How to Use the Lattice MCP in AutoGen

Q: Will AutoGen agents make decisions based on Lattice?

Yes. Agents can debate the data from listgoals to decide on a course of action. You control the prompt and the decision criteria.

Enable multi-agent debate and consensus using Lattice data within your AutoGen systems.

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Connect Lattice MCP to AutoGen

Create your Vinkius account to connect Lattice 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 Lattice with AutoGen

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Perplexity

Debate goals with AutoGen agents

Task multiple agents with interpreting company OKRs. One agent calls `list_goals` while another critiques the progress based on `get_goal` data. This debate uncovers missing context. Agents challenge each other's assumptions until they reach a consensus on the current performance status.

Negotiate feedback using your MCP server

Set up a feedback review pipeline. A security-focused agent monitors `list_feedback` for compliance while a manager-agent reviews the content of `get_feedback`. They negotiate the tone of upcoming reviews. This process ensures that feedback is balanced before it ever reaches the final output.

Delegate HR tasks to specialized agents

Assign specific roles to agents using the full toolset. One agent handles `list_tasks` for pending items, while another verifies user details using `get_user`. Agents coordinate to complete complex workflows. They resolve dependencies by sharing results from one tool call to inform the next.

Setup guide

Set up Lattice 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 Lattice 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="Lattice_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Lattice MCP in AutoGen

Agents pass tool outputs back and forth in the conversation history. One agent calls `list_tasks`, and the next analyzes the results.

Yes. You define the tool list passed to your AssistantAgent. You can omit `get_review` if you want to limit access.

Yes. Agents can debate the data from `list_goals` to decide on a course of action. You control the prompt and the decision criteria.

The adapter handles the conversion between the server and your agents. It supports both HTTP and stdio transports for your environment.

The server operates on a zero-trust model. Only the specific `list_users` or `get_user` objects requested during the conversation are ever visible to the agents.

Use it with your favorite AI tools

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