Judgeval

Open source post-building layer for agents with evals and monitoring.

EstablishedOpen SourceLow lock-in

Pricing

See website

Flat rate

Adoption

Stable

License

Open Source

Data freshness

Overview

What is Judgeval?

Judgeval is an open-source tool that provides a post-training evaluation environment for AI agents, supporting reinforcement learning (RL) and supervised fine-tuning (SFT). It helps in monitoring and improving the performance of trained models through continuous evaluation.

Key differentiator

Judgeval stands out as an open-source solution specifically designed for evaluating and continuously improving AI agents after their initial training, offering a unique focus on post-building evaluation.

Capability profile

Strength Radar

Post-training ev…Supports reinfor…Continuous monit…

Honest assessment

Strengths & Weaknesses

↑ Strengths

Post-training evaluation environment for AI agents

Supports reinforcement learning and supervised fine-tuning

Continuous monitoring of model performance

Fit analysis

Who is it for?

✓ Best for

Teams needing a robust evaluation framework for their AI agents post-training

Data science teams focused on improving model performance through iterative testing and monitoring

✕ Not a fit for

Projects requiring real-time feedback during the training phase (Judgeval focuses on post-training)

Applications where continuous monitoring is not critical to the success of the project

Cost structure

Pricing

Free Tier

None

Starts at

See website

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Alternatives

Next step

Get Started with Judgeval

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →