Endpoint
POST https://api.pipellm.ai/v1/chat/completions
This free route accepts OpenAI-compatible requests and routes only to
OpenAI-compatible platforms. To keep the OpenAI format while calling
Anthropic or Gemini models, use the
OpenAI Format Converter.
Code Examples
- cURL
- Python
- Node.js
- Go
curl https://api.pipellm.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $PIPELLM_API_KEY" \
-d '{
"model": "gpt-4o",
"max_completion_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Why is the sky blue?"
}
]
}'
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv('PIPELLM_API_KEY'),
base_url='https://api.pipellm.ai/v1'
)
response = client.chat.completions.create(
model='gpt-4o',
messages=[
{
'role': 'user',
'content': 'Why is the sky blue?'
}
]
)
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.PIPELLM_API_KEY,
baseURL: 'https://api.pipellm.ai/v1'
});
const response = await client.chat.completions.create({
model: 'gpt-4o',
messages: [
{
role: 'user',
content: 'Why is the sky blue?'
}
]
});
package main
import (
"context"
"os"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig(os.Getenv("PIPELLM_API_KEY"))
config.BaseURL = "https://api.pipellm.ai/v1"
client := openai.NewClientWithConfig(config)
resp, _ := client.CreateChatCompletion(
context.Background(),
openai.ChatCompletionRequest{
Model: "gpt-4o",
Messages: []openai.ChatCompletionMessage{
{
Role: openai.ChatMessageRoleUser,
Content: "Why is the sky blue?",
},
},
},
)
}
Request Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID (e.g., gpt-4o, grok-2) |
messages | array | Yes | Array of message objects |
max_tokens | integer | No | Maximum tokens to generate |
temperature | number | No | Sampling temperature (0-2) |
stream | boolean | No | Enable streaming response |
Response Format
{
"id": "chatcmpl-xxx",
"object": "chat.completion",
"created": 1234567890,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "The sky appears blue because..."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 50,
"total_tokens": 60
}
}
Function Calling
Function Calling allows models to generate structured JSON to call functions in your code.- Python
- Node.js
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
}
}
]
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
tools=tools,
tool_choice="auto"
)
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
# Execute your function and return result
const tools = [
{
type: "function",
function: {
name: "get_weather",
description: "Get the current weather in a location",
parameters: {
type: "object",
properties: {
location: { type: "string", description: "City name" }
},
required: ["location"]
}
}
}
];
const response = await client.chat.completions.create({
model: 'gpt-4.1',
messages: [{ role: 'user', content: "What's the weather in Tokyo?" }],
tools: tools,
tool_choice: 'auto'
});
if (response.choices[0].message.tool_calls) {
const toolCall = response.choices[0].message.tool_calls[0];
// Execute your function and return result
}
Function Calling Documentation
Complete guide on defining functions and handling responses