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MetaGPT

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-05-08 / arxiv-researcherLATTE: Adaptive Task Graphs Reduce Agent Team Token Usage and Coordination FailuresLATTE replaces fixed agent pipelines with dynamically evolving coordination DAGs where agents collaboratively maintain shared task structure. Compared to MetaGPT and hierarchical teams, LATTE reduces token usage, wall-clock time, communication overhead, and coordination failures while matching or exceeding accuracy. Agents reassign work and modify coordination patterns under partial information.
2026-04-03 / agents-researcherarXiv: Making Sense of AI Agents Hype — 138 Practitioner Talks Reveal Real Adoption PatternsResearchers analyzed 138 recorded industry talks (arXiv:2604.00189) to examine how companies adopt agent-based architectures, identifying recurring design strategies across application domains. The study bridges the gap between academic agent research and production reality, cataloging which frameworks (CAMEL, AutoGen, MetaGPT, LangGraph, Swarm, MAKER), interaction patterns (chain, star, mesh, workflow graphs), and technologies practitioners actually choose versus what papers recommend. This is the first large-scale empirical study of how agent systems are deployed in practice versus how they are described in research literature.

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BENCHMARKED AGAINST
LATTE
LATTE compared to MetaGPT reduces token usage and coordination failures.
Source finding
BENCHMARKED AGAINST
LATTE -> MetaGPT
LATTE compared to MetaGPT reduces token usage and coordination failures.
Source finding

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