My workflow.json

{
  "name": "My workflow",
  "nodes": [
    {
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"learnings\": {\n      \"type\": \"array\",\n      \"description\": \"List of learnings, max of 3.\",\n      \"items\": { \"type\": \"string\" }\n    },\n    \"followUpQuestions\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\",\n        \"description\": \"List of follow-up questions to research the topic further, max of 3.\"\n      }\n    }\n  }\n}"
      },
      "id": "bae48bc0-343c-45b5-8681-71be6d655eb0",
      "name": "Structured Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        3728,
        2368
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "df28b12e-7c20-4ff5-b5b8-dc773aa14d4b",
              "name": "request_id",
              "type": "string",
              "value": "={{ $execution.id }}"
            },
            {
              "id": "9362c1e7-717d-444a-8ea2-6b5f958c9f3f",
              "name": "prompt",
              "type": "string",
              "value": "={{ $json['What would you like to research?'] }}"
            },
            {
              "id": "09094be4-7844-4a9e-af82-cc8e39322398",
              "name": "depth",
              "type": "number",
              "value": "={{ $json['input-depth'] }}"
            },
            {
              "id": "3fc30a30-7806-4013-835d-97e27ddd7ae1",
              "name": "breadth",
              "type": "number",
              "value": "={{ $json['input-breadth'] }}"
            }
          ]
        },
        "options": {}
      },
      "id": "5c6a8188-5ef7-447c-be2e-990f0a55562f",
      "name": "Set Variables",
      "type": "n8n-nodes-base.set",
      "position": [
        1072,
        720
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"questions\": {\n      \"type\": \"array\",\n      \"description\": \"Follow up questions to clarify the research direction, max of 3.\",\n      \"items\": {\n          \"type\": \"string\"\n      }\n    }\n  }\n}"
      },
      "id": "5cb34e23-33dc-4e41-b57b-9bc32d3ffd18",
      "name": "Structured Output Parser1",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1584,
        880
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "formTitle": " DeepResearcher",
        "formDescription": "=DeepResearcher is a multi-step, recursive approach using the internet to solve complex research tasks, accomplishing in tens of minutes what a human would take many hours.\n\nTo use, provide a short summary of what the research and how \"deep\" you'd like the workflow to investigate. Note, the higher the numbers the more time and cost will occur for the research.\n\nThe workflow is designed to complete independently and when finished, a report will be saved in a Github Repo.",
        "options": {
          "buttonLabel": "Next",
          "path": "deep_research",
          "ignoreBots": true
        }
      },
      "id": "b8da4808-eacc-4a66-82c2-48811b49e956",
      "name": "On form submission",
      "type": "n8n-nodes-base.formTrigger",
      "position": [
        672,
        720
      ],
      "webhookId": "026629c8-7644-493c-b830-d9c72eea307d",
      "typeVersion": 2.2
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=Given the following prompt from the user, generate a list of SERP queries to research the topic.\nReduce the number of words in each query to its keywords only.\nReturn a maximum of {{ $('JobType Router').first().json.data.breadth }} queries, but feel free to return less if the original prompt is clear. Make sure each query is unique and not similar to each other: <prompt>{{ $('JobType Router').first().json.data.query.trim() }}</prompt>\n\n{{\n$('JobType Router').first().json.data.learnings.length\n  ? `Here are some learnings from previous research, use them to generate more specific queries:\\n${$('JobType Router').first().json.data.learnings.map(text => `* ${text}`).join('\\n')}`\n  : ''\n}}",
        "hasOutputParser": true,
        "messages": {
          "messageValues": [
            {
              "type": "HumanMessagePromptTemplate",
              "message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n  - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n  - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n  - Be highly organized.\n  - Suggest solutions that I didn't think about.\n  - Be proactive and anticipate my needs.\n  - Treat me as an expert in all subject matter.\n  - Mistakes erode my trust, so be accurate and thorough.\n  - Provide detailed explanations, I'm comfortable with lots of detail.\n  - Value good arguments over authorities, the source is irrelevant.\n  - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n  - You may use high levels of speculation or prediction, just flag it for me."
