Instructions to use dongguanting/Agent-Reflex-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dongguanting/Agent-Reflex-8B with Transformers:

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

pipe = pipeline("text-generation", model="dongguanting/Agent-Reflex-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dongguanting/Agent-Reflex-8B")
model = AutoModelForCausalLM.from_pretrained("dongguanting/Agent-Reflex-8B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dongguanting/Agent-Reflex-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dongguanting/Agent-Reflex-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dongguanting/Agent-Reflex-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dongguanting/Agent-Reflex-8B

SGLang

How to use dongguanting/Agent-Reflex-8B 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 "dongguanting/Agent-Reflex-8B" \
    --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": "dongguanting/Agent-Reflex-8B",
		"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 "dongguanting/Agent-Reflex-8B" \
        --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": "dongguanting/Agent-Reflex-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dongguanting/Agent-Reflex-8B with Docker Model Runner:
```
docker model run hf.co/dongguanting/Agent-Reflex-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Agent-Reflex-8B

Agent-Reflex-8B is Qwen3-8B trained with Agent-Reflex, a framework that elicits proactive reflection in tool-using agents: noticing noisy tool feedback and user corrections, and recovering from them.

Training: Qwen3-8B → ReSFT (54,663 reflective trajectories, 5,127 steps) → ORPO (noisy agentic RL with skill-guided on-policy distillation). Skills are used only during training; the model runs skill-free.

Results

Model	VitaBench	τ²-Bench	BFCL V4	ACEBench	ToolSandbox	Overall	AgentNoiseBench
Qwen3-8B	11.4	26.2	40.4	54.0	69.1	40.2	12.3
EnvScaler-8B	15.8	37.9	47.6	60.0	80.5	48.4	17.6
Agent-Reflex-8B	17.5	54.9	48.9	76.0	81.8	55.8	21.4

Usage

Serve with vLLM (native function calling, thinking on), as in our evaluation:

vllm serve dongguanting/Agent-Reflex-8B --served-model-name Qwen/Qwen3-8B \
  --tensor-parallel-size 8 --max-model-len 40960 \
  --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser deepseek_r1

The full evaluation suite (τ²-Bench, BFCL V4, ACEBench, VitaBench, AgentNoiseBench, ToolSandbox) is in the GitHub repo.

Citation

@article{agentreflex2026,
  title  = {Agent-Reflex: Teaching Language Agents to Act Reflectively},
  author = {Anonymous},
  year   = {2026}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for dongguanting/Agent-Reflex-8B

Base model

Qwen/Qwen3-8B-Base

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Quantizations

2 models

Datasets used to train dongguanting/Agent-Reflex-8B

Collection including dongguanting/Agent-Reflex-8B

Agent-Reflex

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