arbitron 0.5.4

Multi-agent collaboration through pairwise comparisons

Arbitron ⚖️

Arbitron is an agentic pairwise comparison engine. Multiple jurors, each with unique value systems, evaluate items head-to-head and produce a set of pairwise comparisons that can be used to derive item's ranks and weights.

• Why pairwise? It's easier to compare two items than to assign absolute scores.
• Why multi-juror? Different models with different perspectives (instructions) lead to more balanced, less biased outcomes.

✨ Features

• 🎯 Arbitrary Sets. Evaluate text, code, products, ideas
• 🤖 Customizable Jurors. Specify custom instructions, tools, providers
• 🛡️ Bias Reduction. Ensemble decision-making
• 🧩 Remixable — Join data with human labels and apply personalized heuristics

🚀 Quickstart

Running your first Arbitron "contest" is easy!

pip install arbitron

Setup your favorite LLM provider's API keys in the environment (e.g: OPENAI_API_KEY) and then run the following code.

from arbitron import Competition, Item, Juror

items = [
    Item(id="arrival"),
    Item(id="interstellar"),
    Item(id="inception"),
]

jurors = [
    Juror(id="SciFi Purist", model="openai:gpt-5-nano"),
]

competition = Competition(
    id="sci-fi-soundtracks",
    description="Which movie has the better soundtrack?",
    jurors=jurors,
    items=items,
)

for comparison in competition.run():
    print(comparison)

print(f"Total cost: {competition.cost}")

🏛️ License

MIT License - see LICENSE file for details.

🙌 Acknowledgments

• DeepGov and their use of AI for Democratic Capital Allocation and Governance.
• Daniel Kronovet for his many writings on the power of pairwise comparisons.

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