AgentPrune is an economical, simple, and robust multi-agent communication framework, which can seamlessly integrate into mainstream multi-agent systems and prunes redundant or even malicious communication messages.
We provide the code of our paper. The algorithm implementation code is in AgentPrune folder, and the experimental code is in experiments folder.
conda create -n agentprune python=3.10
conda activate agentprune
pip install -r requirements.txtBASE_URL = "" # the BASE_URL of OpenAI LLM backend
API_KEY = "" # for OpenAI LLM backendDownload MMLU, HumanEval and GSM8K datasets from MMLU, HumanEval and GSM8K. And put them in different folders.
python experiments/run_mmlu.py --agent_nums 1 --mode DirectAnswer --decision_method FinalMajorVote --agent_names AdverarialAgent --batch_size 4
python experiments/run_mmlu.py --agent_nums 6 --mode FakeChain --decision_method FinalMajorVote --agent_names AdverarialAgent --batch_size 4
python experiments/run_mmlu.py --agent_nums 6 --mode FakeRandom --decision_method FinalMajorVote --agent_names AdverarialAgent --batch_size 4
python experiments/run_mmlu.py --agent_nums 6 --mode FakeAGFull --batch_size 4 --num_iterations 10 --imp_per_iterations 5 --pruning_rate 0.25 --num_rounds 1 --optimized_spatial --optimized_temporalThe above code verifies the experimental results of the mmlu dataset under different topologies.
We also provide experimental code for other datasets and topologies.You can refer to experiments/run_humaneval.py and experiments/run_gsm8k.py.
This code refers to GPTSwarm.