cerebras-api

Integrate Cerebras Chat Completions API into Python and TypeScript applications.

Updated Jan 23, 2026

Search Tags:#api #llm #inference #tool-calling #chat-completions #cerebras

Ready to run

npx skills add https://github.com/diskd-ai/cerebras-api --skill cerebras-api

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cerebras-api
Source: https://github.com/diskd-ai/cerebras-api/tree/main
Command: npx skills add https://github.com/diskd-ai/cerebras-api --skill cerebras-api

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Cerebras API provides ultra-fast AI inference via a cloud-hosted, OpenAI-compatible endpoint, enabling developers to run chat completions, streaming responses, tool calls, and reasoning-enabled outputs with low latency and scalable throughput.

Core Features & Use Cases

Real-time chat completions with 2,000+ tokens/s performance.
Streaming responses and tool-calling for agents.
Structured outputs with JSON schema enforcement and reasoning formats.
Cross-language SDK support (Python cerebras_cloud_sdk and TypeScript @cerebras/cerebras_cloud_sdk).
OpenAI compatibility and quick-start patterns for fast integration.

Quick Start

Install the Cerebras Python or TypeScript SDKs and run a sample chat completion to validate integration.

Frequently Asked Questions about cerebras-api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I integrate ultra-fast AI inference into my application?▼

Ultra-fast AI inference integrates into applications via the Cerebras Chat Completions API. Developers use Python or TypeScript SDKs to run chat completions, streaming responses, and tool calls by setting the base URL to the Cerebras Cloud endpoint.

Does the Cerebras API work with OpenAI-compatible endpoints?▼

Yes, the Cerebras API works with OpenAI-compatible endpoints. It allows developers to run chat completions, streaming responses, and tool calls using the same interface patterns while achieving 2,000+ tokens/s performance.

What's the best way to run real-time chat completions with low latency?▼

Real-time chat completions with low latency are achieved by interfacing with the Cerebras Cloud API. By obtaining an API key and using the Python or TypeScript SDKs, developers can perform streaming responses and tool calls at 2,000+ tokens/s.

Can I use Python and TypeScript SDKs for tool-calling and reasoning workflows?▼

Yes, you can use Python and TypeScript SDKs for tool-calling and reasoning workflows. The cerebras_cloud_sdk and @cerebras/cerebras_cloud_sdk packages support structured outputs with JSON schema enforcement and reasoning-enabled formats.

How do I set up the Cerebras Cloud SDK for my chat application?▼

To set up the Cerebras Cloud SDK, install the Python or TypeScript package, obtain an API key from Cerebras Cloud, and set the base URL to the API endpoint. You can then run a sample chat completion to validate the integration.

What are the limitations of using OpenAI-compatible endpoints for AI inference?▼

OpenAI-compatible endpoints for AI inference require obtaining a valid API key from Cerebras Cloud and configuring the base URL correctly. Users must also use the specific cerebras_cloud_sdk or @cerebras/cerebras_cloud_sdk packages to ensure proper communication.