About Anemoi
Anemoi is an MCP server published by Coral-Protocol in the Developer Tools category: anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol.
The repository has 369 stars and 35 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/Coral-Protocol/AnemoiThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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- ✓README presentThe repository ships documentation.
- !License declaredNo license file. Legally you have no right to use it in a product.
- ✓Tests presentA test suite exists in the repository.
- !Dependencies pinnedNo lockfile. Installs can pull different code over time.
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README
Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol
Anemoi is a semi-centralized multi-agent system (MAS) built on a Agent-to-Agent (A2A) communication MCP server. Unlike traditional context-engineering + centralized paradigms, Anemoi introduces structured, direct inter-agent communication — enabling agents to collaborate much like a real-world team.
🌀 Like winds connecting distant lands, Anemoi enables agents to communicate directly in a semi-centralized network, achieving scalable coordination and seamless information flow.
🚀 Key Features
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Semi-Centralized Architecture Reduces dependency on a single planner agent, supporting adaptive plan updates.
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Direct Agent-to-Agent Collaboration Agents can monitor progress, assess results, identify bottlenecks, and propose refinements in real time.
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Efficient Context Management Minimizes redundant prompt concatenation and information loss, improving scalability and cost-efficiency.
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Benchmark Performance Achieved 52.73% accuracy on the validation set of the GAIA benchmark, setting the state-of-the-art among small-LLM-based systems.
Surpasses OWL (43.63%) by +9.09% in the same worker agents and models configuration (gpt-4.1-mini as planner agent/ gpt-4o as worker agent).
📄 Publication
Our work has been released on arXiv:
If you find this project useful, please consider citing our paper:
@article{ren2025anemoi,
title={Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol},
author={Ren, Xinxing and Forder, Caelum and Zang, Qianbo and Tahir, Ahsen and Georgio, Roman J. and Deb, Suman and Carroll, Peter and Gürcan, Önder and Guo, Zekun},
journal={arXiv preprint arXiv:2508.17068},
year={2025},
url={https://arxiv.org/abs/2508.17068}
}
🧪 Reproduction
Set up environment variables:
echo '
export FIRECRAWL_API_KEY="your_firecrawl_api_key"
export GOOGLE_API_KEY="your_google_api_key"
export HF_HOME="your_hf_home_path"
export OPENROUTER_API_KEY="your_openrouter_api_key"
export SEARCH_ENGINE_ID="your_search_engine_id"
export CHUNKR_API_KEY="your_chunkr_api_key"
' >> ~/.bashrc && source ~/.bashrc
Create environment:
cd Anemoi
/usr/bin/python3.12 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
We made some minor modifications to CAMEL 0.2.70 for our experiments:
rm -rf venv/lib/python3.12/site-packages/camel
cp -r utils/camel venv/lib/python3.12/site-packages/
Run the experiment:
cd ..
./gradlew run --console=plain
README fetched from the repository 5 days ago. The original is authoritative.