buddyMe Framework Analyzes Multi-Paradigm LLM Agent Interaction
A new paper on arXiv (2605.16821) presents a systematic analysis of three principal LLM agent interaction paradigms—Multi-Agent Orchestration (Generator-Evaluator), ReAct Tool-Use Loops, and Memory-Augmented Interaction—as implemented in the open-source buddyMe framework. The study formalizes a five-stage processing pipeline: Requirement Pre-Review, Task Decomposition, ReAct Execution, Real-Execution Verification, and Adversarial Evaluation Discussion, along with a six-dimensional evaluation schema using weighted scoring. Four empirical case studies from real-world deployment logs—covering museum guide generation, scheduled weather tasks, and comprehensive tour planning—yield three key conclusions. First, the Generator-Evaluator paradigm improves output quality through iterative critique. The research provides practical insights for building robust multi-agent systems.
Key facts
- Paper arXiv:2605.16821 analyzes LLM agent interaction paradigms in buddyMe framework.
- Three paradigms: Multi-Agent Orchestration, ReAct Tool-Use Loops, Memory-Augmented Interaction.
- Five-stage pipeline: Requirement Pre-Review, Task Decomposition, ReAct Execution, Real-Execution Verification, Adversarial Evaluation Discussion.
- Six-dimensional evaluation schema with weighted scoring used.
- Four case studies: museum guide generation, scheduled weather tasks, comprehensive tour planning.
- Generator-Evaluator paradigm improves output quality via iterative critique.
- buddyMe is an open-source multi-model agent programming framework.
- Study draws three key conclusions from real-world deployment logs.
Entities
Institutions
- arXiv