Infor SyteLine的统一AI代理编排器
一个全面的工作流编排系统，通过MCP（模型上下文协议）与Infor SyteLine集成，并使用GPT-5 Mini提供统一的AI代理协调。
项目结构
InforMCP Server/
├── orchestrator/ # AI Orchestrator Core
│ ├── ai_orchestrator.py # Main orchestrator engine
│ ├── workflow_coordinator.py # Advanced coordination patterns
│ ├── orchestrator_interface.py # Web dashboard and API
│ └── workflow_examples.py # Pre-built workflow templates
├── mcp/ # MCP Server Components
│ ├── app.py # Flask MCP server
│ ├── tools/ # MCP tool implementations
│ │ ├── get_data.py
│ │ ├── upload_data.py
│ │ └── update_data.py
│ └── config/ # Server configuration
├── utils/ # Shared utilities
│ ├── simple_ido_client.py # SyteLine API client
│ └── ido_integration.py # IDO integration helpers
├── tests/ # Test suites
│ ├── multi_workflow_test.py # Multi-workflow testing
│ └── test_mcp_server.py # MCP server testing
├── templates/ # Web interface templates
│ └── dashboard.html # Orchestrator dashboard
└── docs/ # Documentation
└── workflow_guide.md # Workflow creation guide快速开始
1.启动MCP服务器
python app.py服务器运行于：http://localhost:8000
2.启动编排器界面
python orchestrator_interface.py仪表板位于：http://localhost:5000
创建自己的工作流
基本工作流结构
工作流被定义为一系列具有依赖关系的步骤：
from ai_orchestrator import AIOrchestrator, WorkflowType
orchestrator = AIOrchestrator()
workflow_id = orchestrator.create_workflow(
name="My Custom Workflow",
workflow_type=WorkflowType.DATA_SYNC,
description="My custom business process",
steps_config=[
{
"name": "step_1",
"description": "First step description",
"action_type": "mcp_get_data", # or "ai_reasoning", "file_processing"
"parameters": {
"ido_name": "SLCustomers",
"properties": ["CustNum", "Name"]
},
"dependencies": [], # Steps this depends on
"timeout": 60, # Seconds
"retry_count": 3 # Retry attempts
},
{
"name": "step_2",
"description": "Second step with AI reasoning",
"action_type": "ai_reasoning",
"parameters": {
"prompt": "Analyze the customer data",
"context": {"analysis_type": "customer_segmentation"}
},
"dependencies": ["step_1"], # Runs after step_1 completes
"timeout": 120,
"retry_count": 2
}
]
)
# Start the workflow
orchestrator.start_workflow(workflow_id)可用操作类型
mcp_get_data-从SyteLine检索数据
parameters = {
"ido_name": "SLCustomers",
"properties": ["CustNum", "Name", "City"],
"filter_criteria": "City = 'New York'"
}mcp_upload_data-在SyteLine中创建新记录
parameters = {
"ido_name": "SLCustomers",
"data": {
"Name": "Acme Corp",
"City": "New York",
"Status": "Active"
}
}mcp_update_data-更新现有记录
parameters = {
"ido_name": "SLCustomers",
"data": {"Status": "Inactive"},
"filter_criteria": "City = 'Chicago'"
}ai_reasoning-使用GPT-5 Mini进行分析
parameters = {
"prompt": "Analyze this customer data and identify trends",
"context": {"data_source": "customers"}
}file_processing-处理文件操作
parameters = {
"file_path": "data/invoices.pdf",
"operation": "exists" # or "read"
}使用预构建示例
from workflow_examples import WorkflowExamples
examples = WorkflowExamples(orchestrator)
# Customer onboarding
customer_data = {
"name": "Acme Corporation",
"address": "123 Business St",
"city": "New York",
"credit_limit": 50000
}
workflow_id = examples.create_customer_onboarding_workflow(customer_data)
# Purchase order processing
po_data = {
"vendor_name": "Supplier Inc",
"order_date": "2024-01-15",
"items": [{"item_code": "WIDGET001", "quantity": 100}]
}
workflow_id = examples.create_purchase_order_workflow(po_data)
# Inventory reconciliation
workflow_id = examples.create_inventory_reconciliation_workflow("MAIN")高级协调模式
顺序链
from workflow_coordinator import WorkflowCoordinator
coordinator = WorkflowCoordinator(orchestrator)
# Create workflows
workflow1 = orchestrator.create_workflow(...)
workflow2 = orchestrator.create_workflow(...)
workflow3 = orchestrator.create_workflow(...)
