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 for next-generation agents across both inference and training, enabling them to perceive, act, and explore complex real-world tasks.
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.
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.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.
Uni-Agent is built around a unified interaction loop with three parts: model, tool, and env.
modelis the reasoning backend that decides what to do next,toolis how themodelperceives and acts on theenvenvis 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.
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 aiohttpSee the full installation guide in the docs: Installation.
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 8765See dashboard/README.md for more details.
We compare Uni-Agent with existing agent systems on parallel inference and verification workloads.
| Model | Benchmark | OpenHands | Uni-Agent (1-Attempt, Avg@4) |
|---|---|---|---|
| Qwen3-Coder-30B | SWE-Bench_verified | - | 48.8 |
| Qwen3-Coder-480B | SWE-Bench-Verified | 62.4 | 64.2 |
| Qwen3-Coder-Next | SWE-Bench-Verified | 66.6 | 67.7 |
Uni-Agent supports agent RL training with the same interaction stack used at inference time. A representative recipe is to train Qwen3-30B-A3B-Instruct on R2E-Gym using Fully-Asynchronous RL, Partial Rollout, and GSPO. Example training scripts are available in examples/agent_train.
| Model | Dataset | Train Setup | Base | RL |
|---|---|---|---|---|
| Qwen3-30B-A3B-Instruct | R2E-Gym | GSPO + Fully-Async + Partial Rollout | 22.2 | 36.8 |
| Qwen3-Coder-30B-A3B-Instruct | R2E-Gym | GSPO + Fully-Async + Partial Rollout | 46.2 | 52.0 |
The roadmap below highlights the next major directions for Uni-Agent.
Environment Support
- Local deployment support.
- More cloud deployment backends.
Tool and Task Support
- More built-in tools and task patterns.
- GUI tool support.
Model Support
- DeepSeek model support.
- Multimodal model support.
If you find the project helpful, please cite:
@misc{uniagent_github,
author = {Yuyang Ding and Guangming Sheng and Xibin Wu and Uni-Agent Contributors},
title = {Uni-Agent: Build, Run, and Train Agents at Scale},
year = {2026},
howpublished = {\url{https://github.com/yyDing1/uni-agent}},
urldate = {2026-03-27}
}
Community contributions are welcome. See CONTRIBUTING.md for guidelines on how to get started.