Testing & Quality🔴Developer

DeepEval

Name: DeepEval
Brand: DeepEval
Availability: InStock

DeepEval: Open-source LLM evaluation framework with 50+ research-backed metrics including hallucination detection, tool use correctness, and conversational quality. Pytest-style testing for AI agents with CI/CD integration.

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In Plain English

A testing framework for AI applications — write tests that check if your AI's responses are accurate, helpful, and safe, just like writing unit tests for code.

Overview

DeepEval is an open-source LLM evaluation framework that provides 50+ research-backed metrics for testing AI agents and LLM applications, with the open-source core free under MIT license and Confident AI cloud starting at $19.99/user/month. It targets ML engineers, AI developers, and QA teams building production LLM systems who need pytest-style testing integrated into CI/CD pipelines.

DeepEval powers over 100 million daily evaluations and is used by 150,000+ developers across more than 50% of Fortune 500 companies, making it one of the most widely adopted open-source LLM testing frameworks. The metric suite covers the full spectrum of agent quality assessment: hallucination detection, answer relevancy, faithfulness, contextual precision and recall (for RAG), tool correctness (for agent tool use), conversational relevancy, knowledge retention, bias detection, and toxicity scoring. Each metric is validated against human judgment benchmarks, ensuring scores are meaningful and actionable. Compared to the other testing tools in our directory of 870+ AI tools, DeepEval stands out for its breadth — most competitors specialize in either RAG, agents, or red-teaming, while DeepEval covers all three.

DeepEval's agent testing is particularly strong: the tool correctness metric evaluates whether agents call the right tools with correct parameters, while conversational metrics assess multi-turn interactions for coherence and topic adherence. The framework supports synthetic test data generation from documents and includes a built-in red-teaming module for adversarial testing against prompt injection, bias, and toxicity. Pytest integration enables LLM tests alongside unit tests with deployment gating — if quality scores drop below thresholds, the build fails.

The Confident AI cloud platform layers on top with collaboration features, dataset management, LLM tracing (inputs, outputs, tool calls, latency, token cost), real-time monitoring, and dashboards. Pricing tiers: Starter at $19.99/user/month, Premium at $49.99/user/month, with Team and Enterprise plans offering self-hosted deployment, SOC 2 compliance, SSO, and HIPAA support. Backed by Y Combinator, the framework grew from 14+ to 50+ metrics with active development.

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Key Features

50+ Research-Backed Evaluation Metrics+

DeepEval ships with over 50 metrics spanning hallucination detection, answer relevancy, faithfulness, contextual precision/recall, bias, toxicity, and more. Each metric is grounded in academic research and validated against human judgment benchmarks, so scores are meaningful and reproducible. The library grew from 14+ to 50+ metrics through frequent releases, reflecting active development.

Tool Correctness for Agent Testing+

The tool correctness metric specifically evaluates whether AI agents select the right tools, pass correct parameters, and execute calls in the proper sequence. This is essential for production agent validation because traditional text-based metrics miss tool-call errors entirely. It works with LangChain, CrewAI, OpenAI Agents SDK, and custom function-calling schemas.

Pytest Integration with CI/CD Gating+

DeepEval feels like pytest for LLMs — tests run alongside unit tests using familiar assert-style syntax. Teams can configure quality thresholds that fail builds when hallucination or relevancy scores drop, preventing regressions from reaching production. This integration works with GitHub Actions, GitLab CI, Jenkins, and any standard Python CI runner.

Built-in Red-Teaming Module+

DeepEval includes adversarial testing capabilities that auto-generate prompt injection attempts, bias-eliciting queries, and toxic input variants. Teams can scan agents for vulnerabilities before launch instead of waiting for users to find them. The module covers OWASP LLM Top 10 categories and produces structured vulnerability reports.

Confident AI Tracing and Monitoring+

The Confident AI cloud platform captures full LLM traces including inputs, outputs, tool calls, latency, and token cost across production traffic. Real-time dashboards and alerting surface quality regressions and cost anomalies as they happen. Tracing storage is priced at $1/GB-month with adjustable retention, making long-term observability affordable for high-traffic systems.

