explorium This workflow corresponds to n8n.io template #4840 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "hw7NEOn-q9FiPBWkwYlo8",
"name": "Search business prospects with natural language using Claude AI and Explorium MCP",
"tags": [],
"nodes": [
{
"id": "5a5dc7cc-ece7-42fa-82b7-c54d4d395962",
"name": "Convert to File",
"type": "n8n-nodes-base.convertToFile",
"position": [
912,
0
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "cbadc131-f0f8-4568-9b03-5fea08a8eb0f",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-896,
272
],
"parameters": {
"contextWindowLength": 100
},
"typeVersion": 1.3
},
{
"id": "f1dcdec6-b14e-436b-a9fe-b2eb93f8faa7",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-1472,
172
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "149d8c1c-25f3-4006-96c0-c91de63f7eb7",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-904,
48
],
"parameters": {
"text": "={{ $json.combinedInput }}",
"options": {
"maxIterations": 100,
"systemMessage": "=You are an expert in interpreting natural language queries and converting them into structured JSON requests for the Explorium MCP API.\n\nYour task is to generate the **`data` portion** of a `fetch_prospects` API request, using only Explorium MCP-compatible filters and parameters. Adjust the filters based on both the query and the MCP specifications\u2014not all filters are relevant for every query.\n\n**You may also be asked to revise a previous response that failed validation due to incorrect filters or formatting.** In such cases, carefully review the error message and regenerate the JSON accordingly.\n\nYour response must be a **single JSON object** with this structure:\n\n```json\n{\n \"mode\": \"full\",\n \"size\": 10000,\n \"page_size\": 100,\n \"page\": 1,\n \"filters\": {\n \"has_email\": { \"value\": true },\n \"has_phone_number\": { \"value\": true },\n \"job_level\": { \"values\": [...] },\n \"job_department\": { \"values\": [...] },\n \"business_id\": { \"values\": [...] },\n ...\n }\n}\n```\n\n### Required Structure:\n\n* The fields `mode`, `size`, `page_size`, and `page` must appear **before** the `filters` object.\n* Default values:\n\n * `mode`: `\"full\"`\n * `size`: `10000` (unless otherwise specified by the user; must remain between 1 and 10000)\n * `page_size`: `100` (**must always be less or equal to `size`**)\n * `page`: `1`\n * `has_email`: always `{ \"value\": true }`, unless explicitly requested otherwise\n\n---\n\n### Allowed Filters & Valid Values:\n\nIf any of the following filters appear in your output, their values **must strictly match** these lists:\n\n* **`company_size`**: `'1-10'`, `'11-50'`, `'51-200'`, `'201-500'`, `'501-1000'`, `'1001-5000'`, `'5001-10000'`, `'10001+'`\n* **`company_age`**: `'0-3'`, `'3-6'`, `'6-10'`, `'10-20'`, `'20+'`\n* **`number_of_locations`**: `'0-1'`, `'2-5'`, `'6-20'`, `'21-50'`, `'51-100'`, `'101-1000'`, `'1001+'`\n* **`company_revenue`**: `'0-500K'`, `'500K-1M'`, `'1M-5M'`, `'5M-10M'`, `'10M-25M'`, `'25M-75M'`, `'75M-200M'`, `'200M-500M'`, `'500M-1B'`, `'1B-10B'`, `'10B-100B'`, `'100B-1T'`, `'1T-10T'`, `'10T+'`\n* **`job_level`**: `'director'`, `'manager'`, `'vp'`, `'partner'`, `'cxo'`, `'non-managerial'`, `'senior'`, `'entry'`, `'training'`, `'unpaid'`\n* **`job_department`**: `'customer service'`, `'design'`, `'education'`, `'engineering'`, `'finance'`, `'general'`, `'health'`, `'human resources'`, `'legal'`, `'marketing'`, `'media'`, `'operations'`, `'public relations'`, `'real estate'`, `'sales'`, `'trades'`, `'unknown'`\n* **`country_code`** and **`company_country_code`**: Must be valid **2-letter ISO Alpha-2 codes** (e.g., `us`, `gb`, `de`, etc.)\n* **`total_experience_months`** and **`current_role_months`**: These must be objects with both a `gte` and `lte` key where `gte <= lte`, for example:\n\n```json\n\"total_experience_months\": { \"gte\": 3, \"lte\": 6 }\n```\n\nand\n\n```json\n\"current_role_months\": { \"gte\": 1, \"lte\": 10 }\n```\n\n* If a company name is mentioned (e.g., \"Microsoft\"), include its `business_id` (use a placeholder like `\"abc123\"` if unknown)\n\n---\n\n### Interpretation & Mapping:\n\n* Extract job levels from descriptions like:\n\n * \u201cexecutives\u201d or \u201cleadership\u201d \u2192 `\"cxo\"`\n * \u201cheads\u201d or \u201cvice presidents\u201d \u2192 `\"vp\"`\n* Map department-like terms (e.g., \u201csales\u201d, \u201cmarketing\u201d) to the `job_department` field.\n* Don\u2019t include filters with empty or unrecognized values.\n* Use plural logic: if the query includes a phrase like \u201cfinance and marketing,\u201d return both departments.\n\n---\n\n### Off-topic Handling:\n\nIf the request is unrelated to B2B data intelligence or the MCP use cases, return the following message verbatim:\n\n> I\u2019m the Explorium MCP Playground assistant, built to explore company & prospect intelligence.\n> Example queries:\n> \u2022 Find SaaS firms in New York with 50\u2011200 employees\n> \u2022 Show Microsoft\u2019s technology stack\n> \u2022 Get marketing\u2011director contacts at healthcare companies\n> \u2022 Compare funding rounds of fintech startups\n> How would you like to explore Explorium\u2019s data?"
