Auto arena

Auto Arena Skill

End-to-end automated model comparison using the OpenJudge AutoArenaPipeline:

1. Generate queries — LLM creates diverse test queries from task description
2. Collect responses — query all target endpoints concurrently
3. Generate rubrics — LLM produces evaluation criteria from task + sample queries
4. Pairwise evaluation — judge model compares every model pair (with position-bias swap)
5. Analyze & rank — compute win rates, win matrix, and rankings
6. Report & charts — Markdown report + win-rate bar chart + optional matrix heatmap

Prerequisites

# Install OpenJudge
pip install py-openjudge

# Extra dependency for auto_arena (chart generation)
pip install matplotlib

Gather from user before running

Info	Required?	Notes
Task description	Yes	What the models/agents should do (set in config YAML)
Target endpoints	Yes	At least 2 OpenAI-compatible endpoints to compare
Judge endpoint	Yes	Strong model for pairwise evaluation (e.g. gpt-4, qwen-max)
API keys	Yes	Env vars: OPENAI_API_KEY, DASHSCOPE_API_KEY, etc.
Number of queries	No	Default: 20
Seed queries	No	Example queries to guide generation style
System prompts	No	Per-endpoint system prompts
Output directory	No	Default: ./evaluation_results
Report language	No	"zh" (default) or "en"

Quick start

CLI

# Run evaluation
python -m cookbooks.auto_arena --config config.yaml --save

# Use pre-generated queries
python -m cookbooks.auto_arena --config config.yaml \
  --queries_file queries.json --save

# Start fresh, ignore checkpoint
python -m cookbooks.auto_arena --config config.yaml --fresh --save

# Re-run only pairwise evaluation with new judge model
# (keeps queries, responses, and rubrics)
python -m cookbooks.auto_arena --config config.yaml --rerun-judge --save

Python API

import asyncio
from cookbooks.auto_arena.auto_arena_pipeline import AutoArenaPipeline

async def main():
    pipeline = AutoArenaPipeline.from_config("config.yaml")
    result = await pipeline.evaluate()

    print(f"Best model: {result.best_pipeline}")
    for rank, (model, win_rate) in enumerate(result.rankings, 1):
        print(f"{rank}. {model}: {win_rate:.1%}")

asyncio.run(main())

Minimal Python API (no config file)

import asyncio
from cookbooks.auto_arena.auto_arena_pipeline import AutoArenaPipeline
from cookbooks.auto_arena.schema import OpenAIEndpoint

async def main():
    pipeline = AutoArenaPipeline(
        task_description="Customer service chatbot for e-commerce",
        target_endpoints={
            "gpt4": OpenAIEndpoint(
                base_url="https://api.openai.com/v1",
                api_key="sk-...",
                model="gpt-4",
            ),
            "qwen": OpenAIEndpoint(
                base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
                api_key="sk-...",
                model="qwen-max",
            ),
        },
        judge_endpoint=OpenAIEndpoint(
            base_url="https://api.openai.com/v1",
            api_key="sk-...",
            model="gpt-4",
        ),
        num_queries=20,
    )
    result = await pipeline.evaluate()
    print(f"Best: {result.best_pipeline}")

asyncio.run(main())

CLI options

Flag	Default	Description
--config	—	Path to YAML configuration file (required)
--output_dir	config value	Override output directory
--queries_file	—	Path to pre-generated queries JSON (skip generation)
--save	False	Save results to file
--fresh	False	Start fresh, ignore checkpoint
--rerun-judge	False	Re-run pairwise evaluation only (keep queries/responses/rubrics)

Minimal config file

task:
  description: "Academic GPT assistant for research and writing tasks"

target_endpoints:
  model_v1:
    base_url: "https://api.openai.com/v1"
    api_key: "${OPENAI_API_KEY}"
    model: "gpt-4"
  model_v2:
    base_url: "https://api.openai.com/v1"
    api_key: "${OPENAI_API_KEY}"
    model: "gpt-3.5-turbo"

judge_endpoint:
  base_url: "https://api.openai.com/v1"
  api_key: "${OPENAI_API_KEY}"
  model: "gpt-4"

