How to Use the Flowise MCP in AutoGen

Q: Can AutoGen agents use Flowise to resolve disagreements?

Certainly. You can have one agent run a predict call to fetch data, which then serves as the objective truth for other agents to analyze.

Enable multi-agent debate in AutoGen by connecting Flowise workflows as specialized tools.

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Connect Flowise MCP to AutoGen

Create your Vinkius account to connect Flowise to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Flowise with AutoGen

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Debate Flowise results in AutoGen

Assign the `predict` tool to a specialized agent in your group. When agents debate a problem, one can run a workflow to get evidence, while others verify the result. This creates a system of checks and balances. You aren't relying on a single output; you're building a consensus where the Flowise output is tested by other agents.

Monitor AutoGen agents with Flowise

Use `get_history` to provide agents with a record of past decisions. One agent acts as an auditor, reviewing the history to ensure the group doesn't repeat past errors. It forces your agents to learn from previous cycles. They check the audit logs before proposing a new course of action, keeping the conversation grounded in reality.

Expose Flowise tools to AutoGen agents

Use `list_tools` to dynamically register Flowise functions with your agents. They decide which tool is required to resolve a specific conflict during a debate. It simplifies agent configuration. Instead of hard-coding every interaction, you let the agents negotiate which Flowise capability best solves the current bottleneck.

Setup guide

Set up Flowise MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Flowise tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Flowise_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Flowise data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Flowise_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Flowise data")
    print(result.messages[-1].content)

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Common questions about Flowise MCP in AutoGen

Use the MCP tool adapter to convert Flowise tools into a format AutoGen recognizes. Pass this list to your agent constructor to enable the functionality.

Certainly. You can have one agent run a `predict` call to fetch data, which then serves as the objective truth for other agents to analyze.

Yes, the MCP server acts as a shared tool library. Multiple agents can access the same tools to perform their specific roles in the conversation.

Credentials are never hard-coded. The server manages them via `list_credentials`, and AutoGen agents only access what they need through the authenticated MCP session.

We keep everything ephemeral. Every agent interaction is isolated within the Vinkius sandbox, ensuring that sensitive workflow history and credentials stay private.

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