OpenAI Compatibility

To use the CanopyWave API through the OpenAI client library, simply set api_key to your CanopyWave API key and modify base_url tohttps://inference.canopywave.io/v1

import os
import openai

client = openai.OpenAI(
    api_key=os.environ.get("CANOPYWAVE_API_KEY"),
    base_url="https://inference.canopywave.io/v1",
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.CANOPYWAVE_API_KEY,
  baseURL: "https://inference.canopywave.io/v1",
});

Click to obtain your CANOPYWAVE_API_KEY. If you don't have an account yet, you can register for free.

After completing the above configuration, your OpenAI client is now successfully connected to CanopyWave and ready to invoke our open-source models for inference. For example, try our open-source model—GLM-4.6.

import os
import openai

client = openai.OpenAI(
    api_key=os.environ.get("CANOPYWAVE_API_KEY"),
    base_url="https://inference.canopywave.io/v1",
)

response = client.chat.completions.create(
    model="zai/glm-4.6",
    messages=[
        {
            "role": "system",
            "content": "You are a cook agent.",
        },
        {
            "role": "user",
            "content": "Tell me how to make a hamburger",
        },
    ],
)

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

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.CANOPYWAVE_API_KEY,
  baseURL: 'https://inference.canopywave.io/v1',
});

const response = await client.chat.completions.create({
  model: 'zai/glm-4.6',
  messages: [
    { role: 'user', content: 'What are some fun things to do in New York?' },
  ],
});

console.log(response.choices[0].message.content);

You can use JSON schema to get structured outputs from the model.

from pydantic import BaseModel
from openai import OpenAI
import os, json 

client = OpenAI(
    api_key=os.environ.get("CANOPYWAVE_API_KEY"),
    base_url="https://inference.canopywave.io/v1",
) 

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str] 

completion = client.chat.completions.create(
    model="zai/glm-4.6",
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {
            "role": "user",
            "content": "Timmy and Gavin are planning to cook a big meal on Sunday.. Answer in JSON.",
        },
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "calendar_event",
            "schema": CalendarEvent.model_json_schema(),
        },
    }, 
) 

output = json.loads(completion.choices[0].message.content)
print(json.dumps(output, indent=2)) 

output
{
    "name": "Big Meal Cooking",
    "date": "Sunday",
    "participants": [
        "Timmy",
        "Gavin"
    ]
}

You can use function calling to get structured outputs from the model and call external functions.

from openai import OpenAI
import os, json 

client = OpenAI(
    api_key=os.environ.get("CANOPYWAVE_API_KEY"),
    base_url="https://inference.canopywave.io/v1",
) 

tools = [
    {
        "type": "function",
        "function": {
            "name": "calculate_tip",
            "description": "Calculate the tip amount for a restaurant bill.",
            "parameters": {
                "type": "object",
                "properties": {
                    "bill_amount": {
                        "type": "number",
                        "description": "The total bill amount in USD",
                    },
                    "tip_percentage": {
                        "type": "number",
                        "description": "The percentage of tip to leave (e.g., 15, 18, 20)",
                    }
                },
                "required": ["bill_amount", "tip_percentage"],
                "additionalProperties": False,
            },
            "strict": True,
        },
    }
] 

completion = client.chat.completions.create(
    model="zai/glm-4.6",
    messages=[
        {"role": "user", "content": "Calculate a 20% tip for a $50 bill"}
    ],
    tools=tools,
    tool_choice="auto",
) 

print(
    json.dumps(
        completion.choices[0].message.model_dump()["tool_calls"], indent=2
    )
) 

output 
[
    {
        "id": "call_7b04c99a5ca84b94b710ef40",
        "function": {
            "arguments": "{"bill_amount": 50, "tip_percentage": 20}",
            "name": "calculate_tip"
        },
        "type": "function",
        "index": -1
    }
]

If you have any questions while using our API, please feel free to contact our support team. You can also reach our support staff through the following channels: