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Python main.py

# Simple API usage

                import requests
              
                # Call the API with a simple prompt
              
                response = requests.post(
              
                "https://api.teatree.chat/v1/chat/completions",
              
                json={
              
                "model": "gpt-4o",
              
                "messages": [
              
                {"role": "user", "content": "Hello!"}
              
                ]
              
                }
              
                )
              
                result = response.json()
              
                print(result)

How It Works - No API Key Needed

Simple HTTP Request

Just send a standard HTTP request to our endpoint. No API keys, no authentication, no hassle. Get started in seconds with any programming language.

Choose Your Model

Specify which AI model you want to use. We support OpenAI, Anthropic, XAI, and more. All top models accessible through one unified API interface.

Get Results Instantly

Receive high-quality AI responses immediately. No queues, no delays, just powerful AI at your fingertips whenever you need it.

Use TeaTree AI in Just a Few Lines of Code

Our straightforward API means you can start building with AI in minutes, not days. No complicated setup, no authentication hassles.

Works with any programming language or framework

Standard JSON request and response format

Compatible with existing OpenAI API clients

Advanced features like streaming responses

Python advanced.py

# Advanced example with custom parameters

                import requests
              
                import json
              
                # Function to call TeaTree AI API
              
                def generate_response(prompt, model="gpt-4o"):
              
                response = requests.post(
              
                "https://api.teatree.chat/v1/chat/completions",
              
                json={
              
                "model": model,
              
                "messages": [{"role": "user", "content": prompt}],
              
                "temperature": 0.7,
              
                "max_tokens": 300
              
                }
              
                )
              
                return response.json()["choices"][0]["message"]["content"]
              
                # Example usage
              
                result = generate_response("Explain quantum computing simply")
              
                print(result)

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