DeepEval
Pytest-native LLM evals with 50+ metrics, runs locally in your editor
What is DeepEval?
DeepEval is an Apache 2.0 open-source LLM evaluation framework that runs like pytest, with 50+ research-backed metrics covering hallucination, faithfulness, answer relevancy, and more. It supports synthetic golden generation, multi-modal inputs, and span-level tracing across agent frameworks. Adopted by 150K+ developers and, per the vendor, more than half of the Fortune 500.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Running regression tests for hallucination and faithfulness in CI
Generating synthetic golden datasets for new agent features
Span-level scoring of multi-step agent traces
Local eval loops driven by coding agents
Fit to evaluate
Backend/ML engineers shipping LLM features behind tests
Teams standardizing on pytest for AI quality gates
Open-source-first orgs avoiding closed eval vendors
Developers using Claude Code or Cursor as part of the loop
Business fit
Right for you if your team already lives in pytest and wants evals as code reviewed alongside features, not in a separate dashboard. Skip if non-technical PMs or QA leads need to author tests in a UI. Distinctive feature: native hooks for coding agents like Claude Code that close the build to eval to patch loop. The companion hosted product (Confident AI) is where you go for team dashboards and production observability.
How to evaluate DeepEval
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map DeepEval to one concrete workflow first, such as running regression tests for hallucination and faithfulness in ci. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Shortlist DeepEval against Orgo, Browser Use, Browserbase so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Free and open-source (Apache 2.0). No paid tier on the framework itself; hosted features sold separately via Confident AI. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare DeepEval with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Running regression tests for hallucination and faithfulness in CI, Generating synthetic golden datasets for new agent features |
|---|---|
| Best-fit team | Backend/ML engineers shipping LLM features behind tests, Teams standardizing on pytest for AI quality gates |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | OrgoBrowser UseBrowserbaseHyperbrowser |
DeepEval pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | Free and open-source (Apache 2.0). No paid tier on the framework itself; hosted features sold separately via Confident AI. |
| Checked |
Common questions about DeepEval
What is DeepEval?
DeepEval is an Apache 2.0 open-source LLM evaluation framework that runs like pytest, with 50+ research-backed metrics covering hallucination, faithfulness, answer relevancy, and more. It supports synthetic golden generation, multi-modal inputs, and span-level tracing across agent frameworks. Adopted by 150K+ developers and, per the vendor, more than half of the Fortune 500.
What is DeepEval used for?
Common use cases: Running regression tests for hallucination and faithfulness in CI; Generating synthetic golden datasets for new agent features; Span-level scoring of multi-step agent traces; Local eval loops driven by coding agents.
How much does DeepEval cost?
Free and open-source (Apache 2.0). No paid tier on the framework itself; hosted features sold separately via Confident AI.
Who is DeepEval best for?
DeepEval fits Backend/ML engineers shipping LLM features behind tests, Teams standardizing on pytest for AI quality gates, Open-source-first orgs avoiding closed eval vendors, Developers using Claude Code or Cursor as part of the loop. Right for you if your team already lives in pytest and wants evals as code reviewed alongside features, not in a separate dashboard. Skip if non-technical PMs or QA leads need to author tests in a UI. Distinctive feature: native hooks for coding agents like Claude Code that close the build to eval to patch loop. The companion hosted product (Confident AI) is where you go for team dashboards and production observability.
What are alternatives to DeepEval?
Common alternatives to DeepEval include Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, Anchor Browser.