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RG GraphPilot
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RG GraphPilot Overview

What is RG GraphPilot?

RG GraphPilot is a specialist graph-application agent for real delivery. It does not stop at drawing a graph once. It keeps working through editing, refactoring, implementation, and continuous natural-language iteration.

01

RG GraphPilot can not only draw complex graphs from your natural-language request, but also turn those requests into real graph-app outputs.

02

The graphs it produces remain editable, whether you continue by manual interaction or by telling it in natural language to modify or extend data and interaction features.

03

Its delivery is not limited to a static visualization. It can deliver a graph application with complex interactions, ready to be integrated into your software product or connected directly to your own data APIs as a standalone graph app.

04

It can refactor your current graph application from any web graph framework and any language platform, then output the result in the frontend form you need, including React, Vue, Svelte, or HTML Web Components.

05

Its outputs are not one-off artifacts. Everything stays open to continued iteration through natural language.

Open Online GraphPilotView Plans
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MCP / SKILLS

RG GraphPilot

A graph-drawing agent built on the RG Graph Expert Knowledge Base and hundreds of relation-graph scenario patterns. It generates graph application code that remains AI-iterable, can interact with your own system code, and supports every frontend stack; RG GraphPilot better understands scenarios, data, and algorithms.

  • Outputs code that can continue to be understood, edited, and iterated by AI
  • Can interact with and integrate into your existing system code
  • Supports Vue 2, Vue 3, React, Svelte, and native HTML stacks
  • Understands scenarios: can choose the most suitable viewing or editing approach from the needs and data you describe
  • Understands algorithms: is fluent in dozens of professional graph layout and graph operation algorithms, including center, force, multiple tree layouts, workflow, area-based layouts, shortest path, data flow routing, grouping, undo and redo, and drag-to-create links
What GraphPilot Is

Focused on graph apps, not generic AI noise

RG GraphPilot is a specialist agent for graph visualization and graph editing, with clear scope and delivery-oriented outputs.

  • RG GraphPilot is not an all-purpose AI tool. It is an agent focused on concrete graph-visualization and graph-editing problems.
  • Its core job is writing code and helping you build interactive code that can keep evolving.
  • It is charged by credit consumption, so you do not carry a subscription burden.
RG GraphPilot Workflow

RG GraphPilot Runtime Overview

The online GraphPilot workflow is not just prompt in, image out. It coordinates requirements, knowledge assets, documentation, example patterns, code context, and delivery output together.

Knowledge Foundation

RG GraphPilot is built on a powerful knowledge base

The online GraphPilot agent is built on top of RG-Expert KB. Its base includes not only relation-graph technical knowledge, but also accumulated scenario understanding, data-structure judgment, algorithm experience, and delivery know-how.

The relation-graph team has already provided technical support services for close to one hundred enterprise customers, with deep technical and industry experience.
RG-Expert KB

RG Graph Expert Knowledge Base (RG-Expert KB)

"Beyond code generation, it outputs domain know-how and derives best-fit solutions for complex business scenarios."

  • Includes all technical foundation capabilities from Dev Skill
  • 200+ professional graph-application scenario patterns
  • Supports ultra-complex scenarios such as industrial canvas software and AI editors
  • High-frequency optimization aligned with the latest relation-graph field practices
Best for
Enterprise included / standalone purchase
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Why choose relation-graph
  • Complete coverage for graph display and graph editing:relation-graph covers both graph display and graph editing in a mature and flexible way. It is suitable not only for visual graph views, but also for building truly interactive and editable graph applications.
  • Native multi-framework support with strong consistency:It natively supports React, Vue, Svelte, and Web Components, while keeping strong cross-platform consistency across visual behavior, data structure, interaction logic, and technical concepts. That comes from a rational and stable technical architecture.
  • Rich examples and documentation that keep evolving:The online examples and documentation continue to evolve and significantly reduce learning cost. Combined with AI, they also make it easier to turn ambitious ideas into working code quickly.
  • Naturally suited for LLM and knowledge-base integration:Its clear architecture and usage model are highly legible to LLMs. If stronger internal AI performance is needed, the official knowledge-base license can be added for deeper AI integration.
  • 100% MIT open source means controllable technical risk:relation-graph is MIT-licensed from the project itself through its dependencies, keeping the technical stack transparent and controllable, which is better suited for long-term enterprise use and integration.
Why relation-graph is trustworthy
  • Complete capability backed by real-world validation:It fully covers graph display and graph editing, with a large set of scenario assets proven in real projects, not only demo-oriented examples.
  • Cross-framework consistency sharpened by high-frequency feedback:It natively supports React, Vue, Svelte, and Web Components, and keeps improving stability and usability through frequent issue exchanges with thousands of paying users.
  • Mature enterprise delivery experience with clear execution paths:It has supported close to one hundred enterprise customers and helped them roll out graph applications into their own systems in an orderly and efficient way.
  • A complete engineering support system:It has formed an end-to-end support system across documentation, examples, capability layering, and issue-closure workflows, and can be further amplified with official knowledge assets and AI workflows.
  • Controllable risk and long-term maintainability:Based on MIT open source and a clear architecture, it helps teams start fast while also supporting sustained releases, iteration, and long-term maintenance.