Instructions to use open-thoughts/OpenThinker-Agent-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-thoughts/OpenThinker-Agent-v1 with Transformers:

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

pipe = pipeline("text-generation", model="open-thoughts/OpenThinker-Agent-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinker-Agent-v1")
model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinker-Agent-v1")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use open-thoughts/OpenThinker-Agent-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/open-thoughts/OpenThinker-Agent-v1

SGLang

How to use open-thoughts/OpenThinker-Agent-v1 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 "open-thoughts/OpenThinker-Agent-v1" \
    --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": "open-thoughts/OpenThinker-Agent-v1",
		"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 "open-thoughts/OpenThinker-Agent-v1" \
        --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": "open-thoughts/OpenThinker-Agent-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-thoughts/OpenThinker-Agent-v1 with Docker Model Runner:
```
docker model run hf.co/open-thoughts/OpenThinker-Agent-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Project | SFT dataset | RL dataset | SFT model | RL model

OpenThinker-Agent-v1

OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes datasets, models and our research codebase.

OpenThinker-Agent-v1 is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.

The OpenThinker-Agent-v1 model is post-trained from Qwen/Qwen3-8B. It is SFT-ed on the OpenThoughts-Agent-v1-SFT dataset, then RL-ed on the OpenThoughts-Agent-v1-RL dataset.

This model is the final model after both SFT and RL. For the model after the SFT stage only, see OpenThinker-Agent-v1-SFT.

Homepage: https://www.openthoughts.ai/blog/agent
Repository: https://github.com/open-thoughts/OpenThoughts-Agent

OpenThinker-Agent-v1 Model Performance

Our OpenThinker-Agent-v1 model is the state-of-the-art model at its scale on agent benchmarks.

Model	Harness	Terminal-Bench 2.0	SWE-Bench Verified	OpenThoughts-TB-Dev
Qwen3-8B	Terminus-2	0.0	0.7	5.7
OpenThinker-Agent-v1	Terminus-2	4.9	15.7	17.3
Qwen3-32B	Terminus-2	1.9	5.7	10.2
Qwen/Qwen3-Coder-30B-A3B-Instruct	OpenHands	10.1	49.2	24.5

Data

We built OpenThinker-Agent-v1 in two stages: supervised fine-tuning, followed by reinforcement learning. Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.

OpenThoughts-Agent-v1-SFT is an SFT trace dataset containing approximately 15,200 traces drawn from two different data sources we curate:

nl2bash: Simple synthetically generated tasks where the agent has to format shell commands effectively
InferredBugs: A set of bugs in C# and Java collected by Microsoft that we turned into tasks

OpenThoughts-Agent-v1-RL is an RL dataset containing ~720 tasks drawn from the nl2bash verified dataset.

To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:

Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
Environment stability: remove tasks whose containers take too long to build or tear down. Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.

Citation

@misc{openthoughts-agent,
  author = {Team, OpenThoughts-Agent},
  month = Dec,
  title = {{OpenThoughts-Agent}},
  howpublished = {https://www.open-thoughts.ai/blog/agent},
  year = {2025}
}