DeepEval

DeepEval is a free, open-source framework for engineers building repeatable LLM and agent checks into Python workflows. Its broad metrics and multimodal evaluations make it a strong fit for pre-production quality gates; teams that need production monitoring or centralized collaboration should look elsewhere.

Overview

Pytest-native evaluations run in CI/CD or as Python scripts, giving engineering teams a way to make model checks part of their development process. DeepEval OS keeps results in local files and relies on an engineer-owned test runner, a sensible fit for pre-release regression testing but a poor standalone choice for production oversight. The maker explicitly describes its scope as pre-production testing.

Key features

DeepEval’s 50+ research-backed metrics cover hallucination, faithfulness, answer relevancy, summarization, toxicity, and bias. Teams can evaluate text, images, and audio, including conversational and voice use cases, and choose among G-Eval, DAG, QAG, and JevEval techniques. That breadth suits teams testing varied AI systems; it also means users must decide which checks fit their own evaluation goals.

Synthetic goldens generated from a knowledge base and simulated conversations across user personas can help teams develop test cases. Agent-step tracing supports grading and inspection in the terminal and test runner, which is useful for debugging pre-production behavior without moving evaluation into a separate workflow.

Integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. Evaluation models can come from providers including OpenAI, Azure OpenAI, Ollama, OpenRouter, Anthropic, Amazon Bedrock, Gemini, DeepSeek, Vertex AI, Grok, Moonshot, Portkey, vLLM, LM Studio, and LiteLLM. This range can accommodate varied stacks, while the framework’s value remains centered on developer-run testing.

By default, DeepEval sends basic telemetry to PostHog; it excludes personally identifiable information and stored results. Teams that prefer not to send this telemetry can opt out with DEEPEVAL_TELEMETRY_OPT_OUT=1. Data sent to Confident AI is stored in databases in the maker’s private AWS cloud, except for organizations on the VIP plan.

Pricing

DeepEval is free: its plan costs free and provides an open-source LLM evaluation framework under the Apache 2.0 license, with a local and CI/CD test runner. The plan includes tool-call checks, trace ingestion, safety evaluations, regression runs, custom metrics, LLM-as-a-judge, human review workflows, prompt versioning, and CI/CD integration. It is a compelling option for engineers who want evaluation capabilities in their own development workflow without a paid plan; its pre-production scope is the important trade-off.

For organizations seeking a managed collaboration layer, the separate enterprise offering is available on Confident AI Evals with custom pricing. It can be self-hosted on a customer’s infrastructure or run in the maker’s cloud. Enterprise features include SSO, role-based access control, granular permissions, audit logs, SOC 2 Type II, GDPR compliance, and custom data retention. Shared workspaces, no-code evaluation workflows, and annotation queues address needs that the engineer-owned open-source runner does not.

Platforms

DeepEval supports Linux, macOS, and Windows, as well as self-hosted deployment. Its SDK language support and deployment options are both described as broad, which fits teams working across development environments, though the core workflow remains oriented around Python and CI/CD.

Who it's for

DeepEval is best for engineering teams that want to build repeatable pre-production tests for LLMs, agents, or multimodal systems, especially when they need a wide choice of metrics, evaluation methods, and model-provider integrations. Teams that require production monitoring, shared workspaces, or no-code collaboration should consider a platform layer instead.

Pros and cons

• Pros: More than 50 metrics, multiple evaluation techniques, and text, image, and audio support provide substantial testing range in one framework.
• Pros: Pytest-native checks, regression runs, and CI/CD integration make evaluations suitable for repeatable development gates.
• Pros: Synthetic test-case generation and agent-step tracing help teams build and inspect evaluations.
• Cons: DeepEval OS is limited to pre-production testing, with local results and an engineer-owned runner, so it is not a production-monitoring or centralized collaboration solution.
• Cons: Default telemetry requires an opt-out setting for teams that do not want basic PostHog telemetry sent.

Alternatives

Promptfoo is worth considering for teams that want a free community plan with 10k red-team probes per month, all LLM evaluation features, and local or self-hosted runs.

Giskard may suit teams looking for an open-source library with local deployment and a basic adversarial LLM vulnerability scan and RAG evaluation report.

Braintrust is another freemium option; its Starter plan includes 1 GB processed data, 10,000 scores, 14-day retention, and unlimited users, projects, and datasets.

Confident AI is a related choice for teams seeking a hosted collaboration layer: its free plan has 2 user seats, 1 project, 5 test runs per week, and 1 GB-month of trace spans.

Galileo may be a better fit for teams prioritizing a paid Pro plan with 50,000 traces per month, standard RBAC, advanced analytics, and dedicated Slack support.

Maxim AI offers a free Developer plan with up to 3 seats, 1 workspace, 10k logs per month, 3-day data retention, and email support.

Parea AI is an alternative for small teams wanting a free plan with 2 team members, 3k logs per month with one-month retention, 10 deployed prompts, and Discord community support.

LiveBench is another free option.

For category comparisons, see LLM Evaluation Tools, AI Agent Evaluation Tools, and AI LLM Evaluation Tools.

Verdict

Choose DeepEval if your engineers want a free, flexible framework for repeatable pre-production evaluation across LLMs, agents, and multimodal systems. Its breadth of metrics and developer-owned CI/CD workflow are the main reasons to pick it; look elsewhere when production monitoring or centralized collaboration is essential.