Pydantic Graph Graph and finite state machine library. This library is developed as part of PydanticAI, however it has no dependency on pydantic-ai or related packages and can be considered as a pure graph-based state machine library. You may find it useful whether or not you're using PydanticAI or even building with GenAI. As with PydanticAI, this library prioritizes type safety and use of common Python syntax over esoteric, domain-specific use of Python syntax. pydantic-graph allows you to define graphs using standard Python syntax. In particular, edges are defined using the return type hint of nodes. Full documentation is available at ai.pydantic.dev/graph. Here's a basic example: ```python {noqa="I001" py="3.10"} from future import annotations from dataclasses import dataclass from pydantic_graph import BaseNode, End, Graph, GraphRunContext @dataclass class DivisibleBy5(BaseNode[None, None, int]): foo: int async def run( self, ctx: GraphRunContext, ) -> Increment | End[int]: if self.foo % 5 == 0: return End(self.foo) else: return Increment(self.foo) @dataclass class Increment(BaseNode): foo: int async def run(self, ctx: GraphRunContext) -> DivisibleBy5: return DivisibleBy5(self.foo + 1) fives_graph = Graph(nodes=[DivisibleBy5, Increment]) result = fives_graph.run_sync(DivisibleBy5(4)) print(result.output) > 5 ```
Latest version: 0.2.1 Released: 2025-05-13