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Unlock Your Retrieval Potential with LlamaIndex Eval

The ultimate evaluation toolkit for optimal retrieval pipelines.

shipped Nov 21, 2025buildpaid
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Why it matters

1Comprehensive metric suite for accurate performance evaluation across complex RAG applications.
2Advanced sensitivity testing and outlier detection for improved workflow efficiency and reliability.
3Seamless integration with external tools and automated batch evaluation for scalable performance monitoring.

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

Transform Your Evaluation Processes

LlamaIndex Eval is designed to empower developers and enterprise teams by providing a robust toolkit for evaluating retrieval pipelines. Its powerful metrics and automation features ensure that you maintain the highest standards of retrieval fidelity.

  • Evaluate both response and retrieval accuracy effortlessly.
  • Gain insights into performance with real-time interpretable metrics.
  • Customize evaluation processes to fit your specific needs.

features

Key Features

Discover the features that set LlamaIndex Eval apart from traditional evaluation tools. From comprehensive metrics to advanced sensitivity testing, our toolkit provides everything you need for detailed assessments.

  • Expanded metric suite including correctness, faithfulness, and relevancy.
  • Batch evaluation tools for efficient outlier detection.
  • Automated workflows for scalable model auditing.

use cases

Optimal Use Cases

Whether you're working on document-heavy applications, multi-agent systems, or knowledge bases, LlamaIndex Eval is equipped to handle various scenarios. Leverage its capabilities for effective retrieval management and precision.

  • Perfect for enterprise teams focused on retrieval optimization.
  • Ideal for complex RAG systems where performance is critical.
  • Supports various evaluation workflows tailored to specific projects.

Policies

Free Tier

Vendor website advertises a free tier.

Pricing Page

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