This workflow follows the Chainllm → Execute Workflow 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 →
{
"updatedAt": "2025-12-24T08:53:12.894Z",
"createdAt": "2025-12-23T09:30:25.763Z",
"id": "mbX1lCt14Yi75gTE",
"name": "Continue_Thread",
"description": null,
"active": false,
"isArchived": false,
"nodes": [
{
"parameters": {
"inputSource": "passthrough"
},
"type": "n8n-nodes-base.executeWorkflowTrigger",
"typeVersion": 1.1,
"position": [
6656,
624
],
"id": "cf49146a-7b65-4fe1-8882-9190f8ac80f3",
"name": "ExecuteWorkflowTrigger"
},
{
"parameters": {
"jsCode": "// Prepare batch database queries for Execute_Queries sub-workflow\nconst ctx = $json.ctx;\n\nconst db_queries = [\n {\n key: 'history',\n sql: `SELECT timestamp, role, text FROM thread_history \n WHERE thread_id = $1 AND NOT (role = 'user' AND text = $2) \n ORDER BY timestamp ASC LIMIT 20`,\n params: [ctx.event.thread_id, ctx.event.clean_text]\n },\n {\n key: 'north_star',\n sql: `SELECT value FROM config WHERE key = 'north_star'`\n },\n {\n key: 'activities',\n sql: `SELECT \n (data->>'timestamp')::timestamptz as timestamp,\n data->>'category' as category,\n data->>'description' as description\n FROM projections\n WHERE projection_type = 'activity'\n AND status IN ('auto_confirmed', 'confirmed')\n ORDER BY (data->>'timestamp')::timestamptz DESC\n LIMIT 20`\n },\n {\n key: 'notes',\n sql: `SELECT \n (data->>'timestamp')::timestamptz as timestamp,\n data->>'category' as category,\n data->>'text' as text\n FROM projections\n WHERE projection_type = 'note'\n AND status IN ('auto_confirmed', 'confirmed')\n ORDER BY (data->>'timestamp')::timestamptz DESC\n LIMIT 10`\n }\n];\n\nreturn {\n json: {\n ctx: {\n ...ctx,\n db_queries\n }\n }\n};",
"mode": "runOnceForEachItem"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
6880,
624
],
"id": "prepare-db-queries",
"name": "PrepareDbQueries"
},
{
"parameters": {
"workflowId": {
"__rl": true,
"value": "CgUAxK0i4YhrZ2Wp",
"mode": "list",
"cachedResultName": "Execute_Queries",
"cachedResultUrl": "/workflow/CgUAxK0i4YhrZ2Wp"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"ctx": "={{ $json.ctx }}"
}
},
"options": {
"waitForSubWorkflow": true
}
},
"type": "n8n-nodes-base.executeWorkflow",
"typeVersion": 1.2,
"position": [
7104,
624
],
"id": "execute-queries-read",
"name": "ExecuteQueries(read)"
},
{
"parameters": {
"jsCode": "// Build LLM context from Execute_Queries results\nconst ctx = $json.ctx;\nconst db = ctx.db || {};\n\n// Extract and sort conversation history\nconst history = (db.history?.rows || [])\n .sort((a, b) => new Date(a.timestamp) - new Date(b.timestamp))\n .map(item => ({\n role: item.role,\n text: item.text\n }));\n\n// Extract north star\nconst northStar = db.north_star?.row?.value || 'Not set';\n\n// Extract activities\nconst activities = (db.activities?.rows || []).map(item => ({\n timestamp: item.timestamp,\n category: item.category,\n description: item.description\n}));\n\n// Extract notes \nconst notes = (db.notes?.rows || []).map(item => ({\n timestamp: item.timestamp,\n category: item.category,\n text: item.text\n}));\n\nreturn {\n json: {\n ctx: {\n ...ctx,\n llm: {\n ...ctx.llm,\n north_star: northStar,\n inference_start: Date.now(),\n history: history,\n activities: activities,\n notes: notes\n }\n }\n }\n};",
"mode": "runOnceForEachItem"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
7328,
624
],
"id": "66722f6e-f428-4136-bc15-f60097699a36",
"name": "BuildContext"
},
{
"parameters": {
"promptType": "define",
"text": "=You are an AI life coach helping the user reflect, plan, and think deeply.\n\n## Context Available\n\n**User's Guiding Principle:** {{ $json.ctx.llm.north_star }}\n\nKeep this in mind when helping them, but don't mention it unless directly relevant to the conversation.\n\n**Recent Activities (Last 20)**\n{{ $json.ctx.llm.activities.map(act => `${DateTime.fromISO(act.timestamp).format(\"yyyy-MM-dd hh:mm\")} - ${act.description}`).join('\\n') }}\n\n**Recent Notes (Last 10)**\n{{ $json.ctx.llm.notes.map(n => `[${n.category}] ${n.text}`).join('\\n') }}\n\n## Instructions\n\n1. **Consider the full context**: conversation history, activities, and notes\n2. **Provide grounded insights**: Reference specific activities or notes when relevant\n3. **Be conversational**: This is an ongoing dialogue, not a new conversation\n4. **Ask clarifying questions**: Help them think deeper when appropriate\n5. **Be concise**: 3-5 sentences, respect their time\n6. **Don't force connections**: Only mention the guiding principle if it's naturally relevant to what they're asking about\n\n## Style\n- Warm and supportive\n- Natural and conversational\n- Focus on what they're asking, not on fitting everything into a grand narrative\n\n## Conversation History\n{{ $json.ctx.llm.history.map(msg => `${msg.role.toUpperCase()}: ${msg.text}`).join('\\n\\n') }}\n\nUSER: {{ $json.ctx.event.clean_text }}\n\nASSISTANT:",
"needsFallback": true,
"batching": {}
},
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"typeVersion": 1.7,
"position": [
7552,
748
],
"id": "4119f733-a0a8-4c97-bcf3-a67ab535c4b5",
"name": "GenerateResponse"
},
{
"parameters": {
"model": "xiaomi/mimo-v2-flash:free",
"options": {
"temperature": 0.7
}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"typeVersion": 1,
"position": [
7560,
972
],
"id": "120ceee9-00cb-4175-8df8-57f862e218fd",
"name": "MimoV2Flash",
"credentials": {
"openRouterApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"model": "nvidia/nemotron-nano-9b-v2:free",
"options": {
"temperature": 0.7
}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"typeVersion": 1,
"position": [
7688,
972
],
"id": "9c28edb3-a048-474e-8217-915ddee94e23",
"name": "NemotronNano9b",
"credentials": {
"openRouterApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"resource": "message",
"guildId": {
"__rl": true,
"value": "={{ $json.ctx.event.guild_id }}",
"mode": "id"
},
"channelId": {
"__rl": true,
"value": "={{ $json.ctx.event.thread_id }}",
"mode": "id"
},
"content": "={{ $json.ctx.llm.completion_text }}",
"options": {}
},
"type": "n8n-nodes-base.discord",
"typeVersion": 2,
"position": [
8352,
528
],
"id": "742d2510-b9ca-4252-9f4e-4d2c485050a9",
"name": "PostToThread",
"retryOnFail": true,
"maxTries": 3,
"waitBetweenTries": 1000,
"credentials": {
"discordBotApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"resource": "message",
"operation": "react",
"guildId": {
"__rl": true,
"value": "={{ $json.ctx.event.guild_id }}",
"mode": "id"
},
"channelId": {
"__rl": true,
"value": "={{ $json.ctx.event.channel_id }}",
"mode": "id"
},
"messageId": "={{ $json.ctx.event.message_id }}",
"emoji": "=\ud83d\udcad"
},
"type": "n8n-nodes-base.discord",
"typeVersion": 2,
"position": [
6880,
96
],
"id": "3d6f0aee-f86f-437e-aa57-0683b80c2ced",
"name": "ReactWith\ud83d\udcad",
"retryOnFail": true,
"maxTries": 3,
"waitBetweenTries": 1000,
"credentials": {
"discordBotApi": {
"name": "<your credential>"
}
},
"onError": "continueRegularOutput"
},
{
"parameters": {
"method": "DELETE",
"url": "=https://discord.com/api/v10/channels/{{ $json.ctx.event.channel_id }}/messages/{{ $json.ctx.event.message_id }}/reactions/\ud83d\udd35/@me",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "discordBotApi",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
6880,
1152
],
"id": "remove-blue-reaction-continue-chat",
"name": "Remove\ud83d\udd35Reaction",
"retryOnFail": true,
"maxTries": 3,
"waitBetweenTries": 1000,
"credentials": {
"discordBotApi": {
"name": "<your credential>"
}
},
"onError": "continueRegularOutput"
},
{
"parameters": {
"jsCode": "// Prepare write queries for Execute_Queries (trace + projection)\nconst ctx = $json.ctx || {};\nconst response = ctx.llm?.completion_text || '';\nconst inferenceStart = ctx.llm?.inference_start || Date.now();\nconst durationMs = Date.now() - inferenceStart;\n\n// Build filled prompt (reconstructed from template)\nconst promptText = `You are an AI life coach helping the user reflect, plan, and think deeply.\n\n## Context Available\n\n**User's Guiding Principle:** ${ctx.llm?.north_star || '(not set)'}\n\n**Recent Activities (Last 20)**\n${(ctx.llm?.activities || []).map(act => `${act.timestamp} - ${act.description}`).join('\\n')}\n\n**Recent Notes (Last 10)**\n${(ctx.llm?.notes || []).map(n => `[${n.category}] ${n.text}`).join('\\n')}\n\n## Instructions\n...\n\n## Conversation History\n${(ctx.llm?.history || []).map(msg => `${msg.role.toUpperCase()}: ${msg.text}`).join('\\n\\n')}\n\n## Current Message\nThe user just said: \"${ctx.event?.clean_text || ''}\"\n\nRespond to their message now.`;\n\n// Format trace_chain for PostgreSQL uuid[] type\nconst traceChain = ctx.event?.trace_chain || [];\n\n// Build write queries with chaining\nconst db_queries = [\n {\n key: 'trace',\n sql: `INSERT INTO traces (event_id, step_name, data, trace_chain)\n VALUES (\n $1::uuid,\n 'thread_response',\n jsonb_build_object(\n 'input', jsonb_build_object(\n 'text', $2,\n 'thread_id', $3::text,\n 'history_length', $4::integer\n ),\n 'prompt', $5,\n 'completion', $6,\n 'result', jsonb_build_object(\n 'response_length', $7::integer,\n 'referenced_activities', $8::integer,\n 'referenced_notes', $9::integer\n ),\n 'model', 'xiaomi/mimo-v2-flash:free',\n 'duration_ms', $10::integer\n ),\n $11::uuid[]\n )\n RETURNING id, $11::uuid[] || id AS updated_trace_chain`,\n params: [\n ctx.event?.event_id,\n ctx.event?.clean_text || '',\n ctx.event?.thread_id,\n (ctx.llm?.history || []).length,\n promptText,\n response,\n response.length,\n (ctx.llm?.activities || []).length,\n (ctx.llm?.notes || []).length,\n durationMs,\n traceChain\n ]\n },\n {\n key: 'projection',\n sql: `INSERT INTO projections (\n event_id,\n trace_id,\n trace_chain,\n projection_type,\n status,\n data,\n timezone\n )\n VALUES (\n $1::uuid,\n $2::uuid,\n $3::uuid[],\n 'thread_response',\n 'auto_confirmed',\n jsonb_build_object(\n 'thread_id', $4,\n 'response_text', $5,\n 'role', 'assistant',\n 'timestamp', $6::timestamptz\n ),\n $7\n )\n RETURNING id`,\n params: [\n ctx.event?.event_id,\n '$results.trace.row.id',\n '$results.trace.row.updated_trace_chain',\n ctx.event?.thread_id,\n response,\n ctx.event?.timestamp,\n ctx.event?.timezone\n ]\n }\n];\n\nreturn {\n json: {\n ctx: {\n ...ctx,\n db_queries\n }\n }\n};",
"mode": "runOnceForEachItem"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
8352,
720
],
"id": "prepare-write-queries",
"name": "PrepareWriteQueries"
},
{
"parameters": {
"workflowId": {
"__rl": true,
"value": "CgUAxK0i4YhrZ2Wp",
"mode": "list",
"cachedResultName": "Execute_Queries",
"cachedResultUrl": "/workflow/CgUAxK0i4YhrZ2Wp"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"ctx": "={{ $json.ctx }}"
}
},
"options": {
"waitForSubWorkflow": true
}
},
"type": "n8n-nodes-base.executeWorkflow",
"typeVersion": 1.2,
"position": [
8576,
720
],
"id": "execute-queries-write",
"name": "ExecuteQueries(write)"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "llm-completion",
"name": "ctx.llm.completion_text",
"value": "={{ $json.text }}",
"type": "string"
}
]
},
"options": {
"includeOtherFields": true
}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
7904,
748
],
"id": "set-llm-completion",
"name": "SetLlmCompletion"
},
{
"parameters": {
"mode": "combine",
"combineBy": "combineByPosition",
"options": {}
},
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
8128,
624
],
"id": "merge-llm-result",
"name": "MergeLlmResult"
}
],
"connections": {
"ExecuteWorkflowTrigger": {
"main": [
[
{
"node": "PrepareDbQueries",
"type": "main",
"index": 0
},
{
"node": "ReactWith\ud83d\udcad",
"type": "main",
"index": 0
},
{
"node": "Remove\ud83d\udd35Reaction",
"type": "main",
"index": 0
}
]
]
},
"PrepareDbQueries": {
"main": [
[
{
"node": "ExecuteQueries(read)",
"type": "main",
"index": 0
}
]
]
},
"ExecuteQueries(read)": {
"main": [
[
{
"node": "BuildContext",
"type": "main",
"index": 0
}
]
]
},
"BuildContext": {
"main": [
[
{
"node": "GenerateResponse",
"type": "main",
"index": 0
},
{
"node": "MergeLlmResult",
"type": "main",
"index": 0
}
]
]
},
"GenerateResponse": {
"main": [
[
{
"node": "SetLlmCompletion",
"type": "main",
"index": 0
}
]
]
},
"MimoV2Flash": {
"ai_languageModel": [
[
{
"node": "GenerateResponse",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"NemotronNano9b": {
"ai_languageModel": [
[
{
"node": "GenerateResponse",
"type": "ai_languageModel",
"index": 1
}
]
]
},
"PostToThread": {
"main": [
[]
]
},
"PrepareWriteQueries": {
"main": [
[
{
"node": "ExecuteQueries(write)",
"type": "main",
"index": 0
}
]
]
},
"ExecuteQueries(write)": {
"main": [
[]
]
},
"SetLlmCompletion": {
"main": [
[
{
"node": "MergeLlmResult",
"type": "main",
"index": 1
}
]
]
},
"MergeLlmResult": {
"main": [
[
{
"node": "PrepareWriteQueries",
"type": "main",
"index": 0
},
{
"node": "PostToThread",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"callerPolicy": "workflowsFromSameOwner",
"errorWorkflow": "NOJ7FqVhVLqw0n8D",
"availableInMCP": false,
"executionOrder": "v1"
},
"staticData": null,
"meta": null,
"versionId": "afa8d3be-2df8-4506-baab-4f177983c46a",
"activeVersionId": null,
"versionCounter": 185,
"triggerCount": 0,
"shared": [
{
"updatedAt": "2025-12-23T09:30:25.763Z",
"createdAt": "2025-12-23T09:30:25.763Z",
"role": "workflow:owner",
"workflowId": "mbX1lCt14Yi75gTE",
"projectId": "erM3nntdLL53noWi",
"project": {
"updatedAt": "2025-12-23T09:23:39.658Z",
"createdAt": "2025-12-23T09:16:56.460Z",
"id": "erM3nntdLL53noWi",
"name": "Chris Irineo <chriskevini@gmail.com>",
"type": "personal",
"icon": null,
"description": null,
"projectRelations": [
{
"updatedAt": "2025-12-23T09:16:56.460Z",
"createdAt": "2025-12-23T09:16:56.460Z",
"userId": "2a851a2d-b7e5-4b3c-aefb-6eaaa79e0659",
"projectId": "erM3nntdLL53noWi",
"user": {
"updatedAt": "2025-12-24T08:40:46.063Z",
"createdAt": "2025-12-23T09:16:54.881Z",
"id": "2a851a2d-b7e5-4b3c-aefb-6eaaa79e0659",
"email": "chriskevini@gmail.com",
"firstName": "Chris",
"lastName": "Irineo",
"personalizationAnswers": {
"version": "v4",
"personalization_survey_submitted_at": "2025-12-23T09:23:43.723Z",
"personalization_survey_n8n_version": "1.123.5"
},
"settings": {
"userActivated": true,
"firstSuccessfulWorkflowId": "CgUAxK0i4YhrZ2Wp",
"userActivatedAt": 1766487000077,
"easyAIWorkflowOnboarded": true
},
"disabled": false,
"mfaEnabled": false,
"lastActiveAt": "2025-12-24",
"isPending": false
}
}
]
}
}
],
"tags": [],
"activeVersion": null
}
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.
discordBotApiopenRouterApi
Pro
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About this workflow
Continue_Thread. Uses executeWorkflowTrigger, chainLlm, lmChatOpenRouter, discord. Event-driven trigger; 14 nodes.
Source: https://github.com/chriskevini/kairon/blob/ab924f228ceb22522b9a4dfa1ab4589eb86273ad/n8n-workflows/Continue_Thread.json — original creator credit. Request a take-down →
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