Evaluation & Testing

Judgeval

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

Status
Generally available
Last checked
Checked · Oct 11, 2026
License
Open source
Pricing
Free tier · from $0

What it is

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.

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.

When it fits — and when it doesn’t

A good fit 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

Look elsewhere if

  • 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

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Documentation lacks depth and examples

    Core concepts are briefly covered but lack detailed walkthroughs or advanced use cases

What it does well

  • Post-training evaluation environment for AI agents

  • Supports reinforcement learning and supervised fine-tuning

  • Continuous monitoring of model performance

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up Judgeval

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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Judgeval — tracked on AI Navigator

Markdown

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