nexusx

Write SQLModel classes. Get a complete API.

Define your entities once in SQLModel, and you get GraphQL, REST, and MCP — no repeated data models.

flowchart LR
    sqlmodel["SQLModel"]

    sqlmodel --> graphql["GraphQL"]
    graphql --> mcp1["MCP"]

    sqlmodel --> usecase["UseCaseService"]
    usecase --> rest["REST"]
    usecase --> mcp2["MCP"]
    usecase --> cli["CLI"]

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Install

pip install nexusx
pip install nexusx[fastmcp]  # with MCP support

Requires Python ≥ 3.10.

Quick Start

Step 1 — Entities + GraphQL

Define SQLModel entities and decorate entry-point methods with @query. GraphQLHandler walks the entity graph, generates SDL, and resolves relationships through DataLoader — one batched SQL per relationship level instead of N+1.

from sqlmodel import SQLModel, Field, Relationship, select
from nexusx import query, mutation, GraphQLHandler

class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    posts: list["Post"] = Relationship(back_populates="author")

    @query
    async def get_users(cls, limit: int = 10) -> list["User"]:
        async with get_session() as session:
            return (await session.exec(select(cls).limit(limit))).all()

class Post(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    title: str
    author_id: int = Field(foreign_key="user.id")
    author: User | None = Relationship(back_populates="posts")

handler = GraphQLHandler(base=SQLModel, session_factory=async_session)

User.get_users becomes a GraphQL query field. Querying { userGetUsers(limit: 5) { name posts { title } } } triggers exactly two SQL round-trips — one for the users, one batched SELECT ... WHERE author_id IN (...) for all their posts. The handler is executor-only: mount it on any ASGI app via a POST route that calls handler.execute(query=...) (see demo/blog/app.py for a complete FastAPI example with GraphiQL). handler.get_sdl() returns the schema for codegen or external clients.

The GraphQL mode guide covers filters, pagination (enable_pagination=True wraps lists in Result { items, pagination }), and AutoQueryConfig for auto-generated by_id / by_filter queries across every entity.

Step 2 — Typed REST with DTOs

GraphQL exposes entities directly. For REST handlers or service-layer code you usually want a smaller, intentional shape per endpoint — that's DefineSubset. Declare which fields to keep; relationship fields auto-load when their name matches an ORM relationship, so author: UserDTO | None = None populates itself from the underlying author_id FK without any loader boilerplate.

from nexusx import DefineSubset, ErManager

class UserDTO(DefineSubset):
    __subset__ = (User, ("id", "name"))

class PostDTO(DefineSubset):
    __subset__ = (Post, ("id", "title", "author_id"))
    author: UserDTO | None = None  # auto-loaded — field name matches relationship

Resolver = ErManager(base=SQLModel, session_factory=async_session).create_resolver()
dtos = await Resolver().resolve(posts)

posts is whatever list of ORM instances you fetched — your query, your filter, your permissions. The Resolver traverses the DTO tree level-by-level, batching each level's loads the same way GraphQL does, and returns typed PostDTO instances with relationships filled in. Add resolve_* methods to override the auto-loader for a field, post_* methods for derived/computed fields. See the Core API guide.

Step 3 — MCP + REST from business logic

For operations that compose multiple entities, apply permissions, or go beyond single-table CRUD, write a UseCaseService — a plain class whose @query / @mutation methods hold your business logic. One service class generates both an MCP server (4-layer progressive disclosure for AI agents) and FastAPI routes (one POST per method, types derived from signatures, OpenAPI docs included).

from nexusx import UseCaseService, UseCaseAppConfig, create_use_case_graphql_mcp_server, create_use_case_router

class SprintService(UseCaseService):
    @query
    async def list_sprints(cls) -> list[SprintSummary]:
        """Get all sprints with task counts."""
        ...

config = UseCaseAppConfig(name="project", services=[SprintService])

# MCP for AI agents
mcp = create_use_case_graphql_mcp_server(apps=[config])
mcp.run()

# REST for frontend
app.include_router(create_use_case_router(config))

Methods are regular async functions — they can call Resolver().resolve(...) from Step 2 internally, so business logic and DTO assembly compose freely. Same Python class, three surfaces (MCP / REST / GraphQL-via-MCP). See feature highlights for the full picture.

How It Compares

	nexusx	Strawberry	FastAPI + SQLModel	FastMCP
GraphQL auto-gen	✓	✓	—	—
REST + OpenAPI	✓	—	✓ (manual)	—
MCP	✓	—	—	✓
N+1 prevention	✓ DataLoader	manual	—	—
Relationship auto-loading	✓ implicit	manual	—	—

Demos

git clone https://github.com/allmonday/nexusx.git && cd nexusx && bash start_all.sh

Port	Mode
8000	GraphQL playground
8001	Core API (REST + DTOs)
8005	Paginated GraphQL
8006	UseCase MCP (4-layer)
8007	UseCase FastAPI (REST)
8008	Voyager visualization

AI Agent Skill

A 4-phase skill guides AI coding agents: clarify requirements → build POC → add queries → productize.

ln -s $(pwd)/skill ~/.claude/skills/nexusx-4phase

Development

./scripts/check-ci.sh       # lint + type-check + tests
uv run pytest               # tests only
uv run ruff check src/ tests/  # lint only
uv run mypy src/            # type-check only

Documentation

• API docs — per-mode guides for GraphQL, Core API, and UseCase
• Clean Architecture comparison

License

MIT