Anemoi Agent
Anemoi replaces purely centralized, context-stuffed coordination with an A2A communication server (MCP) that lets agents talk directly, monitor progress, refine plans, and reach consensus. On GAIA, it holds up even with a small planner model and reduces redundant context passing for better cost and scalability.
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Design: A semi-centralized planner proposes an initial plan, while worker agents (web, document processing, reasoning/coding) plus critique and answer-finding agents collaborate via MCP threads. All participants can list agents, create threads, send messages, wait for mentions, and update plans as execution unfolds.
Communication pattern: Five phases structure collaboration: agent discovery, thread initialization with a task plan and tentative allocation, execution with continuous critique, consensus voting before submission, and final answer synthesis. This reduces reliance on a single planner and minimizes token-heavy prompt concatenation.
Results on GAIA: With GPT-4.1-mini as planner and GPT-4o workers, Anemoi reaches 52.73% accuracy (pass@3), beating an OWL reproduction with the same LLM setup by +9.09 points and outperforming several proprietary and open-source systems that use stronger planners.
Why it wins: Most extra solves over OWL come from collaborative refinement enabled by A2A (52%), with smaller gains from reduced context redundancy (8%); remaining differences reflect stochastic worker behavior. OWL’s few wins over Anemoi largely stem from worker stochasticity and web-agent latency.
What still fails: The largest error sources are LLM capability limits (45.6%) and tooling gaps (20.6%), followed by incorrect plans (11.8%) and communication latency (10.3%); minor shares come from benchmark annotation issues and hallucinations.