ControlFlow is a Python framework for building AI-powered applications using large language models (LLMs). It provides a structured, task-centric approach to create sophisticated workflows that leverage the power of AI while adhering to traditional software engineering best practices.
At the core of ControlFlow are three key concepts: Tasks, Agents, and Flows. Understanding how these elements work together is crucial to building effective AI workflows.
Tasks are the fundamental building blocks of ControlFlow workflows. Each task represents a discrete objective or goal that needs to be accomplished, such as generating text, classifying data, or extracting information from a document. Tasks define WHAT needs to be done in your AI workflow.
Key characteristics of tasks include:
ControlFlow provides two ways to create tasks:
Task
class for explicit definition of all task properties.@task
decorator on Python functions, which automatically infers task properties from the function definition.Agents in ControlFlow are AI “workers” responsible for executing tasks. They determine HOW tasks are accomplished. Each agent can have distinct instructions, personality, and capabilities, tailored to specific roles or domains.
Key aspects of agents include:
Agents are assigned to tasks based on their suitability and availability. This separation of “what” (tasks) and “how” (agents) allows for flexible and powerful workflows.
Flows are high-level containers that encapsulate and orchestrate entire AI-powered workflows. They provide a structured way to manage tasks, agents, tools, and shared context.
Key features of flows include:
When designing workflows in ControlFlow:
ControlFlow seamlessly integrates with existing Python codebases, treating AI tasks as first-class citizens. You can mix imperative and declarative programming styles, leverage Python’s control flow and error handling capabilities, and gradually adopt AI capabilities into your applications.
Under the hood, ControlFlow utilizes Prefect, a popular workflow orchestration tool, to provide observability, monitoring, and management features. This allows you to track the progress of your workflows, identify bottlenecks, and optimize performance.
By adhering to software engineering best practices such as modularity, error handling, and clear interfaces between AI and traditional code, ControlFlow enables you to build robust, maintainable, and trustworthy AI-powered applications.