AG Graph Builder

To convert Python scripts into DAG-based automation workflows, provide your script and goals to generate a deterministic graph. The process wraps existing functions into graph nodes using @tool and @graphify forms while preserving your original imports.

Graphify workflow orchestration is the process of structuring scripts into deterministic directed acyclic graphs (DAGs) using @tool and @graphify. You need it when building reliable pipelines that require strict task ordering and typed inputs for complex automation.

Yes, you can add artifact checkpointing to expensive steps in an AetherGraph workflow. This feature enables resumable runs by saving intermediate outputs, which is critical for iterative processing or long-running complex orchestration tasks.

Yes, the graph conversion process explicitly preserves existing Python imports. When wrapping your functions into graph nodes, the original import statements are maintained to ensure the generated @tool and @graphify workflow executes correctly without missing dependencies.

A key constraint is that conversion requires strict graph construction invariants. You must enforce explicit @tool and @graphify forms with typed inputs, meaning scripts lacking clear function boundaries or untyped parameters will require refactoring before orchestration.

The best way to build AetherGraph workflows is to provide clear user intent alongside Python script references. This allows the conversion mechanism to apply strict invariants and generate a deterministic graph that accurately reflects your automation goals.