OpenJiuwen JiuwenClaw 技术教程
2026-03-16 10:47:02
115次阅读
0个评论
最后修改时间：2026-03-16 11:00:18
OpenJiuwen JiuwenClaw 技术教程
概述
jiuwenclaw 是智能 AI Agent，设计为个人 AI 管家。它将 LLM 能力扩展到日常通信应用，提供任务管理、自主进化和多平台访问能力。
技术栈
- 编程语言: Python 3.11-3.13
- 核心框架: 基于 openJiuwen agent-core
- 集成平台: 华为云 MaaS、小艺开放平台
- 包管理器: uv
- 版本: 0.1.5
核心特性
1. 多平台访问
支持多种通信平台，随时随地访问 AI 助手。
Web 界面
from jiuwenclaw import WebChannel
# Web 渠道配置
web_channel = WebChannel(
host="0.0.0.0",
port=8080,
enable_ssl=True,
ssl_cert="/path/to/cert.pem",
ssl_key="/path/to/key.pem"
)
# 启动 Web 服务
await web_channel.start()
Web 界面功能:
- 实时对话
- 历史记录查看
- 任务管理
- 设置配置
飞书 (Lark) 集成
from jiuwenclaw.channel import FeishuChannel
# 飞书渠道配置
feishu_channel = FeishuChannel(
app_id="cli_xxxxxxxxxxxx",
app_secret="your-app-secret",
encrypt_key="your-encrypt-key",
verification_token="your-verification-token"
)
# 处理飞书消息
@feishu_channel.on_message
async def handle_feishu_message(event):
message = event.message
response = await agent.process(message.content)
await feishu_channel.send_message(
receive_id=message.sender_id,
content=response
)
# 启动飞书服务
await feishu_channel.start()
飞书功能:
- 私聊对话
- 群聊@回复
- 卡片消息
- 文件处理
小艺助手 (华为手机)
from jiuwenclaw.channel import XiaoyiChannel
# 小艺渠道配置
xiaoyi_channel = XiaoyiChannel(
skill_id="your-skill-id",
api_key="your-api-key",
endpoint="https://xiaoyi.huawei.com/api"
)
# 处理小艺请求
@xiaoyi_channel.on_request
async def handle_xiaoyi_request(request):
# 解析语音/文本输入
user_input = request.query
# 处理请求
response = await agent.process(user_input)
# 返回语音/文本响应
return {
"text": response.text,
"speech": response.speech_url,
"should_end_session": False
}
# 启动小艺服务
await xiaoyi_channel.start()
小艺功能:
- 语音交互
- 智能家居控制
- 日程管理
- 信息查询
钉钉集成
from jiuwenclaw.channel import DingDingChannel
# 钉钉渠道配置
dingding_channel = DingDingChannel(
webhook="https://oapi.dingtalk.com/robot/send?access_token=xxx",
secret="your-secret",
keyword="AI助手"
)
# 发送钉钉消息
await dingding_channel.send_markdown(
title="任务提醒",
text="## 今日任务\n- 完成报告\n- 参加会议"
)
# 处理钉钉回调
@dingding_channel.on_message
async def handle_dingding_message(message):
response = await agent.process(message.content)
await dingding_channel.reply(message.message_id, response)
Telegram 集成
from jiuwenclaw.channel import TelegramChannel
# Telegram 渠道配置
telegram_channel = TelegramChannel(
bot_token="your-bot-token",
allowed_users=[123456789, 987654321] # 可选: 限制用户
)
# 处理 Telegram 消息
@telegram_channel.on_message
async def handle_telegram_message(update):
chat_id = update.message.chat.id
text = update.message.text
response = await agent.process(text)
await telegram_channel.send_message(
chat_id=chat_id,
text=response.text,
parse_mode="Markdown"
)
# 启动 Telegram Bot
await telegram_channel.start_polling()
Telegram 功能:
- 私聊和群聊
- 内联查询
- 回调查询
- 文件和媒体处理
WhatsApp 集成
from jiuwenclaw.channel import WhatsAppChannel
# WhatsApp 渠道配置
whatsapp_channel = WhatsAppChannel(
phone_number_id="your-phone-number-id",
access_token="your-access-token",
verify_token="your-verify-token"
)
# 处理 WhatsApp 消息
@whatsapp_channel.on_message
async def handle_whatsapp_message(message):
from_number = message.from_number
text = message.text.body
response = await agent.process(text)
await whatsapp_channel.send_message(
to=from_number,
text=response.text
)
# 启动 WhatsApp Webhook
await whatsapp_channel.start_webhook(port=5000)
2. 任务管理
智能任务调度
from jiuwenclaw import TaskScheduler
# 任务调度器
scheduler = TaskScheduler(
max_concurrent_tasks=10,
retry_policy={
"max_retries": 3,
"backoff": "exponential"
}
)
# 添加定时任务
await scheduler.add_task(
task_id="daily_report",
task_func=generate_daily_report,
schedule="0 9 * * *", # 每天9点
timezone="Asia/Shanghai"
)
# 添加一次性任务
await scheduler.add_once(
task_id="reminder_123",
task_func=send_reminder,
execute_at=datetime(2024, 3, 20, 14, 30)
)
# 启动调度器
await scheduler.start()
中途中断处理
from jiuwenclaw import TaskManager
# 任务管理器
task_manager = TaskManager()
# 执行可中断任务
async def long_running_task():
