nexusx

Tools are fixed functions; data is a graph.

Build applications for Human and AI — from the same source, not by wrapping.

nexusx generates GraphQL, REST, MCP, CLI, and TS SDK from the same typed business methods. Every protocol shares one batch-loaded query graph (DataLoader, N+1-proof) and one set of typed DTOs (DefineSubset). This is semantic-level isomorphism — not transport-level wrapping.

For Human — write SQLModel entities + typed DTOs; get REST routes, GraphQL schema, CLI, and TS SDK without boilerplate. Change business logic once → all protocols update in sync.

For AI — MCP is a first-class protocol with strong typing and GraphQL under the hood — the biggest win is context efficiency. Traditional RESTful APIs return large, fixed-shape objects; AI agents have no way to shrink the response, so context windows fill up with irrelevant data. nexusx's MCP runs GraphQL queries under the hood: AI agents select exactly the fields they need (field-level selection, not whole-object dump), keeping responses lean and context-efficient. Every operation carries full typed input/output schemas. Combined with progressive disclosure (app discovery → method overview → schema → execution) and DataLoader batch-loading (one MCP call → fully-nested, N+1-proof data tree), AI gets rich, typed, on-demand data — and only what it asked for.

Quick start

Install the runtime dependencies:

pip install "nexusx[demo]"

Create app.py:

from contextlib import asynccontextmanager

from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
from sqlalchemy.pool import StaticPool
from sqlmodel import Field, Relationship, SQLModel
from sqlmodel.ext.asyncio.session import AsyncSession

from nexusx import AutoQueryConfig, GraphQLHandler


class BaseEntity(SQLModel):
    pass


class Team(BaseEntity, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    heroes: list["Hero"] = Relationship(back_populates="team")


class Hero(BaseEntity, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    team_id: int | None = Field(default=None, foreign_key="team.id")
    team: Team | None = Relationship(back_populates="heroes")


engine = create_async_engine(
    "sqlite+aiosqlite:///:memory:",
    poolclass=StaticPool,
)
session_factory = async_sessionmaker(
    engine,
    class_=AsyncSession,
    expire_on_commit=False,
)
handler = GraphQLHandler(
    base=BaseEntity,
    session_factory=session_factory,
    auto_query_config=AutoQueryConfig(),
)


@asynccontextmanager
async def lifespan(_app: FastAPI):
    async with engine.begin() as connection:
        await connection.run_sync(SQLModel.metadata.create_all)

    async with session_factory() as session:
        team = Team(name="Avengers")
        session.add(team)
        await session.flush()
        session.add(Hero(name="Spider-Man", team_id=team.id))
        await session.commit()

    try:
        yield
    finally:
        await handler.aclose()
        await engine.dispose()


app = FastAPI(lifespan=lifespan)


class GraphQLRequest(BaseModel):
    query: str


@app.get("/graphql", response_class=HTMLResponse)
async def graphiql() -> str:
    return handler.get_graphiql_html()


@app.post("/graphql")
async def graphql(request: GraphQLRequest):
    return await handler.execute(request.query)

Run it:

uvicorn app:app --reload

Open http://127.0.0.1:8000/graphql and run:

{
  Team {
    by_filter {
      id
      name
      heroes {
        id
        name
      }
    }
  }
}

This creates the schema, query roots, and batched relationship loader without a hand-written GraphQL type or resolver. The same runnable source is available at examples/quickstart.py.

