TCAndonRouter is a reasoning-centric multi-intent routing module whose primary role is to perform agent routing in multi-agent systems. Beyond agent routing, TCAndonRouter can be applied to any intent-routing scenario, including agent skill selection.

The main advantages of TCAndonRouter include:

Designed specifically for real-world enterprise applications
Supports dynamic onboarding of new agents (intents) New agents can be added simply by appending their descriptions, without retraining
Provides transparent and interpretable routing decisions, improving explainability, robustness, and cross-domain generalization, and making post-deployment bad-case analysis easier
Effectively resolves agent conflicts caused by overlapping responsibilities, leading to higher-quality final responses. When multiple agents are applicable, TCAndonRouter preserves all relevant agents. Each downstream agent generates its own response, and a Refining Agent subsequently merges these outputs into a single final answer

TCAndonRouter is trained using Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (DAPO), and achieves state-of-the-art performance on large-scale, real-world enterprise datasets, including HWU64, MINDS14, SGD, and the Tencent Cloud ITSM dataset(QCloud).

Models	CLINC150	HWU64	MINDS14	SGD	QCloud
GPT-5.1	93.84	85.59	95.59	73.90	92.80/93.06
Claude-Sonnet-4.5	94.21	87.40	96.20	76.02	88.82/94.25
DeepSeek-v3.1-terminus	88.29	88.10	95.72	79.70	94.09/91.89
ArcRouter	62.98	69.33	91.79	65.59	-
Qwen3-Embedding-4B	57.21	54.27	94.12	37.02	-
Qwen3-4B-Instruct-2507	70.12	80.29	90.08	58.74	82.23/79.44
TCAndonRouter	91.25	91.63	96.70	91.58	95.21/92.78

🔧 How to use

Please refer to GitHub for code usage.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from prompt import router_prompt
from utils import load_config

tokenizer = AutoTokenizer.from_pretrained("tencent/TCAndon-Router")
model = AutoModelForCausalLM.from_pretrained("tencent/TCAndon-Router", device_map="auto")

agents = load_config('config/hwu64_config.xml')
query = "Can you recommend any pub in mg road"
prompt = router_prompt.format(agents=agents) + 'user:' + query

messages = [{"role": "user", "content": prompt}]
encoding = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=False,
    return_tensors="pt"
)

outputs = model.generate(encoding.to(model.device), max_new_tokens=2048)
output_text = tokenizer.decode(outputs[0])

Generate Agent Descriptions

If you want to use TCAndonRouter on your own dataset, you need to provide agent descriptions. The required format is defined in config/xxx_config.xml. You can generate agent descriptions using an LLM via generate_agent_desc.py, or write them manually.

python generate_agent_desc.py --dataset hwu64 --limit 50

🤝 Citation

If you use TCAndonRouter in your work, please cite our paper:

@article{zhao2026TCAndonRouter,
  title={TCAndonRouter: Adaptive Reasoning Router for Multi-Agent Collaboration},
  author={Jiuzhou Zhao, Chunrong Chen, Chenqi Qiao, Lebin Zheng, Minqi Han, Yanchi Liu, Yongzhou Xu, Xiaochuan Xu, Min Zhang},
  journal={arXiv preprint:2601.04544},
  year={2026}
}