How to Use the Flowise MCP in OpenAI Agents SDK

Q: How do I secure credentials when using Flowise with OpenAI Agents SDK?

The server manages keys through listcredentials. Your agent fetches only what it needs, keeping sensitive secrets out of your primary application memory.

Q: Does Flowise support real-time monitoring in OpenAI Agents SDK?

Yes. You call gethistory to retrieve execution logs. This gives your agent visibility into what occurred during past workflow runs.

Connect your OpenAI Agents SDK to Flowise workflows to run predictions and audit execution logs without leaving your production code.

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Connect Flowise MCP to OpenAI Agents SDK

Create your Vinkius account to connect Flowise to OpenAI Agents SDK 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 OpenAI Agents SDK

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Run production predictions with OpenAI Agents SDK

Trigger `predict` directly from your agent logic to execute complex workflows. Your agent handles the input payload, sends it to the server, and processes the result in real-time. This keeps your orchestration logic clean. You don't need custom API wrappers when the `predict` tool handles the handshake for you.

Audit flow history in your OpenAI agent

Use `get_history` to pull past execution data back into your agent's context. This allows your system to reference previous interactions and maintain continuity during long-running tasks. It removes the black-box effect of low-code platforms. Your agent sees exactly what happened previously by parsing the history logs.

Discover available flows using the MCP Server

Map out your architecture by calling `list_chatflows` or `list_agentflows` during initialization. Your agent identifies which workflows are ready for use without manual configuration. It simplifies how your system adapts to changes. If you update a workflow, the agent detects the new structure through the `list_tools` discovery process.

Setup guide

Set up Flowise MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Flowise tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Flowise tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Flowise tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Flowise Agent",
            instructions="You have access to Flowise tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Flowise MCP in OpenAI Agents SDK

The server manages keys through `list_credentials`. Your agent fetches only what it needs, keeping sensitive secrets out of your primary application memory.

Yes. You call `get_history` to retrieve execution logs. This gives your agent visibility into what occurred during past workflow runs.

You control access by limiting the tool definitions exposed to the agent. Simply omit tools like `list_credentials` if your agent doesn't need them.

The server returns an error code that your agent handles via standard try-except blocks. Your code remains in control of the failure state.

Your workflow data stays within the Vinkius sandbox. We use ephemeral sessions to ensure that your chatflow inputs and outputs aren't cached beyond the request lifecycle.

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