This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "Supabase Data Analyst Agent",
"settings": {
"executionOrder": "v1"
},
"nodes": [
{
"parameters": {
"content": "## Supabase Data Analyst Agent\n**What it does:** Turns natural-language questions into SQL queries + charts over a Supabase Postgres database.\n\n**Architecture:** Chat Trigger \u2192 Tools Agent (OpenRouter / Claude Sonnet) with persistent Postgres chat memory + 7 tools for schema discovery, querying, charting, and export.\n\n### Required credentials\n1. **Postgres (Supabase)** \u2014 attach to `list_schema`, `get_table_definition`, `Postgres Chat Memory`, and the sub-workflows.\n2. **OpenRouter API** \u2014 attach to the `OpenRouter Chat Model` node.\n3. **Google Sheets OAuth2** \u2014 attach to `export_to_sheets` (optional, only for export feature).\n\n### Sub-workflows required\n- `[Sub] Run SQL with Row Cap` \u2192 used by `run_sql` tool\n- `[Sub] Generate Chart from Data` \u2192 used by `create_chart` tool\n- `[Sub] Search Schema Docs` \u2192 used by `search_schema_docs` tool\n\n### Before activating\nRun the companion `SETUP.sql` in Supabase to create the readonly role, chat-memory table, and optional schema-docs table.\n\n### Demo prompts\n- *What tables do you have?*\n- *Using demo_orders, show revenue by product category as a bar chart*\n- *Monthly revenue trend \u2014 line chart*\n- *Top 10 customers by lifetime value*",
"height": 540,
"width": 680,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-620,
-580
],
"id": "sticky-main-header",
"name": "README"
},
{
"parameters": {
"content": "### 1. Chat Trigger\nUser-facing entry point. `mode: hostedChat` exposes a public chat URL you can share or embed. The `sessionId` it generates is the key that Postgres Chat Memory uses to isolate conversations.\n\nFor private/auth'd use: set `public: false` and embed via the `@n8n/chat` widget with a JWT in `metadata`.",
"height": 260,
"width": 280,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-620,
-20
],
"id": "sticky-trigger",
"name": "Trigger note"
},
{
"parameters": {
"content": "### 2. The Agent\nLangChain **Tools Agent** loop:\n1. Reads the user message + memory.\n2. Decides which tools to call (maxIterations = 12).\n3. Composes the final markdown response.\n\nThe system message (see node settings) enforces: read-only SQL, always-return-charts policy, strict markdown-table output, and the tool-call order.",
"height": 280,
"width": 300,
"color": 3
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-320,
-40
],
"id": "sticky-agent",
"name": "Agent note"
},
{
"parameters": {
"content": "### 3. Brain (LLM + Memory)\n**OpenRouter Chat Model:** Claude Sonnet 4.6 by default. Swap to any OpenRouter-supported model via the `model` field.\n\n**Postgres Chat Memory:** stores conversation in `public.n8n_chat_histories` keyed by `sessionId`. Last 20 messages are loaded per turn. Needs a WRITABLE Postgres credential (not the readonly role).",
"height": 260,
"width": 320,
"color": 3
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-480,
500
],
"id": "sticky-brain",
"name": "Brain note"
},
{
"parameters": {
"content": "### 4. Schema discovery tools\n**list_schema** \u2014 lists every table + column in the DB (excludes Supabase internal schemas). Call once per conversation.\n\n**get_table_definition** \u2014 columns/types/nullability for ONE table. Call before writing SQL against a table you haven't inspected.\n\n**search_schema_docs** \u2014 trigram-fuzzy search over a human-curated `public.schema_docs` table. Use when the user mentions a business term and you need to map it to tables/columns.",
"height": 320,
"width": 420,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-160,
500
],
"id": "sticky-schema-tools",
"name": "Schema tools note"
},
{
"parameters": {
"content": "### 5. Action tools\n**run_sql** \u2014 calls the `[Sub] Run SQL with Row Cap` sub-workflow. Returns up to 200 rows + a `truncated` flag.\n\n**create_chart** \u2014 calls the `[Sub] Generate Chart from Data` sub-workflow. Takes simple primitives (type, title, labels, series) and returns a QuickChart PNG URL.\n\n**think** \u2014 scratchpad tool. Used after errors to reason before retrying.\n\n**export_to_sheets** \u2014 appends rows to a Google Sheets tab on explicit user request.",