            }
          ]
        }
      },
      "id": "ec9f3015-dd1f-41be-abb8-1d29e1602e59",
      "name": "Generate SERP Queries",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1392,
        2000
      ],
      "typeVersion": 1.5
    },
    {
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"queries\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"query\": {\n            \"type\": \"string\",\n            \"description\": \"The SERP query\"\n          },\n          \"researchGoal\": {\n            \"type\": \"string\",\n            \"description\": \"First talk about the goal of the research that this query is meant to accomplish, then go deeper into how to advance the research once the results are found, mention additional research directions. Be as specific as possible, especially for additional research directions.\"\n          }\n        }\n      }\n    }\n  }\n}"
      },
      "id": "acca8eb2-0a22-4f0d-9e6f-9d28d5b1198d",
      "name": "Structured Output Parser2",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1568,
        2160
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "acb41e93-70c6-41a3-be0f-e5a74ec3ec88",
              "name": "query",
              "type": "string",
              "value": "={{ $('JobType Router').first().json.data.query }}"
            },
            {
              "id": "7fc54063-b610-42bc-a250-b1e8847c4d1e",
              "name": "learnings",
              "type": "array",
              "value": "={{ $('JobType Router').first().json.data.learnings }}"
            },
            {
              "id": "e8f1c158-56fb-41c8-8d86-96add16289bb",
              "name": "breadth",
              "type": "number",
              "value": "={{ $('JobType Router').first().json.data.breadth }}"
            }
          ]
        },
        "options": {}
      },
      "id": "8fc8442f-d1fd-4f55-840e-a937d18f4690",
      "name": "Set Initial Query",
      "type": "n8n-nodes-base.set",
      "position": [
        1840,
        1360
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "fieldToSplitOut": "output.queries",
        "options": {}
      },
      "id": "edc0f01b-e2ed-4345-b114-9c5fd9aed2ef",
      "name": "SERP to Items",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        1728,
        2000
      ],
      "typeVersion": 1
    },
    {
      "parameters": {},
      "id": "41752dc5-46b6-428c-8b3b-0a765f7d946c",
      "name": "Item Ref",
      "type": "n8n-nodes-base.noOp",
      "position": [
        2208,
        2208
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "9acec2cc-64c8-4e62-bed4-c3d9ffab1379",
              "name": "researchGoal",
              "type": "string",
              "value": "={{ $('Item Ref').first().json.researchGoal }}"
            },
            {
              "id": "1b2d2dad-429b-4fc9-96c5-498f572a85c3",
              "name": "learnings",
              "type": "array",
              "value": "={{ $json.output.learnings }}"
            },
            {
              "id": "7025f533-02ab-4031-9413-43390fb61f05",
              "name": "followUpQuestions",
              "type": "string",
              "value": "={{ $json.output.followUpQuestions }}"
            },
            {
              "id": "c9e34ea4-5606-46d6-8d66-cb42d772a8b4",
              "name": "urls",
              "type": "array",
              "value": "={{\n$('Get Markdown + URL')\n  .all()\n  .map(item => item.json.url)\n}}"
            }
          ]
        },
        "options": {}
      },
      "id": "4f160202-347f-4900-a797-d7b9ccdfeaf7",
      "name": "Research Goal + Learnings",
      "type": "n8n-nodes-base.set",
      "position": [
        3888,
        2352
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "db509e90-9a86-431f-8149-4094d22666cc",
              "name": "should_stop",
              "type": "boolean",
              "value": "={{\n$runIndex >= ($('JobType Router').first().json.data.depth)\n}}"
            },
            {
              "id": "90986e2b-8aca-4a22-a9db-ed8809d6284d",
              "name": "all_learnings",
              "type": "array",
              "value": "={{\nArray($runIndex+1)\n  .fill(0)\n  .flatMap((_,idx) => {\n    try {\n      return $('Generate Learnings')\n        .all(0,idx)\n        .flatMap(item => item.json.data.flatMap(d => d.learnings))\n    } catch (e) {\n      return []\n    }\n  })\n}}"
            },
            {
              "id": "3eade958-e8ab-4975-aac4-f4a4a983c163",
              "name": "all_urls",
              "type": "array",
              "value": "={{\nArray($runIndex+1)\n  .fill(0)\n  .flatMap((_,idx) => {\n    try {\n      return $('Generate Learnings')\n        .all(0,idx)\n        .flatMap(item => item.json.data.flatMap(d => d.urls))\n    } catch (e) {\n      return []\n    }\n  })\n}}"
            }
          ]
        },
        "options": {}
      },
      "id": "280ae4aa-4280-4511-b47f-3b3a0a685e85",
      "name": "Accumulate Results",
      "type": "n8n-nodes-base.set",
      "position": [
        2224,
        1360
      ],
      "executeOnce": true,
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "mode": "raw",
        "jsonOutput": "={{ $('Generate Learnings').item.json }}",
        "options": {}
      },
      "id": "12be26c5-95ec-4f55-b30e-c4061e95aa75",
      "name": "DeepResearch Results",
      "type": "n8n-nodes-base.set",
      "position": [
        2592,
        1552
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "fieldToSplitOut": "data",
        "options": {}
      },
      "id": "b9e4cf54-d289-42ed-87db-a4fc96f238c1",
      "name": "Results to Items",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        2752,
        1552
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "d88bfe95-9e73-4d25-b45c-9f164b940b0e",
              "name": "query",
              "type": "string",
              "value": "=Previous research goal: {{ $json.researchGoal }}\nFollow-up research directions: {{ $json.followUpQuestions.map(q => `\\n${q}`).join('') }}"
            },
            {
              "id": "4aa20690-d998-458a-b1e4-0d72e6a68e6b",
              "name": "learnings",
              "type": "array",
              "value": "={{ $('Accumulate Results').item.json.all_learnings }}"
            },
            {
              "id": "89acafae-b04a-4d5d-b08b-656e715654e4",
              "name": "breadth",
              "type": "number",
              "value": "={{ $('JobType Router').first().json.data.breadth }}"
            }
          ]
        },
        "options": {}
      },
      "id": "cb4145f0-539e-4c3a-b2b6-cf05f2a6380d",
      "name": "Set Next Queries",
      "type": "n8n-nodes-base.set",
      "position": [
        2912,
        1552
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "options": {}
      },
      "id": "5cbdcaac-d35c-4c73-a27c-a665d4be8c2f",
      "name": "For Each Query...",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        2000,
        2048
      ],
      "typeVersion": 3
    },
    {
      "parameters": {
        "fieldToSplitOut": "output.questions",
        "options": {}
      },
      "id": "80579c45-a98f-49a3-9f19-fe5f630629de",
      "name": "Feedback to Items",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        1712,
        720
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "formFields": {
          "values": [
            {
              "fieldLabel": "={{ $json[\"output.questions\"] }}",
              "fieldType": "textarea",
              "placeholder": "=",
              "requiredField": true
            }
          ]
        },
        "options": {
          "formTitle": "DeepResearcher",
          "formDescription": "=<img\n  src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n  width=\"100%\"\n  style=\"border:1px solid #ccc\"\n/>\n<p style=\"text-align:left\">\nAnswer the following clarification questions to assist the DeepResearcher better under the research topic.\n</p>\n<hr style=\"display:block;margin-top:16px;margin-bottom:0\" />\n<p style=\"text-align:left;font-family:sans-serif;font-weight:700;\">\nTotal {{ $('Feedback to Items').all().length }} questions.\n</p>",
          "buttonLabel": "Answer"
        }
      },
      "id": "ec8856cf-af53-4541-8aa7-326c5b9f4ba4",
      "name": "Ask Clarity Questions",
      "type": "n8n-nodes-base.form",
      "position": [
        2064,
        800
      ],
      "webhookId": "d3375ba6-0008-4fcb-96bc-110374de2603",
      "typeVersion": 1
    },
    {
      "parameters": {
        "options": {}
      },
      "id": "78c92c6f-831b-4969-89a7-de11cf482383",
      "name": "For Each Question...",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        1888,
        720
      ],
      "typeVersion": 3
    },
    {
      "parameters": {
        "workflowInputs": {
          "values": [
            {
              "name": "requestId",
              "type": "any"
            },
            {
              "name": "jobType"
            },
            {
              "name": "data",
              "type": "object"
            }
          ]
        }
      },
      "id": "dd3bb4bd-d814-4f11-848d-1974bf8830a7",
      "name": "DeepResearch Subworkflow",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
      "position": [
        544,
        2000
      ],
      "typeVersion": 1.1
    },
    {
      "parameters": {
        "content": "## 2. Ask Clarifying Questions\n[Read more about form nodes](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.form/)\n\nTo handle the clarification questions generated by the LLM, I used the same technique found in my \"AI Interviewer\" template ([link](https://n8n.io/workflows/2566-conversational-interviews-with-ai-agents-and-n8n-forms/)).\nThis involves a looping of dynamically generated forms to collect answers from the user.",
        "height": 560,
        "width": 1000,
        "color": 7
      },
      "id": "40b17a07-d8b3-4c96-8fab-6efa6fc77d68",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1280,
        512
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=Given the following query from the user, ask some follow up questions to clarify the research direction. Return a maximum of 3 questions, but feel free to return less if the original query is clear: <query>{{ $json.prompt }}</query>`",
        "hasOutputParser": true,
        "messages": {
          "messageValues": [
            {
              "type": "HumanMessagePromptTemplate",
              "message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n  - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n  - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n  - Be highly organized.\n  - Suggest solutions that I didn't think about.\n  - Be proactive and anticipate my needs.\n  - Treat me as an expert in all subject matter.\n  - Mistakes erode my trust, so be accurate and thorough.\n  - Provide detailed explanations, I'm comfortable with lots of detail.\n  - Value good arguments over authorities, the source is irrelevant.\n  - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n  - You may use high levels of speculation or prediction, just flag it for me."
