# Evaluating Agentic AI: DeepEval, RAGAS & TruLens Frameworks Compared

In this episode of Memriq Inference Digest - Leadership Edition, we unpack the critical frameworks for evaluating large language models embedded in agentic AI systems. Leaders navigating AI strategy will learn how DeepEval, RAGAS, and TruLens provide complementary approaches to ensure AI agents perform reliably from development through production.

In this episode:

- Discover how DeepEval’s 50+ metrics enable comprehensive multi-step agent testing and CI/CD integration

- Explore RAGAS’s revolutionary synthetic test generation using knowledge graphs to accelerate retrieval evaluation by 90%

- Understand TruLens’s production monitoring capabilities powered by Snowflake integration and the RAG Triad framework

- Compare strategic strengths, limitations, and ideal use cases for each evaluation framework

- Hear real-world examples across industries showing how these tools improve AI reliability and speed

- Learn practical steps for leaders to adopt and combine these frameworks to maximize ROI and minimize risk

Key Tools & Technologies Mentioned:

- DeepEval

- RAGAS

- TruLens

- Retrieval Augmented Generation (RAG)

- Snowflake

- OpenTelemetry

Timestamps:

0:00 Intro & Why LLM Evaluation Matters

3:30 DeepEval’s Metrics & CI/CD Integration

6:50 RAGAS & Synthetic Test Generation

10:30 TruLens & Production Monitoring

13:40 Comparing Frameworks Head-to-Head

16:00 Real-World Use Cases & Industry Examples

18:30 Strategic Recommendations for Leaders

20:00 Closing & Resources

Resources:

- Book: "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- This podcast is brought to you by Memriq.ai - AI consultancy and content studio building tools and resources for AI practitioners.

MEMRIQ INFERENCE DIGEST - LEADERSHIP EDITION

Episode: Evaluating Agentic AI: DeepEval, RAGAS & TruLens Frameworks Compared

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Welcome to the Memriq Inference Digest - Leadership Edition. I’m Morgan, here with Casey, and this podcast is brought to you by Memriq AI, a content studio building tools and resources for AI practitioners—do check them out at Memriq.ai.

Today, we’re diving into a hot topic for leaders steering AI strategy—evaluating large language models, or LLMs, especially when embedded in agentic AI systems. We’ll compare three leading frameworks: DeepEval, RAGAS, and TruLens. Each tackles the critical question: how do you know your AI agent is truly working as intended?

If you want to dig deeper with diagrams, detailed explanations, and even hands-on code labs, search for Keith Bourne’s 2nd edition book on Amazon. Keith, who joins us today, is an AI expert who’s written extensively about Retrieval Augmented Generation, or RAG, and his book takes you all the way from fundamentals to cutting-edge agentic AI environments.

Over the next 20 minutes, we’ll explore what these evaluation frameworks do, why they matter now more than ever, their strategic differences, and what this means for you as a leader responsible for delivering reliable, valuable AI products.

So, buckle up—we’re covering the tools, the metrics, the real-world impact, and even some spirited debate on when to pick which approach.

Here’s a surprising headline for you: DeepEval offers over 50 pre-built metrics to assess agent AI—six of those are unique, agent-specific scores you won’t find anywhere else. Meanwhile, RAGAS uses knowledge graphs to generate synthetic tests, slashing test creation time by 90%. That kind of speed-up isn’t just convenient; it revolutionizes how fast you can develop and validate AI agents.

Wait, 90% faster test creation? That’s a game-changer. Imagine cutting your QA cycle by almost tenfold!

But hold on, Morgan. All those metrics and speed sound great, but why is this so critical?

Because without dedicated, rigorous evaluation metrics, AI agents look good in demos but can fail spectacularly in production—leading to costly errors and damaging customer trust. These frameworks are the difference between flashy prototypes and dependable AI services.

That hits home. It’s like having a fancy sports car—you need to know it can perform reliably on the road, not just in the showroom.

Exactly. And these frameworks give leaders the tools to avoid expensive “road test” failures that catch you off guard after launch.

If you remember nothing else from today, here it is: DeepEval, RAGAS, and TruLens are three complementary frameworks for evaluating agentic AI and RAG systems. DeepEval provides a vast library of over 50 metrics focusing on multi-step agent testing and CI/CD integration. RAGAS shines at retrieval pipeline optimization, generating synthetic tests from knowledge graphs with around 25 metrics. TruLens emphasizes production monitoring with about 15 metrics and deep tracing capabilities, recently boosted by Snowflake’s acquisition.

So, your takeaway? Choose DeepEval when you want deep development testing, RAGAS for optimizing retrieval accuracy, and TruLens for tracking live production performance.

Right—and all aim to make AI evaluation scalable and practical for real business needs, not just academic curiosity.

Let’s set the stage. Not long ago, AI agent evaluation was manual, fragmented, and ad hoc—think of it like quality checks done by eyeballing outputs, which just doesn’t scale as agents grow more complex. Enter frameworks like DeepEval, RAGAS, and TruLens that bring systematic rigour.

What’s changed recently to make this surge happen?

Three main drivers: First, enterprises are investing heavily in AI agents—Snowflake’s acquisition of TruLens in May 2024 is a big signal. Second, peer-reviewed research, like the EACL 2024 studies backing RAGAS, brings academic validation. Third, these tools are getting traction on GitHub, with tens of thousands of stars reflecting real-world adoption.

So it’s a mix of market demand, research maturity, and community momentum. But why can’t companies just stick to old evaluation methods?

Because old methods can’t keep up with the complexity of agentic AI—these systems combine multiple tools, memory, and decision steps. Without scalable evaluation, you risk unreliable agents that erode customer trust and waste resources.

It’s like moving from a single inspection of a product to assembly line quality control as production scales up.

That analogy makes sense. For leaders, not adopting these frameworks now risks competitive disadvantage and operational headaches.

At its core, these frameworks tackle the fundamental challenge of evaluating agentic AI, which is inherently non-deterministic—meaning the AI can produce different outputs even with the same input. Traditional testing expects exact answers, but agents act more like complex decision-makers.

So how do they handle that variability?

They use what’s called “LLM-as-a-judge,” where one AI model evaluates another’s output based on multiple criteria. This enables “reference-free evaluation,” meaning you don’t need a fixed ground-truth answer to judge quality. This is essential when agents generate novel responses or multi-step plans.

What about the specific dimensions these frameworks measure?

DeepEval, for example, evaluates six agent metrics including task success, tool correctness, plan quality, and efficiency. RAGAS focuses heavily on retrieval quality using synthetic test sets generated via knowledge graphs—these are structured representations of information that help create smart test queries. TruLens applies the “RAG Triad” framework to detect hallucinations—AI making up facts—and measure faithfulness and relevance of responses in production.

So, it’s a multi-dimensional, strategic measurement, not just a simple pass/fail test.

Exactly. And this shift is critical for building trustable AI agents that can operate reliably in real-world environments.

Let’s get into the direct comparisons. DeepEval shines in comprehensive agent testing during development. It integrates natively with CI/CD pipelines—think automated testing in your software development lifecycle—providing over 50 metrics that cover every aspect of agent behavior.

That sounds powerful, but could the sheer number of metrics overwhelm product teams?

Good point. That’s one trade-off—DeepEval’s richness can be daunting initially. In contrast, RAGAS focuses on optimizing retrieval pipelines—the part where agents pull information from external documents or databases. It leverages synthetic test generation via knowledge graphs to cut test creation time by 90%. That’s huge for teams prioritizing fast iteration on retrieval accuracy.

So RAGAS is like a turbo boost for retrieval tuning.

Precisely. TruLens, on the other hand, targets production observability. It integrates deeply with enterprise data stacks like Snowflake and uses OpenTelemetry for distributed tracing, offering real-time monitoring of agent responses for hallucinations and relevance.

So, use TruLens when you want to keep an eye on your AI agent live in production, catching issues before they impact users?

Exactly. To summarize: Use DeepEval for deep development testing and CI/CD. Use RAGAS to optimize retrieval accuracy with synthetic tests. Use TruLens for scalable, production-grade monitoring. And combining all three can maximize your evaluation coverage.

But remember, each comes with different maturity and focus areas, so aligning choice with your workflow and risk profile is key.

