An open-source 2.5D diagram engine in Go that separates topology from geometry to enable LLMs to generate clean architecture diagrams without spatial hallucinations.
Hey r/AI_agent, Documenting architecture is currently a broken experience for vibe-coding. If you open Figma, you break your flow. If you ask an LLM to write Mermaid or PlantUML, you get an uncontrollable spaghetti mess. The root cause? LLMs are topological geniuses but spatial idiots. They understand the relationship between microservices perfectly, but they can't do 2D coordinate math, resulting in chaotic layouts and crossed lines. I wanted presentation-grade architecture diagrams straight from my agent, so I built iso-topology: an open-source 2.5D diagram engine in Go, designed specifically as an "Agent-First" visual compiler. It fixes the LLM spatial hallucination problem using two mechanisms: 1. Mathematically Preventing Ugly (Separating Topology and Geometry) Instead of giving the LLM an infinite canvas, the engine enforces strict compiler-level design rules: Zero Pixel Pushing: The LLM cannot compute X/Y pixels. It only declares the components (Topology). The underlying layout solvers (Dagre or ELK) compute all cellular gaps and geometry. Aesthetic Constraints: It enforces a "One Hero, One Accent" rule per scene. This completely stops the LLM from generating chaotic "rainbow parts catalogs." Strict Orthogonal Routing: Lines are mathematically forced onto a 30-degree isometric grid, bending around hardware obstacles so the 2.5D depth illusion never breaks. 2. The Autonomous "Produce-Refine-Evaluate" Loop The real magic isn't just the rendering; it's how agents interact with it. Instead of blindly generating text, the engine exposes a native tool-calling loop that allows the agent to negotiate with the layout solver: Discover: The agent dynamically reads syntax rules and fetches from 30+ built-in 3D tech glyphs (e.g., iso://glyph/gpu, iso://glyph/sparkles). Validate (The Compiler): If the agent hallucinates a connection that causes an unavoidable overlap or breaks a visual rule, the engine returns a structured JSON error with an explicit fix (e.g., "suggest": "person"). The LLM auto-corrects its code in milliseconds before it ever renders. Render: Once validation exits 0, it spits out an Apple-keynote-quality 2.5D SVG directly into your chat UI. It plugs directly into Cursor or Claude Desktop via standard tool-calling hooks. You just paste your Terraform or codebase, and watch the agent autonomously negotiate with the compiler to produce a flawless 3D map without you touching a GUI. Would love to hear how you handle visual generation in your agentic workflows, and what you think of this deterministic validation pattern for LLMs!
A new AI tool generates 3D objects by generating code, resulting in objects with separate, functional parts rather than monolithic blobs. It is free and open-source on GitHub.
This paper introduces a neuro-symbolic pipeline using 2.5-D decomposition to improve LLM-based spatial construction accuracy by offloading vertical coordinate calculation to a deterministic executor, achieving high accuracy on benchmarks and edge hardware.
A developer built a pipeline that uses an LLM as a structured code compiler to generate Blender Python code, producing 3D objects with functional, articulated parts instead of monolithic meshes. The tool is open-source and LLM-agnostic.
Atlarix is a desktop environment that pre-parses codebases into a node/edge graph, allowing coding agents to navigate architecture via queries instead of reading raw text, which improves performance of smaller local models.
PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from natural language by combining LLM-based scene understanding with a deterministic constraint solver and a VLM-based verify loop, outperforming existing models on a benchmark of physics problems.