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<b>Layered Architecture</b> • <b>Stack</b> • <b>Swap</b> • <b>Built for Scale</b>
We recommend using uv for dependency and virtual environment management.
# Install dependencies
uv sync
# Activate the virtual environment
source .venv/bin/activate
Create a .env file in the project root and set the following:
OPENAI_API_KEY=your_openai_api_key
MODEL_NAME=gpt-4o-mini
OPENAI_API_BASE=https://api.openai.com/v1
./run_benchmark.sh
math, aimehumaneval, mbppdrop, bbh, mmlu_pro, ifevalsingle_agentsupervisor_mas, swarm, agentverse, chateval, evoagent, jarvis, metagptFor comprehensive guides, tutorials, and API references, visit our complete documentation.
We warmly welcome contributions from the community!
You can contribute in many ways:
🧠 New Agent Systems (MAS): Add novel single- or multi-agent systems to expand the diversity of strategies and coordination models.
📊 New Benchmark Datasets: Bring in domain-specific or task-specific datasets (e.g., reasoning, planning, tool-use, collaboration) to broaden the scope of evaluation.
🛠 New Tools & Toolkits: Extend the framework's tool ecosystem by integrating domain tools (e.g., search, calculators, code editors) and improving tool selection strategies.
⚙️ Improvements & Utilities: Help with performance optimization, failure handling, asynchronous processing, or new visualizations.
$ claude mcp add MASArena \
-- python -m otcore.mcp_server <graph>