Uni-Agent: Build, Run, and Train Agents at Scale

Uni-Agent is a unified framework for general agents at scale.

• All-in-one stack: one framework for building, running, and training agents.
• Unified agent interface: unified abstractions for diverse and complex real-world agent scenarios.

The long-term vision is to build the backend infrastructure across both inference and training, enabling agents to perceive, act, and explore complex real-world tasks.

Highlights ✨

Unified yet decoupled agent stack: Uni-Agent organizes agents around model, tool, and env, so each layer can be swapped independently while still composing into one unified interaction framework.

Large-scale parallel interaction: Uni-Agent supports high-throughput, stable parallel inference, execution, and verification for 1000+ concurrent agent tasks.

One stack from inference to training: Uni-Agent reuses the same interaction stack from large-scale agent execution to RL training, with support for advanced paradigms such as fully-async and partial rollout.

Quickstart 🚀

Start with the docs below:

• Launch: Launch an agent environment to run simple demo scripts.
• Build: Build a simple search agent with minimal customization for arXiv paper search and recommendation.
• Search: Train a search agent on ASearcher with a LocalWiki retrieval service.
• Scale: Run parallel agent interaction for large-scale interaction, inference, and verification workloads.
• Train: Train an agent with reinforcement learning using state-of-the-art training techniques.

Architecture 🧩

Uni-Agent is built around a unified interaction loop with three parts: model, tool, and env.

• model is the reasoning backend that decides what to do next,
• tool is how the model perceives and acts on the env
• env is the runtime environment where actions are executed and state is preserved.

This interaction stack is used for large-scale agent execution and can be connected to verl for scalable RL training.

Installation 📦

Uni-Agent builds on top of latest verl release and can use it as a normal Python package.

git submodule update --init --recursive
pip install --no-deps -e ./verl

# Other Dependencies
pip install swe-rex loguru pydantic pydantic_settings aiohttp

See the full installation guide in the docs: Installation.

Live Dashboard 👀

Uni-Agent includes a lightweight dashboard for monitoring large parallel runs in real time. It is designed for workloads such as parallel inference and reinforcement learning.

Start the dashboard from the repository root:

python -m dashboard.server --log-dir /tmp/swebench_qwen3_coder --port 8765

See dashboard/README.md for more details.

Results 📊

Parallel Inference & Verification

We compare Uni-Agent with existing agent systems on parallel inference and verification workloads.

Model	Benchmark	OpenHands	Uni-Agent	Setting
Qwen3-Coder-30B	SWE-Bench Verified	-	49.2	Avg@4, 100 turns, 128K
Qwen3-Coder-480B	SWE-Bench Verified	62.4	64.2	Avg@4, 500 turns, 256K
Qwen3-Coder-Next	SWE-Bench Verified	66.6	67.6	Avg@4, 300 turns, 128K
Qwen3.5-35B-A3B	SWE-Bench Verified	62.0	68.4	Avg@1, 300 turns, 128K
Qwen3.6-35B-A3B	Terminal-Bench v2	-	42.5	Avg@1, 200K

Agent Reinforcement Learning

Uni-Agent supports agent RL training with the same interaction stack used at inference time. We provide fully async training recipes across multiple tasks, models and datasets, with GRPO/GSPO-style objectives and partial rollout support. Example scripts are available in examples/agent_train.

Model	Dataset	Method	Setting	Base	RL
Qwen3-30B-A3B-Instruct	R2E-Gym	GSPO	Fully Async, 100 turns, 128K	22.2	36.8
Qwen3-Coder-30B-A3B-Instruct	R2E-Gym	GSPO	Fully Async, 100 turns, 128K	46.2	52.0
Qwen3.5-9B	SWE-reBench	GRPO	Fully Async, 100 turns, 128K	53.8	59.2

More training dynamics, including reward, validation score, and average-turn curves, are available in the agent training guide.

Roadmap 🗺️

The roadmap below highlights the next major directions for Uni-Agent.

Environment Support

• Local deployment support.
• Modal deployment support.
• More cloud deployment backends (e.g., Yuanrong Sandbox Management System).

Tool and Task Support

• GUI tool support.
• Integration of Skills.
• More built-in tools and task patterns.

Model Support

• DeepSeek model support.
• Multimodal model support.

Agent Integration

• Black-box integration of additional third-party agents (Ref: RFC #5790).

Performance Optimization

• Optimize Agentic RL rollout performance (Ref: Issue #6383).

Acknowledgement 🙏

Uni-Agent's large-scale parallel interaction and verification rely on remote sandbox backends. We gratefully acknowledge:

• veFaaS: Volcengine Function-as-a-Service, used as a serverless backend for elastically launching agent sandboxes at scale.
• Modal: serverless cloud compute used to spin up isolated, reproducible sandbox environments for agent execution and evaluation.

Citation 📚

If you find the project helpful, please cite:

@misc{uniagent_github,
  author       = {Yuyang Ding and Bo Wen and Guangming Sheng and Xibin Wu and Juntao Li and Min Zhang and Uni-Agent Contributors},
  title        = {Uni-Agent: Build, Run, and Train Agents at Scale},
  year         = {2026},
  howpublished = {\url{https://github.com/yyDing1/uni-agent}},
  note         = {GitHub repository. Supervisor: Xibin Wu and Juntao Li},
  urldate      = {2026-03-27}
}

Contributing 🤝

Community contributions are welcome. See CONTRIBUTING.md for guidelines on how to get started.