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SkillsMay 12, 2026·2 min de lecture

RocketRide — Visual AI Pipelines + Observability

RocketRide is an IDE extension + server runtime for visual `.pipe` AI workflows with tracing; build pipelines in the canvas and deploy via Docker/on-prem.

AI Open Source
AI Open Source · Community
Prêt pour agents

Cet actif peut être lu et installé directement par les agents

TokRepo expose une commande CLI universelle, un contrat d'installation, le metadata JSON, un plan selon l'adaptateur et le contenu raw pour aider les agents à juger l'adaptation, le risque et les prochaines actions.

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Surface agent
Tout agent MCP/CLI
Type
Skill
Installation
Single
Confiance
Confiance : Established
Point d'entrée
Asset
Commande CLI universelle
npx tokrepo install 03e2e98b-6717-5a24-b8c2-3d35b7bd17fe
contrat d'installationJSON metadataplan adaptateurcontenu raw
Introduction

Build and run AI pipelines with a visual .pipe workflow format, then inspect traces (call trees, token usage, memory) to tune before production.

Best for: Teams shipping multi-step AI workflows who want a visual builder plus observability from day one

Works with: RocketRide IDE extension; Docker/on-prem server runtime; Python and TypeScript SDKs (per README)

Setup time: 10–25 minutes

Key facts (verified)

  • README highlights 50+ pipeline nodes and mentions 13 LLM providers plus multiple vector DBs.
  • Docker example maps port 5565:5565 for the runtime container (per README quick start).
  • README includes an Observability section describing tracing of call trees, token usage, and memory consumption.
  • GitHub: 2,480 stars · 454 forks; pushed 2026-05-12 (GitHub API verified).

Main

A pragmatic way to evaluate RocketRide:

  1. Install the IDE extension and create a tiny pipeline with a single source node (chat/webhook/dropper).
  2. Add one transform node (chunking, OCR, or NER) so you can see a meaningful trace.
  3. Run from the canvas and inspect tracing: confirm you can answer “what called what” and “where did tokens go”.
  4. When it’s stable, move the runtime to Docker/on-prem as the README shows, and treat the .pipe file as a deployable artifact.

The goal isn’t “more nodes”; it’s repeatable pipelines with observable cost and latency.

FAQ

Q: Do I need Docker to start? A: Not necessarily. The README describes a local (IDE-pulled) option and also provides Docker deployment steps.

Q: What is a pipeline file? A: The README says pipelines are recognized as *.pipe JSON objects rendered by the IDE canvas.

Q: Does it have observability? A: Yes. The README has an Observability section describing tracing and analytics for running pipelines.

🙏

Source et remerciements

Source: https://github.com/rocketride-org/rocketride-server > License: MIT > GitHub stars: 2,480 · forks: 454

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