Pricing Plans

DeepEval Open Source

Free

✓MIT-licensed open-source framework
✓50+ research-backed evaluation metrics
✓Pytest integration with CI/CD gating
✓Synthetic test data generation
✓Red-teaming module for adversarial testing
✓Local execution (pay only for LLM API calls)

Confident AI Free

✓5 test runs/week
✓1 week data retention
✓2 seats
✓1 project
✓Basic dashboards

Starter

$19.99/user/month

✓Unlimited test runs
✓Dataset management
✓LLM tracing (inputs, outputs, tool calls)
✓Latency and token cost tracking
✓Tracing at $1/GB-month with adjustable retention

Premium

$49.99/user/month

✓Everything in Starter
✓Real-time monitoring and alerting
✓Chat simulation for multi-turn testing
✓Advanced dashboards
✓Performance regression detection

Team / Enterprise

Custom

✓Self-hosted deployment
✓SOC 2 compliance
✓SSO / SAML
✓HIPAA support
✓Dedicated support and onboarding

See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

🎯

CI/CD quality gates for LLM applications: Integrating automated LLM evaluation into CI/CD pipelines using pytest — blocking deployments when hallucination, relevancy, or faithfulness scores drop below defined thresholds

⚡

Agent tool use validation: Testing AI agents to verify they call the correct tools with proper parameters in the right sequence — catching tool misuse, incorrect API calls, and parameter errors before production

🔧

Red-teaming AI systems before deployment: Running automated adversarial testing against customer-facing AI systems to identify vulnerabilities to prompt injection, bias amplification, and toxic output generation

🚀

RAG pipeline quality monitoring: Evaluating retrieval-augmented generation systems with contextual precision, recall, and faithfulness metrics to ensure answers stay grounded in retrieved documents

💡

Production LLM observability via Confident AI: Monitoring production LLM application quality in real-time with tracing, alerting, and dashboards — identifying quality regressions and cost anomalies across model versions

🔄

Synthetic test dataset generation: Auto-generating diverse evaluation test cases from existing documents and knowledge bases — reducing the manual effort required to build robust evaluation suites for new LLM features

Limitations & What It Can't Do

We believe in transparent reviews. Here's what DeepEval doesn't handle well:

⚠Evaluation metrics require LLM API calls — testing 1,000 samples across 5 metrics means 5,000 LLM calls at the evaluator model's pricing
⚠Metric accuracy is only as good as the evaluator model — using GPT-3.5 as an evaluator produces significantly less reliable scores than GPT-4 or Claude
⚠Multi-turn conversational metrics are computationally expensive — evaluating 100 multi-turn conversations can take significant time and cost
⚠Confident AI Free tier limits (5 test runs/week, 1 week retention, 1 project) push teams to paid plans quickly for any real workflow
⚠No built-in support for evaluating image, audio, or multimodal outputs — focused exclusively on text-based LLM evaluation

Pros & Cons

✓ Pros

✓Massive adoption with 150,000+ developers and 100M+ daily evaluations — used by over 50% of Fortune 500 companies, signaling production-grade reliability
✓Comprehensive LLM evaluation metric suite — 50+ metrics covering hallucination, relevancy, tool correctness, bias, toxicity, and conversational quality
✓Pytest integration feels natural for Python developers — LLM tests run alongside unit tests in existing CI/CD pipelines with deployment gating
✓Tool correctness metric specifically designed for validating AI agent behavior — checks correct tool selection, parameters, and sequencing
✓Open-source core (MIT license) runs locally at zero platform cost — only pay for LLM API calls used by metrics
✓Active development with frequent new metrics and features — grew from 14+ to 50+ metrics, backed by Y Combinator with frequent changelog updates

✗ Cons

✗Metrics require LLM API calls (GPT-4, Claude) for evaluation — adds cost that scales with dataset size and metric count
✗Some metrics can be computationally expensive and slow for large evaluation datasets, especially multi-turn conversational metrics
✗Confident AI cloud required for collaboration, dataset management, monitoring, and dashboards — open-source alone lacks team features
✗Metric accuracy depends on the evaluator model quality — weaker models produce less reliable scores, creating cost pressure to use expensive models
✗Free tier of Confident AI is restrictive: 5 test runs/week, 1 week data retention, 2 seats, 1 project

Frequently Asked Questions

How does DeepEval compare to RAGAS?+

DeepEval is broader — it covers RAG metrics (contextual precision, recall, faithfulness) plus agent tool use evaluation, conversational quality metrics, bias/toxicity detection, and red-teaming. RAGAS focuses specifically on RAG pipeline evaluation with deeper RAG-specific metrics. With 50+ metrics versus RAGAS's narrower set, DeepEval is the better choice for teams building agents or multi-turn chatbots. If you only need RAG evaluation, RAGAS may be sufficient; for comprehensive agent and LLM testing across 150,000+ developer workflows, DeepEval covers more ground.

