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OpenJudge

OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards

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Preis unbestätigt★ 816 GitHub-StarsVerzeichnis aktualisiert · 5. Sept. 2026agent-skills skills agent

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OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards

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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",
)

Quelle ansehen ↗

Dateimetadaten

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.

Originaltext anzeigen

---
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",
)
```

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: 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

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: agentscope-ai/OpenJudge
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 3. Aug. 2026
Verzeichnis aktualisiert: 5. Sept. 2026
Anleitungspfad: skills/openjudge/SKILL.md @ 2151def3553e

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

80/100

Stark

Vertrauen

66/100

Nur Sandbox

Audit

80/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

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: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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  "supply": {
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    "scenario": "Data",
    "maintenance": "2mo since push",
    "risk": "Needs review"
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    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
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      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "agentscope-ai-openjudge (OpenJudge)",
      "install_command": "npx skills add agentscope-ai/OpenJudge",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "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"
  }
}

Für Ersteller

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: agentscope-ai
Quelle: agentscope-ai/OpenJudge
Indexiert von: OpenAgentSkill Community-Index

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