Cerebras

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.

Agno v2.7.2 defaults Cerebras.id to the retired llama-4-scout-17b-16e-instruct model. Set id="gpt-oss-120b" or another current Cerebras model explicitly.

Prerequisites

To use Cerebras with Agno, you need to:

Install the required packages:

uv pip install -U cerebras-cloud-sdk agno

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

Use a Cerebras model with Agno:

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

agent = Agent(
    model=Cerebras(id="gpt-oss-120b"),
    markdown=True,
)

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

Supported Models

Cerebras currently serves the following models (see the model list for the latest):

Model Name	Model ID	Parameters	Availability
OpenAI GPT OSS	gpt-oss-120b	120 billion	Production
Gemma 4 31B	gemma-4-31b	31 billion	Preview
Z.ai GLM 4.7	zai-glm-4.7	355 billion	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[Union[httpx.Client, httpx.AsyncClient]]`	`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="gpt-oss-120b"),
    output_schema=MovieScript,
)

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Basics

Advanced

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Additional Resources

Prerequisites

Basic Usage

Supported Models

Parameters

Structured Outputs

Resources

SDK Examples

​Prerequisites

​Basic Usage

​Supported Models

​Parameters

​Structured Outputs

​Resources

​SDK Examples

Prerequisites

Basic Usage

Supported Models

Parameters

Structured Outputs

Resources

SDK Examples