"height": 320,
"width": 460,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
280,
500
],
"id": "sticky-action-tools",
"name": "Action tools note"
},
{
"parameters": {
"public": true,
"mode": "hostedChat",
"initialMessages": "Hi! I'm your Supabase data analyst.\nAsk me anything about your data \u2014 I can run SQL queries, build charts, and export results.",
"options": {
"title": "Supabase Data Analyst",
"subtitle": "Ask questions, get answers and charts from your Supabase database.",
"responseMode": "lastNode"
}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
-600,
0
],
"id": "chat-trigger-001",
"name": "When chat message received"
},
{
"parameters": {
"promptType": "auto",
"options": {
"maxIterations": 12,
"systemMessage": "You are a senior data analyst working over a Supabase Postgres database. Your job is to turn data into clear, visual, decision-ready insights \u2014 not just rows of numbers.\n\n## Tools and when to call them\n- `search_schema_docs(term)` \u2014 search human-curated descriptions. Use ONLY when the user mentions a vague business concept whose table/column is non-obvious (e.g. 'churn', 'MRR', 'active users'). Do NOT call it when the user names a table directly or asks about obvious fields (revenue, orders, customers by country, etc.). Skip it for most questions.\n- `list_schema()` \u2014 lists every table. Call ONLY when you don't know what tables exist yet AND the user hasn't named one. If the user says 'demo_orders', 'orders', or any specific table name, SKIP this and go straight to `get_table_definition`.\n- `get_table_definition(table_name)` \u2014 columns/types for one table. Call this BEFORE any `run_sql` on a table you haven't inspected yet this conversation. If you already inspected it earlier in this session, don't re-call it \u2014 trust your memory.\n- `run_sql(query)` \u2014 single read-only SELECT/WITH, returns up to 200 rows + `truncated` flag.\n- `think(thought)` \u2014 scratchpad for planning or post-error reasoning.\n- `create_chart(chart_config)` \u2014 render a Chart.js v4 config. Use constantly (see chart policy below).\n- `export_to_sheets(spreadsheetId, sheetName, rows)` \u2014 append to Google Sheets on explicit request.\n\n## Efficiency rules\n- Minimum viable tool calls. For most questions the ideal path is: `get_table_definition` \u2192 `run_sql` \u2192 `create_chart` \u2192 answer. Three tool calls total.\n- Never call `list_schema` and `get_table_definition` back-to-back on a table the user already named.\n- Never re-introspect a table you already introspected in this conversation.\n\n## Hard rules\n- Read-only. Refuse INSERT/UPDATE/DELETE/TRUNCATE/DROP/ALTER/CREATE/GRANT.\n- Always qualify tables with their schema (e.g. `public.orders`) and quote case-sensitive identifiers.\n- Always include a `LIMIT` (\u2264500) on exploratory queries unless aggregating.\n- If `run_sql` errors: call `think` \u2192 diagnose (wrong column, missing cast, case-sensitive name) \u2192 retry ONCE. After 2 failures, ask the user.\n- If `run_sql` returns `truncated: true`, tell the user and suggest a narrower filter.\n- Never invent table/column names.\n\n## Chart policy \u2014 BE AGGRESSIVE\nCall `create_chart` by DEFAULT for almost every query. You should be generating a chart in 80%+ of responses. The only exceptions where you may skip the chart:\n- The answer is a single scalar (e.g. 'How many customers do we have?' \u2192 just a number).\n- The user explicitly said 'no chart' or 'table only'.\n- The result has 1 row and 1 column.\n\nChart type selection:\n- Time series (anything grouped by day/week/month/year) \u2192 `line`\n- Category comparison (by country, by segment, by category) \u2192 `bar` (horizontal if >8 categories)\n- Share of total (\u22646 slices) \u2192 `doughnut` or `pie`\n- Two numeric dimensions \u2192 `scatter`\n- Multi-series (e.g. revenue by channel by month) \u2192 grouped or stacked `bar`\n\n### Calling create_chart (SIMPLE \u2014 don't build Chart.js manually)\nPass just these fields. The tool assembles the Chart.js config for you.