The DeepEval alternative
without the flaky judges
DeepEval gives you pytest-style LLM tests. But when LLM judges score your CI runs, expect 15–20% flakiness. Refine AI's structural checks are deterministic — pass is pass, fail is fail, every time.
At a glance
Why teams switch from DeepEval
DeepEval has a great API. These are the failure modes teams hit when using it at CI scale.
LLM judges make CI flaky
DeepEval's metrics (faithfulness, hallucination, contextual recall) call an LLM to score outputs. Teams report 15–20% flakiness: the same code passes Monday and fails Tuesday because the judge model returned slightly different scores. Refine AI's checks are deterministic — step_count is a number, either above threshold or not.
Built for RAG, not agents
DeepEval's metric library is designed around RAG pipelines: contextual recall, faithfulness, answer relevancy. Multi-step agents have entirely different failure modes — step explosions, unexpected tool calls, infinite loops. These aren't "output quality" problems. They're structural behavior regressions.
Judge cost multiplies fast in CI
Running 20 DeepEval metrics × 50 test cases × every PR = thousands of LLM API calls per week. At scale, the eval infrastructure costs more than the product. Refine AI's structural checks cost zero LLM tokens — they analyze run traces directly.
How Refine AI is different
Zero flakiness
Structural checks are deterministic. step_count either exceeded the threshold or it didn't — no variance from run to run.
Zero LLM judge cost
Assertions run against the trace directly. No API calls to a judge model means no cost and no latency added to CI.
Agent-native checks
Built for step counts, tool call sequences, loop detection, and cost-per-run — the failure modes agents actually have.
Automatic PR blocking
No need to watch test output. The GitHub check status says PASS or FAIL and the PR is blocked accordingly.
Who each tool is built for
Use Confident AI / DeepEval if…
- →You're building RAG pipelines and care about faithfulness/relevancy metrics
- →You want pytest-style code tests with a large LLM metric library
- →Output quality scoring is more important than structural behavior
Use Refine AI if…
- →You're shipping agents and need zero-flakiness CI gates
- →LLM judge cost is a concern at your PR volume
- →You need structural behavior assertions — step count, tool calls, loop risk
Get started in 5 minutes
Replace flaky LLM judge assertions with deterministic structural checks.
- name: Assert agent behavior
uses: agentdbg/agentdbg-action@v1
with:
baseline: main
checks: step_count,tool_calls,loop_risk,cost,latencyZero flakiness. Zero judge cost.
Deterministic CI gates for agents. Your PR fails when behavior regresses — reliably, every time.
Add to GitHub Actions