You declare	nexusx generates
SQLModel entity + relationships	GraphQL schema + DataLoader batching (N+1-proof) + ER diagrams
DefineSubset DTO	Minimal-column queries + nested relationship loading + computed fields
UseCaseService method	REST route + GraphQL field + MCP operation + CLI command
Entity __federation_keys__	Cross-service federation (auto-detected + batch-fetched)

flowchart LR
    models["SQLModel entities"]
    data_graph["Data graph<br/>relationships + loaders"]
    dto["Typed DTOs<br/>DefineSubset + Resolver"]
    usecase["Business use cases"]

    models --> data_graph --> dto --> usecase
    data_graph --> data_gql["GraphQL"]
    data_graph --> data_mcp["MCP"]
    data_graph --> er["ER / Voyager"]
    usecase --> rest["REST / OpenAPI"]
    usecase --> operation_gql["GraphQL"]
    usecase --> operation_mcp["MCP"]
    usecase --> cli["CLI"]

Loading

Why nexusx

A SQLModel application usually grows through the same stages:

1. Define entities and relationships.
2. Write resolvers or joins to read nested data.
3. Create response DTOs that do not expose every database column.
4. Repeat the same business operation for REST, GraphQL, and AI tools.
5. Rebuild the relationship map again for documentation and service boundaries.

nexusx keeps those stages connected:

You define	nexusx derives
SQLModel entities and relationships	GraphQL schema, DataLoaders, ER diagrams
A DefineSubset DTO	Minimal SQL columns, nested relationship loading, derived fields
A UseCaseService method	REST route, GraphQL field, MCP operation, CLI command
A non-ORM batch function	A relationship that works with the same loaders, DTOs, and diagrams

The result is less translation code between your database, application layer, web API, and AI interface.

Explore the data graph

For a larger model, define entities as normal SQLModel classes:

from sqlmodel import Field, Relationship, SQLModel


class BaseEntity(SQLModel):
    pass


class User(BaseEntity, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str

    tasks: list["Task"] = Relationship(back_populates="owner")


class Sprint(BaseEntity, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str

    tasks: list["Task"] = Relationship(back_populates="sprint")


class Task(BaseEntity, table=True):
    id: int | None = Field(default=None, primary_key=True)
    title: str
    done: bool = False
    sprint_id: int = Field(foreign_key="sprint.id")
    owner_id: int = Field(foreign_key="user.id")

    sprint: Sprint | None = Relationship(back_populates="tasks")
    owner: User | None = Relationship(back_populates="tasks")

Create a query interface:

from nexusx import AutoQueryConfig, GraphQLHandler

from .database import async_session


handler = GraphQLHandler(
    base=BaseEntity,
    session_factory=async_session,
    auto_query_config=AutoQueryConfig(),
)

The entities now have by_id and by_filter query roots:

{
  Sprint {
    by_filter(limit: 10) {
      id
      name
      tasks {
        id
        title
        done
        owner {
          id
          name
        }
      }
    }
  }
}

No relationship resolver is required. nexusx inspects the SQLAlchemy relationship metadata and creates the DataLoaders.

For the query above, it loads:

1. The requested sprints.
2. All tasks for those sprint IDs in one batch.
3. All owners for those task owner IDs in one batch.

The number of relationship queries grows with the depth of the graph, not with the number of returned rows. Selected fields are also propagated down to SQL, so unrequested columns do not need to be loaded.

Add a small FastAPI endpoint when you want GraphiQL or HTTP access:

from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from pydantic import BaseModel


class GraphQLRequest(BaseModel):
    query: str


app = FastAPI()


@app.get("/graphql", response_class=HTMLResponse)
async def graphiql():
    return handler.get_graphiql_html()


@app.post("/graphql")
async def graphql(request: GraphQLRequest):
    return await handler.execute(request.query)

This is the data graph: a convenient, selection-driven interface for exploring and reading your entity relationships.

Shape application responses

Database entities are not always good API contracts. A task table might contain internal columns that should never be returned, while the application response needs an owner summary and derived fields.

DefineSubset creates an independent Pydantic DTO from selected entity fields:

from nexusx import DefineSubset


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


class TaskSummary(DefineSubset):
    __subset__ = (
        Task,
        ("id", "title", "done", "sprint_id", "owner_id"),
    )

    owner: UserSummary | None = None


class SprintSummary(DefineSubset):
    __subset__ = (Sprint, ("id", "name"))

    tasks: list[TaskSummary] = []
    task_count: int = 0

    def post_task_count(self):
        return len(self.tasks)

The field name owner matches the Task.owner relationship, so nexusx loads it automatically and converts the result to UserSummary. The same applies to SprintSummary.tasks.

Load only the root columns required by the DTO, then resolve its relationship tree:

from nexusx import ErManager, build_dto_select


er = ErManager(
    entities=[User, Sprint, Task],
    session_factory=async_session,
)
Resolver = er.create_resolver()


async def load_sprints() -> list[SprintSummary]:
    statement = build_dto_select(SprintSummary)

    async with async_session() as session:
        rows = (await session.exec(statement)).all()

    dtos = [SprintSummary(**dict(row._mapping)) for row in rows]
    return await Resolver().resolve(dtos)

DefineSubset and Resolver provide the same selection-driven loading model outside GraphQL. They can be used in FastAPI handlers, background jobs, tests, or business services.

As response trees become more advanced, nexusx also provides:

• resolve_* hooks to override how a field is loaded.
• post_* hooks to compute fields after children are ready.
• ExposeAs to pass values from ancestors to descendants.
• SendTo and Collector to aggregate values from descendants.
• Paged for per-parent top-N relationship loading.

Define a business use case

Once the response contract is stable, expose business intent instead of raw database access:

from nexusx import UseCaseService, query


class SprintService(UseCaseService):
    """Sprint planning operations."""

    @query
    async def list_sprints(cls) -> list[SprintSummary]:
        """List sprints with their tasks, owners, and task count."""
        return await load_sprints()

This is plain async Python. It can be tested by calling SprintService.list_sprints() directly.

Now describe the application once:

from nexusx import UseCaseAppConfig


project_api = UseCaseAppConfig(
    name="project",
    services=[SprintService],
    description="Project planning operations",
)

The same configuration can be attached to different delivery protocols.

REST and OpenAPI

from fastapi import FastAPI
from nexusx import create_use_case_router


app = FastAPI()
app.include_router(create_use_case_router(project_api))

The service method becomes a typed FastAPI route and appears in OpenAPI.

MCP for AI agents

Install the optional MCP integration:

pip install "nexusx[fastmcp]"

from nexusx import create_use_case_graphql_mcp_server


mcp = create_use_case_graphql_mcp_server(
    apps=[project_api],
    name="Project API",
)
mcp.run()

AI agents discover the application progressively:

list_apps
    -> describe_compose_schema
        -> describe_compose_method
            -> compose_query

Instead of loading a full GraphQL introspection document into the model context, the agent asks for the application, service, and method details it needs.

CLI

Install the optional CLI integration:

pip install "nexusx[cli]"

from nexusx import create_use_case_cli


cli = create_use_case_cli(project_api)
cli()

The same method is now available as a command without moving business logic into the CLI layer. Services become command groups, methods become commands, and --help works at every layer (myapp --help → myapp <service> --help → myapp <service> <method> --help).

Every method command also takes --select for GraphQL-like field projection — trim the JSON output to just the fields you want:

myapp sprint-service list_sprints --select "name task_count"
myapp task-service get_task --task-id 1 --select "title owner { name }"

A method's --help also lists the return DTO's fields (nested relationships marked selectable), so you know what --select can pick. See demo/use_case/cli_demo.py for a runnable example.

One model, two graphs

nexusx exposes GraphQL in two different places because they solve different problems:

	Data graph	Operation graph
Entry point	GraphQLHandler	UseCaseService
Source	SQLModel entities and relationships	Typed business methods
Main purpose	Browse and slice connected data	Invoke application operations
Typical users	Developers and internal tools	Web clients, integrations, and AI agents
Discovery	Full GraphQL introspection and GraphiQL	Compact service and method discovery

Use the data graph when the caller needs flexible relationship traversal. Use the operation graph when the caller should invoke a stable business capability. Applications can use either one or both.

