Build a pipeline in your IDE

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Build a pipeline in your IDE

The visual canvas in the VS Code extension is the fastest way to author a .pipe file. This walkthrough starts from zero and finishes with a running Chat → LLM pipeline you can talk to.

1. Install the extension

Search for RocketRide in the VS Code Extension Marketplace and install it. The extension also works in VS Code forks (Cursor, Windsurf, VSCodium) via the Open VSX Registry.

2. Deploy a server

Click the RocketRide () icon in your IDE sidebar, then choose how to run the runtime. Local is the right choice here — it pulls the server straight into your IDE with no extra setup. (The other options are covered in Choose How to Run RocketRide.)

3. Create a pipeline file

Create a file ending in .pipe (e.g. my-first-pipeline.pipe). The extension opens it in the visual builder canvas. .pipe files are JSON under the hood, but you author them visually.

4. Build a simple chat pipeline

Every pipeline starts with a source node:

1. Add a Chat source node: an interactive conversational interface.
2. Add an LLM node: pick a provider (OpenAI, Anthropic, Google, …) and set your API key.
3. Connect the Chat source's output lane to the LLM's input lane.

The result is a Chat → LLM pipeline; the LLM's response routes back to the chat interface automatically.

5. Run it

Press the Run button on the source node, or launch from the RocketRide sidebar. Open the chat interface, send a message, and watch the LLM respond in real time. Use the Server Monitor page to trace call trees, token usage, and memory consumption.

Save the .pipe file, you'll run it from code in the next walkthrough.

Next

• Integrate a pipeline with an SDK: run the .pipe file you just built from your own Python or TypeScript application.
• VS Code extension: the full extension guide (canvas, runtime management, tracing).