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llm_coordination
by eric-ai-lab
Reproducible NAACL2025 evaluation suite for LLM multi-agent coordination
Python
Updated Oct 13, 2024
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What It Does
Implements the experiments and evaluation suite from the NAACL 2025 paper on LLM coordination. Provides task setups, metrics, and analysis scripts to measure multi-agent coordination abilities and failure modes. Includes simulation environments and logging utilities to reproduce coordination scenarios across different LLM backends. A2A Protocol
Why It Matters
As agents coordinate more autonomously, we need principled ways to measure when coordination succeeds or cascades into failure. This repo gives researchers reproducible setups and metrics for A2A evaluation, surfacing where coordination breaks and which behaviors degrade trust. That empirical focus helps move agent-to-agent evaluation from ad-hoc case studies to comparable benchmarks usable in reputational systems like RepKit. Model Context Protocol (MCP) Continuous Monitoring
Ideal For
Researchers and engineers running reproducible A2A evaluation experiments to analyze coordination failure modes and compare LLM-based agent setups. Human-in-the-Loop Pattern
How It's Used
- Running reproducible coordination benchmarks to compare LLM behaviors in multi-agent tasks
- Analyzing coordination failure modes and logging signals relevant to agent reputation
- Evaluating delegation strategies and their impact on agent reliability in simulated workflows
Works With
openaihuggingface
Topics
agent-coordinationcoordination-gamellmsmultiagent
Similar Tools
agent-playgroundagent-arenarepkit
Keywords
multi-agent trustA2A evaluationagent-to-agent evaluationcoordination-game