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Cerebras provides ultra-fast inference (3,000+ tokens/sec) for open-source models. With Linkup, you can ground these models in real-time web data, giving them the ability to retrieve current facts, news, and source-backed information beyond their training data. This guide demonstrates the integration using GPT-OSS 120B, but the same approach works with any model available on Cerebras that supports function calling.
1

Get your API Keys

Get your Linkup API key

Create a Linkup account for free to get your API key.

Get your Cerebras API key

Create a Cerebras account for free to get your API key.
2

Install and Setup

Install the required packages:
pip install linkup-sdk cerebras-cloud-sdk
Set up your clients:
from cerebras.cloud.sdk import Cerebras
from linkup import LinkupClient
import json
from datetime import datetime

cerebras_client = Cerebras(api_key="your_cerebras_api_key")
linkup_client = LinkupClient(api_key="your_linkup_api_key")
3

Define the Function Schema

Tell Cerebras about your search function:
tools = [{
    "type": "function",
    "function": {
        "name": "search_web",
        "description": "Search the web for current information. Returns comprehensive content from relevant sources.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query"
                }
            },
            "required": ["query"]
        }
    }
}]
4

Build the Chatbot

Put it all together in a conversational loop:
system_prompt = f"You are a helpful assistant. Today is {datetime.now().strftime('%B %d, %Y')}. Use web search when you need current information."
messages = [{"role": "system", "content": system_prompt}]

print("Chatbot ready! Type 'quit' to exit.\n")

while True:
    user_input = input("You: ").strip()
    if not user_input or user_input.lower() == "quit":
        break

    messages.append({"role": "user", "content": user_input})

    try:
        response = cerebras_client.chat.completions.create(
            model="gpt-oss-120b",
            messages=messages,
            tools=tools
        )

        message = response.choices[0].message

        while message.tool_calls:
            messages.append(message)
            for tool_call in message.tool_calls:
                args = json.loads(tool_call.function.arguments)
                print(f"Searching with Linkup: {args['query']}...")
                linkup_response = linkup_client.search(
                    query=args["query"],
                    depth="standard",
                    output_type="searchResults"
                )
                search_results = json.dumps(
                    [{"content": r.content} for r in linkup_response.results]
                )
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": search_results
                })

            response = cerebras_client.chat.completions.create(
                model="gpt-oss-120b",
                messages=messages,
                tools=tools
            )
            message = response.choices[0].message

        print(f"\nAssistant: {message.content}\n")
        messages.append({"role": "assistant", "content": message.content})

    except Exception as e:
        print(f"Error: {e}\n")
        messages.pop()
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