Three ideas behind nexusx

Selection is a first-class concept

A field selection is not limited to the GraphQL transport. It influences:

• The GraphQL response shape.
• The columns loaded from SQL.
• The fields copied into a DefineSubset DTO.
• The nested relationships resolved by Resolver.
• The fields returned to an MCP caller.
• Whether optional pagination metadata such as total_count is calculated.

Relationships are not limited to the ORM

SQLAlchemy relationships are discovered automatically, but a relationship can also be backed by Redis, a search engine, another database, or an external API. Declare a Relationship with an async batch function and it joins the same loader, DTO, GraphQL, and ER-diagram infrastructure.

Delivery is layered on later

UseCaseService methods do not depend on FastAPI, MCP, GraphQL, or CLI request objects. Protocol-specific builders inspect the same typed signature and add the appropriate adapter. FromContext injects trusted values such as user ID, tenant ID, or request ID without exposing them as client-controlled arguments.

Performance by construction

nexusx uses the requested response shape to plan relationship loading:

• DataLoader batches many-to-one, one-to-many, and many-to-many relationships.
• SQLAlchemy load_only limits selected entity columns.
• Loader requirements from multiple consumers are merged safely.
• Per-parent pagination uses SQL window functions instead of one query per parent.
• total_count is skipped when the response does not request it.
• Dynamic response models are cached by selection structure.

See Feature highlights for the design details and benchmarks for the benchmark suite.

Beyond one database

The same relationship model extends into more advanced architectures:

• Custom relationships connect Redis, search, SDKs, and external APIs.
• Virtual entities use ordinary Pydantic models as non-table graph roots.
• Voyager renders entities, DTOs, use cases, and their dependencies.
• Entity federation composes multiple nexusx data graphs without a central gateway.
• ComposedErManager composes multiple engines within one process (the same-process dual of federation).
• DTO federation loads public DTO trees across service boundaries.
• Multi-app MCP combines independently packaged applications and databases.

Federation is intentionally homogeneous: it composes nexusx services rather than acting as a general-purpose third-party GraphQL supergraph.

When to use nexusx

nexusx is a good fit when:

• Your application already uses SQLModel and has relationship-heavy reads.
• You need REST for applications and MCP for AI agents from the same logic.
• You want stable DTO contracts without duplicating entity field definitions.
• Some relationships come from non-ORM data sources.
• A modular monolith may later evolve into cooperating services.

nexusx is probably not the best fit when:

• You only need a few hand-written REST endpoints.
• You need complete resolver-level control over a large public GraphQL schema.
• Your services use unrelated stacks and require a general federation gateway.
• You prefer explicit protocol-specific code over convention and introspection.

Plain FastAPI is simpler for the first case. Strawberry provides more direct control for a GraphQL-first application.

Installation

pip install nexusx

Optional integrations:

pip install "nexusx[fastmcp]"     # MCP servers
pip install "nexusx[federation]"  # Cross-service composition
pip install "nexusx[cli]"         # Typer CLI generation

nexusx requires Python 3.10 or newer.

Learn in layers

Start with the layer your application needs:

Goal	Guide
Run the first GraphQL query	Quick start
Understand the entity query surface	GraphQL mode
Generate by_id and by_filter	Automatic queries
Build stable application DTOs	Core API
Add derived fields and tree data flow	Core API advanced
Connect a non-ORM data source	Custom relationships
Expose use cases to web and AI	UseCase services
Add an MCP interface	MCP services
Visualize the architecture	Voyager
Compose nexusx services	Federation
Compose engines in one process	ComposedErManager

For complete runnable examples, see demo/. For the progressive Schema-to-SDK development workflow, see the 4-phase nexusx skill.

Project

• Documentation
• Feature highlights
• MCP & context efficiency
• Architecture comparison
• Changelog
• Issue tracker

Testing

nexusx has 88% test coverage across 1511 tests (6 skipped). Run the suite with:

uv run pytest

nexusx follows semantic versioning and is distributed under the MIT license.