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Five production-ready agent pipelines built on the SRAO Framework, each targeting a specific industry with typed I/O schemas, SLA targets, and graceful fallback strategies.

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Quick Start

1

Pick an industry and run

Each template ships a single workflow function you can call immediately:
from praisonaiagents import Agent, tool

# Manufacturing: order → inventory → schedule → quality
from examples.cookbooks.Industry_Templates.manufacturing_template import manufacturing_workflow

result = manufacturing_workflow("Customer ABC needs 100 units of Product-XYZ by March 15th, high priority")
print(result)
2

Customise with IndustryAgentPattern

Every template exposes IndustryAgentPattern — a base class you can mix into any domain:
from examples.cookbooks.Industry_Templates.manufacturing_template import IndustryAgentPattern

# Build a domain-specific parser in one line
parser = IndustryAgentPattern.create_data_parser(
    name="CustomParser",
    domain="pharmaceutical batch records",
    sla_seconds=20
)

result = parser.start("Parse batch record BR-2024-001")
print(result)

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Which Template Should I Use?

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Templates at a Glance

IndustryKey AgentsHighlights
ManufacturingParseOrder, CheckInventory, OptimizeSchedule, DefectDetectMulti-format order processing, real-time inventory, production optimisation, quality inspection
EnergySCADAReader, VibrationAnalyzer, PowerForecaster, MaintenanceSchedulerWind-farm monitoring, vibration-based fault detection, power forecasting, predictive maintenance
HealthcareVitalSignsCapture, EMRRetrieval, TriageRecommendation, ResourceAllocatorEmergency triage workflow, HIPAA-compliant data handling, ESI-based triage, resource allocation
AgricultureMultispectralAnalyzer, DiseaseIdentifier, SprayRecommender, YieldPredictorDrone/satellite multispectral analysis, pest/disease detection, targeted spraying, yield forecasting
TransportationLiDARFusion, DisplacementCalculator, HeatmapGenerator, MaintenancePlannerTunnel/bridge LiDAR analysis, displacement tracking, safety heatmaps, predictive maintenance

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How It Works

All five templates share the same SRAO architecture — only the domain logic differs. Every step includes a fallback strategy — if an agent fails, the workflow degrades gracefully rather than crashing.

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Cross-Industry Reuse with IndustryAgentPattern

IndustryAgentPattern provides four factory methods that power ~70 % of every template: 
from examples.cookbooks.Industry_Templates.manufacturing_template import IndustryAgentPattern

# Generic parser — works for any domain
parser    = IndustryAgentPattern.create_data_parser("MyParser", "logistics data", sla_seconds=15)

# Generic availability checker
checker   = IndustryAgentPattern.create_resource_checker("BedChecker", "hospital bed", sla_seconds=3)

# Generic optimiser
optimizer = IndustryAgentPattern.create_optimizer("IrrigationOpt", "water usage", sla_minutes=5)

# Generic inspector
inspector = IndustryAgentPattern.create_inspector("TunnelInspect", "structural integrity", sla_minutes=2)
MethodPurposeSLA param
create_data_parser(name, domain, sla_seconds)Extract/structure raw inputssla_seconds=30
create_resource_checker(name, resource_type, sla_seconds)Verify availabilitysla_seconds=5
create_optimizer(name, optimization_target, sla_minutes)Find optimal solutionsla_minutes=2
create_inspector(name, inspection_type, sla_minutes)Analyse quality/safetysla_minutes=1

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Pydantic I/O Schemas

Every template uses Pydantic models to enforce typed data between agents:
IndustryInput SchemaOutput Schema
ManufacturingOrderDetailsQualityReport
EnergyTurbineDataMaintenancePlan
HealthcareVitalSignsResourceAllocation
AgricultureMultispectralDataYieldForecast
TransportationLiDARScanMaintenanceSchedule

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Common Patterns

Add a custom tool to any agent 
from praisonaiagents import Agent, tool

@tool
def my_erp_lookup(order_id: str) -> dict:
    """Look up order in ERP system"""
    return {"erp_status": "confirmed", "order_id": order_id}

from examples.cookbooks.Industry_Templates.manufacturing_template import order_parser

order_parser.tools.append(my_erp_lookup)
result = order_parser.start("Parse order ORD-999")
Override the fallback strategy 
def custom_fallback(error: Exception) -> dict:
    return {"status": "queued_for_review", "error": str(error)}

try:
    result = manufacturing_workflow("urgent order text")
except Exception as e:
    result = custom_fallback(e)

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Best Practices

Call manufacturing_workflow(), energy_monitoring_workflow(), etc. before customising. These functions already wire up all agents and fallbacks in the correct order.
Pass structured Pydantic objects (e.g. OrderDetails, VitalSigns) between agents instead of raw strings. This catches type errors early and makes pipelines easier to test.
Each agent declares its SLA in its instructions. Keep custom tools within those bounds — e.g. inventory checks must complete in ≤ 5 seconds to not block downstream agents.
These templates are based on the SRAO Framework (MIT). The IndustryAgentPattern class provides the shared 70 % reuse layer.

Manufacturing Template

Order processing, inventory, scheduling, and quality control agents.

Energy Template

Wind-farm monitoring, vibration analysis, and predictive maintenance agents.

Healthcare Template

Emergency triage, EMR retrieval, and resource allocation agents.

Agriculture Template

Multispectral analysis, disease detection, and yield prediction agents.