The Braintrust alternative
that gates your PRs
Braintrust is excellent for prompt engineering and dataset management. But if you need your CI to automatically block a PR when your agent takes 4× more steps — that's Refine AI's job.
At a glance
Why teams switch from Braintrust
Braintrust is a great eval tool. These are the moments teams realize they need something different.
Braintrust shows problems, it doesn't prevent them
Braintrust is a dashboard. After a regression ships, you open the dashboard and see it. By then, the PR has merged and users are already affected. Refine AI fails the PR at the source — the merge is blocked until the regression is fixed.
LLM judges aren't reliable in CI
Braintrust's eval metrics use LLMs to score outputs. This works for exploratory eval but becomes a liability in CI: the same code can pass Monday and fail Tuesday because the judge model returned slightly different scores. Refine AI's checks are binary — step_count either exceeded threshold or it didn't.
Prompt playgrounds don't cover multi-step agents
Braintrust shines for prompt-in/output-out evaluation. Agents that span multiple steps, invoke tools, and retry on failure have a completely different failure surface. Step count explosions and unexpected tool invocations aren't "bad outputs" — they're structural regressions Braintrust wasn't built to catch.
How Refine AI is different
Fails the PR automatically
No dashboard to check. The GitHub check goes red and the PR is blocked until the regression is resolved.
Zero LLM judge cost
Structural checks analyze the run trace directly. No API calls to a judge model — zero added cost in CI.
Baseline delta comparison
Every assertion compares HEAD vs main. You see exactly what changed: step_count 6 → 22 (+267%).
Any agent, any framework
Wrap any agent in CI. CrewAI, AutoGen, LangChain, custom Python — the check doesn't care.
Who each tool is built for
Use Braintrust if…
- →You're building prompt-heavy LLM features that need human annotation
- →Your team does a lot of prompt A/B testing and dataset curation
- →Output quality scoring (not structural regression) is your primary concern
Use Refine AI if…
- →You're shipping AI agents to production and need CI to catch regressions
- →You want PRs automatically blocked when agent behavior changes structurally
- →You need zero-flakiness, zero-LLM-cost assertions in your pipeline
Get started in 5 minutes
Add one step to your GitHub Actions workflow.
- name: Assert agent behavior
uses: agentdbg/agentdbg-action@v1
with:
baseline: main
checks: step_count,tool_calls,loop_risk,cost,latencyStop catching regressions in dashboards
Let CI catch them before they ship. Your PR fails, your engineer is notified, your users never see the regression.
Add to GitHub Actions