openlayer-ai/jevals

Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Python ◇ agents MIT

View on GitHub Website

★97STARS

⑂9FORKS

!5ISSUES

🏆#6,815GLOBAL RANK

🔥2DAYS TRENDING

🚀

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Star History

Continuous Observations

Interactive star growth chart for openlayer-ai/jevals

Momentum

+10

STARS · LAST 30 DAYS

PER DAY

#824

MOST-STARRED Python

Window	7 days	30 days	90 days
Stars gained	+7	+10	+90
Per day	1	1	1
Forks gained	+1	+3	+10

jevals gained 10 stars in the last 30 days, about 1 a day, and now has 97. It is about 1 year old and has averaged roughly 97 stars a year. It ranks #824 among Python repositories and #6,815 across all languages on GitHubRepo.

Trending Record

jevals has maintained a continuous presence across global trending indexes, peaking at #5613. Below is the 30-day activity profile:

💡 Overview

jevals is an open-source project written in Python: Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Engineered for speed, consistency, and developer ease, it solves common hurdles in agents, evals, guardrails. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

Optimized execution pipeline written in Python for predictable speed.

Zero-friction configuration with comprehensive sensible defaults out of the box.

Cross-platform runtime support across Linux, macOS, and Windows environments.

Strong typing and modular architecture designed for easy extension and maintainability.

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal

$ pip install jevals

$ pip install jevals # or: brew install jevals

> pip install jevals

⚙ System Requirements

Platforms

• macOS
• Linux
• Windows

Runtime & Dependencies

Python >= 3.9, pip, virtualenv

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

jevals coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Autonomous AI Agents

Orchestrate intelligent workflows and tool-calling routines with jevals.

Model Inference & Prompting

Integrate fast, local or cloud-hosted generative AI models directly into production code.

Context Memory & RAG

Augment language models with dynamic vector retrieval and structured project memory.

Developer Productivity

Automate repetitive engineering tasks, code generation, and test creation using AI agents.

🚀 Getting Started

Install jevals using your package manager: `pip install jevals`

Initialize your project workspace or configuration file for jevals.

Import jevals into your codebase or invoke it directly from your terminal.

Execute your test suite or run `jevals --help` to verify successful setup.

👍 Strengths

Active community backing with 97 GitHub stars and verified adoption.

Permissive open-source distribution under the MIT license.

Built in Python for high execution speed and developer familiarity.

Cross-platform compatibility across modern Linux, macOS, and Windows environments.

Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with Python and modern CLI workflows.

Ecosystem extensions may require manual configuration depending on environment constraints.

Active development roadmap means breaking API changes may occur across major versions.

⇄ Alternatives & Direct Competitors

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A collective list of free APIs

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The agent that grows with you

Compare ↗

👥 Who Should Use This

Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

openlayer-ai/jevals is currently ranked #6,815 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

Rank	Repository	Language	Stars	Action
#6,806	Mobaia/PhySpec	Python	★ 98	Compare ↗
#6,806	docker/sandbox-kit-spec	Go	★ 98	Compare ↗
#6,806	ThinkFlowLab/system1-agents	Python	★ 98	Compare ↗
#6,806	tihanyin/REx-skill	Python	★ 98	Compare ↗
#6,806	Simreal-AI/MathmoBench	Python	★ 98	Compare ↗
#6,815	openlayer-ai/jevals This Project	Python	★ 97
#6,815	masumedb/masume	Go	★ 97	Compare ↗
#6,815	lonecoding/QuantumultX-Rules	Python	★ 97	Compare ↗
#6,815	Erutaner/CLADE	Python	★ 97	Compare ↗
#6,815	Albertchamberlain/Awesome-OKF	Python	★ 97	Compare ↗
#6,815	erfnzdeh/my.edu.sharif.edu-sniper	Go	★ 97	Compare ↗

Frequently Asked Questions

What does jevals do? +

Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

What language is jevals written in? +

The primary language is Python. Topics include: agents, evals, guardrails, jev, llm.

Is jevals actively maintained? +

Yes, the last recorded push was on Sep 24, 2026 with 5 open issues being tracked.

How many stars does jevals have? +

jevals has 97 stars and 9 forks on GitHub.

How does jevals rank among GitHub repositories? +

With 97 stars, openlayer-ai/jevals is ranked #6,815 globally across all repositories tracked on GitHubRepo and #824 among Python projects.

What license is jevals distributed under? +

The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.

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LinkedIn

Why jevals is replacing paid subscriptions for engineering teams

Stop paying $50+/month for locked SaaS tools.

Over 97 developers have starred openlayer-ai/jevals, an impressive Python open-source project designed to Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Key Engineering Advantages:
• 100% Local-First: Complete data privacy with zero tracking or telemetry.
• Zero Subscription Costs: No paywalls, per-seat fees, or API markups.
• Complete Customization: Native open-source architecture you can fork, inspect, and deploy anywhere.
• Proven Adoption: 97 GitHub stars and 9 forks.