# Create sequential chain
chain_id = coordinator.create_sequential_workflow_chain(
[workflow1, workflow2, workflow3],
"My Sequential Process"
)
# Start the chain (only first workflow starts automatically)
coordinator.start_workflow_group(chain_id)扇出/扇入模式
master_workflow = orchestrator.create_workflow(...)
worker1 = orchestrator.create_workflow(...)
worker2 = orchestrator.create_workflow(...)
aggregator = orchestrator.create_workflow(...)
pattern_id = coordinator.create_fan_out_fan_in_pattern(
master_workflow,
[worker1, worker2],
aggregator,
"Parallel Processing Pattern"
)错误恢复
from workflow_coordinator import CoordinationRule, CoordinationEvent
# Create rule to start recovery workflow on failure
recovery_rule = CoordinationRule(
id="failure_recovery",
name="Failure Recovery Rule",
trigger_event=CoordinationEvent.WORKFLOW_FAILED,
trigger_conditions={'workflow_id': main_workflow},
actions=[{
'type': 'start_workflow',
'workflow_id': recovery_workflow
}]
)
coordinator.create_coordination_rule(recovery_rule)API终点
工作流管理
GET /api/workflows-列出所有工作流POST /api/workflows-创建新工作流GET /api/workflows/{id}-获取工作流状态POST /api/workflows/{id}/start-启动工作流POST /api/workflows/{id}/stop-停止工作流DELETE /api/workflows/{id}-删除工作流
模板和示例
GET /api/templates/{type}-获取工作流模板POST /api/invoice-processing-创建发票处理工作流
统计
GET /api/stats-获取编排器统计信息
监控与控制
Web仪表板
访问仪表板http://localhost:5000致：
- 实时查看所有活动工作流
- 监控工作流进度和状态
- 启动/停止工作流
- 查看执行统计信息
- 访问工作流模板
程序化监控
# Get workflow status
status = orchestrator.get_workflow_status(workflow_id)
print(f"Status: {status['status']}")
print(f"Current Step: {status['current_step'] + 1}/{status['total_steps']}")
print(f"Results: {status['results']}")
# List all workflows
workflows = orchestrator.list_workflows()
for workflow in workflows:
print(f"{workflow['name']}: {workflow['status']}")
# Get coordination status
coord_status = coordinator.get_coordination_status()
print(f"Active dependencies: {coord_status['active_dependencies']}")测试您的工作流程
运行综合测试
python multi_workflow_test.py测试单个场景
tester = MultiWorkflowTester()
# Test concurrent execution
tester.test_basic_concurrent_workflows()
# Test coordination patterns
tester.test_sequential_coordination()
tester.test_fan_out_fan_in_pattern()
# Test error handling
tester.test_error_handling_and_recovery()最佳实践
- 简单开始：从2-3步工作流程开始，逐渐增加复杂性
- 使用依赖关系：定义明确的步骤依赖关系，以确保正确的执行顺序
- 设置超时：始终为每个步骤设置合理的超时
- 实施检索：对可能暂时失败的步骤使用retry_count
- 监控进度：使用仪表板监视工作流执行
- 测试协调：生产使用前测试协调模式
- 处理错误：始终包括错误恢复机制
SyteLine集成的常见IDO
- 客户:
SLCustomers,SLCustomerDefaults,SLCustomerHistory - 供应商:
SLVendors,SLVendorDefaults - 库存:
SLItemLocs,SLItemForecasts,SLItemMasters - 订单:
SLSoItems,SLPoItems - 财务数据:
SLArOpenItems,SLApOpenItems
故障排除
常见问题
- 工作流未启动：检查MCP服务器是否在端口8000上运行
- 步骤超时：增加超时值或优化步骤逻辑
- 依赖性问题：确保依赖关系中的步骤名称完全匹配
- MCP连接错误：验证SyteLine凭据和网络连接
调试模式
通过设置日志级别启用调试日志记录：
import logging
logging.basicConfig(level=logging.DEBUG)后续步骤
- 探索中的预构建示例
workflow_examples.py - 测试协调模式
multi_workflow_test.py - 使用web仪表板监视工作流
- 根据需要扩展自定义IDO集成
- 为复杂场景实施额外的协调规则