How to Use the Together AI MCP in AutoGen

Resolve complex decisions through multi-agent debate with AutoGen and Together AI.

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Connect Together AI MCP to AutoGen

Create your Vinkius account to connect Together AI 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 Together AI with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-Driven Decision Making

Agents can use `text_completion` to draft initial conclusions, which then become the subject of debate. A 'Critique Agent' might challenge the text based on factual accuracy. This setup lets you build systems where the final answer isn't obvious and requires deliberation between competing perspectives.

Evaluating Assets Through Debate

If an agent needs a visual asset, it runs `generate_image`. A second, 'Review Agent' then evaluates that image against best practices, flagging whether the prompt was too vague or lacked detail. This lets you simulate multi-person review processes right inside your autonomous pipelines.

Model Selection and Comparison

When agents debate which model to use, they can call `list_available_models`. They then discuss the specs—like latency or context window size—to reach a consensus on the best choice. This simulates expert peer review, ensuring decisions are based on measurable technical data.

Setup guide

Set up Together AI 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 Together AI 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="Together AI_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Together AI_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Together AI 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 Together AI. 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 Together AI MCP in AutoGen

The MCP tools provide the factual basis for the debate. One agent might generate text, while another uses `generate_embeddings` to verify if that text aligns with external knowledge.

Yes. You can run a sequence where one agent generates code (`text_completion`), another runs the code, and a third evaluates the result using embeddings.

The output of any tool call—be it text, image metadata, or model lists—is passed back into the agent conversation loop to fuel the next round of debate.

It does. The combination of tools allows agents to run multi-step processes that require multiple specialized inputs and outputs to converge on a single, validated result.

The server handles text prompts, generated content (text or images), model IDs, and structured metadata from listing tools. All these are used in conversation.

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