Now, let’s peek under the hood to understand how these frameworks really work—without getting lost in code. Starting with DeepEval, it uses a pytest-like testing workflow familiar to software teams. You write tests that simulate multi-step agent tasks, and an “@observe” decorator tracks every component’s output—like logging every move in a chess game. Then, DeepEval computes over 50 metrics, including task success and tool correctness.

That decorator approach sounds simple but effective.

Absolutely. It’s designed for easy adoption and tight integration with existing development pipelines. Moving to RAGAS, it’s fascinating how it builds knowledge graphs from your documents. Think of a knowledge graph as a map connecting facts and concepts. RAGAS then generates synthetic, multi-hop test queries—these are questions requiring the agent to connect several pieces of information—without needing pre-labeled answers.

So it’s like training the agent on “needle in a haystack” tests, where it must find specific facts buried in complex data?

Exactly! This reference-free testing is a game-changer for evaluating retrieval systems efficiently. Finally, TruLens integrates OpenTelemetry, an industry-standard tool for distributed tracing, to monitor AI agents live. It applies the RAG Triad framework—checking for hallucinations, faithfulness, and relevance—by using AI to evaluate outputs on the fly. This approach lets you detect if the agent “makes stuff up” or drifts off-topic during real interactions.

That sounds powerful for live production monitoring, especially to catch issues that static tests might miss.

Indeed. And all three frameworks leverage LLMs themselves as evaluators—meaning one AI judges another. This clever approach overcomes the problem of unpredictable AI outputs by applying AI’s natural language understanding to assessment.

Still, relying on AI to evaluate AI introduces its own challenges around bias and cost, right?

Absolutely, but they’re carefully engineered to balance accuracy, efficiency, and practicality. As a whole, these frameworks represent some of the most sophisticated evaluation tools available today.

Let’s talk impact. RAGAS’s synthetic test generation cuts test creation time by 90%. That means what used to take weeks can be done in days—accelerating your development cycles significantly. DeepEval offers the broadest metric coverage—50-plus pre-built metrics—giving you unparalleled insight into agent behavior during development.

Wow, that’s a huge win for teams needing detailed quality assurance.

On GitHub, DeepEval has over 12,000 stars, RAGAS about 11,400, and TruLens close to 10,000—demonstrating strong community adoption and trust. These numbers may seem like vanity metrics, but they reflect real engagement and validation from practitioners worldwide.

What about any downsides or gaps in these results?

Quantitative benchmarks comparing frameworks head-to-head are limited. That means there’s an opportunity—and a risk—for organizations to invest without fully proven comparative data. Still, the adoption metrics and efficiency gains make a strong business case for deploying these tools.

So, practically, investing in these frameworks translates into faster development, more reliable AI agents, and safer production deployments—key to maintaining competitive advantage.

Now, time to play devil’s advocate. DeepEval’s massive metric set can overwhelm product managers and executives who just want actionable insights. Plus, some enterprise-grade features are gated behind paid tiers—so watch out for hidden costs.

That’s a fair concern—complexity can slow decision-making.

RAGAS, while excellent at retrieval evaluation, is less mature for full agent assessment and doesn’t offer built-in production monitoring. That could be a dealbreaker if you want end-to-end coverage. TruLens, strong in production tracing, lacks dedicated agent and tool correctness metrics—relying on custom feedback means more upfront setup.

And all frameworks depend on LLM evaluators, which inherit biases from the models they use and add evaluation cost—especially at scale.

Exactly. So no one tool is a silver bullet. Leaders must be aware of these limitations and consider complementary strategies like adversarial testing or manual audits.

It’s about balancing ambition with pragmatism.

Let’s ground this in reality. DeepEval is often chosen by companies developing multi-step AI agents, like customer support bots that use external tools and databases. Its metrics help ensure the agent completes tasks accurately and efficiently before deployment.

Any specific industry examples?

Sure. In healthcare, AI agents assist with patient triage, where safety and accuracy metrics from DeepEval reduce risk. RAGAS is popular among enterprises using RAG to surface insights from massive document repositories—legal firms, for example, leverage its synthetic test generation to ensure retrieval accuracy.

And TruLens?

