# AgentClash vs DeepEval

DeepEval is categorized here as LLM eval framework. AgentClash focuses on complete, tool-using agent trajectories in isolated sandboxes.

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

## Where each tool fits

DeepEval is an excellent open-source, Pytest-style LLM evaluation framework — 50+ research-backed metrics including tool correctness, task completion, and multi-turn simulation, run locally or in CI. Reach for it when you want code-first, metric-on-trace evals inside your existing test suite.

## Capability comparison

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

## Common questions

### Is AgentClash a DeepEval alternative?

AgentClash and DeepEval overlap but solve different problems. DeepEval is a LLM eval framework 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, DeepEval may be all you need.

### What is the difference between AgentClash and DeepEval?

DeepEval is an excellent open-source, Pytest-style LLM evaluation framework — 50+ research-backed metrics including tool correctness, task completion, and multi-turn simulation, run locally or in CI. Reach for it when you want code-first, metric-on-trace evals inside your existing test suite. 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 DeepEval together?

Yes. Many teams keep DeepEval 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.