How to Use the HiFlow MCP in AutoGen

Assemble teams of AutoGen agents to debate, verify, and execute complex business processes using HiFlow.

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Connect HiFlow MCP to AutoGen

Create your Vinkius account to connect HiFlow 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 HiFlow with AutoGen

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Agents That Debate for Better Results

With AutoGen, you don't have just one agent. You have a team. An 'Accountant' agent can use `list_invoices` to flag an overdue bill. A 'PM' agent can use `get_job` to check if the work is even done yet. They talk to each other before acting. The Accountant says a bill is late, but the PM agent can reply, 'Hold on, the project is still in progress.' This back-and-forth prevents your automation from making simple, costly mistakes.

Build More Reliable Automations

An action is only taken after agents reach a consensus. For example, a 'Provisioner' agent calls `create_customer`. Then, a 'Auditor' agent's whole job is to call `get_customer` and verify the data is correct and complete. The process doesn't move forward until the Auditor agent confirms the work. It's a built-in sanity check that makes your automations far more dependable than a single-agent system that might misinterpret something.

Your AutoGen Agents Are Specialists

You create agents with specific jobs, just like a real team. A 'Billing' agent gets access only to tools like `get_invoice` and `list_estimates`. A 'HR' agent might only be able to use `list_timesheets`. This mirrors a real-world separation of duties. It limits what any single agent can do, reducing the risk of errors and making the whole system easier to debug. Each agent uses the HiFlow MCP Server to do its job, and only its job.

Setup guide

Set up HiFlow 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 HiFlow 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="HiFlow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent HiFlow 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="HiFlow_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HiFlow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about HiFlow MCP in AutoGen

Yes. You'd create a 'Finance' agent to propose the run by calling `list_invoices`. A 'Manager' agent would then need to approve the plan. Once approved, the Finance agent can proceed. It's consensus-driven action.

You set the rules of engagement. In AutoGen, you define the conversation flow, including a maximum number of rounds and termination conditions. This ensures that if agents can't agree, the process stops for human review instead of looping forever.

A great example is customer onboarding. One agent could be a 'Sales' agent that uses `create_customer`. A second 'Project' agent could then use the new customer ID to call `list_jobs` and set up a new project. A third 'Admin' agent could watch the whole conversation and log it.

No, it's straightforward. When you create each agent, you pass in a list of the specific HiFlow tool functions it's allowed to use. The McpToolAdapter makes it easy to give the 'Accountant' billing tools and the 'PM' job tools.

The data, such as invoice details or customer contacts, is passed as messages within the AutoGen conversation. The transport is managed by your code. When using the Streamable HTTP transport, these messages are sent over a secure connection you control, keeping your HiFlow data contained within your agent framework.

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