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AI & researchAgentChat TriggerOpenAI Chat ModelMcp Client ToolMemory Buffer Window

💥🛠️Build a Web Search Chatbot with GPT-4o and MCP Brave Search

Configured with Agent, Chat Trigger, OpenAI Chat Model, Mcp Client Tool, and additional nodes. Review the connections and setup before adapting this template.

Download workflow JSONView files on GitHub ↗6 nodes · MIT
Workflow preview
AI & modelsAI AgentAgentTriggerWhen chat messagereceivedChat TriggerIntegrationMCP Get Brave ToolsMcp Client ToolIntegrationMCP Execute BraveSearchMcp Client ToolData storeSimple MemoryMemory Buffer WindowAI & modelsgpt-4oOpenAI Chat Model

Node categories in this workflow

  • AI & models
  • Trigger
  • Integration
  • Data store

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
NodeCategoryTypeOperation
AI AgentAI & modelsAgentSee node parameters
When chat message receivedTriggerChat TriggerSee node parameters
MCP Get Brave ToolsIntegrationMcp Client ToolSee node parameters
MCP Execute Brave SearchIntegrationMcp Client ToolexecuteTool
Simple MemoryData storeMemory Buffer WindowSee node parameters
gpt-4oAI & modelsOpenAI Chat ModelSee node parameters

Setup

  1. Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
  2. Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
  3. Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
  4. Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

  • mcpClientApi
  • openAiApi

Workflow notes

💥🛠️Your First Simple MCP AI Chatbot using Brave Search

💥🛠️Your First Simple MCP AI Chatbot using Brave Search

https://github.com/nerding-io/n8n-nodes-mcp https://brave.com/search/api/

🛠️ MCP Toolbox

🛠️ MCP Toolbox

https://github.com/nerding-io/n8n-nodes-mcp https://brave.com/search/api/

**Who is this for?**

Who is this for?

This workflow is ideal for developers, automation enthusiasts, and businesses looking to integrate AI-powered chat capabilities into their workflows. It's particularly useful for those leveraging Brave Search and MCP tools to enhance user interactions and streamline data retrieval.

What problem is this workflow solving?

This workflow addresses the challenge of creating an intelligent chatbot that can process user queries, execute searches using Brave Search, and provide responses enriched by AI. It simplifies the integration of multiple tools into a cohesive system, saving time and effort for users who need a robust conversational AI solution.

What this workflow does

  • Listens for incoming chat messages using the Chat Trigger node.
  • Processes user input with an AI Agent powered by GPT-4o.
  • Retrieves relevant tools using the MCP Get Brave Tools node.
  • Executes specific search queries via the MCP Execute Brave Search node.
  • Maintains short-term memory of conversations with the Simple Memory node.

Setup

  1. Prerequisites:

    • Access to an n8n instance (self-hosted).
    • API credentials for OpenAI and MCP Client Tools.
    • Brave Search API key.

  2. Steps:

    • Import the workflow JSON into your n8n instance.
    • Configure the API credentials for OpenAI and MCP Client Tools in their respective nodes.
    • Set up your Brave Search API key in the MCP nodes. https://brave.com/search/api/

  3. Testing:

    • Use the built-in chat interface to send test messages.
    • Verify that the chatbot processes queries and returns results as expected.

How to customize this workflow to your needs

  • Modify the AI Agent's prompt settings to tailor responses to your specific use case.
  • Adjust the memory buffer in the Simple Memory node to retain more or less conversational context.
  • Replace or add additional tools in the MCP nodes to expand functionality.
💥🛠️Build a Web Search Chatbot with GPT-4o and MCP Brave Search

💥🛠️Build a Web Search Chatbot with GPT-4o and MCP Brave Search

Purpose and structure

This folder contains the 💥🛠️Build a Web Search Chatbot with GPT-4o and MCP Brave Search workflow. Its executable nodes use agent, chatTrigger, lmChatOpenAi, mcpClientTool, memoryBufferWindow. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When chat message received

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1104_Stickynote_Create_Triggered.json MIT; preserve source notices.

Canvas section labels

These name areas of the original canvas; they do not include explanatory instructions.

  • 1️⃣ MCP Get Brave Tools
  • 2️⃣ MCP Execute Brave Search
  • Short Term Chat Memory
  • Cloud LLM
  • 👍Try Me!
  • 🤖 AI Agent with Tools

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