If you're looking to streamline your engineering workflows without ballooning software expenses, this is worth checking out.

👉 Full star velocity and architectural report: https://githubrepo.cloud/repo/openlayer-ai/jevals
⭐ GitHub Repository: https://github.com/openlayer-ai/jevals

#OpenSource #SoftwareEngineering #DeveloperTools #Tech #Programming #Python

🤖

Reddit (r/opensource, r/webdev, r/selfhosted)

Show r/opensource: jevals – A free, self-hosted tool that lets you Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya

Hey everyone,

I wanted to share an interesting open-source project called **jevals** (https://github.com/openlayer-ai/jevals) that has been picking up heavy momentum lately (97 stars).

### What problem does it solve?
Many existing tools in this domain require expensive cloud plans or store your data on third-party servers. jevals was built with a local-first philosophy, allowing developers to Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya without recurring subscription costs or privacy compromises.

### Tech Stack & Details:
- **Language:** Python
- **License:** MIT
- **GitHub:** [openlayer-ai/jevals](https://github.com/openlayer-ai/jevals)
- **Live Star Momentum Tracker:** [https://githubrepo.cloud/repo/openlayer-ai/jevals](https://githubrepo.cloud/repo/openlayer-ai/jevals)

Has anyone here tested this in production or integrated it into existing pipelines? Would love to hear your experience and feedback!

🟧

Hacker News (Show HN)

Show HN: jevals – Agent evals and guardrails as Jev decisions: one request per trace,...

Hi HN,

jevals is an open-source Python project designed to Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Rather than relying on closed cloud endpoints, it executes locally on your own machine, providing predictable latency, total privacy, and full control.

Repository: https://github.com/openlayer-ai/jevals
Documentation & Metrics: https://githubrepo.cloud/repo/openlayer-ai/jevals

Feedback and PRs are welcome.

✍️

Dev.to / Hashnode Tags: opensource, python, devtools, webdev

This GitHub repo replaces a $50 a month tool! Stop paying expensive software subscriptions. Meet jevals (97 GitHub Stars)

---
title: This GitHub repo replaces a $50 a month tool! Stop paying expensive software subscriptions. Meet jevals
published: true
tags: opensource, python, devtools, programming
---

## Stop Paying Software Subscriptions For This

Finding high-quality, production-ready open source alternatives to expensive proprietary software can save engineering teams thousands of dollars every year.

Today, we're looking at **[openlayer-ai/jevals](https://github.com/openlayer-ai/jevals)**, a rapidly rising repository with **97 GitHub stars** and **9 forks**.

### What is jevals?

> Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya

Written in **Python**, jevals runs directly on your local workstation or private server, keeping your data secure and eliminating recurring license fees.

### Key Highlights
- **High Performance:** Lightweight, reliable, and built in Python.
- **Privacy & Security:** Complete ownership of your environment and data.
- **Growing Momentum:** Ranked **#6,815** globally across tracked repositories on [GitHubRepo](https://githubrepo.cloud/repo/openlayer-ai/jevals).

### Quick Start
To get started locally:

```bash
git clone https://github.com/openlayer-ai/jevals.git
cd jevals
```

Check out the full repository and star history analytics here:
- **GitHub:** [openlayer-ai/jevals](https://github.com/openlayer-ai/jevals)
- **Star Velocity Tracker:** [jevals on GitHubRepo.cloud](https://githubrepo.cloud/repo/openlayer-ai/jevals)

𝕏

X / Twitter Communities

Thread / Community Post for jevals

Developers are sleeping on this GitHub repo 👇

openlayer-ai/jevals (97 ⭐) replaces expensive monthly software subscriptions with 100% free open source code.

⚡ What it does: Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya
💻 Built with: Python
🔒 100% local execution & privacy

Check live star velocity and benchmarks:
https://githubrepo.cloud/repo/openlayer-ai/jevals

#opensource #developer #coding #tech #programming

🧵

Threads Topics

Topic Discussion: Open Source & Developer Tools

This GitHub repo replaces a $50/month tool! 🚀

If you want to save hours of manual work and stop paying subscriptions, check out openlayer-ai/jevals.

It already has over 97 stars and lets you Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Drop a comment if you've tried it, or check the full star momentum report:
https://githubrepo.cloud/repo/openlayer-ai/jevals

From our network

openlayer-ai/jevals

Star History

Momentum

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💡 Overview

⚡ Key Features

📥 Installation

⚙ System Requirements

Platforms

Runtime & Dependencies

Architecture

🧠 How It Works

🎯 Production Use Cases

Autonomous AI Agents

Model Inference & Prompting

Context Memory & RAG

Developer Productivity

🚀 Getting Started

👍 Strengths

⚠️ Considerations

⇄ Alternatives & Direct Competitors

👥 Who Should Use This

🏆 Nearby in the Rankings

Frequently Asked Questions

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