MCPFast / Tools / TaskFlow: Declarative, Verifiable DAG for AI Agents
TaskFlow offers a declarative, verifiable task graph for AI sub-agents, featuring dynamic fan-out, gates, isolated context, and resumable runs.
View on GitHub→TaskFlow is a Python library designed to streamline the development of complex AI agent workflows. It provides a robust framework for defining, executing, and verifying directed acyclic graphs (DAGs) of tasks, enabling developers to build sophisticated multi-agent systems with greater control and reliability. By abstracting away the complexities of inter-agent communication and state management, TaskFlow empowers developers to focus on the core logic of their AI agents.
TaskFlow allows you to define your AI agent's operational logic as a DAG. Each node in the DAG represents a distinct task or sub-agent. The library handles the orchestration of these tasks, ensuring they are executed in the correct order based on dependencies. It supports dynamic task creation and execution, allowing for flexible and adaptive agent behavior. Furthermore, TaskFlow emphasizes verifiability, providing mechanisms to ensure the integrity and correctness of the agent's execution path.
TaskFlow is an essential tool for AI developers building multi-agent systems, complex automation pipelines, or any application requiring sophisticated task orchestration. It is particularly beneficial for those working with large language models (LLMs) and other AI components that need to be coordinated to achieve a common goal. If you are developing AI agents that require dynamic execution, robust error handling, and verifiable task progression, TaskFlow provides the foundational structure you need.