FlowiseAI MCP Server for AutoGenGive AutoGen instant access to 12 tools to Execute Chatflow Prediction, Get Chatflow Details, Get Server Version, and more
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add FlowiseAI as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
Ask AI about this App Connector for AutoGen
The FlowiseAI app connector for AutoGen is a standout in the Friends Mcp category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="flowiseai_agent",
tools=tools,
system_message=(
"You help users with FlowiseAI. "
"12 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About FlowiseAI MCP Server
Connect your FlowiseAI (self-hosted) instance to any AI agent and take full control of your LLM orchestration and RAG workflows through natural conversation.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use FlowiseAI tools. Connect 12 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Prediction Orchestration — Trigger specific chatflows and retrieve LLM-generated responses programmatically using natural language inputs
- Chatflow Management — List all orchestration flows and retrieve detailed technical structures and metadata to monitor your AI agents
- Vector Intelligence — Programmatically upsert documents or raw data into the vector stores linked to your chatflows to ensure high-fidelity context
- Component Oversight — Access server-wide credentials, custom tools, and global variables to manage your complete Flowise ecosystem
- Operational Visibility — Monitor user feedback, leads, and assistant profiles directly through your agent for instant reporting
The FlowiseAI MCP Server exposes 12 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 FlowiseAI tools available for AutoGen
When AutoGen connects to FlowiseAI through Vinkius, your AI agent gets direct access to every tool listed below — spanning llm-workflows, rag-pipelines, chatbot-development, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Trigger an LLM flow prediction
Get details for a specific chatflow
Get Flowise server version
List OpenAI-style assistants
List user feedback for a chatflow
List all LLM orchestration flows
List custom tools
List captured leads
List global variables
List configured credentials
List chatflow templates
Push data into a vector store
Connect FlowiseAI to AutoGen via MCP
Follow these steps to wire FlowiseAI into AutoGen. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install AutoGen
pip install "autogen-ext[mcp]"Replace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenIntegrate into workflow
Explore tools
Why Use AutoGen with the FlowiseAI MCP Server
AutoGen provides unique advantages when paired with FlowiseAI through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use FlowiseAI tools to solve complex tasks
Role-based architecture lets you assign FlowiseAI tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive FlowiseAI tool calls
Code execution sandbox: AutoGen agents can write and run code that processes FlowiseAI tool responses in an isolated environment
FlowiseAI + AutoGen Use Cases
Practical scenarios where AutoGen combined with the FlowiseAI MCP Server delivers measurable value.
Collaborative analysis: one agent queries FlowiseAI while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from FlowiseAI, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using FlowiseAI data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process FlowiseAI responses in a sandboxed execution environment
Example Prompts for FlowiseAI in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with FlowiseAI immediately.
"List all my chatflows in Flowise."
"Execute chatflow 'cf_1' with question: 'How do I reset my password?'"
"Upsert this data into vector store for chatflow 'cf_2': [data]"
Troubleshooting FlowiseAI MCP Server with AutoGen
Common issues when connecting FlowiseAI to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"FlowiseAI + AutoGen FAQ
Common questions about integrating FlowiseAI MCP Server with AutoGen.