{
  "workflow": {
    "id": 9236,
    "name": "Generate employee retention analytics reports with GPT-4o and Gmail digests",
    "views": 68,
    "recentViews": 0,
    "totalViews": 68,
    "createdAt": "2025-10-03T12:24:27.123Z",
    "description": "## Description\nThis workflow automates employee retention analytics by combining candidate performance data with trait-level retention statistics. It scores candidates, validates data, and generates a polished Retention Digest HTML email using GPT (Azure OpenAI). Hiring managers receive structured insights weekly, highlighting top/weak traits, candidate scores, and actionable JD refinement tips.\n\n## What This Template Does (Step-by-Step)\n⚡ Manual Trigger – Starts workflow execution on demand.\n 📑 Candidate Data Fetch (Google Sheets – Hires Tracking) – Pulls candidate-level details like name, role, traits, start date, and retention status.\n 📑 Trait Summary Fetch (Google Sheets – Retention Summary) – Fetches aggregated trait-level retention statistics, including hires, stayed, left, retention %, and weight adjustments.\n 🔀 Merge Candidate + Trait Data – Combines both datasets into a unified stream for scoring.\n 🧮 Candidate Scoring & Data Normalization (Code Node) –\n- Cleans and standardizes data.\n- Builds a trait → weight map.\n- Calculates each candidate’s Candidate_Score.\n- Outputs normalized JSON.\n-  ✅ Data Validation (If Node) – Ensures both candidate and trait datasets are present.\n  - TRUE → continues to AI digest generation.\n  - FALSE → routes to error logging.\n  -  ⚠️ Error Handling Logic (Google Sheets – Error Log) – Logs any failed or incomplete runs into a dedicated error sheet for auditing.\n  -  🧠 AI Processing Backend (Azure OpenAI) – Prepares candidate + trait data for GPT processing.\n  -  🤖 Retention Digest Generator (LLM Chain) – Uses GPT (gpt-4o-mini) to create a structured HTML Retention Digest, including:\n   - TL;DR summary\n   - Top Traits (positive retention)\n   - Weak Traits (negative retention)\n   - Candidate highlights (scores & retention status)\n  - 3 actionable JD refinement tips\n  -  📧 Email Delivery (Gmail) – Sends the digest directly to hiring managers as a styled HTML email with subject:\n  - Retention Analysis Digest – Weekly Update\n\n## Prerequisites\nGoogle Sheets (Hires Tracking + Retention Summary + Error Log)\nGmail API credentials\nAzure OpenAI access (gpt-4o-mini model)\nn8n instance (self-hosted or cloud)\n\n## Key Benefits\n ✅ Automates retention analytics & reporting\n ✅ Provides AI-powered insights in structured HTML\n ✅ Improves hiring strategy with trait-based scoring\n ✅ Reduces manual effort in weekly retention reviews\n ✅ Ensures reliability with error handling & validation\n\n## Perfect For\nHR & Recruitment teams monitoring post-hire retention\nOrganizations optimizing job descriptions & hiring strategy\nTalent analytics teams needing automated, AI-driven insights\nStakeholders requiring clear weekly digest emails",
    "workflow": {
      "id": "T8iqiPFY6WdVtlC0",
      "meta": {
        "instanceId": "8443f10082278c46aa5cf3acf8ff0f70061a2c58bce76efac814b16290845177"
      },
      "name": "Retention Tracking Post-Hire",
      "tags": [],
      "nodes": [
        {
          "id": "c315357f-e9ad-43c8-89f3-e0a030fb6308",
          "name": "When clicking ‘Execute workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
            -544,
            -32
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "5703baf8-53a0-46a7-b8fe-1bddb3da80a5",
          "name": "Candidate Data Fetch",
          "type": "n8n-nodes-base.googleSheets",
          "position": [
            -256,
            64
          ],
          "parameters": {
            "options": {},
            "sheetName": {
              "__rl": true,
              "mode": "list",
              "value": 1454922719,
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit#gid=1454922719",
              "cachedResultName": "Retention Summary)"
            },
            "documentId": {
              "__rl": true,
              "mode": "list",
              "value": "1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit?usp=drivesdk",
              "cachedResultName": "Interviewer Brief Pack "
            }
          },
          "credentials": {
            "googleSheetsOAuth2Api": {
              "id": "credential-id",
              "name": "googleSheetsOAuth2Api Credential"
            }
          },
          "typeVersion": 4.6
        },
        {
          "id": "76e5ed93-981c-402f-ba97-0f6c07b4642e",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            880,
            464
          ],
          "parameters": {
            "height": 384,
