RocketRide
Open-source AI pipeline engine with a C++ core, Python-extensible nodes, and a VS Code extension.rocketride-org/rocketride-server
- Stars
- 19.6k
- Forks
- 8.5k
- Licence
- MIT
- Language
- Python
- Last update
- 4 days ago
- Type
- Dev tool
What it is
RocketRide is an open-source tool for building and running AI data pipelines, defined as portable JSON and built visually inside VS Code. It runs pipelines on a multithreaded C++ runtime and includes nodes for LLM providers, vector databases, OCR, and agent frameworks like CrewAI and LangChain. People use it to build AI features and run them on their own infrastructure.
- Build a document pipeline that uses OCR, chunking, embeddings, and a vector database
- Debug and observe token usage, LLM calls, and latency while a pipeline runs
- Call a pipeline from a Python or TypeScript app using the SDKs
- Expose pipelines as callable tools for AI assistants through the MCP SDK
- Write a custom Python node and add it to a pipeline
Great for
- Developers who want to build LLM workflows visually inside VS Code
- Teams that want to run AI pipelines on their own infrastructure
- Developers who want to call pipelines from Python or TypeScript apps
- Builders who need to swap between many model providers and vector databases
Not ideal for
- People who only want a ready-made chat app; the README describes a development environment and SDKs, not an end-user product
- Non-technical users without experience with IDEs, Docker, or SDKs, since setup assumes developer tooling
- Projects that need a simple single-script LLM call; the pipeline engine adds structure that may be more than needed
How to get it
- 1
See the README
You'll need: VS Code (for the extension)
Clone the repository
git clone https://github.com/rocketride-org/rocketride-server - 2
Run it
- Install the RocketRide extension from the VS Code marketplace or Open VSX
- Open the RocketRide panel and select Local
- Open or create a pipeline on the visual canvas
- Run the pipeline and inspect its output and observability data
- 3
Get the most out of it
- Use the Python SDK to run pipelines from your own application code
- Use the TypeScript SDK to integrate pipelines into a Node.js or web app
- Expose pipelines as tools for AI assistants via the MCP server
- Write custom Python nodes to extend the pipeline with your own logic
- Deploy with Docker for a self-hosted setup
Downloads
Strengths
- Pipelines are portable JSON that can be version-controlled and shared
- Released under the MIT license with no paid edition described in the README
- Supports Python, TypeScript, MCP, and Docker deployment paths
Trade-offs
- The README is truncated, so some setup details and requirements are not confirmed here
- Combines C++, Python, and TypeScript components, which can make debugging harder for newcomers
- Claims like production-ready and high-throughput are the project's own description and are not independently benchmarked in the README
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| rocketride-server (this) | 19.6k | Dev tool | intermediate | 4 days ago | Developers who want to build LLM workflows visually inside VS Code |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| llama_index | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| TabNine | 10.8k | Dev tool | beginner | 1.2 years ago | Developers who want to understand how TabNine's language settings are structured |
| ai-engineering-from-scratch | 76.3k | Learning resource | intermediate | 4 days ago | Students who want to understand how LLMs, retrieval, and agents work internally |
| ai-engineering-hub | 40.8k | Learning resource | intermediate | 5 days ago | Developers who want working examples of RAG or agent systems to adapt |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.