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Cover Image Flowise is a visual low-code platform for building AI-powered chatflows and agents. By integrating Membit with Flowise, you can enhance your AI agents with real-time social context, enabling them to access current trends, breaking news, and live conversations from across the web through a visual drag-and-drop interface.

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Prerequisites

Before you begin, make sure you have:
  • An active Flowise instance (cloud or self-hosted)
  • A Membit account with an API key get your API key
  • Basic familiarity with Flowise’s visual workflow builder
  • A Google AI Studio API key for the chat model (optional)

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Setting Up Membit Custom MCP

Follow these steps to integrate Membit with your Flowise chatflows:
1

Access Chatflows

Navigate to your Flowise dashboard and click Chatflows in the menu bar to access the workflow builder.
Flowise dashboard showing chatflows menu option
2

Create New Chatflow

Click Add New to start building a new chatflow from scratch.
Flowise add new chatflow button
3

Add Node Panel

Click Add Node in the top left corner to open the node library.
Flowise add node button in workflow editor
4

Search for Custom MCP

In the node search bar, type “Custom MCP” to find the Model Context Protocol integration node.
Searching for Custom MCP node in Flowise node library
The Custom MCP node enables Flowise to connect with external MCP servers like Membit, providing real-time context to your chatflows.
5

Configure Custom MCP Node

Drag and drop the Custom MCP node to your chatflow board, then configure it with your Membit server details.
Custom MCP node configuration interface in Flowise
MCP Server Configuration: Set the configuration using JSON format and replace with your Membit API key:
{
  "url": "https://mcp.membit.ai/mcp",
  "headers": {
    "X-Membit-Api-Key": "{{$vars.MEMBIT_API_KEY}}"
  }
}
To use your API key securely, create a MEMBIT_API_KEY variable in Flowise
After configuration, click the refresh button to load available actions, then select the actions you want to use.

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Building Your First Chatflow

Now let’s create a complete chatflow that uses Membit’s real-time context:
1

Add Chat Model

Search for and add a ChatGoogleGenerativeAI node to handle the conversational AI.
Searching for ChatGoogleGenerativeAI node in Flowise
Configuration:
  • Set up your Google AI Studio API key credential
  • Select your preferred model (e.g., Gemini Pro)
ChatGoogleGenerativeAI node configuration in Flowise
2

Add Tool Agent

Search for and add a Tool Agent node to coordinate between the chat model and external tools.
Searching for Tool Agent node in Flowise
The Tool Agent acts as the orchestrator, managing how your chat model interacts with external tools like Membit.
3

Add Buffer Memory

Search for and add a Buffer Memory node to maintain conversation context.
Searching for Buffer Memory node in Flowise
4

Connect the Nodes

Connect all the nodes to create a functioning chatflow:
Connected Flowise chatflow showing all nodes linked together
Connection Pattern:
  • Custom MCP → Tools (Tool Agent)
  • ChatGoogleGenerativeAI → Tool Calling Chat Model (Tool Agent)
  • Buffer Memory → Memory (Tool Agent)
The visual connections show the data flow - the Tool Agent receives tools from Custom MCP, uses the chat model for responses, and maintains memory for context.
5

Test Your Chatflow

Once all nodes are connected, test your chatflow by starting a conversation.
Flowise chat interface showing conversation with real-time context

Testing the chatflow with real-time context from Membit

Try asking questions like “What’s trending in AI today?” or “Tell me about recent crypto news” to see Membit’s real-time context in action.

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Troubleshooting

Problem: Cannot configure or connect Custom MCP nodeSolutions:Verify the JSON configuration syntax is valid (no trailing commas, proper quotes)Ensure the URL is exactly https://mcp.membit.ai/mcpCheck that the MEMBIT_API_KEY variable is properly set in FlowiseTry clicking the refresh button after configuration to load available actionsRestart Flowise after making configuration changes
Problem: API key variable not recognized in FlowiseSolutions:Create a MEMBIT_API_KEY variable in Flowise’s environment settingsEnsure the variable syntax in JSON uses {{$vars.MEMBIT_API_KEY}}Verify there are no extra spaces or characters in the variable nameCheck that your Membit API key is active and validTry restarting Flowise completely after setting environment variables
Problem: Tool Agent doesn’t connect to Custom MCP properlySolutions:Ensure Custom MCP is connected to the “Tools” input of Tool AgentVerify ChatGoogleGenerativeAI is connected to “Tool Calling Chat Model” inputCheck that Buffer Memory is connected to “Memory” inputConfirm all connection lines are properly established and visibleTry disconnecting and reconnecting nodes in the correct sequence
Problem: AI responses don’t include real-time context from MembitSolutions:Verify the Google AI Studio API key is correctly configuredEnsure the chat model supports function calling (Gemini Pro recommended)Make prompts more explicit: “Use available tools to search for current information about…”Check that the Custom MCP actions are properly loaded and selectedTest with topics that are likely to have recent social media activity
Problem: Chatflow fails to execute or times outSolutions:Check all required inputs are connected and configuredVerify internet connectivity and firewall settingsEnsure your Google AI Studio API key has sufficient quotaReview Flowise logs for specific error messagesTry testing individual components before connecting the full flow
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