Cerebras

Use Cerebras high-speed inference with Agno agents.

Cerebras Inference provides high-speed, low-latency AI model inference powered by Cerebras Wafer-Scale Engines and CS-3 systems. Agno integrates directly with the Cerebras Python SDK, allowing you to use state-of-the-art Llama models with a simple interface.

Prerequisites

To use Cerebras with Agno, you need to:

1. Install the required packages:

   uv pip install cerebras-cloud-sdk
   

2. Set your API key: The Cerebras SDK expects your API key to be available as an environment variable:

   export CEREBRAS_API_KEY=your_api_key_here
   

Basic Usage

Here’s how to use a Cerebras model with Agno:

from agno.agent import Agent
from agno.models.cerebras import Cerebras

agent = Agent(
    model=Cerebras(id="llama-4-scout-17b-16e-instruct"),
    markdown=True,
)

# Print the response in the terminal
agent.print_response("write a two sentence horror story")

Supported Models

Cerebras currently supports the following models (see docs for the latest list):

Model Name	Model ID	Parameters	Knowledge
Llama 4 Scout	llama-4-scout-17b-16e-instruct	109 billion	August 2024
Llama 3.1 8B	llama3.1-8b	8 billion	March 2023
Llama 3.3 70B	llama-3.3-70b	70 billion	December 2023
DeepSeek R1 Distill Llama 70B*	deepseek-r1-distill-llama-70b	70 billion	December 2023

* DeepSeek R1 Distill Llama 70B is available in private preview.

Parameters

Parameter	Type	Default	Description
id	str	"llama-4-scout-17b-16e-instruct"	The id of the Cerebras model to use
name	str	"Cerebras"	The name of the model
provider	str	"Cerebras"	The provider of the model
parallel_tool_calls	Optional[bool]	None	Whether to run tool calls in parallel (automatically set to False for llama-4-scout)
max_completion_tokens	Optional[int]	None	Maximum number of completion tokens to generate
repetition_penalty	Optional[float]	None	Penalty for repeating tokens (higher values reduce repetition)
temperature	Optional[float]	None	Controls randomness in the model’s output (0.0 to 2.0)
top_p	Optional[float]	None	Controls diversity via nucleus sampling (0.0 to 1.0)
top_k	Optional[int]	None	Controls diversity via top-k sampling
strict_output	bool	True	Controls schema adherence for structured outputs
extra_headers	Optional[Any]	None	Additional headers to include in requests
extra_query	Optional[Any]	None	Additional query parameters to include in requests
extra_body	Optional[Any]	None	Additional body parameters to include in requests
request_params	Optional[Dict[str, Any]]	None	Additional parameters to include in the request
api_key	Optional[str]	None	The API key for authenticating with Cerebras (defaults to CEREBRAS_API_KEY env var)
base_url	Optional[Union[str, httpx.URL]]	None	The base URL for the Cerebras API
timeout	Optional[float]	None	Request timeout in seconds
max_retries	Optional[int]	None	Maximum number of retries for failed requests
default_headers	Optional[Any]	None	Default headers to include in all requests
default_query	Optional[Any]	None	Default query parameters to include in all requests
http_client	Optional[httpx.Client]	None	HTTP client instance for making requests
client_params	Optional[Dict[str, Any]]	None	Additional parameters for client configuration
client	Optional[CerebrasClient]	None	A pre-configured instance of the Cerebras client
async_client	Optional[AsyncCerebrasClient]	None	A pre-configured instance of the async Cerebras client

Cerebras is a subclass of the Model class and has access to the same params.

Structured Outputs

The Cerebras model supports structured outputs using JSON schema:

from agno.agent import Agent
from agno.models.cerebras import Cerebras
from pydantic import BaseModel
from typing import List

class MovieScript(BaseModel):
    setting: str
    characters: List[str]
    plot: str

agent = Agent(
    model=Cerebras(id="llama-4-scout-17b-16e-instruct"),
    response_format=MovieScript,
)

Resources

• Cerebras Inference Documentation
• Cerebras API Reference

SDK Examples

• View more examples here.