What it is
HUD (Y Combinator W25, ~15 people) is an open-source platform for building reinforcement-learning environments and evaluations for computer-use agents — agents that browse the web, edit spreadsheets, run terminal commands, navigate real applications. The pitch is straightforward: evals against live software, RL environments designed to plug into actual training pipelines, and a set of public benchmarks the team maintains directly.
Free and open source. Cloud and enterprise pricing are case-by-case for frontier labs.
Why it's different from everything else on this list
The 16 other companies we've covered are, with rare exceptions, built for production observability and offline eval of LLM features in a product. HUD operates a layer down: it measures and trains the agent itself, not the product wrapping the agent. Specifically:
- Evals run against live web apps, not stored input/output pairs.
- The team ships OSWorld-Verified (369+ real desktop tasks) and SheetBench-50 as public benchmarks — they're contributing the standard, not just consuming it.
- The product is positioned for GRPO and similar RL training pipelines, with claims of 5x model performance improvement on their public benchmarks.
- Customers are frontier AI labs, not product engineering teams.
That difference makes HUD nearly orthogonal to Braintrust, Langfuse, and the rest of the all-in-one platforms — the audiences and workflows barely overlap.
Where it shines
- Live-application evaluation. This is the clearest answer to "does my agent actually work?" — not "did it produce reasonable text," but "did it complete the task in the real software?"
- Public benchmarks. OSWorld-Verified is on track to be one of the standard CUA benchmarks. Operating that benchmark is itself a credibility moat.
- RL angle. The training-loop framing is real — they're not retrofitting an observability product into RL talk.
Where it falls short
- Operational youth. Founded in 2025. Production maturity, SOC 2 / enterprise compliance, on-call expectations — all still being built.
- Narrow audience. If you're shipping a chat feature, not training a CUA, this isn't the tool.
- Category itself is early. Computer-use agents are still a frontier-lab problem more than a production reality.
Bottom line
If your work is training or systematically benchmarking agents that operate real software — and you've ever felt like the production-eval platforms don't really fit — HUD is built for you. Outside that audience, the LLM-monitoring platforms remain the right answer.