今天，如何将OpenClaw与Python深度集成，开发高效的自动化脚本？本文将从环境配置到实战案例，手把手教你入门。
一、环境准备
1.1 安装OpenClaw Python SDK
# 使用pip安装
pip install openclaw-python
# 验证安装
python -c "import openclaw; print(openclaw.__version__)"
1.2 配置环境变量
# ~/.bashrc 或 ~/.zshrc
export OPENCLAW_API_KEY="your-api-key-here"
export OPENCLAW_BASE_URL="https://api.openclaw.ai/v1"
1.3 初始化项目
# 创建项目目录
mkdir my_automation_project
cd my_automation_project
# 初始化OpenClaw配置
openclaw init
二、基础API调用
2.1 发送消息
from openclaw import Client
# 创建客户端
client = Client(api_key="your-api-key")
# 发送简单消息
response = client.chat.send(
message="帮我生成一份周报模板",
agent="writing-assistant"
)
print(response.content)
2.2 使用Agent
from openclaw.agents import Agent
# 初始化Agent
agent = Agent(
name="data-processor",
instructions="你是一个数据处理专家，擅长数据清洗和分析"
)
# 执行任务
result = agent.run("分析这个CSV文件的销售趋势",
files=["sales_data.csv"])
print(result)
三、自动化办公实战
3.1 场景一：邮件自动处理
需求：每天自动读取未读邮件，分类并生成摘要。
import imaplib
import email
from openclaw import Client
from datetime import datetime
class EmailProcessor:
def __init__(self):
self.client = Client()
self.agent = self.client.create_agent(
name="email-classifier",
instructions="对邮件进行分类：工作/营销/垃圾邮件"
)
def fetch_unread_emails(self):
"""获取未读邮件"""
mail = imaplib.IMAP4_SSL("imap.gmail.com")
mail.login("user@gmail.com", "password")
mail.select("inbox")
_, search_data = mail.search(None, "UNSEEN")
email_ids = search_data[0].split()
emails = []
for e_id in email_ids[:10]: # 限制处理数量
_, data = mail.fetch(e_id, "(RFC822)")
raw_email = data[0][1]
email_message = email.message_from_bytes(raw_email)
emails.append({
"subject": email_message["Subject"],
"from": email_message["From"],
"body": self.get_body(email_message)
})
return emails
def classify_and_summarize(self, emails):
"""分类并生成摘要"""
summary = []
for mail in emails:
# 使用OpenClaw分类
category = self.agent.run(
f"分类这封邮件：\\n主题：{mail['subject']}\\n内容：{mail['body'][:500]}"
)
summary.append({
"subject": mail["subject"],
"category": category,
"sender": mail["from"]
})
return summary
def get_body(self, msg):
"""提取邮件正文"""
if msg.is_multipart():
for part in msg.walk():
if part.get_content_type() == "text/plain":
return part.get_payload(decode=True).decode()
else:
return msg.get_payload(decode=True).decode()
# 使用示例
processor = EmailProcessor()
emails = processor.fetch_unread_emails()
summary = processor.classify_and_summarize(emails)
print(f"今天收到 {len(emails)} 封未读邮件")
for item in summary:
print(f"[{item['category']}] {item['subject']}")
3.2 场景二：Excel报表自动生成
需求：每周一自动生成销售报表并发送给团队。
import pandas as pd
from openclaw import Client
import schedule
import time
class ReportGenerator:
def __init__(self):
self.client = Client()
def generate_weekly_report(self):
"""生成周报"""
# 读取数据
df = pd.read_excel("sales_data.xlsx")
# 数据分析
summary = {
"total_sales": df["amount"].sum(),
"total_orders": len(df),
"avg_order_value": df["amount"].mean(),
"top_products": df.groupby("product")["amount"].sum().nlargest(5)
}
# 使用OpenClaw生成分析报告
agent = self.client.create_agent("data-analyst")
analysis = agent.run(
f"分析销售数据并生成周报：\\n{summary}",
output_format="markdown"
)
# 保存报告
with open(f"周报_{datetime.now().strftime('%Y%m%d')}.md", "w") as f:
