Evaluating Agentic AI: DeepEval, RAGAS & TruLens Frameworks Compared

MEMRIQ INFERENCE DIGEST - ENGINEERING EDITION

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

Total Duration:: 19:43

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MORGAN: 00:00

Welcome back to Memriq Inference Digest - Engineering Edition, brought to you by Memriq AI. We’re the content studio that builds tools and resources for AI practitioners—so if you want to stay sharp on the bleeding edge of AI engineering, check out Memriq.ai.

CASEY: 00:16

Today, we’re diving into a hot and somewhat thorny topic: evaluating large language model frameworks specifically designed for agentic AI. We’ll be comparing three leading frameworks—DeepEval, RAGAS, and TruLens—and digging into their architectures, metrics, and production readiness.

MORGAN: 00:34

And if you want to go deeper beyond this conversation, I highly recommend searching for Keith Bourne’s second edition book on Amazon. His work walks you through Retrieval-Augmented Generation, or RAG, from the fundamentals all the way to advanced agentic environments. It’s packed with diagrams, hands-on code labs, and thorough explanations.

CASEY: 00:54

In this episode, we’ll explore how these frameworks tackle evaluation challenges unique to multi-step agents, the trade-offs, and how they integrate with popular tools like LangChain, LlamaIndex, and OpenTelemetry. Plus, we’ll get Keith’s practical take on deploying these systems in production.

MORGAN: 01:12

Buckle up—it’s going to be a deep dive with lots of technical goodies for AI engineers and infrastructure teams who want to build reliable agentic AI.

JORDAN: 01:22

Here’s something that caught my eye: DeepEval ships with over 50 pre-built metrics—six of which are specifically designed for agentic AI. That level of coverage is unmatched in the LLM evaluation space right now.

MORGAN: 01:36

Fifty metrics? Six agent-specific? That’s a massive win for teams trying to get granular insights into agent behavior.

JORDAN: 01:43

Exactly. And then there’s RAGAS, which leverages knowledge graphs to generate synthetic tests automatically. This cuts down test set creation time by 90%. Imagine slashing weeks of manual test crafting to just a few hours.

CASEY: 01:57

Hold on—without agent-specific metrics like ToolCorrectnessMetric and StepEfficiencyMetric, aren’t teams flying blind when they push agents to production? That’s a recipe for silent failures lurking in live deployments.

MORGAN: 02:11

Right, and these agent metrics track not just output correctness, but also tool use and step efficiency—critical for multi-step workflows. With faithfulness scores baked in, you get a full picture of both accuracy and trustworthiness.

JORDAN: 02:25

For AI engineers and infrastructure teams, this means comprehensive evaluation isn’t just a nice-to-have; it’s essential to avoid costly production surprises and speed up iteration cycles.

MORGAN: 02:36

That’s a hook if I ever heard one.

CASEY: 02:38

If you remember nothing else from today, here’s the essence: DeepEval, RAGAS, and TruLens form a specialized trio tackling LLM agent evaluation from three angles—broad metric coverage, synthetic test generation, and production observability.

JORDAN: 02:54

Before these frameworks gained traction, evaluation was fragmented, ad hoc, or stuck at basic NLP benchmarks. Agentic AI systems—those that autonomously call tools, plan, and execute multi-step workflows—changed the game. Suddenly, you need standardized, comprehensive evaluation methods covering not just output but tool correctness, plan efficiency, and hallucination detection.

CASEY: 03:17

And it’s not just complexity. Security considerations like adversarial prompt injection and red teaming have pushed evaluation frameworks to evolve. DeepTeam, for example, identifies over 40 vulnerability types that can sabotage agent safety.

JORDAN: 03:32

Exactly. And the landscape shifted further in 2024 when TruLens was acquired by Snowflake, signaling enterprise validation. RAGAS’s peer-reviewed publication at EACL 2024 gave academic endorsement.

MORGAN: 03:45

Don’t forget community momentum—DeepEval, RAGAS, and TruLens collectively have more than 33,000 stars on GitHub. That’s a strong adoption signal and means active development, support, and ecosystem growth.

CASEY: 03:59

For senior engineers and infrastructure leads, this means ignoring these frameworks is riskier than ever. The complexity and security stakes demand they become part of your evaluation toolkit.

TAYLOR: 04:11

At the core, these frameworks share a unified concept: they treat evaluation as a multi-dimensional measurement problem using what’s called an LLM-as-a-judge architecture. Instead of relying solely on ground-truth labels—which are costly and often unavailable—they leverage a large language model to assess agent behavior.

MORGAN: 04:31

That’s the reference-free evaluation paradigm?

TAYLOR: 04:34

Exactly. This lets you measure things like task completion, tool use correctness, plan quality, execution efficiency, response faithfulness, and memory retention—all vital for agentic AI.

