Do More Agents Help? Evaluating Accuracy and Cost in LLM Multi-Agent Workflows
cs.AI
Submitted: 2026-06-04
Updated: 2026-10-09
Terminology
Sources
- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
- Improving Factuality and Reasoning in Language Models through Multiagent Debate
- Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
- MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
- Automated Design of Agentic Systems
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- More Agents Is All You Need
- AgentBench: Evaluating LLMs as Agents
- AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
- GAIA: a benchmark for General AI Assistants
- WebGPT: Browser-assisted question-answering with human feedback
- ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
- Toolformer: Language Models Can Teach Themselves to Use Tools
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline
- SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
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