Template Add Irrigation Planner Api for irrigation planning

templates/irrigation-planner-api/README.md

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+<a href="https://studio.lamatic.ai/template/irrigation-planner-api" target="_blank" style="text-decoration:none;">
+  <div align="right">
+    <span style="display:inline-block;background:#e63946;color:#fff;border-radius:6px;padding:10px 22px;font-size:16px;font-weight:bold;letter-spacing:0.5px;text-align:center;transition:background 0.2s;box-shadow:0 2px 8px 0 #0001;">Deploy on Lamatic</span>
+  </div>
+</a>
+
+# Irrigation Planner API
+
+## About This Flow
+
+This LLM-powered irrigation planning API processes plant data, location, and date inputs to generate structured watering schedules, enabling adaptive and context-aware irrigation planning based on environmental conditions and plant needs.
+
+## Flow Components
+
+This workflow includes the following node types:
+- API Request
+- Generate JSON
+- API Response
+
+## Configuration Requirements
+
+This flow requires configuration for **1 node(s)** with private inputs (credentials, API keys, model selections, etc.). All required configurations are documented in the `inputs.json` file.
+
+## Files Included
+
+- **config.json** - Complete flow structure with nodes and connections
+- **inputs.json** - Private inputs requiring configuration
+- **meta.json** - Flow metadata and information
+- 
+## Usage
+### Input
+The API takes three main inputs:
+* A list of plant types in list of string
+* Location information
+* A target start date 
+
+### Output
+The system produces structured JSON Object including:
+* 7-day irrigation schedule including date and daily watering durations.
+    * Date formatted as MM/DD/YYYY. Duration is in minutes.
+* A reasoning behind the plan
+* A brief reasoning summary for decisions
+
+
+## Next Steps
+
+### Share with the Community
+
+Help grow the Lamatic ecosystem by contributing this flow to AgentKit!
+
+1. **Fork the Repository**
+   - Visit [github.com/Lamatic/AgentKit](https://github.com/Lamatic/AgentKit)
+   - Fork the repository to your GitHub account
+
+2. **Prepare Your Submission**
+   - Create a new folder with a descriptive name for your flow
+   - Add all files from this package (`config.json`, `inputs.json`, `meta.json`)
+   - Write a comprehensive README.md that includes:
+     - Clear description of what the flow does
+     - Use cases and benefits
+     - Step-by-step setup instructions
+     - Required credentials and how to obtain them
+     - Example inputs and expected outputs
+     - Screenshots or diagrams (optional but recommended)
+
+3. **Open a Pull Request**
+   - Commit your changes with a descriptive message
+   - Push to your forked repository
+   - Open a PR to [github.com/Lamatic/AgentKit](https://github.com/Lamatic/AgentKit)
+   - Add a clear description of your flow in the PR
+
+Your contribution will help others build amazing automations! 🚀
+
+## Support
+
+For questions or issues with this flow:
+- Review the node documentation for specific integrations
+- Check the Lamatic documentation at docs.lamatic.ai
+- Contact support for assistance
+
+---
+*Exported from Lamatic Flow Editor*
+*Generated on 4/17/2026*

