openjury 0.4.0

Python SDK for evaluating multiple model outputs using configurable LLM-based jurors

OpenJury 🏛️

A Python SDK for evaluating your agent's response quality using a configurable panel of LLM judges.

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Overview

OpenJury is an agent evaluation framework. Point it at your agent's HTTP endpoint and it will:

1. Send a prompt to your agent and collect the response
2. Pass the response to a panel of LLM judges (jurors), each scoring it against your criteria
3. Return a composite quality score with a full statistical breakdown

The primary output is a single composite_score — a weighted mean of all juror scores across all criteria, plus eight additional canned metrics (median, harmonic mean, weakest link, juror agreement, and more). You can also register a custom scoring function for domain-specific logic.

Why a jury instead of a single judge?

Relying on one LLM to evaluate outputs is common but fragile: it's expensive and prone to intra-model bias. Research from Cohere shows that a panel of smaller, diverse models produces more reliable and less biased evaluations at lower cost.

OpenJury makes this practical: configure jurors declaratively in JSON, wire rubrics per criterion for consistent scoring, and get a structured result you can act on.

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Installation

Requirements: Python 3.11 or newer

pip install openjury

From source

git clone https://github.com/robiscoding/openjury.git
cd openjury
pip install -e .
uv pip install -e ".[dev]"     # optional dev dependencies

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Choose your path

Track	Goal	Time	API keys?
Try it	See output shape, understand AgentEvalResult	2 min	No
Evaluate my agent	Full fetch + score pipeline	10 min	Yes
Production integrate	Batch, resume, custom scoring, CI	30+ min	Yes

Try it (no agent, no keys)

pip install openjury
python examples/hello_world/score_existing.py

→ examples/hello_world/ · offline demo with sample output

Evaluate my agent

# Terminal 1 — mock agent (or use your own endpoint)
python examples/tools/mock_agent.py --port 8080

# Terminal 2
export OPENAI_API_KEY="..." AGENT_API_KEY=demo
python examples/basic_usage/basic_jury_run.py

→ examples/basic_usage/ · docs/endpoint-config.md

Go deeper

• docs/ — architecture, config schema, composable API, CLI
• recipes/ — task-oriented how-tos
• notebooks/ — interactive Jupyter walkthroughs
• examples/ — full examples index

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Quick Start

1. Create a jury config

Set a jury-level llm_provider for shared credentials. Jurors inherit it by default. Use ${ENV_VAR} for secrets.

{
  "name": "Customer Support Jury",
  "score_scale": 5,
  "llm_provider": {
    "provider": "openai_compatible",
    "model_name": "gpt-4o-mini",
    "api_key": "${OPENAI_API_KEY}"
  },
  "jurors": [
    { "name": "Support Expert", "system_prompt": "You are a senior support manager.", "weight": 2.0 },
    { "name": "Customer Perspective", "weight": 1.0 }
  ],
  "criteria": [
    {
      "name": "helpfulness",
      "description": "Does the response resolve the customer's issue?",
      "weight": 2.0,
      "rubric": {
        "1": "Ignores or misunderstands the question",
        "3": "Partially addresses the question",
        "5": "Directly and completely resolves the issue"
      }
    },
    {
      "name": "accuracy",
      "description": "Is the information factually correct?",
      "weight": 2.0,
      "rubric": {
        "1": "Contains factual errors",
        "3": "Mostly accurate with minor gaps",
        "5": "Completely accurate"
      }
    }
  ]
}

Full field reference: docs/config-schema.md

2. Run an evaluation

from openjury import JuryConfig, OpenJury, ResultFormatter
from openjury.endpoint_fetcher import AgentEndpoint

jury = OpenJury(JuryConfig.from_json_file("jury_config.json"))

endpoint = AgentEndpoint(
    url="http://localhost:8080/v1/chat/completions",
    alias="my-agent",
    headers={"Authorization": "Bearer ${AGENT_API_KEY}"},
    request_body_template={
        "model": "my-model",
        "messages": [{"role": "user", "content": "{prompt}"}],
    },
)

result = jury.evaluate(prompt="How do I reset my password?", endpoint=endpoint)
print(ResultFormatter.format_result(result))
print(f"Score: {result.composite_score:.2f} / {result.score_scale}")

score_response() is a backward-compatible alias for evaluate().

CLI:

openjury run \
  --config jury_config.json \
  --endpoints-config endpoints.json \
  --prompt "How do I reset my password?"

3. Read the output

╔══ Quality Evaluation  (scale: 1–5) ══
  composite_score:   3.87 / 5  (0.774 normalized)
  juror_agreement (0–1)        0.880   ← 1 = unanimous
  ...

• composite_score — headline quality number (weighted_mean from trial 1)
• juror_agreement — near 1.0 = high confidence; near 0 = contested
• weakest_link — flags a standout failure even when composite looks okay

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Key Features

• Agent Evaluation — score a single agent response per prompt
• Structured Rubrics — score anchors per criterion improve inter-juror reliability
• Eight Canned Metrics — weighted mean, median, harmonic mean, weakest link, juror agreement, and more
• Custom Scoring — register a Python function for domain-specific composite logic
• Consistency Audit — num_trials > 1 measures response reliability
• Batch Evaluation — inline config datasets, JSONL/CSV files, or evaluate_items()
• Parallel Processing — concurrent jurors and batch items

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Examples

Example	What it shows
examples/hello_world/	Offline demo — no agent, no API keys
examples/basic_usage/	Single prompt, full pipeline, reading AgentEvalResult
examples/provider_configs/	OpenAI, OpenRouter, Ollama, mixed providers
examples/batch_eval/	Inline/JSONL/CSV datasets, batch-eval CLI
examples/custom_scoring/	ScoreAggregator.register(), safety-gate pattern
examples/consistency_audit/	num_trials > 1, ConsistencyResult.score_std
examples/resume_evaluation/	Fetch/score split, crash recovery
examples/web_server/	Flask API wrapping evaluation
examples/tools/	Mock agent for local development

Full index: examples/README.md

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Troubleshooting

Symptom	Fix
ConfigurationError for ${VAR}	Export env vars before OpenJury(...). See provider-config.md
Partial juror override ValidationError	Set all three: model_name, api_key, provider. See config-schema.md
JurorException: missing criterion	Juror JSON keys must match criteria[].name exactly
EndpointFetchError	Check URL, headers, response_path. See endpoint-config.md
Low juror_agreement	Add rubrics, lower juror temperature. See recipes/design-rubrics.md

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Documentation

Resource	Description
docs/	Architecture, config schema, API, CLI
recipes/	Task-oriented cookbook
notebooks/	Interactive tutorials
CONTRIBUTING.md	Development setup

Advanced topics (moved from this README for brevity):

• Composable API — fetch/score split, batch, serialization
• Batch evaluation — inline and JSONL/CSV datasets
• Consistency audit — num_trials
• Custom scoring — safety gates
• Provider setup — OpenAI, OpenRouter, Anthropic, Ollama

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Use Cases

• Customer support agents — score helpfulness, accuracy, and tone per response
• Code review assistants — evaluate correctness, readability, and security
• Content generation — assess clarity, tone, and factuality before publishing
• Production monitoring — track composite_score drift between model versions
• Consistency testing — run num_trials=3 before shipping a prompt change

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License

Apache License 2.0. See LICENSE.

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Contributing

Contributions welcome! See CONTRIBUTING.md.