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OpenJudge
OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards
Overview
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",
)
File metadata
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.
View original text
---
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",
)
```
Review the source
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- License
- Apache-2.0
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Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: 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
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- agentscope-ai/OpenJudge
- License
- Apache-2.0
- Version
- 1.0.0
- Last GitHub push
- Aug 3, 2026
- Registry updated
- Sep 5, 2026
- Instruction path
- skills/openjudge/SKILL.md @ 2151def3553e
Version reported in registry metadata; check source releases before relying on it.
Quality
80/100
Strong
Trust
66/100
Sandbox only
Audit
80/100
Needs review
- 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
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"install": "https://www.openagentskill.com/api/skills/agentscope-ai-openjudge/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/agentscope-ai-openjudge"
}
}For the creator
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