attest vs DeepEval¶
DeepEval is a broad, open-source framework for evaluating LLM apps. attest does one narrower job: it checks a tool-using agent's answer and actions against the outputs its tools actually returned. If you're looking for a DeepEval alternative for that specific job, this page says what differs. For most teams the two are complements, not substitutes.
DeepEval's features below come from its README and the DeepTeam README, checked on 1 November 2026.
Side by side¶
| attest | DeepEval | |
|---|---|---|
| License | AGPL-3.0 | Apache-2.0 |
| Shape | Library and CLI that grade a recorded run and print JSON | pytest-style framework (assert_test, deepeval test run) and evaluate() |
| Scope | Tool-using agent runs and AI code review output | RAG, agentic, multi-turn, multimodal and custom metrics |
| Agent checks | Claims against tool outputs; allowed tools; error recovery and errors reported as success; injection in tool outputs; role adherence; project policies | Task Completion, Tool Correctness, Argument Correctness, Goal Accuracy, Step Efficiency, Plan Adherence, Plan Quality, Tool Use |
| Grounding | Each claim checked against the run's tool outputs and artifacts | Faithfulness and Hallucination compare the output with provided context |
| How it grades | A model sees one claim batch and the evidence, never the agent's reasoning; tool use and the injection scan need no model | Metrics "use LLM-as-a-judge and other NLP models"; G-Eval and DAG for custom judge metrics |
| Output | A pass, review or block verdict with a quote for each failure | Metric scores with thresholds and reasons |
| Prompt injection | Scans tool outputs and files for payloads; optional check that the agent obeyed | Red teaming, including prompt injection attacks, in the companion DeepTeam project |
| UI and tracing | None | @observe tracing; hosted Confident AI platform |
| Measured accuracy | Not yet measured on a labelled benchmark | Not covered here |
Where DeepEval is the better fit¶
- You want many metrics in one framework: RAG, conversational, multimodal and custom LLM-as-a-judge metrics.
- You want tests that read like unit tests and run under pytest.
- You want hosted dashboards, production trace evaluation and annotation through Confident AI.
- You want to generate adversarial attacks against your app (DeepTeam).
Where attest is the better fit¶
- You want each failure tied to a quote from a specific tool output, so a person can check it in seconds.
- You want tool errors followed through the run: retried, replaced by a declared fallback, reported, ignored, or reported as success when they failed.
- You want to scan fetched pages, files and images for injected instructions without an API key, with an optional model-backed deep check of whether the agent acted beyond what it was asked to do.
- You want a
pass,revieworblockverdict and JSON you can gate CI on withjq.
Using both¶
attest reads a plain Trajectory, and DeepEval reads its own test cases, so the same recorded
run can go through both. A common split is DeepEval for broad quality metrics and attest as a
gate on tool-grounded correctness:
from attest import Attest
report = Attest().evaluate(run, checks=("faithfulness", "tool_use", "injection"))
if report.verdict == "block":
raise SystemExit(f"blocked by {report.blocking}")
Where attest is narrower¶
attest has no UI, tracing, user-facing dataset management or synthetic data generation, and fewer checks than DeepEval. Its accuracy hasn't been measured on a labelled benchmark yet, and it trusts that the run it reads is authentic and complete. See Limitations and the full comparison of AI agent evaluation tools.