Developers

Build AI agents in Python

Agents call tools in a loop — searching, reading files, running code — so a single task can take dozens of requests. Tool-calling reliability and total cost per task matter most. Below is a working Python starting point using the official openai Python package.

Python example

python

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ["SOMNUS_API_KEY"],
    base_url="https://gateway-production-c837.up.railway.app/v1",
)

resp = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[
      {"role": "user", "content": "What's the weather in Lisbon right now?"}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Current weather for a city",
            "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
        },
    }],
)
print(resp.choices[0].message.tool_calls)

Models that fit

Model	Maker	List price (in / out per 1M)	Through Somnus	Why
Claude Sonnet 5	Anthropic	$2 / $10	$1 / $5	reliable tool use at a mid price
Claude Opus 5.5	Anthropic	$4 / $20	$2 / $10	long, complex tasks
GPT-5.6 Sol	OpenAI	$5 / $30	$2.5 / $15	alternative strong planner

Somnus column uses the minimum 2× credit; larger top-ups go further.

Making it production-ready in Python

Read the key from an environment variable, never hard-code it.
Use the async client (AsyncOpenAI) for web servers.
Give each tool a precise name and JSON schema.
Stop the loop after a fixed number of steps.

Avoid

Unbounded loops that keep calling tools.
Vague tool descriptions that cause wrong calls and retries.

Questions

Do I need a special SDK for AI agents in Python?

No — the official openai Python package is enough, because Somnus uses the OpenAI chat-completions format.

Run this code with free credit

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