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Aflow
Automated WorkflowDevelopment & Tools#Automation#Workflow#Monte Carlo Tree Search#Agent Workflows#Code OptimizationStandard PicksOpen Source
Overview :
AFlow is a framework designed for the automatic generation and optimization of agent workflows. It employs Monte Carlo Tree Search to identify effective workflows within the code-represented workflow space, replacing manual development and showcasing its potential to surpass handcrafted workflows across various tasks. Key advantages of AFlow include enhanced development efficiency, reduced labor costs, and the ability to adapt to diverse task requirements.
Target Users :
The target audience consists of developers, data scientists, and machine learning engineers who need to automate workflow generation and optimization. AFlow minimizes manual intervention, allowing users to focus on more valuable tasks such as strategy formulation and outcome analysis.
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Use Cases
Automatically generate and optimize workflows on the HumanEval dataset to enhance task execution efficiency.
Conduct experiments using the MATH dataset to verify AFlow's effectiveness in solving mathematical problems.
Test AFlow's performance and accuracy in answering scientific questions with the GSM8K dataset.
Features
- Node: The basic unit of LLM calls, providing an interface to control the LLM, temperature, format, and prompts.
- Operator: A predefined combination of nodes that improves search efficiency by encapsulating common operations.
- Workflow: A sequence of LLM calling nodes, which can be represented as a graph, neural network, or code.
- Optimizer: Explores and refines workflows using LLM in variations of Monte Carlo Tree Search.
- Evaluator: Assesses workflow performance and provides feedback to guide the optimization process.
- Supports custom operators and workflows to accommodate specific datasets and tasks.
- Offers support for experimental datasets and custom datasets, facilitating user experiments and evaluations.
How to Use
1. Configure optimization parameters, including dataset type, number of samples, and path for saving optimization results.
2. Set LLM parameters in config/config2.yaml; refer to examples/aflow/config2.example.yaml for guidance.
3. Define operators in optimize.py as well as optimized_path/template/operator.py and operator.json.
4. On first use, set download(['datasets', 'initial_rounds']) in examples/aflow/optimize.py to download datasets and initial rounds.
5. (Optional) Add custom datasets and corresponding evaluation functions.
6. (Optional) If partial validation data is needed, set va_list in examples/aflow/evaluator.py.
7. Run optimization, starting the process with default parameters or customized parameters.
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