            }
          ]
        }
      },
      "id": "e76e2751-a61c-4a7a-8348-cee41aa3af33",
      "name": "Clarifying Questions",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1392,
        720
      ],
      "typeVersion": 1.5
    },
    {
      "parameters": {
        "content": "## 6. Perform DeepSearch Loop\n[Learn more about the Looping in n8n](https://docs.n8n.io/flow-logic/looping/#creating-loops)\n\nThe key of the Deep Research flow is its extensive data collection capability. In this implementation, this capability is represented by a recursive web search & scrape loop which starts with the original query and extended by AI-generated subqueries. How many subqueries to generate are determined the depth and breadth parameters specified.\n\n\"Learnings\" are generated for each subquery and accumulate on each iteration of the loop. When the loop finishes when depth limit is reached, all learnings are collected and it's these learnings are what we use to generate the report.",
        "height": 640,
        "width": 1360,
        "color": 7
      },
      "id": "ec7a9913-1192-4c94-be78-11d633c93d7f",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1760,
        1120
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "operation": "completion",
        "completionTitle": "=Thank you for using DeepResearcher.",
        "completionMessage": "=You may now close this window.",
        "limitWaitTime": true,
        "resumeAmount": 3,
        "resumeUnit": "minutes",
        "options": {}
      },
      "id": "2197f870-79a2-4b6d-9d88-7bf4713b5a44",
      "name": "End Form",
      "type": "n8n-nodes-base.form",
      "position": [
        3392,
        768
      ],
      "webhookId": "88f2534b-2b82-4b40-a4bc-97d96384e8fd",
      "typeVersion": 1
    },
    {
      "parameters": {
        "workflowId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $workflow.id }}"
        },
        "workflowInputs": {
          "value": {
            "data": "={{\n{\n  \"query\": $('Get Initial Query').first().json.query,\n  \"learnings\": [],\n  \"depth\": $('Set Variables').first().json.depth,\n  \"breadth\": $('Set Variables').first().json.breadth,\n}\n}}",
            "jobType": "deepresearch_initiate",
            "requestId": "={{ $('Set Variables').first().json.request_id }}"
          },
          "schema": [
            {
              "id": "requestId",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "requestId",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "jobType",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "jobType",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "data",
              "type": "object",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "data",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        },
        "mode": "each",
        "options": {
          "waitForSubWorkflow": false
        }
      },
      "id": "9b5fe61d-91d4-4ff5-a3d8-3e233f2fbcd7",
      "name": "Initiate DeepResearch",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        3024,
        768
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "dataToSave": {
          "values": [
            {
              "key": "requestId",
              "value": "={{ $json.requestId }}"
            },
            {
              "key": "=jobType",
              "value": "={{ $json.jobType }}"
            }
          ]
        }
      },
      "id": "78e78ce5-c0b3-4f8e-a889-61de9c4e7c29",
      "name": "Execution Data",
      "type": "n8n-nodes-base.executionData",
      "position": [
        720,
        2000
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "rules": {
          "values": [
            {
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "operator": {
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.jobType }}",
                    "rightValue": "deepresearch_initiate"
                  }
                ]
              },
              "renameOutput": true,
              "outputKey": "initiate"
            },
            {
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "ecbfa54d-fc97-48c5-8d3d-f0538b8d727b",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.jobType }}",
                    "rightValue": "deepresearch_learnings"
                  }
                ]
              },
              "renameOutput": true,
              "outputKey": "learnings"
            },
            {
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "392f9a98-ec22-4e57-9c8e-0e1ed6b7dafa",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $json.jobType }}",
                    "rightValue": "deepresearch_report"
                  }
                ]
              },
              "renameOutput": true,
              "outputKey": "report"
            }
          ]
        },
        "options": {}
      },
      "id": "e6a285ee-ec7f-408b-8f01-3b22236198da",
      "name": "JobType Router",
      "type": "n8n-nodes-base.switch",
      "position": [
        912,
