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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

OpenThinker-Agent-v1 / README.md

nielsr HF Staff

Link model card to OpenThoughts-Agent paper

b0ccd8f verified 16 days ago

preview code

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	---
	base_model:
	- Qwen/Qwen3-8B
	datasets:
	- OpenThoughts-Agent-v1-SFT
	- OpenThoughts-Agent-v1-RL
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- agents
	- terminal
	- code
	- software-engineering
	model-index:
	- name: OpenThinker-Agent-v1
	results: []
	---

	<p align="center">
	<img src="https://huggingface.co/datasets/open-thoughts/OpenThoughts1-Agent-SFT/resolve/main/ota-logo.png" width="50%">
	</p>

	<p align="center">
	<a href="https://huggingface.co/papers/2606.24855" style="margin-right: 24px;">Paper</a> \|
	<a href="https://www.openthoughts.ai/blog/agent" style="margin-right: 24px; margin-left: 24px;">Project</a> \|
	<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT dataset</a> \|
	<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL" style="margin-right: 24px; margin-left: 24px;">RL dataset</a> \|
	<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT model</a> \|
	<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1" style="margin-left: 24px;">RL model</a>
	</p>


	# OpenThinker-Agent-v1

	OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes [datasets](https://huggingface.co/collections/open-thoughts/openthinker-agent), [models](https://huggingface.co/collections/open-thoughts/openthinker-agent) and our [research codebase](https://github.com/open-thoughts/OpenThoughts-Agent). For more details, please see our paper: [OpenThoughts-Agent: Data Recipes for Agentic Models](https://huggingface.co/papers/2606.24855).

	[OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.

	The [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) model is post-trained from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
	It is SFT-ed on the [OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) dataset, then RL-ed on the [OpenThoughts-Agent-v1-RL](https://huggingface.co/datasets/open-thoughts/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](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT).

	- Paper: https://huggingface.co/papers/2606.24855
	- Homepage: https://www.openthoughts.ai/blog/agent
	- Repository: https://github.com/open-thoughts/OpenThoughts-Agent


	# OpenThinker-Agent-v1 Model Performance

	Our [OpenThinker-Agent-v1](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) 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](https://huggingface.co/Qwen/Qwen3-8B) \| Terminus-2 \| 0.0 \| 0.7 \| 5.7 \|
	\| [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) \| Terminus-2 \| 4.9 \| 15.7 \| 17.3 \|
	\| [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) \| Terminus-2 \| 1.9 \| 5.7 \| 10.2 \|
	\| [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) \| OpenHands \| 10.1 \| 49.2 \| 24.5 \|


	# Data

	We built [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/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](https://huggingface.co/datasets/open-thoughts/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](https://huggingface.co/datasets/open-thoughts/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:

	1. Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
	2. 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.


	# Links
	- 📄 [OpenThoughts-Agent Paper](https://huggingface.co/papers/2606.24855)
	- 🌐 [OpenThoughts-Agent project page](https://www.openthoughts.ai/blog/agent)
	- 💻 [OpenThoughts-Agent GitHub repository](https://github.com/open-thoughts/OpenThoughts-Agent)
	- 🧠 [OpenThoughts-Agent-v1-SFT dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT)
	- 🧠 [OpenThoughts-Agent-v1-RL dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL)
	- 🧠 [OpenThoughts-TB-dev dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TB-dev)
	- 🤖 [OpenThinker-Agent-v1 model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)
	- 🤖 [OpenThinker-Agent-v1-SFT model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT)


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