How to Use the Zeev MCP in AutoGen

Q: How does AutoGen use Zeev to manage task delegation?

Agents can challenge ownership by checking gettask and then initiating a formal transfer using delegatetask. This simulates a multi-person review process before the handoff.

Q: Can AutoGen use Zeev to list available workflows?

Yes. One agent can call listprocesses to gather all available workflow definitions, which then becomes input for other agents debating the next action.

Q: If I need to check a cancelled workflow in AutoGen, what data do I use?

You can retrieve details about past workflows by using getrequest or listrequests. The consensus agent can then reason about the implications of that historical record.

Run consensus-driven decision making across Zeev workflows with AutoGen.

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Create your Vinkius account to connect Zeev 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 Zeev with AutoGen

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Debating Workflow Initiation

Multiple agents can debate whether a process needs to start. An 'Audit Agent' might use `get_process` to confirm the workflow exists, while a 'Initiation Agent' uses `list_requests` to check if an instance is already running. The consensus agent then decides if calling `create_request` is warranted based on conflicting inputs.

Negotiating Task Ownership

Agents can simulate a review process. One agent checks the current task owner using `get_task`. A second agent might challenge that ownership, forcing a decision to use `delegate_task`. The entire system converges on whether the task needs re-routing or if the original assignee is correct.

Reviewing Workflow Completion State

You can build agents that monitor completion. One agent checks pending items with `list_tasks`, while another calls `get_task` to verify all required steps are done. If everything clears, the final agent executes `finish_task`. This debate ensures a consensus on whether the process is truly complete before moving forward.

Setup guide

Set up Zeev 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 Zeev 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="Zeev_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Zeev MCP in AutoGen

Agents can challenge ownership by checking `get_task` and then initiating a formal transfer using `delegate_task`. This simulates a multi-person review process before the handoff.

Yes. One agent can call `list_processes` to gather all available workflow definitions, which then becomes input for other agents debating the next action.

It does. You can model complex states by having agents debate whether a request needs to be canceled (`cancel_request`) or if it's ready for completion via `finish_task`.

This server touches process definitions, specific task instances, and request metadata. These are the core structured inputs that allow agents to debate state changes.

You can retrieve details about past workflows by using `get_request` or `list_requests`. The consensus agent can then reason about the implications of that historical record.

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