Full config reference

task

Field	Required	Description
description	Yes	Clear description of the task models will be tested on
scenario	No	Usage scenario for additional context

target_endpoints.<name>

Field	Default	Description
base_url	—	API base URL (required)
api_key	—	API key, supports ${ENV_VAR} (required)
model	—	Model name (required)
system_prompt	—	System prompt for this endpoint
extra_params	—	Extra API params (e.g. temperature, max_tokens)

judge_endpoint

Same fields as target_endpoints.<name>. Use a strong model (e.g. gpt-4, qwen-max) with low temperature (~0.1) for consistent judgments.

query_generation

Field	Default	Description
num_queries	20	Total number of queries to generate
seed_queries	—	Example queries to guide generation
categories	—	Query categories with weights for stratified generation
endpoint	judge endpoint	Custom endpoint for query generation
queries_per_call	10	Queries generated per API call (1–50)
num_parallel_batches	3	Parallel generation batches
temperature	0.9	Sampling temperature (0.0–2.0)
top_p	0.95	Top-p sampling (0.0–1.0)
max_similarity	0.85	Dedup similarity threshold (0.0–1.0)
enable_evolution	false	Enable Evol-Instruct complexity evolution
evolution_rounds	1	Evolution rounds (0–3)
complexity_levels	["constraints", "reasoning", "edge_cases"]	Evolution strategies

evaluation

Field	Default	Description
max_concurrency	10	Max concurrent API requests
timeout	60	Request timeout in seconds
retry_times	3	Retry attempts for failed requests

output

Field	Default	Description
output_dir	./evaluation_results	Output directory
save_queries	true	Save generated queries
save_responses	true	Save model responses
save_details	true	Save detailed results

report

Field	Default	Description
enabled	false	Enable Markdown report generation
language	"zh"	Report language: "zh" or "en"
include_examples	3	Examples per section (1–10)
chart.enabled	true	Generate win-rate chart
chart.orientation	"horizontal"	"horizontal" or "vertical"
chart.show_values	true	Show values on bars
chart.highlight_best	true	Highlight best model
chart.matrix_enabled	false	Generate win-rate matrix heatmap
chart.format	"png"	Chart format: "png", "svg", or "pdf"

Interpreting results

Win rate: percentage of pairwise comparisons a model wins. Each pair is evaluated in both orders (original + swapped) to eliminate position bias.

Rankings example:

  1. gpt4_baseline       [################----] 80.0%
  2. qwen_candidate      [############--------] 60.0%
  3. llama_finetuned      [##########----------] 50.0%

Win matrix: win_matrix[A][B] = how often model A beats model B across all queries.

Checkpoint & resume

The pipeline saves progress after each step. Interrupted runs resume automatically:

• --fresh — ignore checkpoint, start from scratch
• --rerun-judge — re-run only the pairwise evaluation step (useful when switching judge models); keeps queries, responses, and rubrics intact
• Adding new endpoints to config triggers incremental response collection; existing responses are preserved

Output files

evaluation_results/
├── evaluation_results.json     # Rankings, win rates, win matrix
├── evaluation_report.md        # Detailed Markdown report (if enabled)
├── win_rate_chart.png          # Win-rate bar chart (if enabled)
├── win_rate_matrix.png         # Matrix heatmap (if matrix_enabled)
├── queries.json                # Generated test queries
├── responses.json              # All model responses
├── rubrics.json                # Generated evaluation rubrics
├── comparison_details.json     # Pairwise comparison details
└── checkpoint.json             # Pipeline checkpoint

API key by model

Model prefix	Environment variable
gpt-*, o1-*, o3-*	OPENAI_API_KEY
claude-*	ANTHROPIC_API_KEY
qwen-*, dashscope/*	DASHSCOPE_API_KEY
deepseek-*	DEEPSEEK_API_KEY
Custom endpoint	set api_key + base_url in config

Additional resources

• Full config examples: cookbooks/auto_arena/examples/
• Documentation: Auto Arena Guide