Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.hyperbolic.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

​
Integrations

Hyperbolic’s OpenAI-compatible API makes it easy to integrate with your existing tools and frameworks. Simply change the base URL and API key to start using Hyperbolic models with your favorite libraries.

​
OpenAI SDK

The fastest way to integrate with Hyperbolic. Just change base_url and api_key. 
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_HYPERBOLIC_API_KEY",
    base_url="https://api.hyperbolic.xyz/v1"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

​
Installation

pip install openai

​
LangChain

Build AI applications, chains, and agents with LangChain. 
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    openai_api_key="YOUR_HYPERBOLIC_API_KEY",
    openai_api_base="https://api.hyperbolic.xyz/v1",
    model_name="meta-llama/Llama-3.3-70B-Instruct"
)

response = llm.invoke("What is the capital of France?")
print(response.content)

​
Installation

pip install langchain langchain-openai

​
LlamaIndex

Build RAG applications and knowledge-augmented AI with LlamaIndex. 
from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    api_key="YOUR_HYPERBOLIC_API_KEY",
    api_base="https://api.hyperbolic.xyz/v1",
    model="meta-llama/Llama-3.3-70B-Instruct"
)

response = llm.complete("Explain quantum computing in simple terms")
print(response.text)

​
Installation

pip install llama-index llama-index-llms-openai-like

​
Hugging Face

Access Hyperbolic models through the Hugging Face ecosystem. 
from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hyperbolic",
    api_key="YOUR_HYPERBOLIC_API_KEY"
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ],
    max_tokens=500
)

print(response.choices[0].message.content)

​
Authentication Options

MethodAPI KeyBilling
DirectHyperbolic API keyBilled to Hyperbolic account
RoutedHugging Face tokenBilled to Hugging Face account

​
Installation

pip install huggingface_hub>=0.29.0
Browse all Hyperbolic models on Hugging Face.

​
Gradio

Build interactive ML demos with Gradio. 
import gradio as gr
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_HYPERBOLIC_API_KEY",
    base_url="https://api.hyperbolic.xyz/v1"
)

def chat(message, history):
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct",
        messages=[{"role": "user", "content": message}]
    )
    return response.choices[0].message.content

demo = gr.ChatInterface(chat)
demo.launch()

​
Installation

pip install gradio openai

​
Other Compatible Frameworks

Hyperbolic’s OpenAI-compatible API works with any framework that supports the OpenAI API format:
  • AutoGen - Multi-agent conversations
  • CrewAI - AI agent orchestration
  • Semantic Kernel - Microsoft’s AI orchestration SDK
  • Haystack - NLP pipelines and RAG
  • DSPy - Programming with language models
To use any OpenAI-compatible framework with Hyperbolic, set the base URL to https://api.hyperbolic.xyz/v1 and use your Hyperbolic API key.

​
Next Steps

Text APIs

Explore chat completions

Image APIs

Generate images from text

Audio APIs

Text-to-speech generation