Instructions to use tencent/TCAndon-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/TCAndon-Router with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tencent/TCAndon-Router")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/TCAndon-Router")
model = AutoModelForMultimodalLM.from_pretrained("tencent/TCAndon-Router")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/TCAndon-Router with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/TCAndon-Router"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/TCAndon-Router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/TCAndon-Router

SGLang

How to use tencent/TCAndon-Router with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tencent/TCAndon-Router" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/TCAndon-Router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tencent/TCAndon-Router" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/TCAndon-Router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/TCAndon-Router with Docker Model Runner:
```
docker model run hf.co/tencent/TCAndon-Router
```

TCAndon-Router / README.md

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	---
	base_model:
	- Qwen/Qwen3-4B-Instruct-2507
	language:
	- zh
	- en
	license: apache-2.0
	metrics:
	- accuracy
	- f1
	pipeline_tag: text-generation
	library_name: transformers
	---

	# TCAndon-Router

	<p align="center">
	<img src="https://raw.githubusercontent.com/Tencent/TCAndon-Router/refs/heads/main/assets/router.png" width="500"/>
	</p>

	<p align="center">
	<a href="https://github.com/Tencent/TCAndon-Router">Github</a>   \|    📑 <a href="https://arxiv.org/pdf/2601.04544">Paper</a>
	</p>


	## 🌟 Introduction

	In multi-agent systems, the ability to select the appropriate agent(s) to handle a user query is a key determinant of overall system performance.

	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](https://github.com/Tencent/TCAndon-Router) 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.
	```shell
	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}
	}
	```