Openjudge
OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards
Last verified:
What is Openjudge?
OpenJudge is an open-source AI evaluation and quality reward framework designed to solve the core challenge in AI development:
Openjudge pricing
Pricing model: Freemium
OpenJudge is completely open-source and free under the Apache 2.0 license. There is no paid tier - the entire framework is free to use. You can try it online instantly at openjudge.me/app without installation, or install locally via 'pip install py-openjudge'. For LLM-based graders, you need to configure your own OpenAI-compatible API credentials (OpenAI, DashScope/Qwen, or other compatible providers), so API costs from those providers would apply separately.
Openjudge pros
- 50+ production-grade graders covering multiple AI scenarios
- Completely open-source and free under Apache 2.0 license
- Seamless integration with LangSmith and Langfuse
- Supports RL training frameworks like VERL for reward signals
- Each grader validated against benchmark datasets
- Multiple grader building methods including zero-shot rubric auto-generation
- Supports Agent, LLM, multimodal, code, and math reasoning evaluation
- Python SDK and REST API for easy CI/CD integration
- No installation required - can try online instantly
- AI Academic Paper Review supporting 10 disciplines
- Adopted by Alibaba Group core businesses including Alibaba Cloud
- Custom Judge model training capability
- Data-driven grader generation for business-specific needs
- Full-stack monitoring capabilities through platform integration
- Interactive grader testing on openjudge.me/app platform
Openjudge cons
- Requires OpenAI-compatible API credentials for LLM-based graders
- Primarily focused on enterprise/production use cases
- Documentation mostly in Chinese with some English
- Need to configure environment variables for API access
- Learning curve for custom grader development
- Relies on external LLM APIs which may incur costs
- Limited to AI/LLM evaluation - not for general programming contests
- Community support mainly through DingTalk group in China
Frequently asked questions about Openjudge
What is OpenJudge and what problem does it solve?
OpenJudge is an open-source AI evaluation and quality reward framework. It addresses the core challenge in AI development: 'you can't trust what you can't measure.' With 50+ production-grade graders, OpenJudge helps teams systematically evaluate LLMs, Agents, and multimodal models to ensure safety, reliability, and usability of AI applications.
What evaluation scenarios does OpenJudge support?
OpenJudge supports a wide range of AI evaluation scenarios including: Agent Evaluation (covering action alignment, memory, plan feasibility, reflection, tool use), LLM General Evaluation (relevance, correctness, hallucination, harmfulness, instruction following), Multimodal Evaluation (image/video), Code Evaluation, and Math Reasoning Evaluation. Each grader is validated against benchmark datasets.
How do I integrate OpenJudge into my existing AI workflow?
OpenJudge seamlessly integrates with major AI observability platforms like LangSmith and Langfuse for full-stack monitoring. It also works with RL training frameworks like VERL, converting evaluations into reward signals. You can integrate via Python SDK (pip install py-openjudge) or REST API into your CI/CD pipeline.
Is OpenJudge free and how can I try it online?
Yes, OpenJudge is completely open-source and free under the Apache 2.0 license. You can try it online instantly at openjudge.me/app without any installation required, or install locally via 'pip install py-openjudge'.
Which companies are using OpenJudge?
OpenJudge is adopted by multiple core businesses within Alibaba Group, including Alibaba Cloud Bailian, Alibaba Cloud International, Amap (高德地图), and Ant Group (蚂蚁集团).
What are the different ways to build graders in OpenJudge?
OpenJudge supports multiple grader building methods: custom evaluation rules where you define your own criteria, zero-shot rubric auto-generation that creates evaluation rubrics automatically, data-driven grader generation based on your dataset, and training your own custom Judge models for specialized evaluation needs.
What are the built-in graders available in OpenJudge?
OpenJudge provides 50+ built-in graders including: RelevanceGrader (scores how well response addresses query on 1-5 scale), CorrectnessGrader (factually correctness), HallucinationGrader (detects hallucinations), HarmfulnessGrader (safety assessment), InstructionFollowingGrader (follows instructions), plus Agent-specific graders for Action Alignment, Memory (Accuracy/Retrieval/Preservation), Plan Feasibility, Reflection, and Tool Use evaluation.
How do I configure API credentials for OpenJudge?
For LLM-based graders, you need to configure OpenAI-compatible API credentials via environment variables. Examples: For OpenAI set OPENAI_API_KEY and OPENAI_BASE_URL=https://api.openai.com/v1. For DashScope (Qwen) set OPENAI_API_KEY and OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1. OpenJudge uses the OpenAI-compatible API format.
What is the AI Academic Paper Review feature?
The new AI Academic Paper Review feature supports 10 disciplines including Computer Science, Medicine, Physics, Chemistry, Biology, and more. You can upload a PDF and receive professional review covering quality, originality, correctness, and formatting across multiple dimensions. Access it at openjudge.me/paper_review.
How do I join the OpenJudge community?
You can join the OpenJudge community by scanning the QR code to join the OpenJudge DingTalk group (钉钉群). There you can get the latest version updates, technical support, Q&A, and community discussions. Connect with developers from Alibaba, Ant Group, and other companies to exchange AI evaluation best practices.