Open Source · Pydantic v2 · Python 3.10+
Declarative Data Assembly
A BFF toolkit for Python. Compose complex nested APIs declaratively — zero N+1, zero boilerplate.
pip install pydantic-resolve
# Sprint has 10 tasks, each needs owner
for task in sprint.tasks:
task.owner = await get_user(task.owner_id)
# 10 queries!class TaskView(BaseModel):
owner_id: int
owner: Optional[UserView] = None
def resolve_owner(self, loader=Loader(user_loader)):
return loader.load(self.owner_id)
# 1 query!Batch Loading
Auto-batch queries via DataLoader pattern. 100 lookups become 1 query.
Post Processing
Compute derived fields after all nested data is resolved. Counts, aggregates, formatting.
Cross-Layer Data Flow
ExposeAs, SendTo, Collector — pass context down or aggregate data up without traversal code.
ER Diagram + AutoLoad
Centralize relationship declarations. Reuse across models, GraphQL, and MCP services.
GraphQL Generation
Auto-generate GraphQL schema and resolvers from your ER Diagram.
MCP Service
Expose GraphQL APIs to AI agents via Model Context Protocol.
1 resolve_*
→
2 Nested Tree
→
3 post_*
→
4 Cross-Layer
→
5 ERD + AutoLoad
→
6 GraphQL / MCP
Entity + Use Case
ER Diagram defines entities and their relationships — it naturally serves as both the domain model and the use-case boundary.
DB Agnostic
Loaders abstract away the data source. Swap SQLAlchemy for Django or Tortoise ORM — your models stay untouched.
Multi-Presentation
One ERD drives REST API responses, GraphQL queries, and MCP services — without duplication.
Ready to eliminate N+1 queries?
Start with a single resolve method. Scale to ER Diagram when you're ready.