Can DeepEval test multi-turn agent conversations?+

Yes. DeepEval includes conversational metrics for coherence, topic adherence, and knowledge retention across multiple conversation turns. The chat simulation feature in Confident AI Premium ($49.99/user/month) can generate multi-turn test conversations automatically, removing the need to manually script dialogue scenarios. Conversational relevancy and knowledge retention metrics specifically score whether agents maintain context across turns. This is particularly useful for customer support bots, tutoring agents, and any long-running conversational system where single-turn metrics miss the bigger picture.

Does DeepEval work with any agent framework?+

Yes. DeepEval evaluates inputs and outputs regardless of framework — it operates on the text the agent produces rather than hooking into framework internals. It works with LangChain, CrewAI, LlamaIndex, OpenAI Agents SDK, custom agents, and any LLM application that produces text outputs. This framework-agnostic design means you can switch agent frameworks without rewriting your evaluation suite. The tool correctness metric also accepts arbitrary tool call schemas, so agents using custom function-calling formats are supported.

How accurate are the automated metrics?+

DeepEval metrics are validated against human judgment benchmarks, with each of the 50+ metrics backed by academic research. Accuracy varies by metric and evaluator model — using stronger models (GPT-4, Claude Opus) as evaluators produces more accurate scores than GPT-3.5 or smaller models. The framework regularly updates metrics based on new academic findings, and most metrics include confidence scores or reasoning explanations. For mission-critical applications, teams typically run a calibration round comparing DeepEval scores against human-labeled samples to set appropriate thresholds.

What's the difference between DeepEval and Confident AI?+

DeepEval is the free, open-source evaluation framework (MIT license) for running LLM tests locally or in CI. Confident AI is the commercial cloud platform built by the same team — it adds collaboration, dataset management, LLM tracing, real-time monitoring, alerting, and dashboards. Pricing for Confident AI starts at $19.99/user/month for Starter and $49.99/user/month for Premium, with Team and Enterprise tiers offering self-hosted deployment and SOC 2 compliance. DeepEval works standalone; Confident AI layers on top for team and production use.

🔒 Security & Compliance

🏢

SOC2

Enterprise

✅

GDPR

Yes

🏢

HIPAA

Enterprise

🏢

SSO

Enterprise

✅

Self-Hosted

Yes

✅

On-Prem

Yes

—

RBAC

Unknown

—

Audit Log

Unknown

✅

API Key Auth

Yes

✅

Open Source

Yes

✅

Encryption at Rest

Yes

✅

Encryption in Transit

Yes

🦞

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What's New in 2026

DeepEval has expanded from 14+ to 50+ research-backed metrics, with active changelog updates introducing chat simulation for multi-turn testing, expanded tool correctness evaluation for agent frameworks, and Confident AI tracing priced at $1/GB-month with adjustable retention. Adoption has grown to 150,000+ developers and over 50% of Fortune 500 companies, with the platform now powering 100M+ daily evaluations.

Alternatives to DeepEval

RAGAS

AI Memory & Search

Open-source framework for evaluating RAG pipelines and AI agents with automated metrics for faithfulness, relevancy, and context quality.

Promptfoo

Testing & Quality

Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.

Braintrust

Voice Agents

AI observability platform with Loop agent that automatically generates better prompts, scorers, and datasets from production data. Free tier available, Pro at $25/seat/month.

LangSmith

Analytics & Monitoring

LangSmith lets you trace, analyze, and evaluate LLM applications and agents with deep observability into every model call, chain step, and tool invocation.

Arize Phoenix

Analytics & Monitoring

Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host for free with comprehensive tracing, experimentation, and quality assessment for AI applications.

