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, AutoModelForCausalLM

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

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
```

joska commited on Jan 8

Commit

1d8f29b

verified ·

1 Parent(s): e88f00e

Create README.md

Browse files

# TCAndon-Router

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

<p align="center">
<a href="https://github.com/Tencent/TCAndon-Router">Github</a> | 📑 <a href="https://arxiv.org/abs/TCAndonRouter">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 strong performance on large-scale and real-world datasets, including CLINC150, HWU64, MINDS14, SGD, and Tencent Cloud ITSM (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

```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")

agents = load_config('config/hwu64_config.xml')
query = "Can you recommend any pub in mg road"

prompt = router_prompt.format(agents=agents) + 'user:' + query
response = model.generate(prompt)
print(response)
```

## 🤝 Citation

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

```
@article {zhao2025TCAndonRouter,
title={TCAndonRouter: Adaptive Reasoning Router for Multi-Agent Collaboration},
author={Jiuzhou Zhao et al.},
journal={arXiv},
year={2025}
}
```

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+---
+license: apache-2.0
+metrics:
+- accuracy
+- f1
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+---