Glossary
Glossary
Terms used across the RocketRide docs.
Pipeline
A directed graph of components that moves and transforms
data, authored as a .pipe file. The unit of work in RocketRide.
See Pipelines.
Component / node
One vertex of the pipeline graph: a single unit that does one job (call a model,
embed text, query a store, run a tool). Has a unique id, a
provider, and config. See
Nodes.
Provider
What a component is: the value that determines its behaviour (e.g.
llm_openai, qdrant, webhook). Swapping providers is a config change, not a
code change.
Connector
A node at the edge of the graph that reads from or writes to an external system: a source (brings data in) or a target (sends results out).
Class type
The category a provider belongs to (llm, tool, agent, memory, store,
source, target, …), which governs how the node is wired.
Data lane
A typed channel carrying data between nodes (questions, answers, text,
image, …). A node's input declares which lanes it consumes and from where.
See the Execution model.
Control (invoke) connection
A side-channel connection by which an agent invokes a helper (LLM,
tool, memory) instead of streaming data through a lane. Declared as control on
the helper, pointing back at the invoker.
Agent
A node that reasons in a loop, choosing which model to call and which tools to use, over one or more waves until it produces a result. See Agents & tools.
Tool
A capability an agent can invoke at runtime (HTTP request, web search, shell,
another pipeline). Tools have no data lanes; they are wired by control.
Memory
State an agent carries across turns (memory_internal, memory_persistent).
Only agent_rocketride has a memory port.
Engine
The multithreaded C++ runtime that loads a .pipe, instantiates
its nodes, and streams data through the graph. Runs locally, self-hosted,
or on Cloud. See Runtime & engine.
.pipe file
The JSON file that defines a pipeline, conforming to the Pipeline JSON Reference. The same file runs unchanged in every environment.
Task
A single running instance of a pipeline on the engine, identified by a token and controlled over the WebSocket protocol.
MCP
The Model Context Protocol: exposes running pipelines as tools for AI assistants. See MCP.
WebSocket protocol
The engine's native protocol (port 5565) that SDKs and the MCP server use to start pipelines and stream results. See WebSocket.