MetaGPT/examples/aflow

AFlow: Automating Agentic Workflow Generation

AFlow is a framework for automatically generating and optimizing Agentic Workflows. It uses Monte Carlo tree search in a code-represented workflow space to find effective workflows, replacing manual development with machine effort. Our approach shows potential to outperform handcrafted workflows on various tasks.

Read our paper on arXiv

[Insert performance graph/image here]

Framework Components

• Node: Basic unit of LLM invocation. See action_node.py for a flexible interface to control LLM, temperature, format, and prompt.
• Operator: Predefined combinations of Nodes to enhance search efficiency.
• Workflow: [Brief description needed]
• Optimizer: [Brief description needed]
• Evaluator: [Brief description needed]

Datasets

We provide implementations for [list datasets here].

Data is available at [link to data].

For custom tasks, [brief instructions or link to documentation].

Quick Start

1. Configure your search in optimize.py
2. Set up parameters in config/config2.yaml (see examples/aflow/config2.example.yaml for reference)

[Add any additional setup or running instructions]

Contributing

[Instructions for contributing, if applicable]

License

[License information]

Citation

If you use AFlow in your research, please cite our paper:

[Citation details]

For more information, visit our [project website/documentation].