How to Use the Cerebras Inference MCP in AutoGen

Deploy Cerebras Inference agents that debate and decide within your AutoGen multi-agent systems.

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Connect Cerebras Inference MCP to AutoGen

Create your Vinkius account to connect Cerebras Inference 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 Cerebras Inference with AutoGen

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Perplexity

Debate logic using Cerebras Inference

Task multiple agents to generate responses via `create_chat_completion`. You let them challenge each other until they reach a consensus. Each agent uses the same high-speed engine but brings a different perspective. You watch the debate unfold in your conversation logs.

Coordinate batch tasks between AutoGen agents

Assign one agent to manage `list_batches` while another reviews the results. You divide the work based on agent expertise. Your system handles complex scaling tasks without human input. You set the rules; the agents manage the inference workload.

Verify agent performance with Cerebras Inference

Run `get_metrics` to see how your agents impact inference load. You tune your agent behavior based on the hardware response times. Detect if an agent is hitting rate limits or slow endpoints. You adjust your multi-agent strategy to maintain system speed.

Handle files across AutoGen teams

Store and retrieve task files using `get_file_content`. You share context between agents seamlessly during their conversation. Agents pull what they need to inform their debate. You keep the conversation focused by providing specific file access.

Setup guide

Set up Cerebras Inference 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 Cerebras Inference 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="Cerebras Inference_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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