Build an agent

import json
import os
import time

from openai import OpenAI, RateLimitError

client = OpenAI(
    base_url="https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
    api_key=os.environ["LYBER_API_KEY"],
    max_retries=0,
)

def read_file(path: str) -> str:
    with open(path) as file:
        return file.read()[:20_000]

TOOLS = [{
    "type": "function",
    "function": {
        "name": "read_file",
        "description": "Read a text file from the project.",
        "parameters": {
            "type": "object",
            "properties": {"path": {"type": "string"}},
            "required": ["path"],
        },
    },
}]
RUN = {"read_file": read_file}

def step(messages):
    for _ in range(6):
        try:
            return client.chat.completions.create(
                model="minimax-m3.1-flash-abliterated",
                messages=messages,
                tools=TOOLS,
                max_tokens=4096,
                extra_body={"max_cost_micro_usdc": 500_000},  # at most $0.50 per step
            )
        except RateLimitError as error:
            if error.response.headers.get("x-lyber-admission") != "not-admitted":
                raise
            time.sleep(int(error.response.headers.get("retry-after", "5")))
    raise RuntimeError("The model stayed busy")

messages = [
    {"role": "system", "content": "Read files with the tool before you answer."},
    {"role": "user", "content": "What does README.md say about setup?"},
]
for _ in range(10):
    reply = step(messages).choices[0].message
    messages.append(reply)
    if not reply.tool_calls:
        print(reply.content)
        break
    for call in reply.tool_calls:
        result = RUN[call.function.name](**json.loads(call.function.arguments))
        messages.append({"role": "tool", "tool_call_id": call.id, "content": result})

pip install openai-agents

import asyncio
import os

from openai import AsyncOpenAI
from agents import Agent, ModelSettings, OpenAIChatCompletionsModel, Runner, function_tool, set_tracing_disabled

set_tracing_disabled(True)
client = AsyncOpenAI(base_url="https://lyber-paid-acceptance-gateway-production.up.railway.app/v1", api_key=os.environ["LYBER_API_KEY"], max_retries=0)

@function_tool
def get_weather(city: str) -> str:
    """Current weather for a city."""
    return f"21 °C and clear in {city}"

agent = Agent(
    name="Assistant",
    instructions="Answer briefly. Use tools when they help.",
    model=OpenAIChatCompletionsModel(model="minimax-m3.1-flash-abliterated", openai_client=client),
    model_settings=ModelSettings(max_tokens=2048),
    tools=[get_weather],
)

async def main():
    result = await Runner.run(agent, "What's the weather in Buenos Aires?", max_turns=6)
    print(result.final_output)

asyncio.run(main())

npm install ai @ai-sdk/openai-compatible zod

import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText, stepCountIs, tool } from "ai";
import { z } from "zod";

const lyber = createOpenAICompatible({
  name: "lyber",
  baseURL: "https://lyber-paid-acceptance-gateway-production.up.railway.app/v1",
  apiKey: process.env.LYBER_API_KEY,
});

const result = await generateText({
  model: lyber("minimax-m3.1-flash-abliterated"),
  tools: {
    getWeather: tool({
      description: "Current weather for a city.",
      inputSchema: z.object({ city: z.string() }),
      execute: async ({ city }) => ({ city, temperatureC: 21 }),
    }),
  },
  stopWhen: stepCountIs(6),
  maxOutputTokens: 2048,
  maxRetries: 0,
  prompt: "What's the weather in Buenos Aires?",
});
console.log(result.text);