MooseStack

Developer toolkit for building real-time analytical backends in Typescript and Python — MooseStack brings data engineering best practices and a modern web development DX to any engineer building on data infra.

MooseStack modules offer a type‑safe, code‑first developer experience layer for popular open source analytical infrastructure, including ClickHouse, Kafka, Redpanda, and Temporal.

MooseStack is designed for:

1. Software engineers integrating analytics & AI into their apps, and leaning into real-time / OLAP infrastructure best practices
2. Data engineers building software & AI applications on their data infra, and leaning into software development best practices

Why MooseStack?

• Git-native development: Version control, collaboration, and governance built-in
• Local-first experience: Full mirror of production environment on your laptop with moose dev
• Schema & migration management: typed schemas in your application code, with transparent migration support
• Code‑first infrastructure: Declare tables, streams, workflows, and APIs in TS/Python -> MooseStack wires it all up.
• Modular design: Only enable the modules you need. Each module is independent and can be adopted incrementally.
• AI copilot friendly: Designed from the ground up for LLM-powered development

MooseStack Modules

• Moose OLAP: Manage ClickHouse tables, materialized views, and migrations in code.
• Moose Streaming: Real‑time ingest buffers and streaming transformation functions with Kafka/Redpanda.
• Moose Workflows: ETL pipelines and tasks with Temporal.
• Moose APIs: Type‑safe ingestion and query endpoints with auto‑generated OpenAPI docs.
• MooseStack Tooling: Moose Deploy, Moose Migrate, Moose Observability

Quickstart

Also available in the Docs: 5-minute Quickstart

Already running Clickhouse: Getting Started with Existing Clickhouse

Install the CLI

bash -i <(curl -fsSL https://fiveonefour.com/install.sh) moose

Create a project

# typescript
moose init my-project --from-remote <YOUR_CLICKHOUSE_CONNECTION_STRING> --language typescript

# python
moose init my-project --from-remote <YOUR_CLICKHOUSE_CONNECTION_STRING> --language python

Run locally

cd my-project
moose dev

MooseStack will start ClickHouse, Redpanda, Temporal, and Redis; the CLI validates each component.

Deploy with Boreal

The easiest way to deploy your MooseStack Applications is to use Boreal from 514 Labs, the creators of Moose. Boreal provides zero-config deployments, automatic scaling, managed or BYO infrastructure, monitoring and observability integrations.

Get started with Boreal →

Deploy Yourself

MooseStack is open source and can be self-hosted. If you’re only using MooseOLAP, you can use the Moose library in your app for schema management, migrations, and typed queries on your ClickHouse database without deploying the Moose runtime. For detailed self-hosting instructions, see our deployment documentation.

Examples

TypeScript

import { Key, OlapTable, Stream, IngestApi, ConsumptionApi } from "@514labs/moose-lib";

interface DataModel {
primaryKey: Key<string>;
name: string;
}
// Create a ClickHouse table
export const clickhouseTable = new OlapTable<DataModel>("TableName");

// Create a Redpanda streaming topic
export const redpandaTopic = new Stream<DataModel>("TopicName", {
destination: clickhouseTable,
});

// Create an ingest API endpoint
export const ingestApi = new IngestApi<DataModel>("post-api-route", {
destination: redpandaTopic,
});

// Create consumption API endpoint
interface QueryParams {
limit?: number;
}
export const consumptionApi = new ConsumptionApi<QueryParams, DataModel[]>("get-api-route",
async ({limit = 10}: QueryParams, {client, sql}) => {
const result = await client.query.execute(sql`SELECT * FROM ${clickhouseTable} LIMIT ${limit}`);
return await result.json();
}
);

Python

from moose_lib import Key, OlapTable, Stream, StreamConfig, IngestApi, IngestApiConfig, ConsumptionApi
from pydantic import BaseModel

class DataModel(BaseModel):
primary_key: Key[str]
name: str

# Create a ClickHouse table
clickhouse_table = OlapTable[DataModel]("TableName")

# Create a Redpanda streaming topic
redpanda_topic = Stream[DataModel]("TopicName", StreamConfig(
destination=clickhouse_table,
))

# Create an ingest API endpoint
ingest_api = IngestApi[DataModel]("post-api-route", IngestApiConfig(
destination=redpanda_topic,
))

# Create a consumption API endpoint
class QueryParams(BaseModel):
limit: int = 10

def handler(client, params: QueryParams):
return client.query.execute("SELECT * FROM {table: Identifier} LIMIT {limit: Int32}", {
"table": clickhouse_table.name,
"limit": params.limit,
})

consumption_api = ConsumptionApi[RequestParams, DataModel]("get-api-route", query_function=handler)

Docs

• Overview
• 5-min Quickstart
• Quickstart with Existing Clickhouse

Built on

• ClickHouse (OLAP storage)
• Redpanda (streaming)
• Temporal (workflow orchestration)
• Redis (internal state)

Community

Join us on Slack

Cursor Background Agents

MooseStack works with Cursor’s background agents for remote development. The repository includes a pre-configured Docker-in-Docker setup that enables Moose’s Docker dependencies to run in the agent environment.

Quick Setup

1. Enable background agents in Cursor
2. The environment will automatically build with Docker support
3. Run moose dev or other Moose commands in the agent

For detailed setup instructions and troubleshooting, see Docker Setup Documentation.

Contributing

We welcome contributions! See the contribution guidelines.

License

MooseStack is open source software and MIT licensed.