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OpenJudge

OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards

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Precio sin confirmar★ 816 Estrellas de GitHubRegistro actualizado · 5 sept 2026agent-skills skills agent

Resumen

OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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",
)

Ver fuente ↗

Metadatos del archivo

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.

Ver texto original

---
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

```bash
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:

```python
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

```python
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:

```python
# 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",
)
```

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: Apache-2.0
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: agentscope-ai/OpenJudge
Licencia: Apache-2.0
Versión: 1.0.0
Último push de GitHub: 3 ago 2026
Registro actualizado: 5 sept 2026
Ruta de instrucciones: skills/openjudge/SKILL.md @ 2151def3553e

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

80/100

Sólido

Confianza

66/100

Solo sandbox

Auditoría

80/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir API de instalación Formato de texto LLM Abrir Manifest Plan de resolución automática

Más detalles

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  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "agentscope-ai-openjudge",
      "task": "Use OpenJudge in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/agentscope-ai-openjudge",
    "api": "https://www.openagentskill.com/api/agent/skills/agentscope-ai-openjudge",
    "audit": "https://www.openagentskill.com/skills/agentscope-ai-openjudge/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=agentscope-ai-openjudge&task=Use%20OpenJudge%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20OpenJudge%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20OpenJudge%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/agentscope-ai-openjudge/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/agentscope-ai-openjudge"
  }
}

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Creador: agentscope-ai
Fuente: agentscope-ai/OpenJudge
Indexado por: Índice comunitario de OpenAgentSkill

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Resumen

OpenJudge Skill

When to Use This Skill

Sub-documents — Read When Relevant

Install

Architecture Overview

5-Minute Quick Start

Key Data Types

Result Handling Pattern

Model Configuration

Revisar el código fuente

Precio y costes de ejecución

Empieza con una tarea pequeña

Fuente y notas de uso

Acceso para agentes

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