task = await task_manager.create_task(
task_id="analysis_123",
task_func=analyze_large_dataset,
interruptible=True
)
try:
result = await task.execute()
return result
except TaskInterrupted:
# 保存进度
await task.save_checkpoint()
return {"status": "interrupted", "progress": task.progress}
# 恢复中断的任务
await task_manager.resume_task("analysis_123")
动态需求适应
from jiuwenclaw import AdaptiveTaskExecutor
# 自适应任务执行器
executor = AdaptiveTaskExecutor()
# 执行任务时动态调整
async def execute_with_adaptation(user_request):
# 初始任务规划
plan = await executor.plan(user_request)
# 执行过程中根据反馈调整
async for step in executor.execute(plan):
# 检查用户反馈
if step.needs_user_input:
user_input = await request_user_input(step.question)
await executor.adjust_plan(step, user_input)
# 检查环境变化
if step.environment_changed:
await executor.replan(step)
yield step.result
心跳定时任务
from jiuwenclaw import HeartbeatScheduler
# 心跳调度器
heartbeat = HeartbeatScheduler(
interval=60, # 60秒心跳
on_heartbeat=check_system_status
)
# 注册心跳任务
@heartbeat.task(interval=300) # 每5分钟
async def check_reminders():
reminders = await get_pending_reminders()
for reminder in reminders:
await send_notification(reminder)
@heartbeat.task(interval=3600) # 每小时
async def cleanup_expired_data():
await clean_old_conversations()
# 启动心跳
await heartbeat.start()
3. 自主进化
基于反馈的自我改进
from jiuwenclaw.evolution import SelfEvolver
# 自我进化器
evolver = SelfEvolver(
feedback_threshold=0.7,
evolution_interval=86400 # 每天进化一次
)
# 收集用户反馈
await evolver.collect_feedback(
interaction_id="conv_123",
user_rating=4, # 1-5星
user_comment="回答很准确，但可以更简洁"
)
# 触发进化
evolution_result = await evolver.evolve()
print(f"改进了 {evolution_result.improvements_count} 个方面")
print(f"新版本: {evolution_result.new_version}")
自动技能优化
from jiuwenclaw.evolution import SkillOptimizer
# 技能优化器
skill_optimizer = SkillOptimizer()
# 分析技能使用情况
usage_stats = await skill_optimizer.analyze_usage(
skill_name="code_review",
time_range="30d"
)
# 识别改进点
improvements = await skill_optimizer.identify_improvements(usage_stats)
# 应用优化
for improvement in improvements:
await skill_optimizer.apply_improvement(
skill_name="code_review",
improvement=improvement
)
基于错误学习
from jiuwenclaw.evolution import ErrorLearner
# 错误学习器
error_learner = ErrorLearner()
# 记录错误
await error_learner.record_error(
error_type="intent_misunderstanding",
context={
"user_input": "帮我看看这个bug",
"agent_response": "我理解您想...",
"actual_intent": "代码调试"
}
)
# 从错误中学习
learning_result = await error_learner.learn_from_errors()
print(f"识别了 {learning_result.patterns_count} 个错误模式")
print(f"生成了 {learning_result.rules_count} 条新规则")
4. 记忆与学习
智能内存管理
from jiuwenclaw import MemoryManager
# 内存管理器
memory = MemoryManager(
storage_backend="milvus",
max_memory_size=10000,
retention_policy="lru"
)
# 存储记忆
await memory.store(
user_id="user_123",
memory_type="preference",
content="用户喜欢简洁的回答",
importance=0.8
)
# 检索相关记忆
memories = await memory.retrieve(
user_id="user_123",
query="用户偏好",
top_k=5
)
上下文压缩
from jiuwenclaw import ContextCompressor
# 上下文压缩器
compressor = ContextCompressor(
max_tokens=4000,
compression_ratio=0.3
)
# 压缩长对话历史
long_history = await get_conversation_history("conv_123")
compressed = await compressor.compress(
context=long_history,
preserve_key_points=True
)
print(f"原始长度: {len(long_history)} tokens")
print(f"压缩后: {len(compressed)} tokens")
知识保留
from jiuwenclaw import KnowledgeRetention
# 知识保留系统
knowledge_retention = KnowledgeRetention(
knowledge_base="./knowledge",
update_strategy="incremental"
)
# 提取并保留知识
async def learn_from_conversation(conversation):
# 提取知识点
knowledge_points = await knowledge_retention.extract(conversation)
# 验证知识
validated = await knowledge_retention.validate(knowledge_points)
# 存储知识
for kp in validated:
await knowledge_retention.store(kp)
5. 浏览器自动化
from jiuwenclaw import BrowserAutomation
# 浏览器自动化
browser = BrowserAutomation(
headless=True,
user_agent="Mozilla/5.0..."
)
# 执行网页操作
async def search_and_summarize(query):
# 打开搜索引擎
page = await browser.new_page()
await page.goto("https://www.google.com")
# 搜索
await page.fill('input[name="q"]', query)
await page.press('input[name="q"]', 'Enter')
# 等待结果
await page.wait_for_selector('#search')
# 提取结果
results = await page.evaluate('''() => {
const items = document.querySelectorAll('.g');
return Array.from(items).slice(0, 5).map(item => ({
title: item.querySelector('h3')?.textContent,
link: item.querySelector('a')?.href
}));
}''')
await browser.close()
return results
6. 技能系统
自定义技能开发
from jiuwenclaw import Skill
class WeatherSkill(Skill):
name = "weather"
description = "查询天气信息"
async def execute(self, location: str, date: str = None):
# 调用天气 API
weather_data = await self.call_weather_api(location, date)
# 格式化响应
response = self.format_weather_response(weather_data)
return response
async def call_weather_api(self, location, date):
# 实现 API 调用
pass
def format_weather_response(self, data):
# 格式化输出
return f"{data['location']}天气: {data['condition']}, 温度: {data['temp']}°C"
# 注册技能
await skill_manager.register_skill(WeatherSkill())
技能市场
from jiuwenclaw import SkillMarketplace
# 技能市场
marketplace = SkillMarketplace()
# 浏览可用技能
skills = await marketplace.list_skills(
category="productivity",
sort_by="rating"
)
# 安装技能
await marketplace.install_skill("skill_id_123")
# 更新技能
await marketplace.update_skill("skill_id_123")
动态技能加载
from jiuwenclaw import DynamicSkillLoader
# 动态技能加载器
loader = DynamicSkillLoader(
skill_dir="./skills",
auto_reload=True
)
# 加载技能
await loader.load_all()
# 热重载技能
await loader.reload_skill("weather")
# 卸载技能
await loader.unload_skill("old_skill")
架构设计
jiuwenclaw/
├── jiuwenclaw/
│ ├── agentserver/ # Agent 服务器
│ │ ├── server.py
│ │ └── router.py
│ ├── channel/ # 通信渠道
│ │ ├── feishu.py
│ │ ├── xiaoyi_channel.py
│ │ ├── dingding.py
│ │ ├── telegram_channel.py
│ │ ├── whatsapp_channel.py
│ │ └── web_channel.py
│ ├── gateway/ # API 网关
│ │ ├── auth.py
│ │ └── rate_limiter.py
│ ├── evolution/ # 自主进化
│ │ ├── self_evolver.py
│ │ ├── skill_optimizer.py
│ │ └── error_learner.py
│ ├── schema/ # 数据模式
│ │ ├── message.py
│ │ └── task.py
│ ├── web/ # Web 前端
│ │ ├── static/
│ │ └── templates/
│ ├── app.py # 主应用
│ └── app_web.py # Web 应用
├── workspace/ # 用户工作空间
│ ├── skills/
│ ├── memory/
│ └── logs/
└── docs/ # 文档
快速开始
安装
pip install jiuwenclaw
初始化工作空间
jiuwenclaw-init
这将创建以下目录结构:
workspace/
├── skills/ # 自定义技能
├── memory/ # 记忆存储
├── logs/ # 日志文件
├── config.yaml # 配置文件
└── .env # 环境变量
配置
编辑 workspace/config.yaml:
# LLM 配置
llm:
provider: "openai"
model: "gpt-4"
api_key: "${OPENAI_API_KEY}"
# 渠道配置
channels:
feishu:
enabled: true
app_id: "${FEISHU_APP_ID}"
app_secret: "${FEISHU_APP_SECRET}"
telegram:
enabled: true
bot_token: "${TELEGRAM_BOT_TOKEN}"
# 记忆配置
memory:
backend: "milvus"
collection: "jiuwenclaw_memory"
# 进化配置
evolution:
enabled: true
interval: 86400
启动服务
# 启动所有服务
jiuwenclaw-start
# 启动主应用
jiuwenclaw-app
# 启动 Web 界面
jiuwenclaw-web
高级用法
多渠道统一处理
from jiuwenclaw import UnifiedMessageHandler
# 统一消息处理器
handler = UnifiedMessageHandler()
@handler.on_message
async def handle_message(message):
# 统一处理来自不同渠道的消息
response = await agent.process(message.content)
# 根据渠道类型返回响应
if message.channel == "feishu":
await send_feishu_response(message, response)
elif message.channel == "telegram":
await send_telegram_response(message, response)
