DeepEval

DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations can run in CI/CD or as Python scripts. The site lists more than 50 metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity, and bias. The framework evaluates text, images, and audio, including conversational and voice use cases, with methods such as G-Eval, DAG, QAG, and JevEval. It can create synthetic goldens from a knowledge base and simulate conversations across user personas. For agent workflows, DeepEval traces steps for grading and inspection in the terminal and test runner. Integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, and other agent frameworks, alongside a wide range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; opting out is supported through an environment variable. The maker describes DeepEval OS as limited to pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals.

DeepEval provides a local and CI/CD-oriented way to define and run evaluations across several modalities. Its pre-production scope and engineer-owned runner are important boundaries for teams assessing the open-source offering.

DeepEval suits developers building repeatable evaluations for AI systems, especially those running tests in Python or CI/CD. The maker describes its open-source offering as for pre-production testing and provides separate enterprise capabilities through Confident AI Evals.

DeepEval is a free, open-source framework for evaluating LLM applications and agents in Python workflows. It best suits engineers building repeatable pre-production checks across text, images, or audio. Its breadth and CI/CD fit are compelling; its local, engineer-owned workflow is a clear boundary.

Overview

DeepEval turns evaluations into Pytest-native tests that can run in CI/CD or as Python scripts. That makes it a natural choice for teams wanting model checks alongside software development and regression runs, rather than a separate manual review process. The maker describes DeepEval OS as limited to pre-production testing, with results in local files and an engineer-owned runner, so it is not a substitute for production monitoring or a centralized collaboration platform.

Key features

The framework covers more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity, and bias. Support for text, images, and audio, including conversational and voice evaluations, gives teams working across modalities a broad set of checks in one framework. G-Eval, DAG, QAG, and JevEval offer different evaluation techniques; that flexibility is useful when a team wants to shape its approach rather than adopt one fixed method.

Teams can generate synthetic goldens from a knowledge base and simulate conversations across user personas, which can help build evaluation cases. Agent steps can be traced, graded, and inspected in the terminal and test runner. Integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. Evaluation model integrations span providers and deployment options such as OpenAI, Anthropic, Amazon Bedrock, Gemini, Ollama, vLLM, and LiteLLM.

The framework supports tool-call checks, trace ingestion, safety evaluations, regression runs, custom metrics, LLM-as-a-judge, human review workflows, prompt versioning, and CI/CD integration. These capabilities make it adaptable to engineering evaluation pipelines, but they do not change the local and pre-production scope of DeepEval OS.

By default, DeepEval sends basic telemetry to PostHog; it excludes personally identifiable information and stored results, and teams 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. These distinctions matter for teams assessing where evaluation data and telemetry go.

Pricing

DeepEval is free: the DeepEval plan is free. It is an Apache 2.0-licensed open-source evaluation framework with a local and CI/CD test runner. There are no paid tiers or usage caps described for this plan, so it is the straightforward fit for teams able to operate their own evaluation workflow.

Organizations seeking a managed enterprise layer can consider Confident AI Evals, which is available on the maker's cloud or self-hosted on customer infrastructure. Its enterprise offering includes shared workspaces, no-code evaluation workflows, and annotation queues, with custom pricing. Enterprise security features include SSO, role-based access control, granular permissions, audit logs, SOC 2 Type II, GDPR compliance, and custom data retention. Prospective customers can book a demo.

Platforms

DeepEval supports Linux, macOS, and Windows, as well as self-hosted deployment. Its Python-script and Pytest-native execution model is most relevant to teams that want evaluations in their existing development and CI/CD routines.

Who it's for

DeepEval is best for engineering teams that want a free, extensible way to run pre-production evaluations and regression checks for LLM applications or agents. Its modality coverage, synthetic case generation, tracing, and integration breadth are useful for teams working across models and agent frameworks. It is a weaker fit for teams whose priority is centralized production monitoring, shared evaluation operations, or non-engineer-owned workflows.

Pros and cons

• Pros: More than 50 metrics and support for text, image, and audio cover a wide range of evaluation needs.
• Pros: Pytest-native checks and CI/CD integration make repeatable pre-production tests part of an engineering workflow.
• Pros: Synthetic goldens, persona-based conversation simulation, and agent tracing help teams construct and inspect evaluations.
• Cons: DeepEval OS is explicitly pre-production focused, with local results and an engineer-owned runner, limiting its role for production monitoring and shared operations.
• Cons: Default telemetry goes to PostHog, although PII and stored results are excluded and opt-out is supported.

Alternatives

For a managed or team-oriented alternative, Confident AI has a free plan with two user seats, one project, five test runs per week, and one GB-month of trace spans; its enterprise offering adds shared workspaces and no-code workflows. Braintrust may suit teams seeking a free hosted option with unlimited users and projects, alongside 1 GB processed data, 10,000 scores, and 14-day retention.

Promptfoo is another free option, with a Community plan that includes 10k red-team probes per month and local or self-hosted runs. Giskard offers a free open-source library for local deployment, including a basic LLM vulnerability scan and RAG evaluation report. Galileo has a Pro plan at $100 a month billed yearly, with 50,000 traces per month, standard RBAC, advanced analytics, and dedicated Slack support.

Maxim AI offers a free Developer plan with up to three seats, one workspace, up to 10k logs per month, three-day retention, and email support. Parea AI has a free plan for up to two team members, 3k logs per month with one-month retention, and 10 deployed prompts. Arena (formerly Chatbot Arena) is a free web-based option for public leaderboard and head-to-head battles, with a daily usage limit that resets after 24 hours.

For more options, browse LLM Evaluation Tools, AI LLM Evaluation Tools, and AI Agent Evaluation Tools.

Verdict

Choose DeepEval if your engineering team wants a free, open-source way to build broad, multimodal evaluations into Python tests and CI/CD. Its main advantage is the range of checks and evaluation methods; look elsewhere if you need production-oriented monitoring or a collaborative, centrally managed workflow.