DeepEval
Pytest-style unit testing for LLM outputs, 50+ metrics, Apache 2.0
Overview
DeepEval, from Confident AI, is an open-source Python framework that unit-tests LLM and agent outputs the way pytest tests code, with 50-plus research-backed metrics for hallucination, relevancy, safety and RAG quality. It plugs into CI/CD and any major LLM provider or agent framework.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 12 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- Unit and regression testing suite
- Evals in dev and CI/CD
- LLM tracing, prompt versioning
- Custom evaluation metrics
- Online evals on live traffic
- Human annotation and chat simulations
- No-code eval workflows
- Slack/PagerDuty, Jira/Linear integrations
- Custom RBAC, SOC 2 and SSO
- On-premise deployment
- HIPAA compliance
- 24x7 dedicated support
What you can produce with DeepEval
- 50+ research-backed evaluation metrics
- Pytest-native unit and regression testing
- RAG, agent, multimodal and conversational eval support
- Synthetic test-data generation
- CI/CD integration
- Apache 2.0 licensed, no paywalled metrics
ASEAN Perspective
DeepEval in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
DeepEval's whole pitch is that evaluating an LLM shouldn't require learning a new tool — if your team already writes pytest, you already know how to write a DeepEval test. That framing, plus Apache 2.0 licensing with no metric gating, is why it's become close to a default starting point for code-first LLM testing.
Fifty-plus built-in metrics cover more ground than most rivals: hallucination, faithfulness, answer relevancy, bias, toxicity, plus conversational, multimodal and agent-trace-specific checks, alongside synthetic test-data generation for teams without a labeled eval set yet. The open-source library genuinely stands alone; Confident AI's commercial cloud layer adds dashboards, tracing and human annotation for teams that outgrow local test runs. With a claimed 150,000-plus developers and adoption at over half the Fortune 500, it's a low-commitment first eval framework to reach for.
What people say
DeepEval is built by Confident AI and framed deliberately as "pytest for LLMs" — you write assert-style test cases against metrics like answer relevancy or hallucination, and it runs inside whatever CI pipeline already gates your code. That design choice, more than any single metric, seems to be why it's become one of the most cited open-source eval frameworks in the space: engineers don't have to learn a bespoke evaluation DSL or stand up a separate service just to regression-test a prompt change.
The metric library is the other selling point — more than 50 metrics spanning RAG-specific checks (contextual precision, recall, relevancy), agent and multi-turn conversation evaluation, red-teaming and safety metrics, and multimodal support, all described as research-backed rather than ad hoc heuristics. Confident AI's own materials claim 150,000-plus developers and adoption by more than half of Fortune 500 companies, and name enterprise users including Microsoft, AstraZeneca, AXA and Boston Consulting Group; those are vendor-sourced figures rather than independently audited, but the GitHub star count backs up genuine scale — the project passed 10,000 stars in mid-2026 and sits around 12,600 as of this review, unusually high for a testing library rather than an end-user product.
The open-source core (Apache 2.0) isn't artificially limited — the full metric set, CI/CD integration and local execution work with no paywall. Confident AI's commercial platform is where the cloud pieces live: dataset hosting, custom metrics, human annotation, live-traffic monitoring and alerting start at $9.99 per user per month on the Starter tier, with usage-based Team and Enterprise tiers layering in no-code workflows, RBAC, SOC 2 and SSO. Tracing on the paid tiers is priced separately, starting around $1 per GB-month, which Confident AI markets as several times cheaper than comparable observability add-ons — a claim worth checking against your own trace volume rather than taking at face value.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including DeepEval's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with DeepEval unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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