CASEY: 04:45

How does this differ from earlier evaluation approaches?

TAYLOR: 04:49

Older methods focused on simple output accuracy or precision/recall on static datasets. They didn’t cover multi-step reasoning, tool interaction, or hallucination specifically. The new frameworks incorporate chain-of-thought reasoning inside the evaluation LLM, simulate plans, and analyze execution traces.

MORGAN: 05:08

Core architectural choices reflect that. DeepEval uses a pytest-native pipeline with `@observe` decorators to tap into agent internals. RAGAS constructs knowledge graphs for synthetic test generation. And TruLens integrates distributed tracing via OpenTelemetry for production observability.

TAYLOR: 05:26

Precisely. The architectural patterns support different phases of the AI system lifecycle—development testing, retrieval tuning, and live monitoring. Understanding these distinctions is key to choosing the right tool or combination.

TAYLOR: 05:40

Let’s compare them directly. DeepEval shines in multi-step agent testing with its pytest-like interface and a broad metric set. Its six agent-specific metrics—like ToolCorrectnessMetric and StepEfficiencyMetric—help validate complex workflows before deployment. Plus, it integrates seamlessly with CI/CD pipelines to automate validation.

CASEY: 06:00

But DeepEval’s enterprise features are gated behind paid tiers, which could be a barrier. Also, having 50+ metrics can overwhelm teams without clear guidance on prioritization.

TAYLOR: 06:12

Good points. RAGAS, on the other hand, brings strong theoretical foundations with its knowledge graph pipeline for synthetic test generation. This lets you generate complex multi-hop queries automatically, accelerating retrieval tuning for RAG pipelines. However, it lacks native production monitoring features.

MORGAN: 06:30

So RAGAS is great for research and optimization phases but needs complementary tools for production observability.

TAYLOR: 06:38

Exactly. And TruLens focuses on production monitoring using distributed tracing. It taps OpenTelemetry to capture detailed traces and implements the RAG Triad to dissect hallucinations into context relevance, groundedness, and answer relevance.

CASEY: 06:53

But TruLens doesn’t have dedicated agent or tool evaluation metrics, relying instead on custom feedback functions. That adds complexity and may require significant engineering effort to match DeepEval’s coverage.

TAYLOR: 07:06

The recommended approach is hybrid: use RAGAS for retrieval tuning, DeepEval for development and testing, and TruLens for production observability. Each framework brings unique strengths aligned with stages of agentic AI lifecycle.

ALEX: 07:21

Let’s get into the nitty-gritty of how these frameworks work under the hood. Starting with DeepEval, it’s architected as a pytest-native testing framework. Developers instrument agent code by adding `@observe` decorators on key components—like tool calls, sub-plans, or memory accesses.

MORGAN: 07:37

So it captures detailed LLM traces during execution?

ALEX: 07:41

Exactly. These traces are then fed into over 50 pre-built metrics. For example, the ToolCorrectnessMetric evaluates whether each tool invocation’s output matches expected results. The StepEfficiencyMetric measures how many steps the agent took relative to an optimal plan. DeepEval supports both discrete metrics—like Boolean correctness—and numeric metrics, enabling fine-grained analysis.

CASEY: 08:06

How does it run at scale?

ALEX: 08:07

DeepEval integrates tightly with CI/CD through commands like `deepeval test run`, enabling automated regression testing on agent changes. It also supports distributed execution, so large test suites don’t bottleneck builds.

MORGAN: 08:21

Nice. What about RAGAS?

ALEX: 08:23

RAGAS is fascinating. It constructs a knowledge graph pipeline for synthetic test generation. This pipeline includes entity extraction, keyphrase analysis, and relationship mapping to build a rich graph representation of domain knowledge. From here, it generates multi-hop queries that simulate realistic retrieval scenarios.

CASEY: 08:43

So, instead of manually crafting test queries, you get automated, diverse test sets?

ALEX: 08:49

Yes. This slashes test creation time by 90%. Plus, RAGAS uses reference-free evaluation metrics, meaning it doesn’t rely on labeled ground truths. Instead, it measures faithfulness, answer relevancy, and context precision/recall by comparing retrieved contexts to the generated answers using similarity metrics like cosine similarity and precision@k.

MORGAN: 09:10

How does RAGAS fit into a typical AI stack?

ALEX: 09:13

It integrates with LangChain, LlamaIndex, and LangGraph to instrument retrieval pipelines. That way, you can automate evaluation tightly within your retrieval-augmented generation workflows.

CASEY: 09:25

And TruLens?

