# Nexusx

> Buil applications for people and AI.

- **Type:** MCP server
- **Install:** `agentstack add mcp-klr-pattern-nexusx`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [KLR-Pattern](https://agentstack.voostack.com/s/klr-pattern)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [KLR-Pattern](https://github.com/KLR-Pattern)
- **Source:** https://github.com/KLR-Pattern/nexusx
- **Website:** https://klr-pattern.github.io/nexusx/

## Install

```sh
agentstack add mcp-klr-pattern-nexusx
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# nexusx

[](https://pypi.python.org/pypi/nexusx)
[](https://pepy.tech/projects/nexusx)

**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.

| 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) |

```mermaid
flowchart LR
    models["SQLModel entities"]
    data_graph["Data graphrelationships + loaders"]
    dto["Typed DTOsDefineSubset + 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"]
```

## 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.

## First query

Install nexusx:

```bash
pip install nexusx
```

Define entities as normal SQLModel classes:

```python
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:

```python
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:

```graphql
{
  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:

```python
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:

```python
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:

```python
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:

```python
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:

```python
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

```python
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:

```bash
pip install "nexusx[fastmcp]"
```

```python
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:

```text
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:

```bash
pip install "nexusx[cli]"
```

```python
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.

## 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](docs/feature-highlights.md) for the design details and
[benchmarks](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

```bash
pip install nexusx
```

Optional integrations:

```bash
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](docs/guide/quick_start.md) |
| Understand the entity query surface | [GraphQL mode](docs/guide/graphql_mode.md) |
| Generate `by_id` and `by_filter` | [Automatic queries](docs/guide/graphql_auto_query.md) |
| Build stable application DTOs | [Core API](docs/guide/core_api.md) |
| Add derived fields and tree data flow | [Core API advanced](docs/guide/core_api_advanced.md) |
| Connect a non-ORM data source | [Custom relationships](docs/guide/custom_relationship.md) |
| Expose use cases to web and AI | [UseCase services](docs/advanced/use_case_service.md) |
| Add an MCP interface | [MCP services](docs/advanced/mcp_service.md) |
| Visualize the architecture | [Voyager](docs/advanced/voyager.md) |
| Compose nexusx services | [Federation](docs/advanced/federation.md) |
| Compose engines in one process | [ComposedErManager](docs/advanced/composed_er_manager.md) |

For complete runnable examples, see [`demo/`](demo/). For the progressive
Schema-to-SDK development workflow, see the
[4-phase nexusx skill](skills/nexusx-4phase/).

## Project

- [Documentation](docs/index.md)
- [Feature highlights](docs/feature-highlights.md)
- [Architecture comparison](docs/clean-architecture-comparison.md)
- [Changelog](CHANGELOG.md)
- [Issue tracker](https://github.com/allmonday/nexusx/issues)

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

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [KLR-Pattern](https://github.com/KLR-Pattern)
- **Source:** [KLR-Pattern/nexusx](https://github.com/KLR-Pattern/nexusx)
- **License:** MIT
- **Homepage:** https://klr-pattern.github.io/nexusx/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** yes
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-klr-pattern-nexusx
- Seller: https://agentstack.voostack.com/s/klr-pattern
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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