How to Use the Groq MCP in AutoGen

Q: How do I register the Groq MCP Server with an AutoGen agent?

Install autogen-extmcp and use the mcpservertools helper with your HTTP server parameters. Pass the resulting tool list directly into your AssistantAgent constructor to give it access to all LPU tools.

Q: How can an AutoGen agent query available models dynamically?

An agent can run listmodels to see which models are active and then call getmodel to inspect their specific limits. This allows your routing agent to dynamically assign tasks to the most efficient model based on current availability.

Power high-speed multi-agent debates in AutoGen with sub-second Groq LPU inference using this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Groq MCP to AutoGen

Create your Vinkius account to connect Groq 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.

GDPR Free for Subscribers

Setup Groq with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Accelerate AutoGen multi-agent debate loops

The `chat_completion` tool delivers sub-second inference speeds, allowing your AutoGen agents to debate, critique, and reach consensus without lagging. This high-velocity execution makes complex multi-agent conversations practical for real-time production. Instead of waiting seconds for each agent's response, this MCP Server feeds tokens to your `AssistantAgent` instances instantly. Your performance agent can push for speed while your security agent checks inputs, negotiating decisions in milliseconds.

Enforce structured agent outputs in AutoGen

The `structured_output` tool ensures that your AutoGen agents communicate using strict, machine-readable JSON schemas. This prevents formatting errors when agents pass payloads to each other or trigger external system actions. Before any agent commits a decision, it can run `moderate_content` to verify that the generated plan is safe and compliant. If an agent flags an issue, the conversation loop automatically adjusts the prompt to resolve the violation.

Handle audio inputs in multi-agent workflows

The `transcribe_audio` tool lets your AutoGen agents ingest voice files and convert them to text during a conversation. If the voice input is in a foreign language, the `translate_audio` tool translates it directly into English for the rest of the agent group. This lets you build voice-activated agent networks where one agent transcribes the user's command, another runs `list_models` to select the best model, and a third generates the final response.

Setup guide

Set up Groq 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 Groq 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="Groq_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Groq 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 Groq. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Groq now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Groq MCP in AutoGen

Install `autogen-ext[mcp]` and use the `mcp_server_tools` helper with your HTTP server parameters. Pass the resulting tool list directly into your `AssistantAgent` constructor to give it access to all LPU tools.

The MCP tool adapter in the AutoGen extension handles schema translation automatically. It converts the server's tool definitions into the format that AutoGen's conversational agents expect, ensuring smooth execution.

Yes, the server handles concurrent connections, allowing multiple agents in your group chat to trigger `chat_completion` or `create_embedding` at the same time. The LPU architecture processes these parallel requests with minimal queue latency.

An agent can run `list_models` to see which models are active and then call `get_model` to inspect their specific limits. This allows your routing agent to dynamically assign tasks to the most efficient model based on current availability.

All agent messages, audio files, and JSON schemas are processed in memory within an isolated V8 sandbox. Vinkius secures the API keys and enforces zero-trust routing, meaning your multi-agent deliberations remain private and ephemeral.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript