py-openjudge 0.1.8

OpenJudge: A Unified Framework for Holistic Evaluation and Quality Reward

Holistic Evaluation, Quality Rewards: Driving Application Excellence

🌟 If you find OpenJudge helpful, please give us a Star! 🌟

📖 Documentation | 🤝 Contributing | 中文

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📑 Table of Contents

• Key Features
• News
• Installation
• Quickstart
• Integrations
• Contributing
• Citation

OpenJudge is a unified framework designed to drive application excellence through Holistic Evaluation and Quality Rewards.

💡 Evaluation and reward signals are the cornerstones of application excellence. Holistic evaluation enables the systematic analysis of shortcomings to drive rapid iteration, while high-quality rewards provide the essential foundation for advanced optimization and fine-tuning.

OpenJudge unifies evaluation metrics and reward signals into a single, standardized Grader interface, offering pre-built graders, flexible customization, and seamless framework integration.

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✨ Key Features

📦 Systematic & Quality-Assured Grader Library

Access 50+ production-ready graders featuring a comprehensive taxonomy, rigorously validated for reliable performance.

🎯 General Focus: Semantic quality, functional correctness, structural compliance Key Graders: Relevance - Semantic relevance scoring Similarity - Text similarity measurement Syntax Check - Code syntax validation JSON Match - Structure compliance

🤖 Agent Focus: Agent lifecycle, tool calling, memory, plan feasibility, trajectory quality Key Graders: Tool Selection - Tool choice accuracy Memory - Context preservation Plan - Strategy feasibility Trajectory - Path optimization

🖼️ Multimodal Focus: Image-text coherence, visual generation quality, image helpfulness Key Graders: Image Coherence - Visual-text alignment Text-to-Image - Generation quality Image Helpfulness - Image contribution

• 🌐 Multi-Scenario Coverage: Extensive support for diverse domains including Agent, text, code, math, and multimodal tasks. → Explore Supported Scenarios
• 🔄 Holistic Agent Evaluation: Beyond final outcomes, we assess the entire lifecycle—including trajectories, Memory, Reflection, and Tool Use. → Agent Lifecycle Evaluation
• ✅ Quality Assurance: Every grader comes with benchmark datasets and pytest integration for validation. → View Benchmark Datasets

🛠️ Flexible Grader Building Methods

Choose the build method that fits your requirements:

• Customization: Easily extend or modify pre-defined graders to fit your specific needs. 👉 Custom Grader Development Guide
• Data-Driven Rubrics: Have a few examples but no clear rules? Use our tools to automatically generate white-box evaluation criteria (Rubrics) based on your data.👉 Automatic Rubric Generation Tutorial
• Training Judge Models ( Coming Soon🚀): For high-scale and specialized scenarios, we are developing the capability to train dedicated Judge models. Support for SFT, Bradley-Terry models, and Reinforcement Learning workflows is on the way to help you build high-performance, domain-specific graders.

🔌 Easy Integration (🚧 Coming Soon)

We're actively building seamless connectors for mainstream observability platforms and training frameworks. Stay tuned! → See Integrations

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News

• 2025-12-26 - Released OpenJudge v0.2.0 on PyPI - Major Update! This release expands our core capabilities by adding robust support for diverse evaluation scenarios on top of reward construction. By unifying reward and evaluation signals, OpenJudge v0.2.0 provides a more holistic approach to optimizing application performance and excellence. → For v0.1.x Users

• 2025-10-20 - Auto-Rubric: Learning to Extract Generalizable Criteria for Reward Modeling - We released a new paper on learning generalizable reward criteria for robust modeling.

• 2025-10-17 - Taming the Judge: Deconflicting AI Feedback for Stable Reinforcement Learning - We introduced techniques to align judge feedback and improve RL stability.

• 2025-07-09 - Released OpenJudge v0.1.0 on PyPI

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📥 Installation

pip install py-openjudge

💡 More installation methods can be found in the Quickstart Guide.

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🚀 Quickstart

import asyncio
from openjudge.models import OpenAIChatModel
from openjudge.graders.common.relevance import RelevanceGrader

async def main():
    # 1️⃣ Create model client
    model = OpenAIChatModel(model="qwen3-32b")

    # 2️⃣ Initialize grader
    grader = RelevanceGrader(model=model)

    # 3️⃣ Prepare data
    data = {
        "query": "What is machine learning?",
        "response": "Machine learning is a subset of AI that enables computers to learn from data.",
    }

    # 4️⃣ Evaluate
    result = await grader.aevaluate(**data)

    print(f"Score: {result.score}")   # Score: 5
    print(f"Reason: {result.reason}")

if __name__ == "__main__":
    asyncio.run(main())

📚 Complete Quickstart can be found in the Quickstart Guide.

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🔗 Integrations

Seamlessly connect OpenJudge with mainstream observability and training platforms, with more integrations on the way:

Category	Status	Platforms
Observability	🟡 In Progress	LangSmith, LangFuse, Arize Phoenix
Training	🔵 Planned	verl, Trinity-RFT

💬 Have a framework you'd like us to prioritize? Open an Issue!

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🤝 Contributing

We love your input! We want to make contributing to OpenJudge as easy and transparent as possible.

🎨 Adding New Graders — Have domain-specific evaluation logic? Share it with the community!
🐛 Reporting Bugs — Found a glitch? Help us fix it by opening an issue
📝 Improving Docs — Clearer explanations or better examples are always welcome
💡 Proposing Features — Have ideas for new integrations? Let's discuss!

📖 See full Contributing Guidelines for coding standards and PR process.

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📦 For v0.1.x Users

Package renamed from rm-gallery → py-openjudge. Legacy version still available via pip install rm-gallery. Source code preserved in v0.1.6 branch.

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📄 Citation

If you use OpenJudge in your research, please cite:

@software{
  title  = {OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards},
  author = {The OpenJudge Team},
  url    = {https://github.com/modelscope/OpenJudge},
  month  = {07},
  year   = {2025}
}

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Made with ❤️ by the OpenJudge Team

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