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DeepEval is the open-source, Pytest-style LLM evaluation framework from Confident AI. Ship it in your test suite the same way you ship pytest — it runs 50+ research-backed metrics (hallucination, tool-use correctness, contextual precision/recall, conversational quality) offline in dev and inline in CI/CD, then optionally streams the results to Confident AI's paid cloud for team dashboards and prompt versioning.
DeepEval is the open-source, Pytest-style LLM evaluation framework from Confident AI. Ship it in your test suite the same way you ship pytest — it runs 50+ research-backed metrics (hallucination, tool-use correctness, contextual precision/recall, conversational quality) offline in dev and inline in CI/CD, then optionally streams the results to Confident AI's paid cloud for team dashboards and prompt versioning.
DeepEval is the fastest way for a Python team to add tests for their LLM app that actually mean something. The library is MIT-licensed and installs as pip install deepeval; from there you write pytest-style test cases against inputs, expected outputs, and retrieval contexts, and DeepEval runs research-backed metrics such as G-Eval, GPT-4-as-a-judge, DAG (Deep Acyclic Graph), summarization, hallucination, faithfulness, contextual precision/recall/relevancy, answer relevancy, toxicity, bias, tool-use correctness, and multi-turn conversational quality. All metrics run offline in your dev loop and can gate your CI/CD pipeline exactly the way pytest does — pass/fail, thresholds, and reports.
The paid layer is Confident AI (formerly known just as DeepEval Cloud). Verified on confident-ai.com/pricing (July 2026): Free forever at $0 with the full LLM unit/regression testing suite, CI evals, LLM tracing, prompt versioning, and community support; Starter from $9.99/user/month adds cloud datasets, custom metrics, online evals on live traffic, human annotation, chat simulations, downstream observability workflows, real-time alerting, and full Project API access; Team is Custom-priced with no-code eval workflows, alert integrations (Slack, PagerDuty), Jira/Linear links, annotation queues, metric/dataset versioning, git-based prompt workflows, custom RBAC, SOC 2, SSO, and dedicated support; Enterprise adds on-prem deployment, HIPAA, org management API, 24x7 support, and add-on AI red teaming (DeepTeam) and AI governance modules. Tracing storage is quoted from $1/GB-month with configurable retention.
DeepEval is the correct choice when your team writes Python, wants evals in the same test file as the code, and is willing to grow into a paid cloud only if it becomes worth it.
DeepEval is the most comprehensive open-source LLM evaluation framework available, with 50+ metrics covering everything from basic relevancy to complex agent tool use and adversarial robustness. The pytest-style integration makes it natural for development teams already using Python testing workflows. Free for self-hosted use with Confident AI cloud providing collaboration and monitoring from $19.99/month. Best for teams needing rigorous, automated quality testing for AI applications.
Comprehensive metric suite covering hallucination detection, answer relevancy, faithfulness, contextual precision/recall, tool correctness, conversational coherence, knowledge retention, bias, toxicity, and more — each validated against human judgment benchmarks.
Use Case:
Running a full quality audit on a customer support chatbot using hallucination, relevancy, and faithfulness metrics to catch responses that fabricate information or drift from the knowledge base.
Tool correctness metric specifically evaluates whether AI agents call the right tools with correct parameters and in the right sequence — essential for validating agent behavior in production.
Use Case:
Testing an e-commerce agent to verify it correctly calls the inventory API before the order API, passes valid product IDs, and handles out-of-stock scenarios without hallucinating availability.
Write LLM tests using familiar pytest patterns with decorators and assertions. Tests run alongside unit tests in existing CI/CD pipelines. Failed quality thresholds block deployments automatically.
Use Case:
Adding DeepEval tests to a GitHub Actions pipeline that runs on every pull request — if hallucination scores exceed 10% or relevancy drops below 0.85, the PR can't merge.
Generate diverse test datasets from your documents using LLMs. Creates edge cases, adversarial inputs, and comprehensive test coverage without manual data curation.
Use Case:
Generating 500 test questions from a product documentation corpus, including paraphrases, multi-hop questions, and out-of-scope queries to stress-test a RAG chatbot.
Automated adversarial testing that generates prompt injection attempts, bias probes, toxicity triggers, and jailbreak prompts to test agent robustness before deployment.
Use Case:
Running red-team evaluations against a customer-facing agent to verify it resists prompt injection, doesn't generate biased responses, and handles toxic inputs gracefully.
Cloud platform layering on DeepEval with LLM tracing (full context: inputs, outputs, tool calls, latency, token costs), real-time monitoring, performance alerting, collaborative dataset management, prompt versioning, and dashboards. Available as SaaS or self-hosted.
Use Case:
Monitoring a production RAG system's quality in real-time — receiving alerts when hallucination rates spike, drilling into individual traces to identify root causes, and tracking quality trends across model versions.
$0 forever
From $9.99/user/month
Custom
Custom
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We believe in transparent reviews. Here's what DeepEval doesn't handle well:
DeepEval expanded to 50+ evaluation metrics (from 14+ in 2024), including enhanced agent tool use evaluation and conversational metrics. Confident AI platform added LLM tracing at $1/GB-month, no-code evaluation workflows, auto-dataset curation from traces, real-time alerting, and self-hosted deployment. Y Combinator backed. SOC 2 compliance added for Team and Enterprise tiers.
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Pricing and features change often. If anything on this DeepEval page is wrong or missing, tell us and include a link to the source. We review every correction.