TruLens has found traction in finance and e-commerce, industries that demand real-time monitoring of AI decisions to catch hallucinations and maintain compliance. Integration with Snowflake’s data platform allows seamless tracing and alerting.

So these frameworks are already powering mission-critical AI across sectors.

Exactly, offering measurable improvements in reliability, speed, and maintainability.

Picture this scenario: an e-commerce customer service AI agent that answers questions, processes returns, and fetches order data. Who picks which framework?

I’d lean on DeepEval during development to get detailed metrics on task success, tool correctness, and efficiency. You want to catch problems early.

But RAGAS shines by generating diverse synthetic test queries to evaluate how well the agent retrieves product info from databases—crucial for accuracy in customer responses.

Once live, TruLens becomes indispensable for production monitoring. Its RAG Triad detects hallucinations—say, if the agent invents a return policy—and tracks response relevance in real time.

So a hybrid approach: DeepEval for pre-deployment testing, RAGAS for retrieval tuning, and TruLens for live monitoring. Each framework tackles a different pain point.

That’s a smart strategy, but it requires coordination and investment—something leadership must plan for.

And it’s a reminder there’s no one-size-fits-all—your choice depends on your agent’s complexity, domain, and risk tolerance.

For teams starting out, begin with DeepEval’s pytest-native testing. Its simple decorator approach and CI/CD integration lower the learning curve.

And if you want to accelerate retrieval evaluation, adopt RAGAS’s knowledge graph synthetic test generation. It uncovers edge cases you might miss with manual tests.

When moving to production, implement TruLens’s RAG Triad framework for structured hallucination detection and integrate it with your existing data stack—Snowflake is a great example.

Combine these frameworks where possible to cover development, optimization, and live monitoring. Avoid relying solely on one tool to reduce blind spots.

And don’t forget adversarial testing tools like DeepTeam and solutions like the Confident AI platform to guard against security vulnerabilities like prompt injection.

Strategic integration and ongoing evaluation cycles are the keys to sustainable AI success.

Quick plug—Keith Bourne’s book “Unlocking Data with Generative AI and RAG” is a fantastic resource. While it focuses on RAG, it walks you through fundamentals to advanced agentic AI implementations. If you want a solid foundation to understand today’s frameworks better, definitely check it out.

Memriq AI is an AI consultancy and content studio building tools and resources for AI practitioners. This podcast is produced by Memriq AI to help engineers and leaders stay current with the rapidly evolving AI landscape.

Head to Memriq.ai for deep-dives, practical guides, and research breakdowns that empower your AI decisions.

Despite progress, major challenges remain. There’s no industry-standard benchmark to reliably compare agent evaluation frameworks on accuracy or effectiveness, making investment decisions tricky.

And relying on LLMs as judges introduces evaluator bias and cost concerns at scale, which leadership must factor into ROI calculations.

Memory evaluation—how well agents recall past interactions—and multi-agent coordination are underdeveloped areas across all frameworks.

Plus, balancing real-time evaluation speed with cost constraints is an open problem. You don’t want to slow down production or overspend on monitoring.

These gaps represent both risks and opportunities—leaders who navigate them wisely will have a competitive edge.

My takeaway? Investing in evaluation frameworks is no longer optional. It’s mission-critical to deliver reliable AI products that build trust and drive value.

Remember, no single tool does it all. Strategic combinations tailored to your business needs yield the best ROI and risk management.

The speed and depth of evaluation directly influence your AI development velocity and quality—don’t underestimate that leverage.

Understanding the architectural trade-offs behind these tools helps align them with your workflows and priorities.

The clever use of AI to evaluate AI is fascinating—a sign we’re entering a new era of scalable, intelligent quality assurance.

Real-world deployments show these frameworks work today, across industries, to reduce risk and accelerate innovation.

And from my perspective, mastering these evaluation strategies is key to unlocking the full potential of agentic AI responsibly. For those wanting a solid foundation, my book is a great place to start.

Keith, thanks for giving us the inside scoop today.

My pleasure—this is such an important topic, and I hope listeners dig deeper into it.

Thanks to everyone for listening. Remember, robust AI evaluation is your best friend in this fast-moving landscape. See you next time!