View All Alternatives & Detailed Comparison →

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Overview

Key Features

50+ Research-Backed Evaluation Metrics+

Tool Correctness for Agent Testing+

Pytest Integration with CI/CD Gating+

Built-in Red-Teaming Module+

Confident AI Tracing and Monitoring+

Pricing Plans

DeepEval Open Source

Free

✓MIT-licensed open-source framework
✓50+ research-backed evaluation metrics
✓Pytest integration with CI/CD gating
✓Synthetic test data generation
✓Red-teaming module for adversarial testing
✓Local execution (pay only for LLM API calls)

Confident AI Free

✓5 test runs/week
✓1 week data retention
✓2 seats
✓1 project
✓Basic dashboards

Starter

$19.99/user/month

✓Unlimited test runs
✓Dataset management
✓LLM tracing (inputs, outputs, tool calls)
✓Latency and token cost tracking
✓Tracing at $1/GB-month with adjustable retention

Premium

$49.99/user/month

✓Everything in Starter
✓Real-time monitoring and alerting
✓Chat simulation for multi-turn testing
✓Advanced dashboards
✓Performance regression detection

Team / Enterprise

Custom

✓Self-hosted deployment
✓SOC 2 compliance
✓SSO / SAML
✓HIPAA support
✓Dedicated support and onboarding

Best Use Cases

🎯

CI/CD quality gates for LLM applications: Integrating automated LLM evaluation into CI/CD pipelines using pytest — blocking deployments when hallucination, relevancy, or faithfulness scores drop below defined thresholds

⚡

Agent tool use validation: Testing AI agents to verify they call the correct tools with proper parameters in the right sequence — catching tool misuse, incorrect API calls, and parameter errors before production

🔧

Red-teaming AI systems before deployment: Running automated adversarial testing against customer-facing AI systems to identify vulnerabilities to prompt injection, bias amplification, and toxic output generation

🚀

RAG pipeline quality monitoring: Evaluating retrieval-augmented generation systems with contextual precision, recall, and faithfulness metrics to ensure answers stay grounded in retrieved documents

💡

Production LLM observability via Confident AI: Monitoring production LLM application quality in real-time with tracing, alerting, and dashboards — identifying quality regressions and cost anomalies across model versions

🔄

Synthetic test dataset generation: Auto-generating diverse evaluation test cases from existing documents and knowledge bases — reducing the manual effort required to build robust evaluation suites for new LLM features

Limitations & What It Can't Do

We believe in transparent reviews. Here's what DeepEval doesn't handle well:

⚠Evaluation metrics require LLM API calls — testing 1,000 samples across 5 metrics means 5,000 LLM calls at the evaluator model's pricing

⚠Metric accuracy is only as good as the evaluator model — using GPT-3.5 as an evaluator produces significantly less reliable scores than GPT-4 or Claude

⚠Multi-turn conversational metrics are computationally expensive — evaluating 100 multi-turn conversations can take significant time and cost

⚠Confident AI Free tier limits (5 test runs/week, 1 week retention, 1 project) push teams to paid plans quickly for any real workflow

⚠No built-in support for evaluating image, audio, or multimodal outputs — focused exclusively on text-based LLM evaluation

Pros & Cons

✓ Pros

✓Massive adoption with 150,000+ developers and 100M+ daily evaluations — used by over 50% of Fortune 500 companies, signaling production-grade reliability
✓Comprehensive LLM evaluation metric suite — 50+ metrics covering hallucination, relevancy, tool correctness, bias, toxicity, and conversational quality
✓Pytest integration feels natural for Python developers — LLM tests run alongside unit tests in existing CI/CD pipelines with deployment gating
✓Tool correctness metric specifically designed for validating AI agent behavior — checks correct tool selection, parameters, and sequencing
✓Open-source core (MIT license) runs locally at zero platform cost — only pay for LLM API calls used by metrics
✓Active development with frequent new metrics and features — grew from 14+ to 50+ metrics, backed by Y Combinator with frequent changelog updates

✗ Cons

✗Metrics require LLM API calls (GPT-4, Claude) for evaluation — adds cost that scales with dataset size and metric count
✗Some metrics can be computationally expensive and slow for large evaluation datasets, especially multi-turn conversational metrics
✗Confident AI cloud required for collaboration, dataset management, monitoring, and dashboards — open-source alone lacks team features
✗Metric accuracy depends on the evaluator model quality — weaker models produce less reliable scores, creating cost pressure to use expensive models
✗Free tier of Confident AI is restrictive: 5 test runs/week, 1 week data retention, 2 seats, 1 project