---
title: "Caveman vs Braintrust for agent evaluation and cost improvement"
description: "Braintrust makes sense when evaluations and production observations need to inform each other. Caveman's public local tools make sense when you want to reduce eligible agent context while keeping the "
canonical: https://caveman.so/compare/braintrust
last-updated: 2026-09-07
---

# Caveman vs Braintrust for agent evaluation and cost improvement

Braintrust makes sense when evaluations and production observations need to inform each other. Caveman's public local tools make sense when you want to reduce eligible agent context while keeping the rest of your development workflow. Caveman Platform, in private development, brings that cost question into a broader view of agent work and proposed improvements.

If your team already relies on Braintrust experiments and scorers, use them to evaluate Caveman. Replacing the evaluation system at the same time as the optimization removes a useful control.

## Compare more than a dashboard

| Decision | Braintrust | Caveman |
| --- | --- | --- |
| Build an evaluation workflow | Datasets, experiments, scorers, and production logs | Local benchmarkable tools; private Platform evaluation workflows |
| Investigate a failure | Observability and Loop-assisted investigation | Platform evidence and intervention workflows in private development |
| Manage prompts | Prompt development and management workflows | Do not assume a replacement for an existing prompt registry |
| Reduce tool-output context | Application or agent changes | Public recoverable compression on supported paths |
| Relate spend to engineering work | Verify against the evidence your integration provides | A central Platform use case to validate in a pilot |

[Braintrust's Loop](https://www.braintrust.dev/docs) helps investigate results and build evaluation assets. If your team uses it, include that workflow in the comparison: how quickly can you find a failure, propose a change, and check it against representative tasks?

## Keep the scorer when testing compression

Suppose an agent reads a large repository report and returns a patch. A compression experiment should keep the repository state, task, model, tools, and acceptance test fixed. Only the context path changes.

A smaller input is encouraging. A passing patch with lower total usage is stronger evidence. If the agent retries, recovers original content, or produces a patch that needs repair, count that work. Failed tasks remain failures even if they were cheap.

Braintrust can remain the place where your team inspects the experiment. Caveman's local reports supply another view with their own measurement basis. Reconcile run identifiers and units rather than expecting every displayed dollar amount to match automatically.

## Evaluate suggested fixes with independent cases

Both an analysis tool and a human can overfit to the failure in front of them. A proposed prompt edit may make that example pass while weakening ordinary tasks. Keep unseen cases and rerun the existing acceptance suite.

For a Caveman Platform pilot, follow a proposal back to the captured evidence and inspect the scope of the intervention. Confirm what must be approved and what actually executed. A proposed saving, a successful test, and a provider bill are three different records.

The [agent evaluation guide](/guides/agent-evaluations) covers this separation without requiring a particular vendor.

## Choose Caveman for a specific cost hypothesis

Try local Caveman when tool output or repeated context is a measurable part of your agent's cost. Consider a Platform pilot when your team needs to connect that cost with ownership, delivery, and a tested intervention.

Keep Braintrust where its experiments, datasets, scorers, or prompt workflows already work. Moving those assets is justified only when the replacement improves the job enough to pay for the migration.

The [Braintrust switching guide](/switch/braintrust) starts from an existing experiment and shows which artifacts to retain before changing the collection or review workflow.