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ControlFlow

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v0.11.3: Exception-al Service

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控制流横幅

控制流

ControlFlow是用于构建代理AI工作流程的Python框架。

ControlFlow提供了一个结构化的，以开发人员为中心的框架，用于定义工作流并将工作委派给LLM，而无需牺牲控制或透明度：

创建离散的，可观察到的任务，供AI进行工作。
为每个任务分配一个或多个专业的AI代理。
将任务结合在一起，以协调更复杂的行为。

例子

最简单的ControlFlow Workflow具有一个任务，一个默认代理和自动线程管理：

 import controlflow as cf

result = cf . run ( "Write a short poem about artificial intelligence" )

print ( result )

结果：

 In circuits and code, a mind does bloom,
With algorithms weaving through the gloom.
A spark of thought in silicon's embrace,
Artificial intelligence finds its place.

为什么要控制流？

ControlFlow解决了构建功能强大且可预测的AI驱动应用程序的挑战：

？以任务为中心的体系结构：将复杂的AI工作流扩散到可管理的，可观察的步骤中。
结构化结果：桥接AI和传统软件之间的差距，并具有类型的安全性，经过验证的输出。
？专业代理：部署特定于任务的AI代理，以有效解决问题。
？灵活的控制：连续调整工作流中控制和自主权的平衡。
？️多机构编排：在单个工作流或任务中协调多个AI代理。
？本机可观察性：以完整的3.0支持，监视和调试您的AI工作流程。
？生态系统集成：与您现有的代码，工具和更广泛的AI生态系统无缝合作。

安装

使用pip安装控制流：

pip install controlflow

接下来，配置您的LLM提供商。 ControlFlow的默认提供商是OpenAI，它需要OPENAI_API_KEY环境变量：

 export OPENAI_API_KEY=your-api-key

要使用其他LLM提供商，请参见LLM配置文档。

工作流示例

这是一个更涉及的示例，可以展示用户交互，多步工作流和结构化输出：

 import controlflow as cf
from pydantic import BaseModel


class ResearchProposal ( BaseModel ):
    title : str
    abstract : str
    key_points : list [ str ]


@ cf . flow
def research_proposal_flow ():

    # Task 1: Get the research topic from the user
    user_input = cf . Task (
        "Work with the user to choose a research topic" ,
        interactive = True ,
    )
    
    # Task 2: Generate a structured research proposal
    proposal = cf . run (
        "Generate a structured research proposal" ,
        result_type = ResearchProposal ,
        depends_on = [ user_input ]
    )
    
    return proposal


result = research_proposal_flow ()

print ( result . model_dump_json ( indent = 2 ))

点击查看结果

对话：

 Agent: Hello! I'm here to help you choose a research topic. Do you have 
any particular area of interest or field you would like to explore? 
If you have any specific ideas or requirements, please share them as well.

User: Yes, I'm interested in LLM agentic workflows

提议：

{
    "title" : " AI Agentic Workflows: Enhancing Efficiency and Automation " ,
    "abstract" : " This research proposal aims to explore the development and implementation of AI agentic workflows to enhance efficiency and automation in various domains. AI agents, equipped with advanced capabilities, can perform complex tasks, make decisions, and interact with other agents or humans to achieve specific goals. This research will investigate the underlying technologies, methodologies, and applications of AI agentic workflows, evaluate their effectiveness, and propose improvements to optimize their performance. " ,
    "key_points" : [
        " Introduction: Definition and significance of AI agentic workflows, Historical context and evolution of AI in workflows " ,
        " Technological Foundations: AI technologies enabling agentic workflows (e.g., machine learning, natural language processing), Software and hardware requirements for implementing AI workflows " ,
        " Methodologies: Design principles for creating effective AI agents, Workflow orchestration and management techniques, Interaction protocols between AI agents and human operators " ,
        " Applications: Case studies of AI agentic workflows in various industries (e.g., healthcare, finance, manufacturing), Benefits and challenges observed in real-world implementations " ,
        " Evaluation and Metrics: Criteria for assessing the performance of AI agentic workflows, Metrics for measuring efficiency, accuracy, and user satisfaction " ,
        " Proposed Improvements: Innovations to enhance the capabilities of AI agents, Strategies for addressing limitations and overcoming challenges " ,
        " Conclusion: Summary of key findings, Future research directions and potential impact on industry and society "
    ]
}