HyperGraphOS: Graph-Centric MetaOS

• HyperGraphOS is a web-based meta-operating system that replaces file and folder paradigms with a unified, graph-centric model using labeled, attributed graphs.
• It integrates domain-specific languages and AI tools like LLMs to enable dynamic code generation, real-time model modification, and automated task planning.
• Its modular architecture, including a Node.js backend and browser-based visualization, supports scalable, persistent, and infinite workspaces for complex workflows.

HyperGraphOS is a web-based meta-operating system (MetaOS) designed to address the complex demands of scientific and engineering users by replacing traditional file/folder and application-based paradigms with a unified, graph-centric modeling environment. All user data, processes, models, and tools are encoded as labeled attributed graphs and manipulated through extensible Domain-Specific Languages (DSLs), providing an infinite workspace (OmniSpace) and dynamic integration with computational tools and AI services (Ceravola et al., 2024, Ceravola et al., 2024).

1. System Architecture and Formal Foundations

HyperGraphOS is delivered via a web server, requiring only a modern web browser for access. Its architecture comprises several interconnected modules: a kernel interface (for UI rendering and OS-level calls), a Node.js–based back end (handling workspace JSON graphs and server-side logic), browser-based front ends (using the GoJS diagramming library for graph visualization), external cloud services (LLM integration, storage, security), and data management relying primarily on JSON documents, with optional YAML support (Ceravola et al., 2024).

The central abstraction is a labeled, attributed directed graph: $G = (N, E, \alpha_N, \alpha_E)$ where:

• $N$ is a set of nodes, each carrying a type in a type hierarchy and a map of name–value attributes.
• $E \subseteq N \times L \times N$ is a set of typed, directed edges labeled $l \in L$.
• $\alpha_N\colon N \to \mathrm{Attr}_N$ and $\alpha_E\colon E \to \mathrm{Attr}_E$ assign attribute maps to nodes and edges respectively.

Container nodes ($c$) define subgraphs by membership, enabling encapsulation and scoping: $$N_c = \alpha_N(c).\mathit{children}, \qquad E_c = \{\,e \in E \mid \mathrm{src}(e) \in N_c \text{ and } \mathrm{tgt}(e) \in N_c\}$$

Every workspace (OmniSpace) corresponds to such a graph, persisted as a JSON file.

Multi-level modeling is supported: any vertex or edge may simultaneously serve as a meta-model, model, and implementation (“fractal design”). Meta-models themselves are graphs, and instantiation is expressed as a mapping $\sigma : M \mapsto G$ that preserves types and relationships (Ceravola et al., 2024).

2. Domain-Specific Languages (DSLs) and Workspace Navigation

All interaction in HyperGraphOS is mediated by DSLs. The platform provides a “Basic DSL” for OS-like operations (e.g., open("Home"), ls("Home"), mv(nodeID, containerID)) and supports definition of custom meta-models and meta-meta-models fully within an OmniSpace using a DSL-creation DSL. A typical grammar for a dataflow DSL is given in BNF-like notation, supporting node and link definition plus arbitrary lists of annotated attributes: $N$1 Such DSL grammars are automatically translated into palettes of draggable elements, with integrated property editors and code generation. Some of the pre-defined DSLs include Dialog DSL, DataFlow DSL, CodeFlow DSL, CodeGeneration DSL, UserInterface DSL, Animator DSL, and Deployment DSL (Ceravola et al., 2024).

Code generation is driven by template-based DSLs. For example, a control-flow rule can be specified with DSL annotations such as: $N$2

3. Computational Tools, AI Integration, and Execution

HyperGraphOS incorporates a "Control Engine" within each OmniSpace, supporting invocation of external computational resources. Integration points include:

• LLMs via OpenAI APIs (e.g., GPT-4) for model-driven planning, authoring, and dynamic pipeline steps.
• A local JavaScript engine for runtime execution of embedded scripts.
• ROS communication for robotic control and feedback.
• Template-based code generation, with support for mustache-like and annotated templates (Ceravola et al., 2024, Ceravola et al., 2024).