\n- `chart_type`: one of `bar`, `line`, `pie`, `doughnut`, `horizontalBar`\n- `title`: e.g. 'Revenue by Product Category'\n- `labels`: array of strings, aligned with series values, e.g. `['Jan','Feb','Mar']`\n- `series`: array of `{name, values}`, one per metric. Example for single series: `[{\"name\":\"Revenue\",\"values\":[12000,15000,18000]}]`. Example for two series: `[{\"name\":\"Orders\",\"values\":[26,86,67]},{\"name\":\"Revenue\",\"values\":[12105,36418,23797]}]`\n- `x_label`, `y_label`: optional axis titles\n\n### Rule for mixed-magnitude series\nIf you want to plot two metrics with very different scales (e.g. orders 26-150 alongside revenue $12K-$60K), DO NOT put them in the same chart \u2014 the small one flatlines. Instead call `create_chart` TWICE (once per metric) or pick the single most relevant metric.\n\n## Output formatting \u2014 STRICT\nFollow this exact structure for every response:\n\n1. **Headline insight** \u2014 ONE bold sentence with the key takeaway and the most important number(s). Example: `**Revenue tripled from $12K in Apr 2025 to $46K in Apr 2026, driven by a spike in March.**`\n\n2. **Chart** \u2014 embed the chart URL from `create_chart` as a markdown image on its own line:\n ``\n\n3. **Data table** \u2014 present the rows using PROPER MARKDOWN PIPE TABLES. Never use space-aligned ASCII. Example:\n ```\n | Month | Orders | Revenue |\n | -------- | -----: | -------: |\n | 2025-04 | 26 | $12,105 |\n | 2025-05 | 86 | $36,418 |\n ```\n - Right-align numeric columns with `---:`\n - Format money as `$1,234` (thousands separators, no decimals unless <$10)\n - Format percents as `12.3%`\n - Format dates as `2025-04` (months), `2025-04-15` (days)\n - Never dump more than 15 rows \u2014 summarize or aggregate instead.\n\n4. **SQL** \u2014 fenced ```sql block with the exact query used. One query only, no comments.\n\n5. **Key observations** \u2014 2-4 bullets of NON-OBVIOUS insights. Examples: trend inflections, anomalies, category concentration, week-over-week deltas, notable outliers. Never restate the headline. Never pad with filler like 'the data shows'.\n\nSeparate each section with a blank line. Keep the whole response tight \u2014 no introductions, no 'Here is your analysis', no closing pleasantries."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 1.9,
"position": [
-300,
0
],
"id": "agent-001",
"name": "Data Analyst Agent"
},
{
"parameters": {
"model": "anthropic/claude-sonnet-4.6",
"options": {
"temperature": 0.2
}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"typeVersion": 1,
"position": [
-460,
240
],
"id": "openrouter-001",
"name": "OpenRouter Chat Model"
},
{
"parameters": {
"sessionIdType": "customKey",
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"contextWindowLength": 20,
"tableName": "n8n_chat_histories"
},
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"typeVersion": 1.3,
"position": [
-300,
240
],
"id": "memory-001",
"name": "Postgres Chat Memory"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Lists every table and column in the user's Supabase database (excluding internal Supabase/Postgres schemas). Call this once per conversation to discover what data is available. Takes no input.",
"operation": "executeQuery",
"query": "SELECT table_schema, table_name, column_name, data_type\nFROM information_schema.columns\nWHERE table_schema NOT IN ('pg_catalog','information_schema','auth','storage','realtime','extensions','graphql','graphql_public','net','vault','pgsodium','pgsodium_masks','supabase_functions','_analytics','_realtime')\nORDER BY table_schema, table_name, ordinal_position;",
"options": {}
},
"type": "n8n-nodes-base.postgresTool",
"typeVersion": 2.6,
"position": [
-140,
240
],
"id": "tool-list-schema",
"name": "list_schema"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Returns columns, data types, nullability, and length for ONE table. Call this BEFORE writing a SQL query for any table you have not yet inspected, so you don't hallucinate column names. Input: `table_name` (string, unqualified, e.g. 'orders').",
"operation": "executeQuery",
