DeepEval
Open-source pytest-style evaluation framework for LLMs and agents
Overview
DeepEval, by Confident AI, is an open-source LLM evaluation framework that works like pytest for AI systems, letting teams unit-test outputs in CI/CD. It ships 50+ research-backed metrics including hallucination, faithfulness, answer relevancy, toxicity and bias, plus conversational, multimodal and agent-trace evaluations and synthetic test-data generation. It is Apache 2.0 licensed and integrates with major providers and agent frameworks.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 4 Jun 2026 and may be out of date or incomplete.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.
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 has become a de facto standard for code-first LLM evaluation, largely because the pytest mental model lets engineers fold AI tests into existing CI without learning a new paradigm. Its 50+ research-backed metrics cover RAG, agents, safety and multimodal cases.
The open-source framework is genuinely capable on its own; the commercial Confident AI platform adds dashboards and production monitoring. For any team that wants regression testing for prompts and agents, it is a near-default starting point.
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 Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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