# AgentClash vs Braintrust

Braintrust 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-braintrust
Markdown export: https://www.agentclash.dev/md/compare/agentclash-vs-braintrust

## Where each tool fits

Braintrust is a strong choice for prompt and LLM evaluation — datasets, scoring functions, and logging across your app's model calls. Reach for it when the unit you evaluate is a prompt or a single model response.

## Capability comparison

| Capability | AgentClash | Braintrust |
| --- | --- | --- |
| 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 Braintrust alternative?

AgentClash and Braintrust overlap but solve different problems. Braintrust 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, Braintrust may be all you need.

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

Braintrust is a strong choice for prompt and LLM evaluation — datasets, scoring functions, and logging across your app's model calls. Reach for it when the unit you evaluate is a prompt or a single model response. 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 Braintrust together?

Yes. Many teams keep Braintrust 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.