JSON Schema Template for Graph-Based AI Agent Workflows
A basic JSON schema template has been shared for structuring AI agent workflows, defining metadata, nodes, edges, and execution flows. Each node in this schema is required to have at least an ID, name, type, description, and responsibilities. Standardizing workflow structures using JSON helps developers design, visualize, and exchange graph-based agent configurations across different orchestration frameworks. This approach simplifies the integration of multi-agent systems by defining clear boundaries and responsibilities for each processing step. The schema represents agent logic as a directed graph where nodes represent processing steps or tools, and edges define the transitions and data flow between them. The flows array maps out sequential steps, allowing developers to orchestrate complex, multi-step agent behaviors.
## BACKGROUND
Modern AI agent frameworks, such as LangGraph and Flowise, model agentic workflows as graphs rather than linear chains to support complex decision-making and loops. In these architectures, nodes typically represent LLM calls, tool executions, or conditional logic, while edges determine the execution path based on state changes. Defining these components in structured formats like JSON allows for declarative agent design and easier serialization.