> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Industry Templates > Pre-built agent workforces for Manufacturing, Energy, Healthcare, Agriculture, and Transportation — ready to run in minutes. 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. 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. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from examples.cookbooks.Industry_Templates.manufacturing_template import manufacturing_workflow agent = Agent(name="ops-lead", instructions="Route industry template workflows.") result = manufacturing_workflow( "Customer ABC needs 100 units of Product-XYZ by March 15th, high priority" ) print(result) ``` The user picks an industry template; agents parse the request and return a structured operations report. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Industry Templates" SRAO["🏭 SRAO-Based Templates"] --> MFG["⚙️ Manufacturing"] SRAO --> ENG["⚡ Energy"] SRAO --> HC["🏥 Healthcare"] SRAO --> AG["🌾 Agriculture"] SRAO --> TR["🚇 Transportation"] end classDef hub fill:#8B0000,stroke:#7C90A0,color:#fff classDef industry fill:#189AB4,stroke:#7C90A0,color:#fff class SRAO hub class MFG,ENG,HC,AG,TR industry classDef agent fill:#8B0000,color:#fff classDef tool fill:#189AB4,color:#fff ``` ## Quick Start Each template ships a single workflow function you can call immediately: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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) ``` Every template exposes `IndustryAgentPattern` — a base class you can mix into any domain: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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) ``` *** ## Which Template Should I Use? ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Q1{"What do you need?"} Q1 -->|"Order → Production → QC"| MFG["⚙️ Manufacturing"] Q1 -->|"Turbine/grid monitoring"| ENG["⚡ Energy"] Q1 -->|"Patient triage & EMR"| HC["🏥 Healthcare"] Q1 -->|"Crop health & yield"| AG["🌾 Agriculture"] Q1 -->|"Tunnel/bridge safety"| TR["🚇 Transportation"] classDef decision fill:#F59E0B,stroke:#7C90A0,color:#fff classDef leaf fill:#189AB4,stroke:#7C90A0,color:#fff class Q1 decision class MFG,ENG,HC,AG,TR leaf ``` *** ## Templates at a Glance | Industry | Key Agents | Highlights | | ------------------ | ---------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | **Manufacturing** | `ParseOrder`, `CheckInventory`, `OptimizeSchedule`, `DefectDetect` | Multi-format order processing, real-time inventory, production optimisation, quality inspection | | **Energy** | `SCADAReader`, `VibrationAnalyzer`, `PowerForecaster`, `MaintenanceScheduler` | Wind-farm monitoring, vibration-based fault detection, power forecasting, predictive maintenance | | **Healthcare** | `VitalSignsCapture`, `EMRRetrieval`, `TriageRecommendation`, `ResourceAllocator` | Emergency triage workflow, HIPAA-compliant data handling, ESI-based triage, resource allocation | | **Agriculture** | `MultispectralAnalyzer`, `DiseaseIdentifier`, `SprayRecommender`, `YieldPredictor` | Drone/satellite multispectral analysis, pest/disease detection, targeted spraying, yield forecasting | | **Transportation** | `LiDARFusion`, `DisplacementCalculator`, `HeatmapGenerator`, `MaintenancePlanner` | Tunnel/bridge LiDAR analysis, displacement tracking, safety heatmaps, predictive maintenance | *** ## How It Works All five templates share the same SRAO architecture — only the domain logic differs. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant Workflow participant Agent1 as "Parser / Reader" participant Agent2 as "Analyser / Checker" participant Agent3 as "Optimiser / Recommender" participant Agent4 as "Inspector / Planner" User->>Workflow: Call workflow(input) Workflow->>Agent1: Parse / capture raw data Agent1-->>Workflow: Structured schema Workflow->>Agent2: Validate / analyse Agent2-->>Workflow: Analysis result Workflow->>Agent3: Optimise / recommend Agent3-->>Workflow: Decision Workflow->>Agent4: Inspect / plan Agent4-->>Workflow: Final report Workflow-->>User: Complete result dict ``` Every step includes a fallback strategy — if an agent fails, the workflow degrades gracefully rather than crashing. *** ## Cross-Industry Reuse with `IndustryAgentPattern` `IndustryAgentPattern` provides four factory methods that power \~70 % of every template: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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) ``` | Method | Purpose | SLA param | | ----------------------------------------------------------- | ---------------------------- | ---------------- | | `create_data_parser(name, domain, sla_seconds)` | Extract/structure raw inputs | `sla_seconds=30` | | `create_resource_checker(name, resource_type, sla_seconds)` | Verify availability | `sla_seconds=5` | | `create_optimizer(name, optimization_target, sla_minutes)` | Find optimal solution | `sla_minutes=2` | | `create_inspector(name, inspection_type, sla_minutes)` | Analyse quality/safety | `sla_minutes=1` | *** ## Pydantic I/O Schemas Every template uses Pydantic models to enforce typed data between agents: | Industry | Input Schema | Output Schema | | -------------- | ------------------- | --------------------- | | Manufacturing | `OrderDetails` | `QualityReport` | | Energy | `TurbineData` | `MaintenancePlan` | | Healthcare | `VitalSigns` | `ResourceAllocation` | | Agriculture | `MultispectralData` | `YieldForecast` | | Transportation | `LiDARScan` | `MaintenanceSchedule` | *** ## Common Patterns **Add a custom tool to any agent** ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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** ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} 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) ``` *** ## 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)](https://github.com/beixuan577/SRAO-Framework). The `IndustryAgentPattern` class provides the shared 70 % reuse layer. *** ## Related Order processing, inventory, scheduling, and quality control agents. Wind-farm monitoring, vibration analysis, and predictive maintenance agents. Emergency triage, EMR retrieval, and resource allocation agents. Multispectral analysis, disease detection, and yield prediction agents.