ALEX: 09:26

TruLens is all about production observability. It plugs into OpenTelemetry to collect distributed traces from deployed agents. Its standout feature is the RAG Triad framework, which decomposes hallucination detection into three components: Context Relevance—how related the provided context is to the query; Groundedness—whether the answer is supported by source documents; and Answer Relevance—how well the answer addresses the user’s question.

MORGAN: 09:53

That’s clever—breaking hallucinations into finer-grained signals helps debugging and alerting.

ALEX: 09:59

Exactly. TruLens offers composable feedback functions that chain these metrics together. It supports multiple LLM providers, so you can customize the evaluation logic based on your stack. The modular design also aids scalability and extensibility.

CASEY: 10:15

How does it handle latency and cost in production?

ALEX: 10:19

By integrating with OpenTelemetry and Snowflake analytics, TruLens offloads heavy trace analysis to scalable data warehouses, balancing detail with operational cost. Plus, its dashboards built on Streamlit or Snowflake UI enable real-time monitoring without developer overhead.

MORGAN: 10:36

Sounds like a well-thought-out architecture.

ALEX: 10:39

Let’s talk results. RAGAS’s synthetic test generation reduces test creation time by an astonishing 90% compared to manual query crafting. That’s a game changer for teams tuning RAG pipelines.

MORGAN: 10:51

That’s huge! Faster iterations mean more tuning cycles and better retrieval quality.

ALEX: 10:56

DeepEval leads on coverage with 50+ pre-built metrics, offering unparalleled insights into agent behavior. Its 12.2k+ GitHub stars reflect strong community trust.

CASEY: 11:07

But I noticed there’s a lack of quantitative benchmarks on evaluation accuracy, latency, or cost for these frameworks. That’s a concern for production deployments.

ALEX: 11:17

Absolutely. It highlights an open research and engineering area. Without standard benchmarks, teams must conduct their own pilot evaluations.

MORGAN: 11:26

TruLens, with around 10k stars and Snowflake backing, shines in production observability, but its metrics count is lower—around 15 core metrics focused on hallucination and context relevance.

ALEX: 11:39

Right. Each framework’s strengths address different operational needs, so the payoff is in combining them strategically.

CASEY: 11:47

Let’s get real about limitations. DeepEval’s huge metric set can introduce decision paralysis. Teams might struggle to pick which metrics to prioritize or how to interpret conflicting signals.

MORGAN: 11:59

And the fact that enterprise features require paid tiers means startups or smaller teams might have limited access to crucial capabilities.

CASEY: 12:07

RAGAS’s agent evaluation methods are still nascent, and its lack of native production monitoring means you’ll need additional tooling, increasing complexity. Plus, the knowledge graph-based synthetic generation comes with a steep learning curve.

MORGAN: 12:23

What about TruLens?

CASEY: 12:24

TruLens lacks dedicated agent-specific metrics like tool correctness. It relies on custom feedback functions, which require engineering resources and expertise to develop effectively. Also, all three frameworks depend heavily on LLM-as-a-judge architectures—meaning evaluator bias and high API costs remain major risks.

CASEY: 12:45

Thanks, Keith. So, integrating these frameworks demands significant upfront effort, and teams must plan for ongoing tuning and cost management.

SAM: 12:53

Let’s switch gears to real-world deployments. DeepEval is widely used in multi-step agent development across fintech and healthcare, where strict compliance and auditability require comprehensive metrics and CI/CD integration.

MORGAN: 13:07

That makes sense—complex workflows need robust validation before hitting production in regulated domains.

SAM: 13:14

RAGAS is favored in research and e-commerce for retrieval tuning. Its synthetic test generation accelerates search quality improvements and personalization tests.

CASEY: 13:24

Any examples of TruLens in production?

SAM: 13:27

TruLens powers monitoring for conversational AI platforms deployed by large enterprises, especially after Snowflake’s acquisition. They use it to detect hallucinations in customer support agents, feeding alerts into Datadog and Cortex AI platforms for rapid incident response.

MORGAN: 13:44

Interesting. So, enterprises combine TruLens’s distributed tracing with observability stacks like OpenTelemetry and Datadog to maintain trust in deployed agents.

SAM: 13:54

Exactly. And DeepTeam complements all this by providing adversarial red teaming to catch prompt injection and other vulnerabilities before release.

SAM: 14:04

Here’s a scenario—evaluating an e-commerce customer service AI agent with multi-step workflows, tool use for order lookups, and memory for past interactions. Let’s debate which framework suits best.

TAYLOR: 14:16

DeepEval is my pick here. Its `@observe` instrumentation and detailed agent-specific metrics like TaskCompletionMetric and ToolCorrectnessMetric validate multi-step behavior before deployment. Strong CI/CD integration means fast feedback cycles.

CASEY: 14:30

But DeepEval lacks native production observability. If you want to catch hallucinations or tool failures live, you’ll need TruLens. Its RAG Triad framework is tailor-made for operational hallucination detection and integrates with Snowflake and Streamlit dashboards.

ALEX: 14:47

RAGAS also deserves a mention. Its synthetic multi-hop query generation tests retrieval and tool call accuracy without labeled data—ideal for tuning the knowledge base supporting the agent. It’s less about end-to-end agent testing and more focused on retrieval quality.

SAM: 15:03

So the optimal strategy combines them: RAGAS for retrieval tuning, DeepEval for development testing, and TruLens for production monitoring. That hybrid approach covers the full lifecycle and ensures reliability at scale.

MORGAN: 15:17

Sounds like a powerful team-up.

SAM: 15:20

For engineers building evaluation pipelines, here’s some actionable advice. Start with DeepEval’s pytest-native testing and `@observe` to instrument your agent components. Use commands like `deepeval test run` to integrate tests in your CI/CD workflows.

JORDAN: 15:35

To generate diverse, realistic retrieval tests, leverage RAGAS’s knowledge graph synthetic test generation. It automates complex multi-hop queries, saving 90% of test creation time.

SAM: 15:47

In production, adopt TruLens’s RAG Triad framework for hallucination detection. Its composable feedback functions allow mixing metrics and chaining reasoning steps, which is great for complex agents.

CASEY: 15:59

Avoid relying on a single framework. Each has gaps. A hybrid strategy maximizes coverage and resilience.

MORGAN: 16:06

And thanks to shared ecosystem integration with LangChain, LlamaIndex, and LangGraph, combining these frameworks is technically feasible without reinventing the wheel.

MORGAN: 16:16

Before we continue, a quick shout-out: Keith Bourne’s book, "Unlocking Data with Generative AI and RAG," is an invaluable resource. While it focuses on RAG, it walks you through everything from fundamentals to implementing RAG in the most advanced agentic AI environments you’ll find today. If you want hands-on code labs and clear diagrams to deepen your understanding, grab the second edition on Amazon.

MORGAN: 16:41

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.

CASEY: 16:55

Head over to Memriq.ai for more AI deep-dives, practical guides, and cutting-edge research breakdowns.

SAM: 17:01

Despite the progress, major open problems remain. There’s no standardized benchmark to compare agent evaluation accuracy and reliability across frameworks, complicating tool selection.

KEITH: 17:12

Another challenge is evaluator calibration—ensuring the LLM-as-a-judge reliably assesses agent outputs without bias or inconsistency is still an open research area.

SAM: 17:23

Evaluation cost and latency at scale are largely unquantified, which complicates decisions to deploy these frameworks in production environments.

CASEY: 17:32

Metrics for memory retention and multi-agent coordination are missing or underdeveloped, even though these are vital for complex agent ecosystems.

MORGAN: 17:41

Also, the trade-offs between real-time streaming evaluation and batch offline scoring need exploration for various production scenarios.

SAM: 17:50

Research and industry standardization efforts are nascent. Those building evaluation pipelines today are pioneers, but the field is shaping up rapidly.

MORGAN: 17:59

My takeaway—comprehensive multi-dimensional evaluation is no longer optional for agentic AI. DeepEval, RAGAS, and TruLens each have strengths; combining them thoughtfully is the way forward.

CASEY: 18:12

Always be critical of evaluation results. No framework is perfect—beware evaluator bias, cost complexities, and integration overhead. Plan accordingly.

JORDAN: 18:21

The pace of innovation here is thrilling. Synthetic test generation and reference-free evaluation are redefining how we build trust in AI agents.

TAYLOR: 18:30

Architect your evaluation pipeline matching your system lifecycle: development testing, retrieval optimization, and production monitoring are distinct problems needing different tools.

ALEX: 18:40

Under the hood, these frameworks showcase clever engineering: knowledge graphs for test creation, distributed tracing for observability, and rich metric suites for agent validation. Dive deep!

SAM: 18:52

Keep an eye on security and adversarial testing as a crucial evaluation dimension. Red teaming frameworks like DeepTeam close critical gaps.

KEITH: 19:01

From practical experience, the biggest win comes from layered evaluation—combining frameworks, calibrating your metrics, and embedding evaluation into your CI/CD and production workflows. And for those wanting a deeper dive, my book on RAG is a solid foundation—especially for grasping the intricacies of agentic AI evaluation.

MORGAN: 19:21

Keith, thanks for giving us the inside scoop today.

KEITH: 19:24

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

CASEY: 19:30

Thanks, everyone, for tuning in. Remember, evaluation is the backbone of reliable agentic AI.

MORGAN: 19:36

Catch you next time on Memriq Inference Digest - Engineering Edition. Keep building, keep questioning, and stay curious!