This article provides a complete, practical walkthrough of the Http Stickynote Automate Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**  
   Building a Multi-AI Agent Chatbot with n8n for Postgres Databases and Visualizations via QuickChart
   
   **Meta Description:**  
   Discover how to build an AI-powered chatbot in n8n that queries Postgres or Supabase databases and generates dynamic charts using QuickChart—powered by OpenAI’s GPT-4o and LangChain agents.
   
   **Keywords:**  
   n8n, AI chatbot, OpenAI, GPT-4o, LangChain, Postgres, Supabase, QuickChart, database visualization, AI workflow automation, SQL agent, AI chart generator, multi-agent orchestration, AI tools
   
   ---
   
   # Building a Multi-AI Agent Chatbot with n8n for Postgres Databases and Visual Visualizations via QuickChart
   
   In today’s data-driven world, being able to ask natural language questions and get back both insightful answers and beautiful visualizations is not just a luxury—it’s a game-changer. Using n8n, an open-source automation and workflow platform, you can now build your own AI-powered, multi-agent chatbot interfacing directly with Postgres or Supabase databases and delivering real-time visualizations using QuickChart.
   
   This article breaks down the architecture and functionality of an n8n workflow titled:  
   ✨📊Multi-AI Agent Chatbot for Postgres/Supabase DB and QuickCharts + Tool Router
   
   ---
   
   ## 🧠 Overview: What This Workflow Does
   
   At its core, this n8n workflow allows users to interact with their database simply by chatting. Behind the scenes, intelligent agents—powered by OpenAI's GPT-4o via LangChain—interpret the user's intent, extract and execute SQL queries, and even create charts via QuickChart. It provides a seamless natural language interface to data analytics.
   
   This architecture provides:
   - Natural language interpretation of user input via GPT-4o
   - Smart tool routing for query execution or chart creation
   - On-the-fly SQL generation and execution against a Postgres/Supabase DB
   - Dynamic chart creation using JSON configurations and QuickChart
   - Persistent chat memory for contextually rich answers
   
   ---
   
   ## 🔗 Key Components
   
   ### 🤖 1. Primary Multitool AI Agent  
   The main driver is a built-in LangChain Agent node configured with the system prompt detailing two tools:
   - `query_database_tool`: Executes SQL queries
   - `generate_chart_tool`: Creates charts from results
   
   This agent determines the user’s intent and passes along the appropriate route.
   
   ### 🔀 2. Tool Router Logic  
   A Switch node determines the routing path based on the selected tool. Depending on the prompt, it routes to:
   - Secondary Postgres Agent for database queries
   - Secondary QuickChart Agent for visualization requests
   
   ### 🧮 3. Postgres/Supabase Integration  
   Several customized Postgres nodes are tightly integrated:
   - `Execute SQL Query`: Executes generated SQL statements
   - `DB Schema and Tables`: Lists table info for agent context
   - `Table Definitions`: Fetches detailed column metadata
   These allow for smart SQL generation grounded in the actual DB schema.
   
   ### 🧠 4. Chat Memory  
   Context is preserved via the `Postgres Chat Memory` node, ensuring continuity in conversations with the AI—like recalling previous prompts or datasets.
   
   ### 📈 5. Chart Generation via QuickChart  
   For visual output:
   - `Secondary QuickChart Agent` generates a proper JSON object using GPT-4o
   - `QuickChart GET URL` builds the QuickChart-compatible URL string
   - `Create QuickChart` performs the actual GET request to render the visualization
   
   Charts are dynamically adjusted based on the dataset and the structure generated by the AI model.
   
   ---
   
   ## 🛠️ Agent Specialization
   
   To better compartmentalize responsibilities, this workflow uses specialized AI agents:
   
   - 🧠 GPT-4o (via OpenAI API) is utilized in three different nodes:
     - Primary Agent: Manages multi-tool logic and user intent parsing
     - Secondary Postgres Agent: Specializes in SQL query generation and schema understanding
     - Secondary QuickChart Agent: Understands Chart.js configurations and output schema
   
   Each agent benefits from LangChain’s support for tools and memory, enabling more accurate responses.
   
   ---
   
   ## 🚀 How It Works (Step-by-Step)
   
   1. A user sends a chat message such as “Show me a bar chart of recent listings and prices.”
   2. The Primary Agent parses the intent and either routes to SQL querying or chart creation.
   3. If querying:
      - SQL query is generated.
      - Table schema and column information enrich the query quality.
      - The SQL result is retrieved from Postgres.
   4. If visualization:
      - Chart object is built via AI and converted into QuickChart JSON format.
      - A shareable QuickChart URL is created to visualize the result.
   5. Final output includes data results and/or a chart link.
   
   ---
   
   ## 🌐 Third-Party APIs and Tools Used
   
   This workflow tightly integrates several third-party services:
   
   - **OpenAI API**
     - Powers all AI-driven agent nodes (GPT-4o-mini)
     - Handles prompt interpretation, SQL generation, JSON object building for charts
   
   - **QuickChart API**
     - Generates dynamic chart images
     - Accepts Chart.js JSON and returns an image via RESTful URL
   
   - **Supabase/Postgres**
     - Underlying database system queried
     - Provides table and schema metadata accessed directly through SQL
   
   ---
   
   ## ⚙️ Setup Instructions
   
   To get started with your own version of this intelligent chatbot:
   
   1. Set up a Postgres-compatible database—Supabase works perfectly.
   2. Input OpenAI API and Postgres credentials in n8n.
   3. Open the chat UI and begin asking natural-language questions about your data.
   
   Example prompt:  
   “Give me average house prices grouped by city, and plot it as a bar chart.”
   
   The system takes care of the rest 🚀
   
   ---
   
   ## 🧩 Extending the Workflow
   
   You can adapt and enhance this workflow in many ways:
   
   - Add additional chart types (pie, line, radar)
   - Share charts with external teams via webhooks or Slack
   - Extend SQL logic for more advanced joins and filters
   - Swap GPT-4o with other LLMs (Claude, Gemini)
   - Enrich visual output with customizable themes
   
   ---
   
   ## 🔚 Final Thoughts
   
   This n8n-powered chatbot showcases the real potential of no-code meets AI. By orchestrating multiple specialized agents, connecting directly to your database, and offering dynamic visualization—all triggered from simple chat input—it enables business users and techies alike to explore and interact with data in fundamentally more intuitive ways.
   
   Whether you're automating internal data analysis, enhancing a BI tool, or offering next-gen support experiences, this workflow provides the ideal backbone for any intelligent assistant.
   
   ---
   
   Looking forward to your own AI-powered dashboard? Spin up this workflow... and just start chatting with your data!
   
   ---
   
   **🔗 Source Repositories and Docs:**
   - n8n Docs: https://docs.n8n.io
   - QuickChart: https://quickchart.io/documentation
   - OpenAI API: https://platform.openai.com/docs
   - Supabase: https://supabase.com/docs

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.