How agentprdiff compares¶
The LLM-evaluation space is crowded, and several of the tools in it are excellent. This page is an honest map: what agentprdiff does that they don't, what they do that agentprdiff doesn't, and when you should use them instead. Claims here reflect the tools as of this writing — check their docs for current capabilities.
The one-sentence difference¶
Most eval tools answer "how good is my agent?" with scores on a dashboard. agentprdiff answers "what did my agent stop doing since the last known-good commit?" with a diff in the pull request. Scores need interpretation; diffs demand action.
At a glance¶
| agentprdiff | promptfoo | DeepEval | Braintrust | |
|---|---|---|---|---|
| What it is | Snapshot tests for agent behavior | Prompt/output eval runner | pytest-style metric framework | Hosted eval platform |
| Open source | MIT | MIT | Apache 2.0 (cloud: Confident AI) | SaaS (free tier) |
| Where results live | JSON baselines committed to your repo | Config + web viewer / CI output | Test results / cloud dashboard | Their platform |
| Regression unit | Assertion flips vs a git baseline, per case | Score deltas between runs | Metric thresholds per test | Score thresholds vs experiment history |
| Agent-trace assertions (tool calls, sequences) | Built-in graders (tool_called, tool_sequence, …) |
Partial (provider-dependent) | Agent metrics (judge-scored) | Via custom scorers |
| Judge dependence | Optional — 9 of 10 graders deterministic; --strict-judge refuses silent judge downgrade |
Mixed (asserts + judges) | Judge-centric metrics | Judge/scorer-centric |
| CI gate | Exit 1 + GitHub Action posting the behavioral diff as a PR comment | CI-runnable | pytest exit codes | GitHub Action with merge blocking |
| Reviewer artifact | The baseline diff in the PR itself | Web UI / terminal | Test log / dashboard | Dashboard links |
| Account required | No | No (cloud optional) | No (cloud optional) | Yes |
When to use them instead¶
Use promptfoo when the thing under test is a prompt, not an agent: comparing many prompt × model combinations side by side, red-teaming, or jailbreak scanning. Its matrix-eval UX is more mature than anything agentprdiff offers, and its red-team tooling has no counterpart here at all. Where agentprdiff differs: baselines are committed and diffed per-PR (promptfoo compares runs, but the known-good reference isn't a git artifact your reviewers see in the diff), and trace-level assertions — which tools fired, in what order — are first-class.
Use DeepEval when you want research-grade metrics — hallucination, faithfulness, RAG relevancy — inside pytest. Its metric library is far larger than agentprdiff's ten graders. Where agentprdiff differs: DeepEval's metrics are predominantly LLM-judged (with the cost and nondeterminism that implies per CI run), and its open-source form doesn't keep a persistent known-good baseline across runs — each run scores fresh. agentprdiff is the inverse: deterministic first, judge optional, baseline permanent.
Use Braintrust (or LangSmith / Langfuse / Arize Phoenix) when you need the full lifecycle: production observability, dataset curation, experiment history, team dashboards. These platforms do far more than agentprdiff ever will — that's their job. Where agentprdiff differs: no account, no data leaving your repo, free at any scale, and the review artifact is a git diff rather than a dashboard link. Teams commonly run both: a platform for observability, agentprdiff as the free per-PR behavioral gate.
Use RAGAS for reference-free RAG-pipeline scoring specifically; it's a metrics library, not a regression harness, and pairs fine with agentprdiff.
What agentprdiff deliberately doesn't do¶
No hosted dashboard, no dataset management, no production tracing, no prompt playground, no red-teaming, no ELO/pairwise ranking. The roadmap keeps it that way — the project's bet is that a narrow, free, deterministic CI gate that reviewers see inside the PR is worth more than a broader platform you have to adopt.
Not to be confused with "Agent Diff"¶
There's a separate project called Agent Diff (agent-diff on PyPI,
agentdiff.dev) — sandboxed replicas of third-party APIs (Slack, Linear,
Box, Google Calendar) for evaluating and RL-training agents against
simulated services. Different tool, different job: Agent Diff gives you
environments to run agents in; agentprdiff gives you regression
tests for the agent you already run in your own environment. The names
are close; the products aren't. (If you're building suites for an agent
that talks to those APIs, they even combine: run your agent against an
Agent Diff sandbox inside an agentprdiff case.)
The evidence¶
The claims above are testable. The benchmark is a reproducible experiment — a model downgrade caught breaking an agent's format contract while every answer stayed correct, with all-deterministic assertions — that you can re-run for a few cents.