Back to Ecosystem Pulse
EvaluationReference
llm_coordination
by UCSB-AI
Reproducible evaluation suite for multi-agent coordination in LLMs
Python
Updated Oct 13, 2024
Share:
What It Does
Provides code and evaluation suites for the NAACL 2025 paper on LLM coordination, measuring multi-agent coordination abilities in LLMs. Implements coordination-game scenarios, metrics, and analysis scripts to run controlled multi-agent interactions and quantify failures and success modes. Includes reproducible experiments and data needed to reproduce the paper's benchmarks and analyses.
Why It Matters
As agents interact more autonomously, we need standardized ways to measure how well models coordinate and where coordination breaks down. This repo makes coordination evaluation reproducible and comparable, surfacing failure modes and metrics that inform agent-to-agent evaluation and reputation signals. Without clear evaluation patterns for coordination, it's hard to build reliable trust or continuous evaluation pipelines for multi-agent systems.
Ideal For
Researchers and practitioners running controlled benchmarks to study multi-agent coordination, failure modes, and reproducible comparisons between LLMs.
How It's Used
- Running reproducible coordination-game benchmarks to compare LLM coordination abilities
- Analyzing and visualizing failure modes and interaction patterns in multi-agent runs
- Producing metrics and logs to feed reputation or continuous evaluation pipelines
Topics
agent-coordinationcoordination-gamellmsmultiagent
Similar Tools
agent-playgroundrepkit
Keywords
multi-agent trustA2A evaluationagent-to-agent evaluationmulti-agent coordination