| Name | pydantic-graph JSON |
| Version |
0.4.7
JSON |
| download |
| home_page | None |
| Summary | Graph and state machine library |
| upload_time | 2025-07-24 21:47:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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No coveralls.
|
# Pydantic Graph
[](https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml?query=branch%3Amain)
[](https://coverage-badge.samuelcolvin.workers.dev/redirect/pydantic/pydantic-ai)
[](https://pypi.python.org/pypi/pydantic-graph)
[](https://github.com/pydantic/pydantic-ai)
[](https://github.com/pydantic/pydantic-ai/blob/main/LICENSE)
Graph and finite state machine library.
This library is developed as part of [Pydantic AI](https://ai.pydantic.dev), 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 Pydantic AI or even building with GenAI.
As with Pydantic AI, 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](https://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
```
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"description": "# Pydantic Graph\n\n[](https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml?query=branch%3Amain)\n[](https://coverage-badge.samuelcolvin.workers.dev/redirect/pydantic/pydantic-ai)\n[](https://pypi.python.org/pypi/pydantic-graph)\n[](https://github.com/pydantic/pydantic-ai)\n[](https://github.com/pydantic/pydantic-ai/blob/main/LICENSE)\n\nGraph and finite state machine library.\n\nThis library is developed as part of [Pydantic AI](https://ai.pydantic.dev), however it has no dependency\non `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 Pydantic AI or even building with GenAI.\n\nAs with Pydantic AI, this library prioritizes type safety and use of common Python syntax over esoteric, domain-specific use of Python syntax.\n\n`pydantic-graph` allows you to define graphs using standard Python syntax. In particular, edges are defined using the return type hint of nodes.\n\nFull documentation is available at [ai.pydantic.dev/graph](https://ai.pydantic.dev/graph).\n\nHere's a basic example:\n\n```python {noqa=\"I001\" py=\"3.10\"}\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\n\nfrom pydantic_graph import BaseNode, End, Graph, GraphRunContext\n\n\n@dataclass\nclass DivisibleBy5(BaseNode[None, None, int]):\n foo: int\n\n async def run(\n self,\n ctx: GraphRunContext,\n ) -> Increment | End[int]:\n if self.foo % 5 == 0:\n return End(self.foo)\n else:\n return Increment(self.foo)\n\n\n@dataclass\nclass Increment(BaseNode):\n foo: int\n\n async def run(self, ctx: GraphRunContext) -> DivisibleBy5:\n return DivisibleBy5(self.foo + 1)\n\n\nfives_graph = Graph(nodes=[DivisibleBy5, Increment])\nresult = fives_graph.run_sync(DivisibleBy5(4))\nprint(result.output)\n#> 5\n```\n",
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