In robotic task planning (CoPAL), workflows are modeled as dataflow graphs; nodes may trigger LLM calls for subtask synthesis or directly execute ROS actions. A hierarchical replanning routine is supported, informally characterized analogously to a POMDP update loop: $$\text{State}_{t+1} = f_{\mathrm{LLM}}(\text{State}_t, \text{Context}_t)$$ Here, each LLM invocation is packaged as a node effecting $N$0.

The platform’s İMBSE assistant provides context-aware suggestions, inference rule completions, and real-time search. The “Multi-Agent Editor Office” (MAEO) demonstrates multi-agent AI-assisted authoring workflows.

4. Dynamic Model Modification, Interactivity, and Persistence

All OmniSpace graphs present a dual representation: a GoJS-based visual diagram and an underlying JSON structure. Edits via the JavaScript shell are immediately reflected visually, and all drag-and-drop changes are written back to the JSON.

HyperGraphOS provides a client-side JavaScript API for direct model manipulation: $N$3 Programmatic modifications and DSL-driven processes are both first-class. An integrated search engine indexes node/link attributes and graph connectivity for efficient inspection and querying.

Persistence is inherent: navigation history, layout, and open views are automatically preserved in JSON files. No dedicated database is required, and JSON OmniSpaces can be distributed on local disks or cloud storage (Ceravola et al., 2024).

5. Evaluation and Representative Use Cases

Empirical evaluation of HyperGraphOS has been conducted across several domains, demonstrating flexibility, scalability, and rapid iteration:

Case	Model Size	Key Results	DSLs/Tools Used
Virtual Receptionist	4,246 nodes, 3,890 links	Code-gen: 4,033 dict lines, 4,659 weights, 2,410 intents; <3s for gen.	Dialog DSL, CodeGen DSL
Robotic Task Planning (CoPAL)	414 nodes, 355 links	Real-time feedback, replanning, execution in 2 weeks	DataFlow DSL, LLM
Thebes Research Projects	-	Prototype in 1 day, C++/ROS code-gen in days	Thebes DSL
Paper Authoring via MAEO	-	Entire pipeline for writing/editing coordinated in 3 days	DataFlow, Document DSL

HyperGraphOS supports sub-second to second-scale code generation even for multi-thousand node graphs. Scalability tests indicate no bottlenecks for distribution across hundreds of projects using the JSON backing model (Ceravola et al., 2024, Ceravola et al., 2024).

6. Benefits, Limitations, and Future Directions

Substantial benefits include:

• Unified modeling across documents, dataflows, and computation within a single attributed graph representation (OmniSpace paradigm).
• Extensible DSL ecosystem reducing accidental complexity and enabling domain-specific modeling at each layer.
• Minimal footprint via browser-only client, no per-user installation.
• Tight integration of LLM-based AI for planning, authoring, and DSL evolution.
• Persistent, infinite workspace unconstrained by window or folder metaphors (Ceravola et al., 2024).

Limitations are observed in several areas:

• Security and privacy are presently delegated to external APIs; no built-in encryption, isolation, or access control.
• The JSON-based persistence may not scale to very large models requiring ACID semantics or advanced graph indexing.
• Real-time multi-user collaboration is under development.
• Lack of formal model verification facilities compared to specialized model management systems (e.g., ConceptBase, MetaEdit+).
• The learning curve for custom DSL and meta-model design is high (Ceravola et al., 2024).

Future avenues include addition of built-in security (role-based access, encryption), scalable document- or graph-store backends, WebSocket-powered real-time collaboration, integration of constraint solvers or model checkers, and enhanced cross-OmniSpace search (Ceravola et al., 2024, Ceravola et al., 2024).

7. Context and Significance in Scientific and Engineering Computing

HyperGraphOS represents a radical departure from traditional file/folder-based operating systems for scientific and engineering workflows by elevating model-based engineering, graph models, and domain-specific languages to first-class OS abstractions. The demonstrated ability to represent, execute, and adapt entire research, engineering, or robotic pipelines inside an interactive, infinite graph workspace situates it at the leading edge of intelligent model-based software engineering environments. A plausible implication is that such meta-operating system environments could become foundational for collaborative, AI-assisted scientific discovery and dynamic engineering processes (Ceravola et al., 2024, Ceravola et al., 2024).