openclaw_trello_integration.py

openclaw AI小龙虾攻略 2026-04-09 2

我来为你提供AI小龙虾OPENCLAW与Trello集成的详细教程,OPENCLAW是一个AI驱动的自动化工具，可以与Trello结合实现智能项目管理。

openclaw_trello_integration.py-第1张图片-官方获取 | OpenClaw下载 - openclaw官网

准备工作

1 Trello端准备

创建Trello账户（如果还没有）
- 访问 trello.com 注册
- 创建你的第一个看板（Board）

获取Trello API凭证

步骤：
1. 登录Trello → 右上角头像 → 设置
2. 点击"开发者API密钥"
3. 点击"生成新的API密钥"
4. 复制API Key和Secret
5. 生成Token（需要授权）

2 OPENCLAW端准备

安装OPENCLAW

# 方法1：Docker安装
docker pull openclaw/ai-assistant
# 方法2：直接安装
pip install openclaw-ai

配置环境变量

export TRELLO_API_KEY="your_api_key"
export TRELLO_API_TOKEN="your_api_token"
export OPENCLAW_API_KEY="your_openclaw_key"

基础集成配置

1 使用Webhooks自动同步

import json
from flask import Flask, request
app = Flask(__name__)
# Trello Webhook配置
TRELLO_CONFIG = {
    "description": "OPENCLAW Integration",
    "callbackURL": "https://your-domain.com/trello-webhook",
    "idModel": "your_board_id"  # Trello看板ID
}
def setup_trello_webhook():
    url = f"https://api.trello.com/1/tokens/{TRELLO_TOKEN}/webhooks"
    response = requests.post(url, json=TRELLO_CONFIG)
    return response.json()

2 OPENCLAW Trello客户端

class TrelloOpenClawClient:
    def __init__(self):
        self.base_url = "https://api.trello.com/1"
        self.auth_params = {
            "key": TRELLO_API_KEY,
            "token": TRELLO_API_TOKEN
        }
    def create_card_with_ai(self, board_id, list_name, task_description):
        """使用AI分析任务并创建卡片"""
        # 1. AI分析任务
        ai_analysis = openclaw.analyze_task(task_description)
        # 2. 创建Trello卡片
        card_data = {
            "name": ai_analysis["title"],
            "desc": f"{task_description}\n\nAI分析:\n{ai_analysis['details']}",
            "idList": self.get_list_id(board_id, list_name),
            "labels": ai_analysis["labels"],
            "due": ai_analysis.get("due_date")
        }
        response = requests.post(
            f"{self.base_url}/cards",
            params={**self.auth_params, **card_data}
        )
        return response.json()

核心功能实现

1 智能任务分配

def intelligent_task_assignment():
    """
    AI根据以下因素自动分配任务：
    1. 成员技能匹配度
    2. 当前工作负载
    3. 任务优先级
    4. 截止日期
    """
    # 获取所有未分配任务
    cards = trello_client.get_cards(board_id)
    for card in cards:
        if not card['idMembers']:
            # 使用OPENCLAW分析最佳负责人
            best_member = openclaw.analyze_best_assignee(
                card['desc'],
                available_members,
                skill_matrix
            )
            # 分配任务
            trello_client.assign_card(card['id'], best_member['id'])
            # AI生成个性化说明
            personalized_note = openclaw.generate_assignment_note(
                card, 
                best_member
            )
            trello_client.add_comment(card['id'], personalized_note)

2 自动化工作流

def automate_workflows():
    """配置自动化规则"""
    # 规则1：卡片移动到"进行中"时自动添加时间戳
    automation_rules = [
        {
            "trigger": "card_moved",
            "condition": "list_after == '进行中'",
            "action": "add_time_tracking"
        },
        {
            "trigger": "due_date_approaching",
            "condition": "due_in_hours < 24",
            "action": "send_reminder"
        },
        {
            "trigger": "card_completed",
            "condition": "list_after == '已完成'",
            "action": "generate_report"
        }
    ]
    # 使用OPENCLAW优化规则
    optimized_rules = openclaw.optimize_workflow_rules(
        automation_rules,
        historical_data
    )

