Python SDK

Use the official OpenAI Python SDK with AltLLM for seamless integration.

Installation

pip install openai

Quick Start

from openai import OpenAI

client = OpenAI(
    base_url="https://altllm-api.viber.autonome.fun/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="altllm-standard",
    messages=[
        {"role": "user", "content": "What's the price of Bitcoin?"}
    ]
)

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

Environment Variables

Use environment variables for secure configuration:

# .env file
OPENAI_API_KEY=your_altllm_api_key
OPENAI_BASE_URL=https://altllm-api.viber.autonome.fun/v1

import os
from openai import OpenAI

# Client automatically reads from environment
client = OpenAI()

response = client.chat.completions.create(
    model="altllm-standard",
    messages=[{"role": "user", "content": "Hello!"}]
)

Streaming Responses

from openai import OpenAI

client = OpenAI(
    base_url="https://altllm-api.viber.autonome.fun/v1",
    api_key="YOUR_API_KEY"
)

stream = client.chat.completions.create(
    model="altllm-standard",
    messages=[{"role": "user", "content": "Tell me about Ethereum"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Async Client

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    base_url="https://altllm-api.viber.autonome.fun/v1",
    api_key="YOUR_API_KEY"
)

async def main():
    response = await client.chat.completions.create(
        model="altllm-standard",
        messages=[{"role": "user", "content": "Hello async!"}]
    )
    print(response.choices[0].message.content)

    # Async streaming
    stream = await client.chat.completions.create(
        model="altllm-standard",
        messages=[{"role": "user", "content": "Stream this"}],
        stream=True
    )
    async for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="")

asyncio.run(main())

System Messages

response = client.chat.completions.create(
    model="altllm-pro",
    messages=[
        {
            "role": "system",
            "content": "You are a crypto analyst. Be concise and data-driven."
        },
        {
            "role": "user",
            "content": "Analyze Bitcoin's current market position"
        }
    ],
    temperature=0.7,
    max_tokens=1000
)

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

Custom Tool Calling

Define custom tools alongside the built-in crypto tools:

import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_portfolio",
            "description": "Get user's crypto portfolio balance",
            "parameters": {
                "type": "object",
                "properties": {
                    "wallet_address": {
                        "type": "string",
                        "description": "Ethereum wallet address"
                    }
                },
                "required": ["wallet_address"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="altllm-pro",
    messages=[
        {"role": "user", "content": "Show my portfolio for 0x742d35Cc6634C0532925a3b844Bc9e7595f5eE"}
    ],
    tools=tools,
    tool_choice="auto"
)

# Handle tool calls
message = response.choices[0].message
if message.tool_calls:
    for tool_call in message.tool_calls:
        print(f"Function: {tool_call.function.name}")
        print(f"Arguments: {tool_call.function.arguments}")

        # Execute your function
        args = json.loads(tool_call.function.arguments)
        result = get_portfolio(args["wallet_address"])  # Your implementation

        # Send result back
        follow_up = client.chat.completions.create(
            model="altllm-pro",
            messages=[
                {"role": "user", "content": "Show my portfolio..."},
                message,
                {
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": json.dumps(result)
                }
            ]
        )
        print(follow_up.choices[0].message.content)

Multi-turn Conversation

messages = [
    {"role": "system", "content": "You are a helpful crypto assistant."}
]

def chat(user_message):
    messages.append({"role": "user", "content": user_message})

    response = client.chat.completions.create(
        model="altllm-standard",
        messages=messages
    )

    assistant_message = response.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_message})

    return assistant_message

# Multi-turn conversation
print(chat("What's the price of ETH?"))
print(chat("How about BTC?"))
print(chat("Compare their 24h performance"))

Usage Tracking

response = client.chat.completions.create(
    model="altllm-standard",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Track usage
usage = response.usage
print(f"Prompt tokens: {usage.prompt_tokens}")
print(f"Completion tokens: {usage.completion_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Estimate cost (altllm-standard: $0.60/$2.40 per 1M)
input_cost = usage.prompt_tokens * 0.0000006
output_cost = usage.completion_tokens * 0.0000024
total_cost = input_cost + output_cost
print(f"Estimated cost: $" + f"{total_cost:.6f}")

Timeout & Retries

from openai import OpenAI

client = OpenAI(
    base_url="https://altllm-api.viber.autonome.fun/v1",
    api_key="YOUR_API_KEY",
    timeout=60.0,      # 60 second timeout
    max_retries=3      # Retry up to 3 times
)

# Per-request timeout
response = client.with_options(timeout=120.0).chat.completions.create(
    model="altllm-max",
    messages=[{"role": "user", "content": "Complex analysis..."}]
)