This platform enables users to convert proprietary software and web applications into reinforcement learning environments for agent training and evaluation. It provides a unified API endpoint compatible with OpenAI clients to access various large language models for inference testing. The infrastructure supports scalable, concurrent environment execution with low latency for rapid benchmarking and analysis.
Funding
Funding not disclosed
Founders
Product
Problem
Evaluating AI agents across diverse environments and tasks is complex and time-consuming, hindering rapid iteration and performance improvement. Existing methods often require significant setup for each evaluation cycle, limiting the speed at which agents can be refined and deployed.
Solution
HUD provides a comprehensive platform for evaluating AI agents, enabling accelerated development through efficient orchestration of evaluation environments. The platform supports a wide array of pre-built benchmarks and allows for the creation of custom tasks and environments, catering to specific agent capabilities and workflows. By leveraging a robust SDK and a scalable infrastructure, HUD facilitates rapid iteration cycles, allowing developers to quickly identify regressions and optimize agent performance. Integration with existing agent stacks and models is streamlined, ensuring that developers can evaluate their agents using their preferred tools and architectures.
Target Audience
The primary users are AI researchers and developers building and evaluating AI agents, particularly those focused on computer use, web navigation, and complex reasoning tasks.
Features
- Python SDK for programmatic agent evaluation and task definition.
- Support for multiple environment types including browser-based (e.g., WebVoyager, Mind2Web), desktop OS (e.g., OSWorld), and custom Dockerfile environments.
- Pre-built tasksets for common agent evaluation benchmarks such as GAIA, WebVoyager, Mind2Web, and OSWorld.
- Capability to define custom tasks with specific setup and evaluation criteria.
- Framework for building and integrating custom evaluation environments.
- Automatic tracing of Model Context Protocol (MCP) calls for detailed agent behavior analysis.
- Cloud-based platform for managing and visualizing evaluation results and leaderboards.
- API key management for secure access to evaluation services.
- Support for integrating various agent architectures and models, including LLMs and VLMs.