            "content": "## ⚠️ Error Handling Logic (Google Sheets – Error Log)  \n\n**Action:**  \n- Appends failed runs into the *Error Log Sheet*.  \n\n**Description:**  \n- Records details like error_id and error message.  \n- Provides visibility into workflow issues.  \n- Ensures graceful handling of bad data without breaking the workflow.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "d48ac595-d48e-401d-88bd-a31c3cd596d4",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            592,
            176
          ],
          "parameters": {
            "height": 448,
            "content": "## ✅ Data Validation   \n\n**Action:**  \n- Validates whether both candidate and trait arrays contain records.  \n\n**Description:**  \n- Condition checks: `candidates.length > 0` AND `traits.length > 0`.  \n- If TRUE → proceeds to LLM digest generation.  \n- If FALSE → workflow routes to error logging.  \n- Prevents empty datasets from reaching AI/email stages.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "bddb3bf5-c3bb-4899-b6ff-aa22b953b366",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            304,
            -496
          ],
          "parameters": {
            "width": 304,
            "height": 464,
            "content": "## 🧮 Candidate Scoring & Data Normalization (Code Node)  \n\n**Action:**  \n- Cleans, normalizes, and enriches the merged dataset.  \n\n**Description:**  \n- Splits rows into two arrays: `candidates[]` and `traits[]`.  \n- Normalizes headers, trims spaces, and standardizes data.  \n- Builds a trait → weight lookup map from the Retention Summary.  \n- Calculates each candidate’s `Candidate_Score` based on their traits.  \n- Outputs clean JSON with both candidates and traits.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "549cdd14-5514-4413-9e91-ec3ddd9aa771",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            80,
            192
          ],
          "parameters": {
            "width": 272,
            "height": 384,
            "content": "## 🔀 Merge Candidate + Trait Data  \n\n**Action:**  \n- Combines candidate-level and trait-level rows into one dataset.  \n\n**Description:**  \n- Unifies inputs from both sheets into a single stream.  \n- Ensures both granular (candidates) and aggregated (traits) data are processed together.  \n- Passes consolidated data into the scoring and enrichment step.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "b9f7939e-a01f-400f-b5b1-45b9202f6bc4",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -352,
            -576
          ],
          "parameters": {
            "width": 288,
            "height": 400,
            "content": "## 📑 Trait Summary Fetch (Google Sheets – Retention Summary)  \n\n**Action:**  \n- Retrieves aggregated trait-level data from the Retention Summary sheet.  \n\n**Description:**  \n- Collects retention stats like hires, stayed, left, retention %, and weight adjustment.  \n- Identifies which traits are positively or negatively correlated with retention.  \n- Feeds into candidate scoring logic to adjust weights dynamically.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "a5260ee9-3a6a-4cc1-ae50-426cb4e7d372",
          "name": "Sticky Note5",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -336,
            240
          ],
          "parameters": {
            "width": 272,
            "height": 416,
            "content": "## 📑 Candidate Data Fetch (Google Sheets – Hires Tracking)  \n\n**Action:**  \n- Retrieves the candidate’s details from the designated Google Sheet.  \n\n**Description:**  \n- Pulls structured information such as candidate name, role, traits, start date, and retention status.  \n- This sheet acts as the source of truth for post-hire outcomes.  \n- Ensures accurate and up-to-date records for downstream scoring and reporting.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "5b4a9840-0dd7-4fcc-b6d1-a2ca015d5daf",
          "name": "Sticky Note6",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1168,
            192
          ],
          "parameters": {
            "height": 384,
            "content": "## 🧠 AI Processing Backend (Azure OpenAI Node)  \n\n**Action:**  \n- Executes GPT processing using Azure OpenAI.  \n\n**Description:**  \n- Takes candidate + trait JSON input.  \n- Applies strict prompting rules (no hallucination, only dataset values).  \n- Returns formatted HTML insights for downstream use.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "94d3d781-c9a4-4778-8029-440612132c7f",
          "name": "Sticky Note7",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1024,
            -512
          ],
          "parameters": {
            "width": 368,
            "height": 464,
            "content": "## 🤖 Retention Digest Generator (LLM Chain)  \n\n**Action:**  \n- Generates an HTML Retention Digest using Azure OpenAI.  \n\n**Description:**  \n- Summarizes retention insights into a structured email.  \n- Sections include:  \n  - TL;DR Summary  \n  - Top Traits (positive)  \n  - Weak Traits (negative)  \n  - Candidate Highlights with scores  \n  - 3 Actionable JD Refinement Tips  \n- Ensures valid, styled HTML output ready for email.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "b6bf37e0-ebfd-48d4-a47b-66d6bbaa2991",
          "name": "Sticky Note8",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1504,
            -496
          ],
          "parameters": {
            "height": 432,
            "content": "## 📧 Email Delivery (Gmail – Send Digest)  \n\n**Action:**  \n- Sends the Retention Digest via Gmail.  \n\n**Description:**  \n- Uses the HTML generated by the LLM as the email body.  \n- Subject: *Retention Analysis Digest – Weekly Update*.  \n- Recipients: Hiring managers / stakeholders.  \n- Automates communication of insights directly to decision-makers.  \n"
          },
          "typeVersion": 1
        },
        {
          "id": "82cb57ca-7214-4789-ba0e-c49871938f13",
          "name": " Trait Summary Fetch ",
          "type": "n8n-nodes-base.googleSheets",
          "position": [
            -256,
            -144
          ],
          "parameters": {
            "options": {},
            "sheetName": {
              "__rl": true,
              "mode": "list",
              "value": 834845387,
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit#gid=834845387",
              "cachedResultName": "Hires Tracking"
            },
            "documentId": {
              "__rl": true,
              "mode": "list",
              "value": "1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit?usp=drivesdk",
              "cachedResultName": "Interviewer Brief Pack "
            }
          },
          "credentials": {
            "googleSheetsOAuth2Api": {
              "id": "credential-id",
              "name": "googleSheetsOAuth2Api Credential"
            }
          },
          "typeVersion": 4.6
        },
        {
          "id": "41c489c1-9aa2-4ea7-a000-c57e0cd237f0",
          "name": "Merge Candidate + Trait Data",
          "type": "n8n-nodes-base.merge",
          "position": [
            128,
            0
          ],
          "parameters": {},
          "typeVersion": 3.2
        },
        {
          "id": "88119bb1-b48a-4d71-95da-6f012e87830e",
          "name": "Candidate Scoring & Data Normalization",
          "type": "n8n-nodes-base.code",
          "position": [
            416,
            0
          ],
          "parameters": {
            "jsCode": "// ---- STEP 1: Separate inputs ----\nconst allRows = $input.all();\n\n// Identify rows with candidate info (Hire Tracking) vs traits (Retention Summary)\nconst candidateRaw = allRows.filter(r => r.json[\"Candidate \"] || r.json.Candidate);\nconst traitsRaw = allRows.filter(r => r.json.Trait);\n\n// ---- STEP 2: Normalize Candidate Data ----\nconst candidates = candidateRaw.map(c => {\n  return {\n    row_number: c.json.row_number,\n    Candidate: (c.json[\"Candidate \"] || c.json.Candidate || \"\").trim(),\n    Role: (c.json[\"Role              \"] || c.json.Role || \"\").trim(),\n    Traits: (c.json[\"Traits                   \"] || c.json.Traits || \"\").trim(),\n    Start_Date: c.json[\"Start Date\"] || null,\n    Status: (c.json[\"Status   \"] || c.json.Status || \"\").trim(),\n    Retention_30: c.json[\"Retention_30 \"] ?? c.json.Retention_30 ?? null,\n    Retention_90: c.json[\"Retention_90\"] ?? null,\n    Candidate_Score: c.json[\"Candidate_Score\"] || 0\n  };\n});\n\n// ---- STEP 3: Normalize Traits Summary ----\nconst traits = traitsRaw.map(t => {\n  return {\n    Trait: t.json.Trait,\n    Total_Hires: parseInt(t.json.Total_Hires, 10) || 0,\n    Stayed_90: parseInt(t.json.Stayed_90, 10) || 0,\n    Left_90: parseInt(t.json.Left_90, 10) || 0,\n    \"Retention_%\": parseFloat(t.json[\"Retention_%\"]) || 0,\n    Weight_Adjust: parseInt(t.json.Weight_Adjust, 10) || 0,\n    Candidate_Score: t.json.Candidate_Score || 0\n  };\n});\n\n// ---- STEP 4: Build Trait Weight Lookup ----\nconst weightMap = {};\nfor (const t of traits) {\n  weightMap[t.Trait] = t.Weight_Adjust;\n}\n\n// ---- STEP 5: Calculate Candidate Scores ----\nfor (const c of candidates) {\n  let score = 0;\n  const candidateTraits = (c.Traits || \"\").split(\",\").map(t => t.trim());\n  candidateTraits.forEach(trait => {\n    score += weightMap[trait] ?? 0;\n  });\n  c.Candidate_Score = score;\n}\n\n// ---- STEP 6: Final Combined Output ----\nreturn [\n  {\n    json: {\n      candidates,\n      traits\n    }\n  }\n];\n"
          },
          "typeVersion": 2
        },
        {
          "id": "b563b0b1-393d-48a3-8f8f-d8c55fa1d8b7",
          "name": " Data Validation",
          "type": "n8n-nodes-base.if",
          "position": [
            672,
            0
          ],
          "parameters": {
            "options": {},
            "conditions": {
              "options": {
                "version": 2,
                "leftValue": "",
                "caseSensitive": true,
                "typeValidation": "strict"
              },
              "combinator": "and",
              "conditions": [
                {
                  "id": "01f729d5-7169-4f69-89cc-90bc194d11b5",
                  "operator": {
                    "type": "number",
                    "operation": "gt"
                  },
                  "leftValue": "={{ $json.candidates.length }}",
                  "rightValue": 0
                },
                {
                  "id": "ef01e17b-4779-4819-a879-742246d8a3f4",
                  "operator": {
                    "type": "number",
                    "operation": "gt"
                  },
                  "leftValue": "={{ $json.traits.length }}",
                  "rightValue": 0
                }
              ]
            }
          },
          "typeVersion": 2.2
        },
        {
          "id": "71be08e3-2031-416a-851f-9833953b1e25",
          "name": " Error Handling Logic",
          "type": "n8n-nodes-base.googleSheets",
          "position": [
            928,
            304
          ],
          "parameters": {
            "columns": {
              "value": {},
              "schema": [
                {
                  "id": "error_id",
                  "type": "string",
                  "display": true,
                  "removed": false,
                  "required": false,
                  "displayName": "error_id",
                  "defaultMatch": false,
                  "canBeUsedToMatch": true
                },
                {
                  "id": "error",
                  "type": "string",
                  "display": true,
                  "removed": false,
                  "required": false,
                  "displayName": "error",
                  "defaultMatch": false,
                  "canBeUsedToMatch": true
                }
              ],
              "mappingMode": "defineBelow",
              "matchingColumns": [
                "error_id"
              ],
              "attemptToConvertTypes": false,
              "convertFieldsToString": false
            },
            "options": {},
            "operation": "append",
            "sheetName": {
              "__rl": true,
              "mode": "list",
              "value": 1338537721,
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit#gid=1338537721",
              "cachedResultName": "error log sheet"
            },
            "documentId": {
              "__rl": true,
              "mode": "list",
              "value": "1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit?usp=drivesdk",
              "cachedResultName": "Interviewer Brief Pack "
            }
          },
          "credentials": {
            "googleSheetsOAuth2Api": {
              "id": "credential-id",
              "name": "googleSheetsOAuth2Api Credential"
            }
          },
          "typeVersion": 4.6
        },
        {
          "id": "62e4914b-c97a-46b7-bd66-1fc1cbd4d0df",
          "name": " Retention Digest Generator",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            1104,
            -16
          ],
          "parameters": {
            "text": "=Here is the retention dataset:\n\n{{ JSON.stringify($json, null, 2) }}\n\nGenerate one **Retention Digest** email using this dataset. \nThe HTML should include the following sections:\n\n1. **TL;DR Summary** – one short paragraph summarizing retention insights.  \n2. **Top Traits (Strong Retention + Positive Weights)** – list traits with Retention_% = 1 and Weight_Adjust > 0.  \n3. **Weak Traits (Poor Retention + Negative Weights)** – list traits with Retention_% = 0 or Weight_Adjust < 0.  \n4. **Candidate Highlights** – list each candidate, their traits, retention status, and Candidate_Score (positive/negative).  \n5. **Actionable Tips** – provide 3 practical JD refinement recommendations based only on this dataset.  \n\n📌 **Formatting requirements**:  \n- Blue header bar (#0073e6) with white bold title “Retention Insights Digest”.  \n- White card-style container with light shadow + rounded corners.  \n- Section headings:  \n   • Blue (#0073e6) for TL;DR and Top Traits  \n   • Red (#d9534f) for Weak Traits  \n   • Green (#28a745) for Actionable Tips  \n- Candidate list in a table (Name, Traits, Score, Retention_90).  \n- Green CTA button (#28a745) at the bottom labeled “View Full Report”.  \n- Output only valid HTML, no markdown or code fences.  \n",
            "batching": {},
            "messages": {
              "messageValues": [
                {
                  "message": "You are an HR Analytics Assistant.  STRICT RULES:  - Use ONLY the traits and candidate data from the provided dataset.  - Do not invent or hallucinate new traits, values, or candidates.  - Always echo the exact Retention_% and Weight_Adjust values.  - Show candidate scores exactly as calculated.  - Output must be valid, production-ready HTML email with inline CSS styling (email-safe). - Do not include markdown or code fences (no ```html)."
                }
              ]
            },
            "promptType": "define"
          },
          "typeVersion": 1.7
        },
        {
          "id": "9fc86f99-9bc5-4161-9145-9cce505d808c",
          "name": " AI Processing Backend ",
          "type": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi",
          "position": [
            1072,
            160
          ],
          "parameters": {
            "model": "gpt-4o-mini",
            "options": {}
          },
          "credentials": {
            "azureOpenAiApi": {
              "id": "credential-id",
              "name": "azureOpenAiApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "489d85e3-e51c-4db9-ab20-e466db61aa1e",
          "name": "Email Delivery",
          "type": "n8n-nodes-base.gmail",
          "position": [
            1552,
            -16
          ],
          "parameters": {
            "toList": [
              "user@example.com"
            ],
            "message": " Weekly Update",
            "subject": "=Retention Analysis Digest - Weekly Update\n",
            "resource": "message",
            "htmlMessage": "={{ $json.text }}",
            "includeHtml": true,
            "additionalFields": {
              "ccList": []
            }
          },
          "credentials": {
            "gmailOAuth2": {
              "id": "credential-id",
              "name": "gmailOAuth2 Credential"
            }
          },
          "typeVersion": 1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
      },
      "versionId": "1c0b5bac-8ce0-4e48-a2e1-b9bf0868d16a",
      "connections": {
        " Data Validation": {
          "main": [
            [
              {
                "node": " Retention Digest Generator",
                "type": "main",
                "index": 0
              }
            ],
            [
              {
                "node": " Error Handling Logic",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Candidate Data Fetch": {
          "main": [
            [
              {
                "node": "Merge Candidate + Trait Data",
                "type": "main",
                "index": 1
              }
            ]
          ]
        },
        " Trait Summary Fetch ": {
          "main": [
            [
              {
                "node": "Merge Candidate + Trait Data",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        " AI Processing Backend ": {
          "ai_languageModel": [
            [
              {
                "node": " Retention Digest Generator",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        " Retention Digest Generator": {
          "main": [
            [
              {
                "node": "Email Delivery",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Merge Candidate + Trait Data": {
          "main": [
            [
              {
                "node": "Candidate Scoring & Data Normalization",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "When clicking ‘Execute workflow’": {
          "main": [
            [
              {
                "node": "Candidate Data Fetch",
                "type": "main",
                "index": 0
              },
              {
                "node": " Trait Summary Fetch ",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Candidate Scoring & Data Normalization": {
          "main": [
            [
              {
                "node": " Data Validation",
                "type": "main",
                "index": 0
              }
            ]
          ]
        }
      }
    },
    "lastUpdatedBy": 1,
    "workflowInfo": {
      "nodeCount": 19,
      "nodeTypes": {
        "n8n-nodes-base.if": {
          "count": 1
        },
        "n8n-nodes-base.code": {
          "count": 1
        },
        "n8n-nodes-base.gmail": {
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        },
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        "n8n-nodes-base.googleSheets": {
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    "status": "published",
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      },
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        "icon": "file:gmail.svg",
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                },
                {
                  "url": "https://n8n.io/blog/how-to-get-started-with-crm-automation-and-no-code-workflow-ideas/",
                  "icon": "👥",
                  "label": "How to get started with CRM automation (with 3 no-code workflow ideas"
                },
                {
                  "url": "https://n8n.io/blog/automate-google-apps-for-productivity/",
                  "icon": "💡",
                  "label": "15 Google apps you can combine and automate to increase productivity"
                },
                {
                  "url": "https://n8n.io/blog/your-business-doesnt-need-you-to-operate/",
                  "icon": " 🖥️",
                  "label": "Hey founders! Your business doesn't need you to operate"
                },
                {
                  "url": "https://n8n.io/blog/using-automation-to-boost-productivity-in-the-workplace/",
                  "icon": "💪",
                  "label": "Using Automation to Boost Productivity in the Workplace"
                }
              ],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.gmail/"
                }
              ],
              "credentialDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/credentials/google/oauth-single-service/"
                }
              ]
            },
            "categories": [
              "Communication",
              "HITL"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "HITL": [
                "Human in the Loop"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Gmail"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Gmail",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 6,
            "name": "Communication"
          },
          {
            "id": 28,
            "name": "HITL"
          }
        ]
      },
      {
        "id": 565,
        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
        "codex": {
          "data": {
            "alias": [
              "Comments",
              "Notes",
              "Sticky"
            ],
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers"
              ]
            }
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Sticky Note",
          "color": "#FFD233"
        },
        "iconData": {
          "icon": "sticky-note",
          "type": "icon"
        },
        "displayName": "Sticky Note",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 834,
        "icon": "file:code.svg",
        "name": "n8n-nodes-base.code",
        "codex": {
          "data": {
            "alias": [
              "cpde",
              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
            ],
            "details": "The Code node allows you to execute JavaScript in your workflow.",
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
                }
              ]
            },
            "categories": [
              "Development",
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Code"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Code",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 5,
            "name": "Development"
          },
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 838,
        "icon": "fa:mouse-pointer",
        "name": "n8n-nodes-base.manualTrigger",
        "codex": {
          "data": {
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.manualworkflowtrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0"
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When clicking ‘Execute workflow’",
          "color": "#909298"
        },
        "iconData": {
          "icon": "mouse-pointer",
          "type": "icon"
        },
        "displayName": "Manual Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1123,
        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainLlm",
        "codex": {
          "data": {
            "alias": [
              "LangChain"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Basic LLM Chain",
          "color": "#909298"
        },
        "iconData": {
          "icon": "link",
          "type": "icon"
        },
        "displayName": "Basic LLM Chain",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1253,
        "icon": "file:azure.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatazureopenai/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Azure OpenAI Chat Model"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Azure OpenAI Chat Model",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      }
    ],
    "categories": [
      {
        "id": 17,
        "name": "HR"
      },
      {
        "id": 49,
        "name": "AI Summarization"
      }
    ],
    "image": []
  }
}