MegaAgent is a framework designed to manage autonomous cooperation in large-scale LLM-powered multi-agent systems, enabling dynamic agent generation, task auto-splitting, and enhanced communication without relying on predefined operating procedures.
This repository contains two parts: the latest version of MegaAgent at ., and examples at examples/.
To run the latest version, you can add your key and change the prompt in config.py, and run main.py. The output will be located at files/. The default prompt is used for automatically generating a Gobang game.
Some experiments are shown in examples/ using an older version of MegaAgent. You can use the same prompt while substituting other files with the latest version.
| Model | MBPP | HumanEval | MATH | GSM-8k |
|---|---|---|---|---|
| MetaGPT | 81.7% | 82.3% | N/A | N/A |
| Camel | 78.1% | 57.9% | 22.3% | 45.6% |
| AgentVerse | 82.4% | 89.0% | 54.5% | 81.2% |
| AutoGen | 85.3% | 85.9% | 69.5% | 87.8% |
| MegaAgent | 92.2% | 93.3% | 69.0% | 93.0% |
(Quantitative experiments using gpt-4o as backbone)
We also used GPT-4o to achieve the following results on TravelPlanner. The submission file is included in examples/travel planner.
- Delivery Rate: 100.0%
- Commonsense Constraint Micro Pass Rate: 81.88%
- Commonsense Constraint Macro Pass Rate: 27.22%
- Hard Constraint Micro Pass Rate: 40.48%
- Hard Constraint Macro Pass Rate: 23.89%
- Final Pass Rate: 10.0%
This repository is under license CC BY 4.0.