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Understanding RocketRide

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Understanding RocketRide

RocketRide has a small number of moving parts. Once you know how they fit together, the rest of the docs map cleanly onto them.

The pipeline​

A pipeline is a graph of nodes defined in a .pipe file (JSON). Data flows between nodes along typed data lanes: a node declares which input lanes it consumes and which output lanes it produces, and the engine routes data accordingly. See Pipelines and the Execution model.

Nodes​

Nodes are the building blocks: LLM providers, vector stores, embedding models, preprocessors, OCR/NER, web tools, agents, and sources like Chat. Each node ships a schema (its config, inputs, and outputs) and runs inside the engine. Connectors are the nodes that read from and write to external systems. See Nodes and Agents & tools.

The runtime engine​

Pipelines execute on a multithreaded C++ engine (the runtime). It loads the .pipe definition, instantiates the nodes, and streams data through the graph. The same engine runs locally, on-premises, and on RocketRide Cloud. See Runtime & engine.

Talking to the engine​

You start and feed pipelines through one of two protocols:

  • WebSocket: the native engine protocol (port 5565). The TypeScript and Python SDKs speak it for you: use() to start a pipeline, send()/pipe() to stream data, chat() for conversational flows, terminate() to stop.
  • MCP: expose a pipeline as a tool for AI assistants like Claude and Cursor.

How you build​

  • Visually: the VS Code extension opens .pipe files on a canvas; wire nodes by connecting lanes and press Run.
  • In code: author or run the same pipeline from your application with the SDKs.

Putting it together​

A typical flow: author a .pipe visually → run it locally to iterate → integrate it into your app via an SDK → deploy the engine on-prem or to Cloud. The pipeline JSON never changes across those steps.

See the Use cases walkthroughs to do this end to end.