f.write(analysis)
# 发送邮件
self.send_report(analysis)
def send_report(self, content):
"""发送报告"""
self.client.email.send(
to=["team@company.com"],
subject=f"销售周报 – {datetime.now().strftime('%Y年%m月%d日')}",
body=content
)
# 定时任务
reporter = ReportGenerator()
schedule.every().monday.at("09:00").do(reporter.generate_weekly_report)
# 保持运行
while True:
schedule.run_pending()
time.sleep(60)
3.3 场景三：会议纪要自动整理
需求：将会议录音转文字后，自动提取关键信息和待办事项。
from openclaw import Client
import speech_recognition as sr
class MeetingAssistant:
def __init__(self):
self.client = Client()
self.agent = self.client.create_agent(
name="meeting-summarizer",
instructions="整理会议纪要，提取关键决策和待办事项"
)
def transcribe_audio(self, audio_file):
"""语音转文字"""
recognizer = sr.Recognizer()
with sr.AudioFile(audio_file) as source:
audio = recognizer.record(source)
try:
text = recognizer.recognize_google(audio, language="zh-CN")
return text
except Exception as e:
return f"转录失败: {e}"
def summarize_meeting(self, transcript):
"""总结会议内容"""
prompt = f"""
请整理以下会议内容：
1. 提取关键决策
2. 列出待办事项（含负责人）
3. 记录时间节点
4. 生成简洁的会议纪要
会议内容：
{transcript}
"""
summary = self.agent.run(prompt)
return summary
def process_meeting(self, audio_file):
"""处理完整流程"""
print("正在转录音频…")
transcript = self.transcribe_audio(audio_file)
print("正在整理纪要…")
summary = self.summarize_meeting(transcript)
# 保存结果
output_file = f"会议纪要_{datetime.now().strftime('%Y%m%d')}.md"
with open(output_file, "w") as f:
f.write(summary)
print(f"会议纪要已保存至: {output_file}")
return summary
# 使用示例
assistant = MeetingAssistant()
assistant.process_meeting("meeting_recording.wav")
四、进阶技巧
4.1 错误处理与重试
from openclaw import Client
from tenacity import retry, stop_after_attempt, wait_exponential
class RobustAutomation:
def __init__(self):
self.client = Client()
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10)
)
def call_with_retry(self, prompt):
"""带重试机制的API调用"""
try:
return self.client.chat.send(prompt)
except Exception as e:
print(f"调用失败，准备重试: {e}")
raise
4.2 批量处理优化
from concurrent.futures import ThreadPoolExecutor
import openclaw
class BatchProcessor:
def __init__(self):
self.client = openclaw.Client()
def process_batch(self, items, max_workers=5):
"""批量处理"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [
executor.submit(self.process_item, item)
for item in items
]
results = []
for future in futures:
try:
results.append(future.result())
except Exception as e:
results.append({"error": str(e)})
return results
def process_item(self, item):
"""处理单个项目"""
agent = self.client.create_agent("data-processor")
return agent.run(f"处理数据: {item}")
五、最佳实践
5.1 安全建议
5.2 性能优化
5.3 调试技巧
import logging
# 开启调试日志
logging.basicConfig(level=logging.DEBUG)
# 使用上下文管理器追踪性能
from contextlib import contextmanager
import time
@contextmanager
def timed_execution(name):
start = time.time()
yield
elapsed = time.time() – start
print(f"{name} 耗时: {elapsed:.2f}秒")
# 使用示例
with timed_execution("数据分析"):
result = agent.run("分析大量数据…")
六、总结
通过OpenClaw与Python的集成，我们可以：
自动化办公不再是遥不可及的技术，而是每个人都能掌握的生产力工具。
示例代码仓库：https://github.com/openclaw/python-examples
官方文档：https://docs.openclaw.ai/python-sdk
