openjudge

name openjudge description Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system. OpenJudge Skill Build evaluation pipelines for LLM applications using the openjudge library. When to Use This Skill User wants to evaluate LLM output quality (correctness, relevance, hallucination, etc.) User wants to compare two or more models and rank them User wants to design a scoring rubric and automate evaluation User wants to analyze evaluation results statistically User wants to build a reward model or quality filter Sub-documents — Read When Relevant Topic File Read when… Grader selection & configuration graders.md User needs to pick or configure an evaluator Batch evaluation pipeline pipeline.md User needs to run evaluation over a dataset Auto-generate graders from data generator.md No rubric yet; generate from labeled examples Analyze & compare results analyzer.md User wants win rates, statistics, or metrics Read the relevant sub-document before writing any code. Install pip install py-openjudge Architecture Overview Dataset (List[dict]) │ ▼ GradingRunner ← orchestrates everything │ ├─► Grader A ──► EvaluationStrategy ──► _aevaluate() ──► GraderScore / GraderRank ├─► Grader B ──► EvaluationStrategy ──► _aevaluate() ──► GraderScore / GraderRank └─► Grader C ... │ ├─► Aggregator (optional) ← combine multiple grader scores into one │ └─► RunnerResult ← {grader_name: [GraderScore, ...]} │ ▼ Analyzer ← statistics, win rates, validation metrics 5-Minute Quick Start Evaluate responses for correctness using a built-in grader: import asyncio from openjudge.models.openai_chat_model import OpenAIChatModel from openjudge.graders.common.correctness import CorrectnessGrader from openjudge.runner.grading_runner import GradingRunner # 1. Configure the judge model (OpenAI-compatible endpoint) model = OpenAIChatModel( model= "qwen-plus" , api_key= "sk-xxx" , base_url= "https://dashscope.aliyuncs.com/compatible-mode/v1" , ) # 2. Instantiate a grader grader = CorrectnessGrader(model=model) # 3. Prepare dataset dataset = [ { "query" : "What is the capital of France?" , "response" : "Paris is the capital of France." , "reference_response" : "Paris." , }, { "query" : "What is 2 + 2?" , "response" : "The answer is five." , "reference_response" : "4." , }, ] # 4. Run evaluation async def main (): runner = GradingRunner( grader_configs={ "correctness" : grader}, max_concurrency= 8 , ) results = await runner.arun(dataset) for i, result in enumerate (results[ "correctness" ]): print ( f"[ {i} ] score= {result.score} reason= {result.reason} " ) asyncio.run(main()) Expected output: [0] score=5 reason=The response accurately states Paris as capital... [1] score=1 reason=The response gives the wrong answer (five vs 4)... Key Data Types Type Description GraderScore Pointwise result: .score (float), .reason (str), .metadata (dict) GraderRank Listwise result: .rank (List[int]), .reason (str), .metadata (dict) GraderError Error during evaluation: .error (str), .reason (str) RunnerResult Dict[str, List[GraderResult]] — keyed by grader name Result Handling Pattern from openjudge.graders.schema import GraderScore, GraderRank, GraderError for grader_name, grader_results in results.items(): for i, result in enumerate (grader_results): if isinstance (result, GraderScore): print ( f" {grader_name} [ {i} ]: score= {result.score} " ) elif isinstance (result, GraderRank): print ( f" {grader_name} [ {i} ]: rank= {result.rank} " ) elif isinstance (result, GraderError): print ( f" {grader_name} [ {i} ]: ERROR — {result.error} " ) Model Configuration All LLM-based graders accept either a BaseChatModel instance or a dict config: # Option A: instance from openjudge.models.openai_chat_model import OpenAIChatModel model = OpenAIChatModel(model= "gpt-4o" , api_key= "sk-..." ) # Option B: dict (auto-creates OpenAIChatModel) model_cfg = { "model" : "gpt-4o" , "api_key" : "sk-..." } grader = CorrectnessGrader(model=model_cfg) # OpenAI-compatible endpoints (DashScope / local / etc.) model = OpenAIChatModel( model= "qwen-plus" , api_key= "sk-xxx" , base_url= "https://dashscope.aliyuncs.com/compatible-mode/v1" , )