},
"promptType": "define",
"hasOutputParser": true
},
"retryOnFail": true,
"typeVersion": 1.7
},
{
"id": "db8d1555-bde5-4182-85bc-b2d041ee2a31",
"name": "Extract \"data\"",
"type": "n8n-nodes-base.splitOut",
"position": [
464,
0
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "31da183f-0575-46db-9d90-f7f172d72730",
"name": "Merge All Pages",
"type": "n8n-nodes-base.code",
"position": [
240,
0
],
"parameters": {
"jsCode": "// Initialize a single array to hold all data items\nconst mergedData = [];\n\n// Loop over all input items\nfor (const item of $input.all()) {\n if (item.json.data && Array.isArray(item.json.data)) {\n mergedData.push(...item.json.data); // Spread and merge the data arrays\n }\n}\n\n// Return a single item with the combined data array\nreturn [\n {\n json: {\n data: mergedData\n }\n }\n];"
},
"typeVersion": 2
},
{
"id": "a8045e5a-256c-41e4-ae31-32531d60e422",
"name": "Prepare for CSV",
"type": "n8n-nodes-base.code",
"position": [
688,
0
],
"parameters": {
"jsCode": "const items = [];\n\nfor (const item of $input.all()) {\n items.push({\n prospect_id: item.json.prospect_id ?? null,\n first_name: item.json.first_name ?? null,\n last_name: item.json.last_name ?? null,\n full_name: item.json.full_name ?? null,\n country_name: item.json.country_name ?? null,\n region_name: item.json.region_name ?? null,\n city: item.json.city ?? null,\n linkedin: item.json.linkedin ?? null,\n experience: Array.isArray(item.json.experience) ? item.json.experience.join('; ') : null,\n skills: Array.isArray(item.json.skills) ? item.json.skills.join(', ') : null,\n interests: Array.isArray(item.json.interests) ? item.json.interests.join(', ') : item.json.interests ?? null,\n company_name: item.json.company_name ?? null,\n company_website: item.json.company_website ?? null,\n company_linkedin: item.json.company_linkedin ?? null,\n job_department: item.json.job_department ?? null,\n job_seniority_level: Array.isArray(item.json.job_seniority_level) ? item.json.job_seniority_level.join(', ') : null,\n job_title: item.json.job_title ?? null,\n business_id: item.json.business_id ?? null,\n });\n}\n\nreturn items;"
},
"typeVersion": 2
},
{
"id": "d9247bd5-9f0f-489b-a50b-b87f8f4f7119",
"name": "API Call Validation",
"type": "n8n-nodes-base.code",
"position": [
-432,
48
],
"parameters": {
"jsCode": "const allowedFilters = [\n \"country_code\",\n \"region_country_code\",\n \"company_country_code\",\n \"company_region_country_code\",\n \"company_size\",\n \"company_revenue\",\n \"company_age\",\n \"google_category\",\n \"naics_category\",\n \"linkedin_category\",\n \"company_name\",\n \"number_of_locations\",\n \"city_region_country\",\n \"website_keywords\",\n \"has_email\",\n \"has_phone_number\",\n \"job_level\",\n \"job_department\",\n \"job_title\",\n \"business_id\",\n \"total_experience_months\",\n \"current_role_months\"\n];\n\nconst validCompanySizes = ['1-10', '11-50', '51-200', '201-500', '501-1000', '1001-5000', '5001-10000', '10001+'];\nconst validAges = ['0-3', '3-6', '6-10', '10-20', '20+'];\nconst validLocations = ['0-1', '2-5', '6-20', '21-50', '51-100', '101-1000', '1001+'];\nconst validRevenue = [\n \"0-500K\", \"500K-1M\", \"1M-5M\", \"5M-10M\", \"10M-25M\", \"25M-75M\", \"75M-200M\", \"200M-500M\",\n \"500M-1B\", \"1B-10B\", \"10B-100B\", \"100B-1T\", \"1T-10T\", \"10T+\"\n];\nconst validJobLevel = [\"director\", \"manager\", \"vp\", \"partner\", \"cxo\", \"non-managerial\", \"senior\", \"entry\", \"training\", \"unpaid\"];\nconst validDepartment = [\n \"customer service\", \"design\", \"education\", \"engineering\", \"finance\", \"general\", \"health\", \"human resources\",\n \"legal\", \"marketing\", \"media\", \"operations\", \"public relations\", \"real estate\", \"sales\", \"trades\", \"unknown\"\n];\n\nconst countryCodeRegex = /^(aw|af|ao|ai|ax|al|ad|ae|ar|am|as|aq|tf|ag|au|at|az|bi|be|bj|bq|bf|bd|bg|bh|bs|ba|bl|by|bz|bm|bo|br|bb|bn|bt|bv|bw|cf|ca|cc|ch|cl|cn|ci|cm|cd|cg|ck|co|km|cv|cr|cu|cw|cx|ky|cy|cz|de|dj|dm|dk|do|dz|ec|eg|er|eh|es|ee|et|fi|fj|fk|fr|fo|fm|ga|gb|ge|gg|gh|gi|gn|gp|gm|gw|gq|gr|gd|gl|gt|gf|gu|gy|hk|hm|hn|hr|ht|hu|id|im|in|io|ie|ir|iq|is|il|it|jm|je|jo|jp|kz|ke|kg|kh|ki|kn|kr|kw|la|lb|lr|ly|lc|li|lk|ls|lt|lu|lv|mo|mf|ma|mc|md|mg|mv|mx|mh|mk|ml|mt|mm|me|mn|mp|mz|mr|ms|mq|mu|mw|my|yt|na|nc|ne|nf|ng|ni|nu|nl|no|np|nr|nz|om|pk|pa|pn|pe|ph|pw|pg|pl|pr|kp|pt|py|ps|pf|qa|re|ro|ru|rw|sa|sd|sn|sg|gs|sh|sj|sb|sl|sv|sm|so|pm|rs|ss|st|sr|sk|si|se|sz|sx|sc|sy|tc|td|tg|th|tj|tk|tm|tl|to|tt|tn|tr|tv|tw|tz|ug|ua|um|uy|us|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|xk|ye|za|zm|zw)$/;\n\nconst item = $input.first().json;\nconst output = item.output || {};\nconst filters = output.filters || {};\nconst errors = [];\n\nfor (const key of Object.keys(filters)) {\n const filter = filters[key];\n\n if (!allowedFilters.includes(key)) {\n errors.push(`Invalid filter key: '${key}'`);\n continue;\n }\n\n if ('value' in filter) {\n if (typeof filter.value !== 'boolean') {\n errors.push(`Filter '${key}' has a 'value' but it's not a boolean.`);\n }\n continue;\n }\n\n if ('values' in filter) {\n if (!Array.isArray(filter.values) || filter.values.length === 0) {\n errors.push(`Filter '${key}' must contain a non-empty 'values' array.`);\n continue;\n }\n\n // Normalize to lowercase for fields that require it\n if ([\"job_level\", \"job_department\", \"country_code\", \"region_country_code\", \"company_country_code\", \"company_region_country_code\"].includes(key)) {\n filter.values = filter.values.map(v => typeof v === 'string' ? v.toLowerCase() : v);\n }\n\n const validateValues = (validList, keyName) => {\n const invalid = filter.values.filter(v => !validList.includes(v));\n if (invalid.length > 0) {\n errors.push(`Invalid ${keyName} values: ${invalid.join(', ')}. Valid values are: ${validList.join(', ')}`);\n }\n };\n\n switch (key) {\n case 'company_size':\n validateValues(validCompanySizes, 'company_size');\n break;\n case 'company_age':\n validateValues(validAges, 'company_age');\n break;\n case 'number_of_locations':\n validateValues(validLocations, 'number_of_locations');\n break;\n case 'company_revenue':\n validateValues(validRevenue, 'company_revenue');\n break;\n case 'job_level':\n validateValues(validJobLevel, 'job_level');\n break;\n case 'job_department':\n validateValues(validDepartment, 'job_department');\n break;\n case 'country_code':\n const invalid = filter.values.filter(v => !countryCodeRegex.test(v));\n if (invalid.length > 0) {\n errors.push(`Invalid country_code values: ${invalid.join(', ')}. Must be ISO 3166-1 alpha-2 codes.`);\n }\n break;\n }\n\n const unique = new Set(filter.values);\n if (unique.size < filter.values.length) {\n errors.push(`Duplicate values found in filter '${key}'`);\n }\n\n continue;\n }\n\n // Handle total_experience_months and current_role_months as a range objects\n if (key === 'total_experience_months' || key === 'current_role_months') {\n const range = filter;\n \n if (!('gte' in range) && !('lte' in range)) {\n errors.push(`Filter '${key}' must include at least 'gte' or 'lte'.`);\n continue;\n }\n\n if ('gte' in range && typeof range.gte !== 'number') {\n errors.push(`Filter '${key}' has a non-numeric 'gte' value.