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📰Tech Blog | 📄Tech Report
Apodex-1.1 is a reasoning-first model for complex, long-horizon research tasks. Beyond searching and writing reports, it works directly with files, data, code, and tools to complete tasks from input to verifiable deliverables. Powered by AgentOS and an asynchronous Agent Team, it can maintain task state, adapt its plan, coordinate parallel work, and incorporate user feedback throughout execution.
Try Apodex at apodex.ai.
To prevent potential information leakage (e.g., retrieving benchmark answers from public repositories), we block access to relevant benchmark-hosting websites during evaluation.
Apodex 1.1 Agent Team delivers frontier-level performance across professional work, finance, scientific research, and general reasoning, achieving 38.5 on APEX-Agents, 78.8 on GDPVal, 54.3 on FrontierFinance, 63.3 on FrontierScience-Research, 35.3 on BioMysteryBench, and 56.1 on Humanity’s Last Exam. It consistently improves over the ReAct setup across all six benchmarks and achieves the highest scores among the compared systems on FrontierFinance and FrontierScience-Research.
Apodex-1.1-mini remains competitive with frontier models, leading FrontierFinance with 50.2 and nearly matching the best result on APEX-Agent with 27.7. Its Agent Team setup also consistently outperforms ReAct across all three benchmarks. You can try Apodex-1.1-mini with our Agent Team harness.
Apodex follows the Qwen3.5 chat template — tool calls are emitted as <tool_call><function=...><parameter=...></tool_call> and reasoning as .... Launch with the matching parsers so the server returns standard OpenAI-style tool_calls and reasoning_content fields.
We recommend deploying Apodex with the latest SGLang for an OpenAI-compatible endpoint.
For optimal performance in agentic tasks, we recommend:
Apodex is trained for native function calling — tool schemas are passed via the tools= parameter of the chat-completions API and rendered into the prompt by the chat template, so the system prompt itself only needs to set the role and the high-level objective. We recommend the prompt below (this is the prompt used in our internal evaluation runs):
Substitute {today_date} with the current date (e.g. 2026-06-01). Do not inline tool descriptions in the system prompt — pass them via tools= so the Qwen3.5 chat template can emit the correct <tool_call><function=...> format and the server-side qwen3_coder parser can recover structured tool_calls for you.
The example below runs Apodex as a tool-using agent against an OpenAI-compatible endpoint (the SGLang / vLLM server launched above). The agent loops — executing the requested tools and feeding results back as role="tool" messages — until the model produces a final answer with no tool calls.
Before running, set the endpoint:
Apodex-1.1 is released under Apache 2.0.
If you find this project useful in your research, please consider citing:
Reach the Apodex Team via our website.
python3 -m sglang.launch_server \ --model-path apodex/Apodex-1.1-mini-FP8 \ --tp 1 \ --tool-call-parser qwen3_coder --reasoning-parser qwen3 \ --context-length 262144 \ --speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mamba-scheduler-strategy extra_buffer --mamba-ssm-dtype bfloat16temperature: 1.0top_p: 0.95repetition_penalty: 1.05max_context_length: 262144max_tokens: 32768You 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 serversexport BASE_URL="http://localhost:1234/v1"import jsonimport osfrom datetime import datefrom 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-FP8", 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}}