"query": "SELECT table_schema, table_name, column_name, data_type, is_nullable, character_maximum_length, column_default\nFROM information_schema.columns\nWHERE table_name = $1\n AND table_schema NOT IN ('pg_catalog','information_schema','auth','storage','realtime','extensions','graphql','graphql_public','net','vault','pgsodium','pgsodium_masks','supabase_functions','_analytics','_realtime')\nORDER BY table_schema, ordinal_position;",
"options": {
"queryReplacement": "={{ $fromAI('table_name', 'Unqualified table name to inspect, e.g. orders', 'string') }}"
}
},
"type": "n8n-nodes-base.postgresTool",
"typeVersion": 2.6,
"position": [
20,
240
],
"id": "tool-get-table-def",
"name": "get_table_definition"
},
{
"parameters": {
"name": "run_sql",
"description": "Runs a single read-only SQL SELECT (or WITH...SELECT) against the Supabase Postgres database and returns up to 200 rows with a `truncated` flag and `total_rows` count. Input: `query` \u2014 the SQL string. Always qualify tables with their schema (e.g. public.orders) and add LIMIT to exploratory queries.",
"workflowId": {
"__rl": true,
"value": "F119zKphZYCqFS4z",
"mode": "id"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"query": "={{ $fromAI('query', 'A single read-only SQL SELECT or WITH statement.', 'string') }}"
},
"matchingColumns": [],
"schema": [
{
"id": "query",
"displayName": "query",
"required": true,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"typeVersion": 2.1,
"position": [
180,
240
],
"id": "tool-run-sql",
"name": "run_sql"
},
{
"parameters": {
"name": "create_chart",
"description": "Renders a chart and returns a public PNG URL. Pass these simple fields \u2014 do NOT try to build a full Chart.js config. Inputs: chart_type (bar|line|pie|doughnut|horizontalBar), title (string), labels (array of strings for the x-axis or slice names), series (array of {name, values}) \u2014 one object per data series, values aligned with labels, x_label (optional string for x-axis title), y_label (optional string for y-axis title).",
"workflowId": {
"__rl": true,
"value": "HmxF5hPr6ELWkY7e",
"mode": "id"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"chart_type": "={{ $fromAI('chart_type', 'One of: bar, line, pie, doughnut, horizontalBar', 'string') }}",
"title": "={{ $fromAI('title', 'Chart title, e.g. \"Revenue by Product Category\"', 'string') }}",
"labels": "={{ $fromAI('labels', 'Array of string labels for x-axis or slices, e.g. [\"Jan\",\"Feb\",\"Mar\"]', 'json') }}",
"series": "={{ $fromAI('series', 'Array of series objects, each {\"name\":\"...\",\"values\":[...]}. Example: [{\"name\":\"Revenue\",\"values\":[100,200,150]}]', 'json') }}",
"x_label": "={{ $fromAI('x_label', 'X-axis title, optional', 'string') }}",
"y_label": "={{ $fromAI('y_label', 'Y-axis title, optional', 'string') }}"
},
"matchingColumns": [],
"schema": [
{
"id": "chart_type",
"displayName": "chart_type",
"required": true,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
},
{
"id": "title",
"displayName": "title",
"required": true,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
},
{
"id": "labels",
"displayName": "labels",
"required": true,
"type": "array",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
},
{
"id": "series",
"displayName": "series",
"required": true,
"type": "array",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
},
{
"id": "x_label",
"displayName": "x_label",
"required": false,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
},
{
"id": "y_label",
"displayName": "y_label",
"required": false,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"typeVersion": 2.1,
"position": [
340,
240
],
"id": "tool-create-chart",
"name": "create_chart"
},
{
"parameters": {
"name": "search_schema_docs",
"description": "Fuzzy-search the human-curated `public.schema_docs` table for tables/columns whose business meaning matches a term (e.g. 'churn', 'revenue', 'pharmaceutical form'). Input: `term` (string). Use this BEFORE list_schema when the user asks about a business concept. Returns up to 10 doc rows.",
"workflowId": {
"__rl": true,
"value": "V55XKSUOJoFkkQN2",
"mode": "id"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"term": "={{ $fromAI('term', 'Search term \u2014 a business concept, table guess, or column guess.', 'string') }}"
},
"matchingColumns": [],