`);\n }\n\n if ('lte' in range && typeof range.lte !== 'number') {\n errors.push(`Filter '${key}' has a non-numeric 'lte' value.`);\n }\n\n if ('gte' in range && 'lte' in range && range.gte > range.lte) {\n errors.push(`Filter '${key}' has 'gte' greater than 'lte'.`);\n }\n\n continue;\n}\n\n errors.push(`Filter '${key}' must include either 'value', 'values', or a valid range.`);\n}\n\nreturn [\n {\n json: {\n ...item,\n isValid: errors.length === 0,\n validationErrors: errors\n }\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "47a6c37c-30f0-429b-83cb-92ef6aee7fd3",
"name": "Validation Prompter",
"type": "n8n-nodes-base.code",
"position": [
16,
268
],
"parameters": {
"jsCode": "const sessionId = $('When chat message received').first().json.sessionId;\nconst userQuery = $('When chat message received').first().json.chatInput;\nconst aiOutput = $('AI Agent').first().json.output;\nconst validationErrors = $('API Call Validation').first().json.validationErrors;\n\n// Safely stringify output and errors\nconst formattedOutput = typeof aiOutput === 'object' ? JSON.stringify(aiOutput, null, 2) : aiOutput;\nconst formattedErrors = Array.isArray(validationErrors) ? validationErrors.join('\\n- ') : validationErrors;\n\nreturn [\n {\n sessionId,\n action: \"sendMessage\",\n source: \"validation\",\n errorInput: \n`Your response did not pass validation.\n\n\ud83d\udcdd **User Query**:\n${userQuery}\n\n\ud83e\udd16 **Your Output**:\n${formattedOutput}\n\n\u26a0\ufe0f **Validation Errors**:\n- ${formattedErrors}\n\n\ud83d\udd27 Please review the validation errors and regenerate a corrected output. \nEnsure that all values strictly match the allowed formats and valid categories.\n\nReformulate your response accordingly.`\n }\n];"
},
"typeVersion": 2
},
{
"id": "5045558e-5520-4694-bdb1-a424157f1c81",
"name": "Is API Call Valid?",
"type": "n8n-nodes-base.if",
"position": [
-208,
48
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "9b2c3127-07c4-47d0-8dc3-44daa2a2d6e7",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.isValid }}",
"rightValue": "true"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "c836920a-5b5d-417b-9e21-3619e0031ec4",
"name": "Chat or Refinement",
"type": "n8n-nodes-base.code",
"position": [
-1248,
172
],
"parameters": {
"jsCode": "const chat = $json.chatInput;\nconst error = $json.errorInput;\n\nreturn [\n {\n json: {\n ...$json,\n combinedInput: error ?? chat\n }\n }\n];"
},
"typeVersion": 2
},
{
"id": "9ebcdea9-dcc8-4a1b-aecb-14d0c426ec99",
"name": "Anthropic Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"position": [
-1024,
272
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "claude-sonnet-4-5-20250929",
"cachedResultName": "Claude Sonnet 4.5"
},
"options": {
"thinking": false
}
},
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "541ad4a5-c275-44fc-a326-5c3a8400b257",
"name": "Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-640,
272
],
"parameters": {
"jsonSchemaExample": "{\n \"mode\": \"full\",\n \"size\": 10000,\n \"page_size\": 100,\n \"page\": 1,\n \"filters\": {\n \"has_email\": {\n \"value\": true\n },\n \"has_phone_number\": {\n \"value\": true\n },\n \"job_level\": {\n \"values\": [\n \"cxo\",\n \"manager\"\n ]\n },\n \"job_department\": {\n \"values\": [\n \"Customer service\",\n \"Engineering\"\n ]\n },\n \"business_id\": {\n \"values\": [\n \"8adce3ca1cef0c986b22310e369a0793\"\n ]\n },\n \"total_experience_months\": {\n \"gte\": 1,\n \"lte\": 20\n },\n \"country_code\": {\n \"values\": [\n \"us\",\n \"dk\",\n \"uk\"\n ]\n },\n \"region_country_code\": {\n \"values\": [\n \"us-ca\",\n \"us-ny\"\n ]\n },\n \"current_role_months\": {\n \"gte\": 1,\n \"lte\": 200\n },\n \"company_size\": {\n \"values\": [\n \"5001-10000\"\n ]\n },\n \"company_revenue\": {\n \"values\": [\n \"500K-1M\",\n \"5M-10M\"\n ]\n },\n \"google_category\": {\n \"values\": [\n \"construction\"\n ]\n },\n \"naics_category\": {\n \"values\": [\n \"541512\"\n ]\n },\n \"linkedin_category\": {\n \"values\": [\n \"retail\",\n \"software development\"\n ]\n },\n \"job_title\": {\n \"values\": [\n \"Software Engineer\"\n ]\n },\n \"company_country_code\": {\n \"values\": [\n \"us\",\n \"jp\"\n ]\n },\n \"company_region_country_code\": {\n \"values\": [\n \"us-ca\"\n ]\n },\n \"city_region_country\": {\n \"values\": [\n \"Paris, FR\"\n ]\n },\n \"company_name\": {\n \"values\": [\n \"Microsoft\"\n ]\n }\n }\n}"
},
"typeVersion": 1.2
},
{
"id": "c57c1b15-1c90-48d6-960d-1286d1a8d05a",
"name": "Explorium API",
"type": "@exploriumai/n8n-nodes-explorium-ai.exploriumApiNode",
"position": [
16,
0
],
"parameters": {
"mode": "full",
"size": 100,
"type": "prospects",
"operation": "fetch",
"page_size": 100
},
"credentials": {},
"typeVersion": 1
},
{
"id": "57fe3aba-8de1-4869-a3a3-112ea2ef1128",
"name": "Explorium MCP",
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
"position": [
-768,
272
],
"parameters": {
"options": {},
"endpointUrl": "https://mcp-n8n.explorium.ai/mcp",
"authentication": "headerAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "",
"connections": {
"AI Agent": {
"main": [
[
{
"node": "API Call Validation",
"type": "main",
"index": 0
}
]
]
},
"Explorium API": {
"main": [
[
{
"node": "Merge All Pages",
"type": "main",
"index": 0
}
]
]
},
"Explorium MCP": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Extract \"data\"": {
"main": [
[
{
"node": "Prepare for CSV",
"type": "main",
"index": 0
}
]
]
},
"Merge All Pages": {
"main": [
[
{
"node": "Extract \"data\"",
"type": "main",
"index": 0
}
]
]
},
"Prepare for CSV": {
"main": [
[
{
"node": "Convert to File",
"type": "main",
"index": 0
}
]
]
},
"Chat or Refinement": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Is API Call Valid?": {
"main": [
[
{
"node": "Explorium API",
"type": "main",
"index": 0
}
],
[
{
"node": "Validation Prompter",
"type": "main",
"index": 0
}
]
]
},
"API Call Validation": {
"main": [
[
{
"node": "Is API Call Valid?",
"type": "main",
"index": 0
}
]
]
},
"Validation Prompter": {
"main": [
[
{
"node": "Chat or Refinement",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Chat or Refinement",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApihttpHeaderAuth
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Explorium Prospects Search Chatbot
Source: https://n8n.io/workflows/4840/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow implements an advanced AI-powered system for generating, and executing Claude Skills stored on GitHub.
Chat Trigger, Memory Buffer Window, Mcp Client Tool +9
This n8n workflow transforms natural language queries into targeted B2B prospecting campaigns by combining Explorium's data intelligence with AI-powered research and personalized email generation. Sim
Memory Buffer Window, Agent, Output Parser Structured +5
This Chatbot automates the process of discovering job openings and generating tailored job application emails.
Chat Trigger, OpenAI Chat, Mcp Client Tool +12
ModelRouter. Uses chatTrigger, agent, modelSelector, httpRequest. Chat trigger; 28 nodes.
Chat Trigger, Agent, Model Selector +8
This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classifica
Chat Trigger, Agent, Model Selector +7