Judgeval
Open source post-building layer for agents with evals and monitoring.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
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
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary development and maintenance focus is on Python, with limited official support for other languages
Core concepts are briefly covered but lack detailed walkthroughs or advanced use cases
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
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Judgeval
Step-by-step setup guide with code examples and common gotchas.