# ...
工作流自动化
from jiuwenclaw import WorkflowAutomation
# 工作流自动化
automation = WorkflowAutomation()
# 定义工作流
workflow = automation.define_workflow(
name="daily_summary",
trigger="schedule:0 18 * * *", # 每天18点
steps=[
{"action": "collect_data", "source": "calendar"},
{"action": "collect_data", "source": "email"},
{"action": "summarize", "template": "daily_summary"},
{"action": "send", "channel": "feishu", "to": "user_123"}
]
)
# 启动自动化
await automation.start()
插件扩展
from jiuwenclaw import PluginInterface
class CustomPlugin(PluginInterface):
name = "custom_plugin"
version = "1.0.0"
async def on_load(self):
# 插件加载时执行
pass
async def on_message(self, message):
# 处理消息
pass
async def on_unload(self):
# 插件卸载时执行
pass
# 注册插件
await plugin_manager.register(CustomPlugin())
部署
Docker 部署
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
RUN jiuwenclaw-init
CMD ["jiuwenclaw-start"]
# docker-compose.yml
version: '3.8'
services:
jiuwenclaw:
build: .
ports:
- "8080:8080"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
volumes:
- ./workspace:/app/workspace
Kubernetes 部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: jiuwenclaw
spec:
replicas: 3
selector:
matchLabels:
app: jiuwenclaw
template:
metadata:
labels:
app: jiuwenclaw
spec:
containers:
- name: jiuwenclaw
image: jiuwenclaw:latest
ports:
- containerPort: 8080
env:
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: jiuwenclaw-secrets
key: openai-api-key
最佳实践
1. 安全配置
# 环境变量管理
from pydantic import BaseSettings
class Settings(BaseSettings):
openai_api_key: str
feishu_app_secret: str
telegram_bot_token: str
class Config:
env_file = ".env"
settings = Settings()
2. 错误处理
from jiuwenclaw import ErrorHandler
# 错误处理器
error_handler = ErrorHandler()
@error_handler.on_error
async def handle_error(error, context):
# 记录错误
logger.error(f"Error: {error}", exc_info=True)
# 通知用户
await send_error_notification(context.user_id, error)
# 尝试恢复
if error.recoverable:
await attempt_recovery(context)
3. 性能监控
from jiuwenclaw import PerformanceMonitor
# 性能监控
monitor = PerformanceMonitor()
@monitor.track
async def process_message(message):
# 处理消息
response = await agent.process(message.content)
return response
# 查看性能指标
metrics = monitor.get_metrics()
print(f"平均响应时间: {metrics.avg_response_time}ms")
print(f"成功率: {metrics.success_rate}%")
常见问题
Q: 如何添加新的通信渠道?
A: 实现 ChannelInterface 接口，注册到渠道管理器。
Q: 如何处理大量并发消息?
A: 使用消息队列和异步处理，设置合理的并发限制。
Q: 如何保护用户隐私?
A: 加密存储敏感信息，定期清理过期数据，遵守隐私政策。
相关资源
总结
jiuwenclaw 作为个人 AI 管家，通过多平台集成、智能任务管理和自主进化能力，为用户提供全方位的智能助手服务。无论是日常沟通、任务管理还是知识学习，都能提供个性化的支持。
00
