Apodex-1.1-mini

# SGLang
python3 -m sglang.launch_server --model-path apodex/Apodex-1.1-mini --tp 8 --host 0.0.0.0 --port 1234 --context-length 262144 --tool-call-parser qwen3_coder --reasoning-parser qwen3

# vLLM
vllm serve apodex/Apodex-1.1-mini --tensor-parallel-size 8 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3

temperature: 1.0
top_p: 0.95
repetition_penalty: 1.05
max_context_length: 262144
max_tokens: 32768

You are Apodex, an AI assistant developed by Apodex AI.

Apodex is the flagship agent of Apodex AI. Rather than a conventional conversational LLM, it is a general-purpose solver designed for mission-critical tasks.

Current time: {today_date}. In this environment you have access to a set of tools you can use to answer the user's question.

You only have access to the tools provided. You can use multiple tools per message, and will receive the results of those tools in the user's next response. You use tools step-by-step to accomplish a given task.

# General Objective

You accomplish a given task iteratively, breaking it down into clear steps and working through them methodically.

export OPENAI_API_KEY="EMPTY"           # any non-empty string for local servers
export BASE_URL="http://localhost:1234/v1"

import json
import os
from datetime import date
from openai import OpenAI


# -------- 1. Tool implementations --------
def get_weather(location: str, unit: str = "celsius") -> str:
    """Get current weather information for a city (simulated)."""
    table = {
        "London":   {"temperature": 15, "condition": "sunny",  "humidity": 45},
        "New York": {"temperature": 20, "condition": "cloudy", "humidity": 60},
        "Tokyo":    {"temperature": 25, "condition": "rainy",  "humidity": 75},
    }
    w = dict(table.get(location, {"temperature": 18, "condition": "unknown", "humidity": 50}))
    if unit == "fahrenheit":
        w["temperature"] = w["temperature"] * 9 / 5 + 32
        w["unit"] = "°F"
    else:
        w["unit"] = "°C"
    return json.dumps(w, ensure_ascii=False)


def calculate(expression: str) -> str:
    """Evaluate a Python-style arithmetic expression."""
    try:
        return json.dumps({"expression": expression, "result": eval(expression)}, ensure_ascii=False)
    except Exception as e:
        return json.dumps({"expression": expression, "error": str(e)}, ensure_ascii=False)


available_tools = {"get_weather": get_weather, "calculate": calculate}


# -------- 2. Tool schemas (OpenAI function-calling format) --------
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather information for a city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name, e.g. 'London'."},
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit (default: celsius).",
                    },
                },
                "required": ["location"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "calculate",
            "description": "Evaluate a Python-style arithmetic expression.",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Expression to evaluate, e.g. '(25 + 15) * 3 - 10'.",
                    },
                },
                "required": ["expression"],
            },
        },
    },
]


# -------- 3. System prompt --------
SYSTEM_PROMPT = f"""You are Apodex, an AI assistant developed by Apodex AI.

Apodex is the flagship agent of Apodex AI. Rather than a conventional conversational LLM, it is a general-purpose solver designed for mission-critical tasks.

Current time: {date.today()}. In this environment you have access to a set of tools you can use to answer the user's question.

You only have access to the tools provided. You can use multiple tools per message, and will receive the results of those tools in the user's next response. You use tools step-by-step to accomplish a given task.

# General Objective

You accomplish a given task iteratively, breaking it down into clear steps and working through them methodically."""


# -------- 4. Agentic loop --------
def run_agent(user_query: str, model: str = "apodex/Apodex-1.1-mini", max_turns: int = 20):
    client = OpenAI(
        api_key=os.environ.get("OPENAI_API_KEY", "EMPTY"),
        base_url=os.environ.get("BASE_URL", "<http://localhost:1234/v1>"),
    )

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user",   "content": user_query},
    ]
    print(f"\\n{'=' * 60}\\nUser: {user_query}\\n{'=' * 60}\\n")

    for turn in range(max_turns):
        resp = client.chat.completions.create(
            model=model,
            messages=messages,
            tools=tools,
            parallel_tool_calls=True,
            temperature=1.0,
            top_p=0.95,
            max_tokens=16384,
            extra_body={"repetition_penalty": 1.05},
        )
        msg = resp.choices[0].message

        # Optional: print reasoning if the server exposes it (qwen3 reasoning parser)
        reasoning = getattr(msg, "reasoning_content", None)
        if reasoning:
            print(f"[think] {reasoning.strip()}\\n")
        if msg.content:
            print(f"[assistant] {msg.content.strip()}\\n")

        messages.append(msg)

        # No more tool calls -> final answer
        if not msg.tool_calls:
            print(f"💬 Final answer:\\n{msg.content}\\n")
            return msg.content

        # Execute every tool call requested in this turn
        for call in msg.tool_calls:
            name = call.function.name
            args = json.loads(call.function.arguments or "{}")
            print(f"🔧 call {name}({args})")
            try:
                result = available_toolsname
            except Exception as e:
                result = json.dumps({"error": f"{type(e).__name__}: {e}"}, ensure_ascii=False)
            print(f"   ↳ {result}\\n")
            messages.append({
                "role": "tool",
                "tool_call_id": call.id,
                "content": result,
            })

    print("⚠️  Reached max_turns without a final answer.")
    return None


if __name__ == "__main__":
    run_agent("What's the weather in London in Fahrenheit, and what's (25 + 15) * 3 - 10?")

@article{apodex2026,
  title={Apodex 1.1: Scaling Agentic Intelligence for Complex Work},
  author={Apodex Team},
  year={2026},
  url={https://arxiv.org/abs/2608.23283}
}