Continuous-Eval

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continuous-eval is an open-source package for data-driven evaluation of LLM-powered applications. It offers modularized evaluation with tailored metrics for various pipeline modules.

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At a glance

Pricing

open-source

Free tier

Yes

API

—

Skill level

Technical

About

What is continuous-eval?

continuous-eval is an open-source package designed for the data-driven evaluation of applications powered by Large Language Models (LLMs). It provides a modular approach to evaluation, allowing users to apply tailored metrics to each specific module within their LLM pipeline. The tool includes a comprehensive library of metrics to facilitate thorough assessment. It supports the evaluation of diverse LLM use cases, including Retrieval-Augmented Generation (RAG), code generation, and the utilization of agent tools.

Best used for

Evaluating the performance and effectiveness of various modules within LLM-powered applications, including RAG and code generation.

Common actions

Evaluate LLM performance

Improve LLM applications

Measure RAG effectiveness

Assess code generation quality

Monitor agent tool usage

low-code/no-codeopen-sourceautomated workflowworkflowsdeepfakecollaborationgithub copilot"AI Agents"face swapping

Capabilities

Key features

Open-source package
Data-driven evaluation
Modular evaluation
Comprehensive metric library
Supports RAG, code generation

Target Audience

LLM DevelopersAI EngineersData ScientistsResearchers

Integrations

Not yet documented

Pricing & Plans

open-source

Free

FAQs

Since continuous-eval is open-source, are there any hidden costs or limitations to its features?

No, continuous-eval is genuinely open-source under the Apache 2.0 license, meaning all features are freely available without hidden costs, subscriptions, or usage limits. Users can inspect, modify, and distribute the code.

What kind of technical expertise is required to effectively use continuous-eval for LLM evaluation?

Users should have intermediate programming skills, particularly in Python, and a foundational understanding of LLM concepts and evaluation methodologies. Familiarity with setting up development environments will also be beneficial.

Can continuous-eval be integrated with existing MLOps pipelines or CI/CD workflows for automated evaluation?

Yes, continuous-eval is designed to be modular and can be integrated into existing MLOps and CI/CD pipelines. Its Python-based nature allows for scripting and automation of evaluation processes within your development lifecycle.

Does continuous-eval provide tools for visualizing evaluation results or comparing different LLM models?

While continuous-eval focuses on generating comprehensive evaluation metrics, it provides the data in a structured format that can be easily exported. Users can then leverage external visualization tools like Matplotlib, Seaborn, or dashboarding solutions to analyze and compare results.

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