3 AI生成报告

def generate_ai_report(board_id):
    """AI生成项目分析报告"""
    # 1. 收集数据
    board_data = trello_client.get_board(board_id)
    cards = trello_client.get_cards(board_id)
    activities = trello_client.get_actions(board_id)
    # 2. AI分析
    analysis = openclaw.analyze_project_health({
        "completion_rate": calculate_completion_rate(cards),
        "burndown_chart": generate_burndown(activities),
        "bottlenecks": identify_bottlenecks(cards),
        "team_velocity": calculate_velocity(activities)
    })
    # 3. 生成报告并发送到Trello
    report_card = trello_client.create_card(
        "项目周报",
        analysis['report'],
        list_id="报告"
    )
    # 4. 发送通知
    openclaw.send_notification(
        recipients=team_members,
        message=analysis['summary'],
        priority=analysis['priority']
    )

高级功能

1 自然语言命令

@app.route('/trello-command', methods=['POST'])
def handle_natural_language_command():
    """处理自然语言命令"""
    user_command = request.json.get('command')
    # 使用OPENCLAW解析命令
    parsed = openclaw.parse_command(user_command)
    if parsed['action'] == 'create_task':
        # 创建任务
        trello_client.create_card(
            parsed['title'],
            parsed.get('description', ''),
            parsed.get('list', '待办事项')
        )
    elif parsed['action'] == 'move_task':
        # 移动任务
        trello_client.move_card(
            parsed['card_id'],
            parsed['target_list']
        )
    return {"status": "success", "action": parsed}

2 智能预测

def predict_project_timeline():
    """AI预测项目时间线"""
    # 训练预测模型
    model = openclaw.train_prediction_model(
        features=[
            "task_complexity",
            "assignee_experience",
            "dependencies",
            "historical_completion_times"
        ],
        target="actual_duration"
    )
    # 对新任务进行预测
    predictions = {}
    for card in upcoming_tasks:
        prediction = model.predict(card_features(card))
        # 更新Trello卡片
        trello_client.update_card(
            card['id'],
            due=prediction['estimated_completion'],
            labels=[f"预计:{prediction['confidence']}%"]
        )

配置示例

1 Docker Compose配置

# docker-compose.yml
version: '3.8'
services:
  openclaw:
    image: openclaw/ai-assistant:latest
    environment:
      - TRELLO_API_KEY=${TRELLO_API_KEY}
      - TRELLO_TOKEN=${TRELLO_TOKEN}
      - OPENCLAW_MODEL=gpt-4
    volumes:
      - ./config:/app/config
  trello-bridge:
    image: nginx
    ports:
      - "8080:80"
    depends_on:
      - openclaw

2 环境变量文件

# .env
TRELLO_API_KEY=your_trello_key
TRELLO_TOKEN=your_trello_token
TRELLO_BOARD_ID=main_project_board
OPENCLAW_API_KEY=your_openclaw_key
OPENCLAW_WEBHOOK_SECRET=your_secret
SLACK_WEBHOOK_URL=your_slack_webhook

最佳实践

1 安全建议

使用环境变量存储敏感信息
限制API权限：只授予必要权限
定期轮换Token
启用双因素认证

2 性能优化

批量操作：合并API调用
缓存机制：缓存频繁访问的数据
异步处理：使用队列处理耗时操作
监控告警：设置性能监控

3 故障排除

def diagnose_integration_issues():
    """诊断集成问题"""
    checklist = [
        ("API凭证有效", check_api_credentials()),
        ("网络连接", check_network_connectivity()),
        ("权限足够", check_trello_permissions()),
        ("Webhook配置", check_webhook_setup()),
        ("OPENCLAW服务", check_openclaw_health())
    ]
    for item, status in checklist:
        if not status:
            openclaw.suggest_fix(item)

扩展功能

1 与其它工具集成

Slack集成：Trello通知发送到Slack
GitHub集成：代码提交关联Trello卡片
Google Calendar：截止日期同步到日历
JIRA同步：双向同步任务状态

2 自定义AI模型

# 训练专属项目管理AI
custom_model = openclaw.fine_tune_model(
    base_model="gpt-4",
    training_data=project_history,
    custom_skills=["estimation", "risk_assessment", "resource_allocation"]
)

开始使用

快速测试：

# 测试连接
python test_integration.py
# 运行示例
python examples/basic_automation.py

监控仪表板：访问 http://localhost:3000/dashboard 查看集成状态

这个集成方案可以帮助你：

自动创建和分配任务
智能优化工作流程
生成AI驱动的洞察报告
自然语言交互管理项目

需要根据你的具体需求调整配置参数,建议先从基础集成开始，逐步添加高级功能。

标签： OpenClaw Trello

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