It is a Python framework designed for building agentic AI workflows, which are processes that delegate at least some of their work to an LLM (Large Language Model) agent.
It is a Python framework designed for building agentic AI workflows, which are processes that delegate at least some of their work to an LLM (Large Language Model) agent. An agent in this context is an autonomous entity that is repeatedly invoked to make decisions and perform complex tasks. ControlFlow provides a structured, developer-focused framework for defining workflows and delegating tasks to LLMs while maintaining control and transparency. This task-centric approach allows developers to harness the power of AI for complex workflows, balancing AI autonomy with precise oversight to build sophisticated AI-powered applications.
ControlFlow enables developers to define clear objectives and constraints for each task, ensuring fine-grained control over the workflow. For example, a simple ControlFlow script can create a task, assign it to an agent, and execute it using the `run()` function, which serves as the main entry point. This function can be customized to handle tasks, agents, and flows according to specific requirements.
Key features of ControlFlow include the ability to return structured data types supported by Pydantic, output lists of strings, or choose from predefined options. Developers can provide Python functions as tools for agents to use, assign multiple agents to a task for collaboration, and enable agents to interact with users through chat. Additionally, flows allow the creation of complex workflows by running tasks with a shared context and message history.
By offering a structured yet flexible approach to AI development, ControlFlow empowers developers to create robust and intelligent applications with confidence. It combines the power of LLMs with developer control, making it suitable for building advanced AI-driven workflows.
It is a durable execution platform that ensures workflows never fail by managing failures, network outages, flaky endpoints, and long-running processes.
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