rocketride-org/rocketride-server
by Various
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
MCP
rocketride-org/rocketride-server
Added 1 June 2026
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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