The HoneyHive alternative
that enforces automatically
HoneyHive gives you rich eval workflows and session recording. But nothing stops a PR from merging after a regression. Refine AI is the gate that blocks it automatically.
At a glance
Why teams switch from HoneyHive
HoneyHive is great for human-in-the-loop quality review. These are the moments that model breaks down.
Eval without enforcement is just data
HoneyHive generates great eval scores and session traces. But nothing stops a PR from merging after a regression. There's no feature that fails a GitHub check. You see the data, then decide manually. Refine AI skips the decision — the assertion either passes or fails the PR.
Manual review cycles slow down shipping
The HoneyHive workflow is: run evals → check dashboard → route to reviewer → decide. For teams shipping agents daily, this becomes a bottleneck. Refine AI's workflow: push PR → assertions run → PASS or FAIL. No human in the critical path for routine checks.
LLM judges add cost and flakiness at CI scale
HoneyHive's quality metrics use LLM judges. At CI scale (every PR, multiple scenarios), this adds latency and token cost. The same scenario can score differently on different runs. Refine AI's structural checks complete in milliseconds and never flake.
How Refine AI is different
Automatic PR blocking
No reviewer needed for structural regressions. The check fails and the PR is blocked until resolved.
No LLM judge cost
Deterministic analysis of run traces. Zero tokens consumed in CI, zero flakiness from judge variance.
Agent behavior-level checks
step_count, tool_calls, loop_risk — the actual failure surface of production agents.
Baseline delta on every PR
Compare HEAD vs main automatically. See exactly what changed, not just a score.
Who each tool is built for
Use HoneyHive if…
- →You need rich human-in-the-loop feedback and annotation workflows
- →Session recording and manual quality review are part of your process
- →You want a polished eval dashboard for stakeholder reporting
Use Refine AI if…
- →You want automated CI enforcement without manual review cycles
- →You need zero-flakiness assertions on agent structural behavior
- →You want regressions caught at the PR, not the production dashboard
Get started in 5 minutes
One YAML step. Automatic PR enforcement on every push.
- name: Assert agent behavior
uses: agentdbg/agentdbg-action@v1
with:
baseline: main
checks: step_count,tool_calls,loop_risk,cost,latencyAutomatic enforcement, zero manual review
Stop routing regressions to human reviewers. Let CI catch them before they merge.
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