"schema": [
{
"id": "term",
"displayName": "term",
"required": true,
"type": "string",
"display": true,
"canBeUsedToMatch": true,
"defaultMatch": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"typeVersion": 2.1,
"position": [
500,
240
],
"id": "tool-schema-docs",
"name": "search_schema_docs"
},
{
"parameters": {
"name": "think",
"description": "Internal scratchpad. Write down a brief plan, decompose a complex question, or reason about a tool error before retrying. Input: `thought` (string). Returns the thought back. Use this generously before complex SQL and ALWAYS after a tool error.",
"language": "javaScript",
"jsCode": "const t = typeof query === 'string' ? query : (query && query.thought) ? query.thought : JSON.stringify(query);\nreturn `Thought recorded at ${new Date().toISOString()}: ${t}`;",
"specifyInputSchema": true,
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"thought\": { \"type\": \"string\", \"description\": \"What you're thinking / planning / diagnosing.\" }\n },\n \"required\": [\"thought\"]\n}"
},
"type": "@n8n/n8n-nodes-langchain.toolCode",
"typeVersion": 1.3,
"position": [
660,
240
],
"id": "tool-think",
"name": "think"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Append rows to an existing Google Sheet tab. Use only when the user explicitly asks to export. Inputs: spreadsheet ID, sheet/tab name, and the rows to append (array of objects with column-name keys).",
"operation": "append",
"documentId": {
"__rl": true,
"value": "={{ $fromAI('spreadsheetId', 'The Google Sheets file ID (from the URL).', 'string') }}",
"mode": "id"
},
"sheetName": {
"__rl": true,
"value": "={{ $fromAI('sheetName', 'Tab name in the spreadsheet to append to.', 'string') }}",
"mode": "name"
},
"columns": {
"mappingMode": "autoMapInputData",
"value": {},
"matchingColumns": []
},
"options": {}
},
"type": "n8n-nodes-base.googleSheetsTool",
"typeVersion": 4.5,
"position": [
820,
240
],
"id": "tool-sheets",
"name": "export_to_sheets"
}
],
"connections": {
"When chat message received": {
"main": [
[
{
"node": "Data Analyst Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter Chat Model": {
"ai_languageModel": [
[
{
"node": "Data Analyst Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "Data Analyst Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"list_schema": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"get_table_definition": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"run_sql": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"create_chart": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"search_schema_docs": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"think": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"export_to_sheets": {
"ai_tool": [
[
{
"node": "Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}
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How this works
This workflow empowers you to query your Supabase database using natural language, delivering instant insights without writing complex SQL code. It's ideal for data analysts, business owners, or teams needing quick data-driven decisions from PostgreSQL-backed Supabase projects. The core step involves an AI agent that interprets your chat queries, leverages the Postgres tool for schema exploration and data retrieval, and responds via OpenRouter's language model for accurate, context-aware analysis.
Use this when you want conversational access to Supabase data for ad-hoc reporting or exploratory analysis, especially in chat-based interfaces like Slack or custom apps. Avoid it for high-volume batch processing or real-time dashboards requiring custom visualisations, as it's optimised for interactive queries. Common variations include adding toolWorkflow nodes for external API integrations or memoryPostgresChat for persistent conversation history across sessions.
About this workflow
Supabase Data Analyst Agent. Uses stickyNote, chatTrigger, agent, lmChatOpenRouter. Chat trigger; 17 nodes.
Source: https://github.com/MinaSaad1/n8n-data-analyst-agent/blob/main/workflows/01-main-agent.json — original creator credit. Request a take-down →
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