Flowise Connector for AI agents.
7 live capabilities
Manage low-code AI workflows and run live predictions from your chat interface.
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Why people use Flowise
Flowise for Faster AI Workflow Debugging
This Connector cuts out the middleman by bringing that data directly to you. You can just ask your agent to show you the execution history or run a live prediction, getting the answer you need in seconds without ever leaving your chat window.
What Vinkius changes
You get a direct command line to your Flowise instance through your favorite AI chat interface.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Debugging a broken chain
An engineer asks the agent to show the history for a specific flow to see why a RAG step failed.
- Real-world use case 02
Testing a new prompt
A developer uses predict to see how a new system instruction changes the output of a customer support bot.
- Real-world use case 03
Inventory check
A product manager asks to list all chatflows to see which ones are live in the production environment.
Complete set · 7capabilities
The complete Flowise capability set.
These are the exact actions your AI can choose when you ask it to work with Flowise.
01—04
4 capabilities in this set.
Part of 7 available through Flowise.
- 01 Capability
List credentials
This capability lists the credentials stored in your instance. This helps you verify which auth components are currently active.
- 02 Capability
Get history
This capability gets the chat execution history for a flow. Use this to pull logs and debug exactly where a logic chain broke.
- 03 Capability
List chatflows
This capability lists all the chatflows currently in your Flowise instance. It helps you quickly see what's deployed.
- 04 Capability
Get chatflow
This capability gets the specific details for a single chatflow. Use this to see the architecture of a specific flow.
05—07
3 capabilities in this set.
Part of 7 available through Flowise.
- 05 Capability
Predict
This capability runs a live prediction on a chatflow. This lets you test how the AI responds to a specific user question right now.
- 06 Capability
List agentflows
This capability lists your agentflows. This is how you see the complex, multi-step reasoning paths you've built.
- 07 Capability
List capabilities
This capability lists the capabilities and integrations in your environment. Use this to see what capabilities your agent can actually call.
Set up in minutes
One URL. Then ask Flowise to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Flowise from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Flowise, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Flowise for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Flowise URL.
- Step 03
Save and start
Save the connection and enable Flowise in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"flowise": {
"url": "https://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Flowise
Open Agent mode in chat and ask: "Using Flowise, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"flowise": {
"url": "https://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Flowise
Ask Copilot: "Using Flowise, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"flowise": {
"url": "https://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Flowise
Open Cascade and ask: "Using Flowise, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"flowise": {
"url": "https://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Flowise
Ask Cline: "Using Flowise, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add flowise --transport http "https://edge.vinkius.com/vk_preview_x0CXvywaUNOIkFSe5hI4681xfnamFZZaTdsop8is/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Flowise
Ask Claude: "Using Flowise, show me...". 7 tools are ready
Where the request belongs
Work Flowise can move forward.
This is for the AI engineer who spends more time debugging broken nodes than actually building features. It's for the automation pro who needs to see why an agent failed at 3am without digging through a messy dashboard.
AI Developer
Debugging chatflows and agentflows directly in the editor to speed up deployment.
Automation Engineer
Triggering live predictions and monitoring execution history via natural language.
Product Manager
Auditing conversational logs and verifying available capabilities for specific use cases.
Build the capability set
Add more capabilities.
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Bring your own AI
Change the model, client or framework. Keep Flowise connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Flowise.
The practical details behind the request, access and result.
What is the Flowise MCP?
It's a way to connect your Flowise AI instance to your AI client so you can manage workflows and run tests using natural language.
Can I use Flowise MCP to run live tests?
Yes, you can use the predict capability to send a prompt to any of your chatflows and see the AI's response instantly.
How do I see my execution logs with Flowise MCP?
You can ask your agent to pull the history for any flow, and it will show you the past traces and conversational logs.
Does Flowise MCP support agentflows?
Yes, it allows you to access and manage compound agentflows that handle multi-step reasoning and complex tasks.
Can I check my credentials using Flowise MCP?
You can use it to list the credentials stored in your Flowise instance to verify which auth components are active.
Is Flowise MCP good for debugging?
It's great for debugging because it lets you pull specific logs and see why a logic chain failed without leaving your chat.
How do I connect Flowise MCP to my agent?
You just need to provide your Flowise Base URL and API Key in your AI client settings to get started.
Can my agent run a prediction against a specific Flowise chatflow?
Yes. Use the 'predict' capability. Provide the 'chatflow_id' and your question. The agent will command the Flowise backend to process the logic chain and return the AI-generated response directly in your chat.
How do I see the past conversational logs for a chatflow via chat?
Use the 'get_history' capability with the 'chatflow_id'. Your agent will retrieve the past execution traces and logs, helping you understand how users have interacted with that specific logic chain natively.
Can I list all custom capabilities configured in my Flowise instance through the agent?
Absolutely. Use the 'list_tools' capability. Your agent will retrieve custom capabilities and integrations configured in your environment, allowing you to audit available capabilities through natural conversation.
One connection away
Give your agent a direct line to Flowise.
Connect Flowise once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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