# AgentClash vs OpenAI Evals

OpenAI Evals is categorized here as prompt eval. AgentClash focuses on complete, tool-using agent trajectories in isolated sandboxes.

Source: https://www.agentclash.dev/compare/agentclash-vs-openai-evals
Markdown export: https://www.agentclash.dev/md/compare/agentclash-vs-openai-evals

## Where each tool fits

OpenAI Evals is a solid open framework for building and running model and prompt evals, especially within the OpenAI ecosystem. It fits when you're scoring model outputs against datasets.

## Capability comparison

| Capability | AgentClash | OpenAI Evals |
| --- | --- | --- |
| Multi-turn agent loops | Yes | Partial |
| Sandboxed tool execution | Yes | No |
| Same-task concurrent eval | Yes | No |
| Trajectory scoring | Yes | No |
| Cross-provider tool-call normalisation | Yes | No |
| Four-vantage composite verdict | Yes | Partial |
| Failures auto-promote to regression | Yes | No |

## Common questions

### Is AgentClash a OpenAI Evals alternative?

AgentClash and OpenAI Evals overlap but solve different problems. OpenAI Evals is a prompt eval tool, while AgentClash is an agent-evaluation platform that runs agents on real tasks in a sandbox, scores the full trajectory, and gates CI on regressions. If you need to evaluate tool-using agents end-to-end, AgentClash is the closer fit; for single-call prompt and output scoring, OpenAI Evals may be all you need.

### What is the difference between AgentClash and OpenAI Evals?

OpenAI Evals is a solid open framework for building and running model and prompt evals, especially within the OpenAI ecosystem. It fits when you're scoring model outputs against datasets. AgentClash focuses on multi-turn agents that take actions: each model gets a fresh microVM, real tools, the same time budget, and a same-task eval run, and the verdict scores the trajectory — not just the final text.

### Can I use AgentClash and OpenAI Evals together?

Yes. Many teams keep OpenAI Evals for prompt-level evaluation and observability and add AgentClash for end-to-end, sandboxed agent evals and CI regression gates. They are complementary layers of an evaluation stack.