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

In this episode of Memriq Inference Digest - Engineering Edition, we explore the cutting-edge evaluation frameworks designed for agentic AI systems. Dive into the strengths and trade-offs of DeepEval, RAGAS, and TruLens as we unpack how they address multi-step agent evaluation challenges, production readiness, and integration with popular AI toolkits.

In this episode:

- Compare DeepEval’s extensive agent-specific metrics and pytest-native integration for development testing

- Understand RAGAS’s knowledge graph-powered synthetic test generation that slashes test creation time by 90%

- Discover TruLens’s production-grade observability with hallucination detection via the RAG Triad framework

- Discuss hybrid evaluation strategies combining these frameworks across the AI lifecycle

- Learn about real-world deployments in fintech, e-commerce, and enterprise conversational AI

- Hear expert insights from Keith Bourne on calibration and industry trends

Key tools & technologies mentioned:

DeepEval, RAGAS, TruLens, LangChain, LlamaIndex, LangGraph, OpenTelemetry, Snowflake, Datadog, Cortex AI, DeepTeam

Timestamps:

00:00 - Introduction to agentic AI evaluation frameworks

03:00 - Key metrics and evaluation challenges

06:30 - Framework architectures and integration

10:00 - Head-to-head comparison and use cases

14:00 - Deep technical overview of each framework

17:30 - Real-world deployments and best practices

19:30 - Open problems and future directions

Resources:

1. "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition
2. This podcast is brought to you by Memriq.ai - AI consultancy and content studio building tools and resources for AI practitioners.