<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> Uni-Agent provides a unified, all-in-one framework for building, running, and training general AI agents at scale. It targets developers creating complex, real-world agent applications, offering a streamlined stack that simplifies development and enables massive parallelization for inference and training workloads.

How It Works

The framework uses a decoupled model, tool, and env interaction loop, allowing independent component swapping while composing a cohesive system. This architecture facilitates high-throughput, stable parallel execution for over 1000 concurrent agent tasks and reuses the stack for both large-scale inference and advanced RL training paradigms like fully-async and partial rollout.

Quick Start & Requirements

Installation involves cloning submodules, then installing the local package and dependencies: git submodule update --init --recursive, pip install --no-deps -e ./verl, pip install swe-rex loguru pydantic pydantic_settings aiohttp. A full installation guide is in the docs. While specific hardware isn't mandated, large-scale parallel interaction implies significant computational resources are beneficial.

Highlighted Details

• Supports high-throughput, stable parallel inference and verification for 1000+ concurrent agent tasks.
• Features a lightweight live dashboard for real-time monitoring of large parallel runs.
• Enables agent reinforcement learning training using the same interaction stack, with advanced recipes for fully async training, GRPO/GSPO objectives, and partial rollouts.
• Demonstrates competitive performance on SWE-Bench Verified and Terminal-Bench v2 benchmarks against existing agent systems.

Maintenance & Community

The project lists "Uni-Agent Contributors" and provides a roadmap detailing future enhancements in environment support, tool integration, model capabilities, and performance optimization. Contribution guidelines are available in CONTRIBUTING.md.

Licensing & Compatibility

The provided README does not explicitly state the project's license. This omission requires further investigation for commercial use or integration into closed-source projects. Compatibility is built upon the verl release.

Limitations & Caveats

The project's long-term vision includes advanced agents like "Milo" and "Miko," indicating some aspirational features are not yet implemented. The roadmap highlights planned additions like GUI tool support, broader cloud backend integration, and multimodal model support, implying these are current gaps.