        2000
      ],
      "typeVersion": 3.2
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "14b77741-c3c3-4bd2-be6e-37bd09fcea2b",
              "name": "query",
              "type": "string",
              "value": "=Initial query: {{ $('Set Variables').first().json.prompt }}\nFollow-up Questions and Answers:\n{{\n$input.all()\n  .map(item => {\n    const q = Object.keys(item.json)[0];\n    const a = item.json[q];\n    return `question: ${q}\\nanswer: ${a}`;\n  })\n  .join('\\n')\n}}"
            }
          ]
        },
        "options": {}
      },
      "id": "a2d0f5d1-2d2c-4065-ad00-9a9f1d1214c5",
      "name": "Get Initial Query",
      "type": "n8n-nodes-base.set",
      "position": [
        2064,
        640
      ],
      "executeOnce": true,
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"title\": {\n      \"type\": \"string\",\n      \"description\":\" A short title summarising the research topic\"\n    },\n    \"description\": {\n      \"type\": \"string\",\n      \"description\": \"A short description to summarise the research topic\"\n    }\n  }\n}"
      },
      "id": "8d43e110-192b-4629-b1ff-16d64f6801c5",
      "name": "Structured Output Parser4",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        2592,
        912
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=Create a suitable title for the research report which will be created from the user's query.\n<query>{{ $json.query }}</query>",
        "hasOutputParser": true
      },
      "id": "db4dca3b-38ad-44c3-a421-cf9962edc9a2",
      "name": "Report Page Generator",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        2400,
        768
      ],
      "typeVersion": 1.5
    },
    {
      "parameters": {
        "content": "## 4. Trigger DeepResearch Asynchronously\n[Learn more about the Execute Trigger node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflow/)\n\nn8n handles asynchronous jobs by spinning them off as separate executions. This basically means the user doesn't have to wait or keep their browser window open for our researcher to do its job.\n\nOnce we initiate the Deepresearcher job, we can close out the onboarding journey for a nice user experience.",
        "height": 560,
        "width": 640,
        "color": 7
      },
      "id": "59780b23-88e1-49f5-861a-80005080d039",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2928,
        512
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "content": "## 7. Generate Search Queries\n[Learn more about the Basic LLM node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nMuch like a human researcher, the DeepResearcher will rely on web search and content as the preferred source of information. To ensure it can cover a wide range of sources, the AI can first generate relevant research queries of which each can be explored separately.",
        "height": 540,
        "width": 620,
        "color": 7
      },
      "id": "4cf5cebc-769f-4fdb-9aeb-2c9d21fdc7dc",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1264,
        1808
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "75d18d88-6ba6-43df-bef7-3e8ad99ad8bd",
              "operator": {
                "type": "boolean",
                "operation": "true",
                "singleValue": true
              },
              "leftValue": "={{ $json.should_stop }}",
              "rightValue": ""
            }
          ]
        },
        "options": {}
      },
      "id": "fc4e1a01-1189-43d7-b9c3-af6d0986a0df",
      "name": "Is Depth Reached?",
      "type": "n8n-nodes-base.if",
      "position": [
        2384,
        1360
      ],
      "typeVersion": 2.2
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "90b3da00-dcd5-4289-bd45-953146a3b0ba",
              "name": "all_learnings",
              "type": "array",
              "value": "={{ $json.all_learnings }}"
            },
            {
              "id": "623dbb3d-83a1-44a9-8ad3-48d92bc42811",
              "name": "all_urls",
              "type": "array",
              "value": "={{ $json.all_urls }}"
            }
          ]
        },
        "options": {}
      },
      "id": "15548283-7f64-467d-a4eb-f6fa643b7831",
      "name": "Get Research Results",
      "type": "n8n-nodes-base.set",
      "position": [
        2592,
        1360
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "content": "## 8. Web Search and Extracting Web Page Contents using [APIFY.com](https://www.apify.com?fpr=414q6)\n[Read more about the HTTP Request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest/)\n\nHere is where I deviated a little from the reference implementation. I opted not to use Firecrawl.ai due to (1) high cost of the service and (2) a regular non-ai crawler would work just as well and probably quicker. Instead I'm using [APIFY.com](https://www.apify.com?fpr=414q6) which is a more performant, cost-effective and reliable web scraper service. If you don't want to use Apify, feel free to swap this out with your preferred service.\n\nThis step is the most exciting in terms of improvements and optimisations eg. mix in internal data sources! Add in Perplexity.ai or Jina.ai! Possibilities are endless.",
        "height": 740,
        "width": 1340,
        "color": 7
      },
      "id": "2c9dc342-1ec7-44a9-bcb4-eb3a424253dd",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1904,
        1808
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "content": "## 9. Compile Learnings with Reasoning Model\n[Read more about the Basic LLM node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nWith our gathered sources, it's now just a case of giving it to our LLM to compile a list of \"learnings\" from them. For our DeepResearcher, we'll use OpenAI's o3-mini which is the latest reasoning model at time of writing. Reasoning perform better than regular chat models due their chain-of-thought or \"thinking\" process that they perform.\n\nThe \"Learnings\" are then combined with the generated research goal to complete one loop.",
        "height": 580,
        "width": 800,
        "color": 7
      },
      "id": "22319d12-9fcd-44e6-8448-d6d9a34d2372",
      "name": "Sticky Note7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        3280,
        1968
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "content": "## 1. Let's Research!\n[Learn more about the form trigger node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger)\n\nn8n forms are a really nice way to get our frontend up and running quickly and compared to chat, offers a superior user interface for user input. I've gone perhaps a little extra with the custom html fields but I do enjoy adding a little customisation now and then.",
        "height": 560,
        "width": 680,
        "color": 7
      },
      "id": "bef328ae-cd91-450f-87dd-8537cd03d421",
      "name": "Sticky Note11",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        592,
        512
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=You are an expert, insight‑driven researcher tasked with creating a **final report** on the user’s query.  \nThe report must:\n\n1. **Length** – ≥ 3 pages (≈ 1,500–2,000 words).  \n2. **Content** – integrate every learning from the <learnings> block; do not omit or summarize them away.  \n3. **Sources** – cite **every URL** listed in <urls> with inline parentheses [n].  \n   * Each citation number must appear next to the claim or fact it supports.  \n   * At the end of the document, list the reference numbers followed by the full URLs in a Markdown numbered list.  \n4. **Structure** – use the following hierarchy (feel free to add sub‑headings as needed, but keep the order):  \n   - # Introduction  \n   - ## Background / Context  \n   - ## Methodology / Research Approach  \n   - ## Findings / Results  \n   - ## Discussion  \n   - ## Conclusions & Recommendations  \n   - ## Future Work / Next Steps (include only if relevant)  \n5. **Formatting** – **Markdown only**.  \n   * Headings (#, ##, ###)  \n   * Ordered/unordered lists  \n   * Tables where they clarify data or comparisons  \n   * No plain paragraphs unless a complex explanation is needed.  \n6. **Tone** – professional, research‑grade, no high‑level one‑sentence summaries.  \n7. **No summarization of learnings** – each learning must appear verbatim or as a direct quotation.  \n\n**Placeholders**\n\n<prompt>{{ $('JobType Router').first().json.data.query }}</prompt>\n<learnings>{{\n$('JobType Router').first().json.data\n  .all_learnings\n  .map(item => `<learning>${item}</learning>`)  \n  .join('\\n')\n}}</learnings>\n<urls>{{ $('JobType Router').first().json.data.all_urls }}</urls>"
      },
      "id": "ae44d5e7-cc88-459f-ac99-31b25d1fa341",
      "name": "DeepResearch Report",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1568,
        2784
      ],
      "typeVersion": 1.5,
      "retryOnFail": true,
      "onError": "continueRegularOutput"
    },
    {
      "parameters": {
        "promptType": "define",
        "text": "=Given the following contents from a SERP search for the query <query>{{ $('Item Ref').first().json.query }}</query>, generate a list of learnings from the contents. Return a maximum of 3 learnings, but feel free to return less if the contents are clear. Make sure each learning is unique and not similar to each other. The learnings should be concise and to the point, as detailed and infromation dense as possible. Make sure to include any entities like people, places, companies, products, things, etc in the learnings, as well as any exact metrics, numbers, or dates. The learnings will be used to research the topic further. The learning will be placed into a json object that looks like <format> so be very careful how you format each learning and ensure its quotable so the output can be formatted for the \"items\" attribute.\n\n<contents>\n{{\n$input\n  .all()\n  .map(item =>`<content>\\n${item.json.markdown.substr(0, 25_000)}\\n</content>`)\n  .join('\\n')\n}}\n</contents>\n\n\n\n<format>\n{\n  \"type\": \"object\",\n  \"properties\": {\n    \"learnings\": {\n      \"type\": \"array\",\n      \"description\": \"List of learnings, max of 3.\",\n      \"items\": { \"type\": \"string\" }\n    },\n    \"followUpQuestions\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\",\n        \"description\": \"List of follow-up questions to research the topic further, max of 3.\"\n      }\n    }\n  }\n}\n</format>",
        "hasOutputParser": true,
        "messages": {
          "messageValues": [
            {
              "type": "HumanMessagePromptTemplate",
              "message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n  - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n  - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n  - Be highly organized.\n  - Suggest solutions that I didn't think about.\n  - Be proactive and anticipate my needs.\n  - Treat me as an expert in all subject matter.\n  - Mistakes erode my trust, so be accurate and thorough.\n  - Provide detailed explanations, I'm comfortable with lots of detail.\n  - Value good arguments over authorities, the source is irrelevant.\n  - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n  - You may use high levels of speculation or prediction, just flag it for me."
            }
          ]
        }
      },
      "id": "5806921c-21b1-4fca-868f-0f5fdfe8d706",
      "name": "DeepResearch Learnings",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        3552,
        2208
      ],
      "executeOnce": true,
      "typeVersion": 1.5,
      "retryOnFail": true,
      "onError": "continueRegularOutput"
    },
    {
      "parameters": {
        "workflowId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $workflow.id }}"
        },
        "workflowInputs": {
          "value": {
            "data": "={{\n{\n  ...Object.assign({}, $json),\n  query: $('JobType Router').first().json.data.query\n}\n}}",
            "jobType": "deepresearch_report",
            "requestId": "={{ $('JobType Router').first().json.requestId }}"
          },
          "schema": [
            {
              "id": "requestId",
              "display": true,
              "required": false,
              "displayName": "requestId",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "jobType",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "jobType",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "data",
              "type": "object",
              "display": true,
              "required": false,
              "displayName": "data",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        },
        "options": {
          "waitForSubWorkflow": false
        }
      },
      "id": "d8c900b3-d501-434c-9519-496fb302d0a2",
      "name": "Generate Report",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        2912,
        1360
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "workflowId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $workflow.id }}"
        },
        "workflowInputs": {
          "value": {
            "data": "={{ $json }}",
            "jobType": "deepresearch_learnings",
            "requestId": "={{ $('JobType Router').first().json.requestId }}"
          },
          "schema": [
            {
              "id": "requestId",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "requestId",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "jobType",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "jobType",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "data",
              "type": "object",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "data",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        },
        "mode": "each",
        "options": {
          "waitForSubWorkflow": true
        }
      },
      "id": "ff2b8eba-72df-4a98-a1b2-6449191ec373",
      "name": "Generate Learnings",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        2048,
        1360
      ],
      "typeVersion": 1.2
    },
    {
      "parameters": {
        "options": {
          "formTitle": "DeepResearcher",
          "formDescription": "=<img\n  src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n  width=\"100%\"\n  style=\"border:1px solid #ccc\"\n/>\n<p style=\"text-align:left\">\n<strong style=\"display:block;font-family:sans-serif;font-weight:700;font-size:16px;margin-top:12px;margin-bottom:0;\">Your Report Is On Its Way!</strong>\n<br/>\nDeepResearcher will now work independently to conduct the research and the compiled report will be uploaded to the <a href=\"https://github.com/bbourqu/deep-research/blob/main/research/{{$workflow.id}}-{{$('Initiate DeepResearch').first().json.requestId}}.md\">following github page</a> when finished.\n<br/><br/>\nPlease click the \"Done\" button to complete the form.\n</p>\n<hr style=\"display:block;margin-top:16px;margin-bottom:0\" />",
          "buttonLabel": "Done"
        }
      },
