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rocketride-org/rocketride-server

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High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent

R

MCP

rocketride-org/rocketride-server

Added 1 June 2026

#ai #cpp #data-pipeline #data-processing #machine-learning #mcp #python #sdk

Overview

Rocketride is a high-performance AI pipeline engine with a C++ core and over 50 Python-extensible nodes. It allows developers to build, debug, and scale LLM workflows from their IDE, supporting 13+ model providers, 8+ vector databases, and agent orchestration. The tool includes a VS Code extension, TypeScript and Python SDKs, and Docker deployment.

Best for

Best for
Developers building complex, multi-provider LLM pipelines who need high performance and IDE integration.

Use cases

  • Orchestrating multi-step LLM pipelines with custom nodes
  • Debugging and scaling agent workflows in the IDE
  • Integrating multiple model providers and vector databases in one pipeline

How to use

Tested with

Cursor, VS Code

Notes

Rocketride is a high-performance AI pipeline engine with a C++ core and over 50 Python-extensible nodes. It allows developers to build, debug, and scale LLM workflows from their IDE, supporting 13+ model providers, 8+ vector databases, and agent orchestration. The tool includes a VS Code extension, TypeScript and Python SDKs, and Docker deployment.

3,737 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

  • Orchestrating multi-step LLM pipelines with custom nodes
  • Debugging and scaling agent workflows in the IDE
  • Integrating multiple model providers and vector databases in one pipeline

Pros

  • High-performance C++ core for low-latency pipeline execution
  • Extensible with 50+ Python nodes and SDKs for TypeScript and Python
  • Supports a wide range of model providers and vector databases

Cons

  • Requires familiarity with C++ for core modifications
  • Docker deployment adds overhead for simple projects
  • Limited to IDE-based workflow (VS Code) for full debugging features

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • High-performance C++ core for low-latency pipeline execution
  • Extensible with 50+ Python nodes and SDKs for TypeScript and Python
  • Supports a wide range of model providers and vector databases

Cons

  • Requires familiarity with C++ for core modifications
  • Docker deployment adds overhead for simple projects
  • Limited to IDE-based workflow (VS Code) for full debugging features
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