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",
)