      "id": "63550950-bd64-4fd3-adf7-bfc6808d06b4",
      "name": "Confirmation",
      "type": "n8n-nodes-base.form",
      "position": [
        3200,
        768
      ],
      "webhookId": "2eb17c47-c887-4e95-8641-1b3796452ab9",
      "typeVersion": 1
    },
    {
      "parameters": {
        "formFields": {
          "values": [
            {
              "fieldLabel": "What would you like to research?",
              "fieldType": "textarea",
              "requiredField": true
            },
            {
              "fieldLabel": "input-depth",
              "fieldType": "number",
              "placeholder": "1",
              "requiredField": true
            },
            {
              "fieldLabel": "input-breadth",
              "fieldType": "number",
              "placeholder": "1",
              "requiredField": true
            }
          ]
        },
        "options": {
          "formTitle": "DeepResearcher",
          "formDescription": "=<img\n  src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n  width=\"100%\"\n  style=\"border:1px solid #ccc\"\n/>"
        }
      },
      "id": "142774ee-6ed7-4050-8ac4-7e45649af6b1",
      "name": "Research Request",
      "type": "n8n-nodes-base.form",
      "position": [
        864,
        720
      ],
      "webhookId": "46142c14-3692-40f6-80e5-f3d976e95191",
      "typeVersion": 1
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "1de40158-338b-4db3-9e22-6fd63b21f825",
              "name": "ResearchGoal",
              "type": "string",
              "value": "={{ $('Item Ref').first().json.researchGoal }}"
            },
            {
              "id": "9f59a2d4-5e5a-4d0b-8adf-2832ce746f0f",
              "name": "learnings",
              "type": "array",
              "value": "={{ [] }}"
            },
            {
              "id": "972ab5f5-0537-4755-afcb-d1db4f09ad60",
              "name": "followUpQuestions",
              "type": "array",
              "value": "={{ [] }}"
            },
            {
              "id": "90cef471-76b0-465d-91a4-a0e256335cd3",
              "name": "urls",
              "type": "array",
              "value": "={{ [] }}"
            }
          ]
        },
        "options": {}
      },
      "id": "a163c5f1-5e67-4d63-bf52-42809a025c5c",
      "name": "Empty Response",
      "type": "n8n-nodes-base.set",
      "position": [
        3072,
        2352
      ],
      "typeVersion": 3.4
    },
    {
      "parameters": {
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "1ef1039a-4792-47f9-860b-d2ffcffd7129",
              "operator": {
                "type": "object",
                "operation": "notEmpty",
                "singleValue": true
              },
              "leftValue": "={{ $json }}",
              "rightValue": ""
            }
          ]
        },
        "options": {}
      },
      "id": "8aed1ac8-1e2f-429a-9d94-aa367dc352dd",
      "name": "Has Content?",
      "type": "n8n-nodes-base.if",
      "position": [
        2912,
        2208
      ],
      "typeVersion": 2.2
    },
    {
      "parameters": {
        "aggregate": "aggregateAllItemData",
        "options": {}
      },
      "id": "c2d2c200-486b-4b6c-b0b4-71e5b0309f2f",
      "name": "Combine & Send back to Loop",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        2208,
        2048
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "content": "![](https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw#full-width)",
        "height": 240,
        "width": 520,
        "color": 7
      },
      "id": "46f30cac-1fe5-4953-b364-1e6751993bbe",
      "name": "Sticky Note17",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -64,
        672
      ],
      "typeVersion": 1
    },
    {
      "parameters": {
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "f44691e4-f753-47b0-b66a-068a723b6beb",
              "operator": {
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.crawl.requestStatus }}",
              "rightValue": "handled"
            },
            {
              "id": "8e05df2b-0d4a-47da-9aab-da7e8907cbca",
              "operator": {
                "type": "string",
                "operation": "notEmpty",
                "singleValue": true
              },
              "leftValue": "={{ $json.markdown }}",
              "rightValue": ""
            }
          ]
        },
        "options": {}
      },
      "id": "b1581368-f02e-4a45-9c6d-4d5148e2e857",
      "name": "Valid Results",
      "type": "n8n-nodes-base.filter",
      "position": [
        2720,
        2208
      ],
      "typeVersion": 2.2,
      "alwaysOutputData": true
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/apify~rag-web-browser/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendQuery": true,
        "queryParameters": {
          "parameters": [
            {
              "name": "memory",
              "value": "4096"
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