templates/irrigation-planner-api/config.json

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+{
+  "nodes": [
+    {
+      "id": "triggerNode_1",
+      "data": {
+        "modes": {},
+        "nodeId": "graphqlNode",
+        "values": {
+          "id": "triggerNode_1",
+          "nodeName": "API Request",
+          "responeType": "realtime",
+          "advance_schema": "{\n  \"plants\": [\n    {}\n  ],\n  \"day\": \"string\",\n  \"location\": \"string\"\n}"
+        },
+        "trigger": true
+      },
+      "type": "triggerNode",
+      "measured": {
+        "width": 216,
+        "height": 93
+      },
+      "position": {
+        "x": 0,
+        "y": 0
+      },
+      "selected": false
+    },
+    {
+      "id": "InstructorLLMNode_862",
+      "data": {
+        "label": "dynamicNode node",
+        "modes": {},
+        "nodeId": "InstructorLLMNode",
+        "values": {
+          "id": "InstructorLLMNode_862",
+          "tools": [],
+          "schema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"watering data\": {\n      \"type\": \"string\",\n      \"required\": true\n    },\n    \"reasoning\": {\n      \"type\": \"string\",\n      \"required\": true\n    },\n    \"reasoning summary\": {\n      \"type\": \"string\",\n      \"required\": true\n    }\n  }\n}",
+          "prompts": [
+            {
+              "id": "187c2f4b-c23d-4545-abef-73dc897d6b7b",
+              "role": "system",
+              "content": "You are an expert irrigation planning assistant. You have a strong background in horticulture."
+            },
+            {
+              "id": "187c2f4b-c23d-4545-abef-73dc897d6b7d",
+              "role": "user",
+              "content": "Generate a weekly irrigation plan in JSON format based on the provided inputs.\nYour task is to generate a weekly irrigation plan using the provided inputs.\nInputs:\n\\- {{triggerNode_1.output.plants}}\n\\- {{triggerNode_1.output.day}}\n\\- {{triggerNode_1.output.location}}\nThe output must include:\n\\- \"watering_data\":An array containing information for all 7 days of the week in chronological order.Each element must be an object with:\n\"day\": The date of the watering day, starting from the given day, formatted as MM/DD/YYYY.\n\"duration\": The watering duration (in minutes) for that day. If no watering is needed, the value must be 0.\n\\- \"reasoning\":A detailed explanation of how plant types and weather conditions influenced the irrigation plan.\n\\- \"reasoning_summary\":A short and concise summary of the reasoning.\nYou must:\nConsider plant types and the weekly weather forecast for the given location when planning irrigation.\nAdjust watering dynamically:\nReduce watering on rainy days\nIncrease watering on hot or dry days\nEnsure the plan is reasonable and consistent with both plant needs and weather conditions."
+            }
+          ],
+          "memories": "[]",
+          "messages": "[]",
+          "nodeName": "Generate JSON",
+          "attachments": "",
+          "generativeModelName": ""
+        }
+      },
+      "type": "dynamicNode",
+      "measured": {
+        "width": 216,
+        "height": 93
+      },
+      "position": {
+        "x": 0,
+        "y": 130
+      },
+      "selected": false
+    },
+    {
+      "id": "responseNode_triggerNode_1",
+      "data": {
+        "label": "Response",
+        "nodeId": "graphqlResponseNode",
+        "values": {
+          "id": "responseNode_triggerNode_1",
+          "headers": "{\"content-type\":\"application/json\"}",
+          "retries": "0",
+          "nodeName": "API Response",
+          "webhookUrl": "",
+          "retry_delay": "0",
+          "outputMapping": "{\n  \"watering data\": \"{{InstructorLLMNode_862.output.watering data}}\",\n  \"reasoning\": \"{{InstructorLLMNode_862.output.reasoning}}\",\n  \"reasoning summary\": \"{{InstructorLLMNode_862.output.reasoning summary}}\"\n}"
+        },
+        "isResponseNode": true
+      },
+      "type": "responseNode",
+      "measured": {
+        "width": 216,
+        "height": 93
+      },
+      "position": {
+        "x": 0,
+        "y": 260
+      },
+      "selected": false
+    }
+  ],
+  "edges": [
+    {
+      "id": "triggerNode_1-InstructorLLMNode_862",
+      "type": "defaultEdge",
+      "source": "triggerNode_1",
+      "target": "InstructorLLMNode_862",
+      "selected": false,
+      "sourceHandle": "bottom",
+      "targetHandle": "top"
+    },
+    {
+      "id": "InstructorLLMNode_862-responseNode_triggerNode_1",
+      "type": "defaultEdge",
+      "source": "InstructorLLMNode_862",
+      "target": "responseNode_triggerNode_1",
+      "sourceHandle": "bottom",
+      "targetHandle": "top"
+    },
+    {
+      "id": "response-trigger_triggerNode_1",
+      "type": "responseEdge",
+      "source": "triggerNode_1",
+      "target": "responseNode_triggerNode_1",
+      "selected": false,
+      "sourceHandle": "to-response",
+      "targetHandle": "from-trigger"
+    }
+  ]
+}

templates/irrigation-planner-api/inputs.json

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+{
+  "InstructorLLMNode_862": [
+    {
+      "name": "generativeModelName",
+      "label": "Generative Model Name",
+      "type": "model",
+      "mode": "instructor",
+      "description": "Select the model to generate text based on the prompt.",
+      "modelType": "generator/text",
+      "required": true,
+      "isPrivate": true,
+      "defaultValue": [
+        {
+          "configName": "configA",
+          "type": "generator/text",
+          "provider_name": "",
+          "credential_name": "",
+          "params": {}
+        }
+      ],
+      "typeOptions": {
+        "loadOptionsMethod": "listModels"
+      }
+    }
+  ]
+}

templates/irrigation-planner-api/meta.json

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+{
+  "name": "Irrigation Planner API",
+  "description": "Lamatic Agent API to automate irrigation planning using an LLM",
+  "tags": [],
+  "testInput": "",
+  "githubUrl": "",
+  "documentationUrl": "",
+  "deployUrl": "",
+  "author": {
+    "name": "N-han"
+  }
+}