DeepEval
Open sourceUpdated recentlyPricing may have changed — not yet verifiedPytest-style unit testing framework purpose-built for LLM outputs.
by Confident AI · Primary workflow: Code-native RAG/LLM metric evaluation inside Python test suites
scored
No verified editorial score yet. Scores appear only after documented evaluation.
What it is
DeepEval brings pytest-like structure to LLM evaluation: metrics (faithfulness, relevance, toxicity and more) run against your cases, integrate into CI, and power a hosted 'Confident' dashboard for teams wanting UI aggregation.
Editorial basis: vendor documentation and public information reviewed by our editors; written originally for this catalog. Hands-on testing has not yet been performed. Last editorial update 2026-08-26.
Editorial assessment
- Python teams embedding evals into existing pytest flows
- RAG faithfulness measurement
- Non-Python stacks
- Visual playground-first workflows
- Metric library maps to RAG failure modes
- pytest integration feels native
- Metric implementations are heuristics/models — validate against your domain
- Dashboard optional but pushed
Hands-on evidence
Vendor information reviewed; hands-on testing not yet completed.Vendor information reviewed; hands-on testing not yet completed.
When we test this tool ourselves, this section will document the test date, exact scenario, setup experience, example output, integration result and discovered limitations — with reproducibility notes so you can replicate it.
Read our evidence policy →Pricing
Confident AI cloud
See vendorUnverifiedOSS
FreeUnverified
Pricing reflects publicly listed information at research time and is marked unverified until an editor re-checks the vendor’s pricing page. Always confirm on the official site.
Integrations & compatibility
- Deployment
- Library, Self-hosted CI, Cloud
- Platforms
- Windows, macOS, Linux
- Languages / SDKs
- Python
- API available
- Yes
- Learning curve
- Low
- Documentation quality
- GOOD
- Team fit
- Solo, Small team, Mid-size
- Support
- See vendor
Security, privacy & AI transparency
Metrics often use LLM-as-judge internally; docs discuss reliability caveats.
Vendor response
Vendors can respond to listings and reviews through the vendor portal. Responses are moderated and labeled. No response has been received for this listing yet.