GenAgent
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Genagent
Overview :
GenAgent is a framework that builds collaborative AI systems by creating workflows and converting them into code that large language model (LLM) agents can better understand. GenAgent learns from human-designed tasks and creates new workflows, which can be interpreted as collaborative systems to tackle complex tasks.
Target Users :
The target audience for GenAgent includes AI researchers and developers, particularly those who need to build collaborative AI systems and automate workflow generation. It is suitable for scenarios that require rapid iteration and testing of new workflows, as well as for teams looking to reduce the manual workload involved in designing workflows.
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Use Cases
AI researchers use GenAgent to quickly build and test new workflows.
Developers leverage GenAgent to automate code generation, enhancing development efficiency.
Businesses utilize GenAgent to construct collaborative systems, optimizing the handling processes of complex tasks.
Features
Automated workflow generation: GenAgent can generate workflows automatically, reducing the manual effort required for workflow design.
Code conversion: It translates workflows into code, facilitating AI understanding and execution.
Learning and innovation: GenAgent can learn from existing workflows and create new workflows.
Building collaborative systems: By generating workflows, it constructs collaborative systems to accomplish complex tasks.
Flexible configuration: Users can set the HTTP proxy address, OpenAI API key, and ComfyUI server address by modifying the config file.
Experimental reproduction: Provides commands for reproducing experiments, allowing users to validate and compare the performance of different agents.
Cost-effective: Benchmark evaluations cost approximately $30, offering a significant cost advantage over manual workflow design.
How to Use
Clone the repository and navigate to the project directory.
Create a new conda environment and install the necessary dependencies.
Modify the config.yaml file to set the HTTP proxy address, OpenAI API key, and ComfyUI server address.
Execute the GenAgent pipeline by inputting task requirements, agent name, and save path via the command line.
Run the command for benchmark evaluation and ablation studies to verify and compare the performance of different agents.
Ensure that the OpenAI API key is set in the config.yaml file.
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