RocketRide is an open-source AI pipeline orchestration platform that enables building, running, and deploying complex multi-step AI workflows. The system is built as a multi-language monorepo with a high-performance C++ engine core (apLib/engLib) that executes declarative JSON pipelines (.pipe files) composed of 100+ modular Python nodes covering LLM providers (OpenAI, Anthropic, Gemini, Mistral, DeepSeek, Ollama, etc.), vector databases (Pinecone, Qdrant, Milvus, Chroma, Weaviate, Elasticsearch), document processing (OCR, parsing, chunking, embedding), agent frameworks (CrewAI, LangChain, LlamaIndex, DeepAgent), and external tool integrations (Slack, Gmail, Google Drive, Microsoft 365, GitHub, Notion, etc.). The engine supports local, on-premise, Docker, and cloud deployment modes with GPU acceleration and real-time observability (token usage, latency, memory, call-tree tracing via WebSocket DAP channel). The architecture follows a modular plugin pattern: each pipeline node implements a consistent IGlobal/IInstance lifecycle with beginGlobal/endGlobal hooks, dynamic dependency loading via a depends() mechanism, and declarative configuration via services.json files. The platform includes multiple front-end applications: a VS Code extension (visual pipeline builder with engine management), a web shell (Module Federation host for cloud/on-prem UIs), and specialized apps (SQL explorer, file dropper, event monitor, chat widget, chaos testing tool). Client SDKs in Python and TypeScript allow embedding pipelines into external applications, and an MCP server exposes pipelines as tools for AI assistants. The system also provides an n8n integration, an embeddable chat widget, and a Helm chart for Kubernetes deployment. Key data flows begin with pipeline definition (JSON or visual canvas), which is parsed and validated by the engine. The engine resolves node configurations, installs required dependencies, and executes nodes in topological order, passing typed data (text, documents, images, audio, tables, embeddings) through the pipeline graph. Nodes communicate via a shared context and can branch, merge, loop, and invoke sub-pipelines. Results are streamed back through WebSocket connections for real-time UI updates, and full execution traces are stored for debugging and observability. The platform also includes a model server for GPU-accelerated inference (OCR, embeddings, vision, TTS) with device locking and proxy mode. External dependencies are extensive: the C++ engine relies on OpenSSL, libcurl, libxml2, and platform-specific system calls; Python nodes depend on dozens of AI/ML libraries (transformers, sentence-transformers, openai, anthropic, langchain, llama-index, crewai, etc.); TypeScript/React frontends use MUI, ReactFlow, and RJSF. The build system uses pnpm workspaces, vcpkg for C++ dependencies, and a custom builder script. The project is MIT-licensed with an active community (17.7k stars, 7.5k forks) and regular releases with semantic versioning.