This workflow follows the Chainllm → HTTP Request 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 →
{
"name": "Query Agent",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "query-agent",
"responseMode": "responseNode",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
0,
300
],
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"name": "Webhook"
},
{
"parameters": {
"promptType": "define",
"text": "=Task: {{ $json.body.task }}\nDatabase: {{ $json.body.database }}\n{% if $json.body.context %}Additional context: {{ $json.body.context }}{% endif %}",
"messages": {
"messageValues": [
{
"message": "You are a Query Agent for an inventory data system.\nYou have access to two DuckDB databases:\n\n1. /data/source.duckdb \u2014 read-only source data in named schemas:\n- erp.products (product_id, product_name, sku, unit_cost, list_price, safety_stock, reorder_point, product_line, product_class, size, weight_lbs)\n- erp.inventory_levels (product_id, location_id, shelf, bin, qty_on_hand, last_updated)\n- erp.locations (location_id, location_name, cost_rate, capacity)\n- erp.work_orders (wo_id, product_id, qty_required, qty_stocked, qty_scrapped, start_date, end_date, due_date)\n- procurement.vendors (vendor_id, account_number, vendor_name, credit_rating, is_active, is_preferred, last_updated)\n- procurement.purchase_orders (po_id, vendor_id, status, status_code, order_date, ship_date, subtotal, tax_amount, total_due)\n- procurement.po_lines (po_id, line_id, product_id, qty_ordered, unit_price, qty_received, qty_rejected, qty_stocked, due_date)\n- wms.warehouses (warehouse_id, warehouse_name, city, capacity_sqft)\n- wms.stock_movements (movement_id, product_id, location_id, movement_type, quantity, movement_date, reference_doc)\n\n2. /data/vault.duckdb \u2014 pipeline output in the main schema (no schema prefix):\n- hub_*, lnk_*, sat_* \u2014 Data Vault tables\n- mart_* \u2014 Information Mart tables\n- stg_* \u2014 temporary staging tables (present only during active pipeline runs)\n\nGiven a task, generate a DuckDB-compatible SQL query.\n\nRules:\n- The database path is provided in the request body \u2014 use it exactly as-is. Do NOT substitute a different path.\n- For source data queries (erp.*, procurement.*, wms.*): the database will be /data/source.duckdb\n- For vault/mart queries (hub_*, lnk_*, sat_*, mart_*, stg_*): the database will be /data/vault.duckdb\n- Always qualify source table names with their schema: erp.products, procurement.vendors, wms.stock_movements, etc.\n- Vault and mart tables use no schema prefix: SELECT * FROM hub_product, SELECT * FROM mart_low_stock\n- For schema inspection: use SHOW ALL TABLES or SELECT * FROM information_schema.tables\n- For data extraction: add LIMIT 20 unless told otherwise\n- For final results: return the complete query\n- Use DuckDB SQL syntax (not PostgreSQL or MySQL)\n\nRespond ONLY with valid JSON, no markdown code fences:\n{\"sql\": \"SELECT ...\", \"explanation\": \"what this query does\"}"
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"typeVersion": 1.4,
"position": [
300,
300
],
"id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
"name": "Generate SQL"
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "claude-haiku-4-5-20251001"
},
"options": {
"maxTokensToSample": 2048
}
},
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"typeVersion": 1.3,
"position": [
300,
500
],
"id": "c3d4e5f6-a7b8-9012-cdef-123456789012",
"name": "Anthropic Chat Model",
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "const llmOutput = $input.first().json.text;\n\nlet parsed;\ntry {\n // Try to parse directly\n parsed = JSON.parse(llmOutput);\n} catch (e) {\n // Try to extract JSON from markdown code fences\n const match = llmOutput.match(/```(?:json)?\\s*([\\s\\S]*?)```/);\n if (match) {\n parsed = JSON.parse(match[1].trim());\n } else {\n throw new Error('Could not parse LLM output as JSON: ' + llmOutput.substring(0, 200));\n }\n}\n\n// Get the original request body for database path\nconst webhookData = $('Webhook').first().json.body;\n\nreturn {\n json: {\n sql: parsed.sql,\n explanation: parsed.explanation,\n database: webhookData.database || '/data/source.duckdb'\n }\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
600,
300
],
"id": "d4e5f6a7-b8c9-0123-defa-234567890123",
"name": "Parse SQL"
},
{
"parameters": {
"method": "POST",
"url": "http://duckdb-service:8001/query",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({sql: $json.sql, database: $json.database}) }}",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
900,
300
],
"id": "e5f6a7b8-c9d0-1234-efab-345678901234",
"name": "Run Query"
},
{
"parameters": {
"jsCode": "const queryResult = $input.first().json;\nconst parseData = $('Parse SQL').first().json;\n\n// Handle error from duckdb-service\nif (queryResult.status === 'error' || queryResult.detail) {\n return {\n json: {\n status: 'error',\n output: {\n error: queryResult.detail || 'Query execution failed',\n sql: parseData.sql,\n explanation: parseData.explanation\n }\n }\n };\n}\n\nreturn {\n json: {\n status: 'success',\n output: {\n columns: queryResult.columns || [],\n data: queryResult.rows || [],\n row_count: queryResult.row_count || 0,\n sql: parseData.sql,\n explanation: parseData.explanation\n }\n }\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1200,
300
],
"id": "f6a7b8c9-d0e1-2345-fabc-456789012345",
"name": "Format Response"
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ $json }}",
"options": {}
},
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
1500,
300
],
"id": "a7b8c9d0-e1f2-3456-abcd-567890123456",
"name": "Respond to Webhook"
}
],
"connections": {
"Webhook": {
"main": [
[
{
"node": "Generate SQL",
"type": "main",
"index": 0
}
]
]
},
"Generate SQL": {
"main": [
[
{
"node": "Parse SQL",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[
{
"node": "Generate SQL",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Parse SQL": {
"main": [
[
{
"node": "Run Query",
"type": "main",
"index": 0
}
]
]
},
"Run Query": {
"main": [
[
{
"node": "Format Response",
"type": "main",
"index": 0
}
]
]
},
"Format Response": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
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.
anthropicApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Query Agent. Uses chainLlm, lmChatAnthropic, httpRequest. Webhook trigger; 7 nodes.
Source: https://github.com/thinhhoangpham/pantex-n8n/blob/ebf807cb5136c0c4ced8c8e8daca15b5525709f9/workflows/query_agent.json — original creator credit. Request a take-down →
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