How to Use the Sally MCP in AutoGen

Debate your frontline workflows with AutoGen. Let agents negotiate project tasks and team comments using the Sally MCP server.

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Connect Sally MCP to AutoGen

Create your Vinkius account to connect Sally to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sally with AutoGen

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Consensus-driven tasks with AutoGen

One agent proposes a change via `create_task` while another checks the project constraints. They debate until they agree on the priority level. This ensures every task created is verified by multiple perspectives. You avoid the errors that happen when a single agent makes decisions in isolation.

Negotiate comments using AutoGen agents

Agents use `add_comment` to discuss project blockers. They simulate a real team conversation to resolve issues before posting the final update. This keeps your communication professional and accurate. You get a record of the deliberation process attached to every task.

Multi-agent project oversight

Assign a security agent to monitor `get_timesheet_report` for anomalies. If it finds an issue, it challenges the performance agent to justify the project hours. This keeps your operations transparent. The agents hold each other accountable, using the MCP server as their shared source of truth.

Setup guide

Set up Sally 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 Sally 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="Sally_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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