Alternatives
Products that do what A Satellite View for Python Code does
Hi HN, I built ast-visualizer.com because I wanted a way to visualize the architecture/structure of a Python repo before dived into the code. Most tools tell you what the code does; I wanted to see how it's built. The Problem: Onboarding onto a large codebase is a nightmare. LLMs help with single functions, but they struggle to show you the "God Objects," circular dependencies, or high-complexity hotspots across 50+ files. What it does: Dependency Graph: Visualizes imports and file complexity to find architectural bottlenecks. Radial AST Heatmaps: Maps individual files and color-codes…
- 1VA
I explored an alternative way to view codebases to the typical folder/file list, showing a bird's-eye-view of its structure. https://octo.github.com/projects/repo-visualization
2021 · octo.github.com
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Turn codebases into interactive maps, graphs, and governance
Jul 2026
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- 4WC
Hi all, I'm Ivan, and together with Alex, we're building a diagram visualization tool for codebases. Alex and I are devs, and we've noticed that recently we've been super productive at writing code (prompting :D). But when it comes to understanding big systems, prompting doesn't work that well — for that, diagrams are best imo. Most tools out there don't scale to big projects (e.g. PyTorch), so we're building CodeBoarding — a recursive visualizer for codebases. It starts from the highest level of abstractions and lets you dive deeper. We use static analysis and LLM agents. The control-flow…
2025 · github.com
- 5PS
2021 · github.com
- 6GP
2016 · veniversum.github.io
- 7
- 8OC
2012 · assembly.ynh.io
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- 10CC
2020 · codemap.app
- 11CV
I worked as a software engineer at Amazon, SAP, and on open source. In all 3 places I have struggled with the friction of understanding codebases before I can make a contribution. I think this brain-fatiguing process can be improved. I am trying to solve it with a tool I built over the last 4 years called CodeCanvas: https://docs.code-canvas.com CodeCanvas visualizes codebases through interactive diagrams linked directly to source code. Users can record 'simulations' to demonstrate data flow and business logic. I’ve also recently added an LLM chat where it takes only the relevant…
2025 · pie-crepe-38f.notion.site
- 12RA
OP here. I built RepoReaper to solve code context fragmentation in RAG. Unlike standard chat-with-repo tools, it simulates a senior engineer's workflow: it parses Python AST for logic-aware chunking, uses a ReAct loop to JIT-fetch missing file dependencies from GitHub, and employs hybrid search (BM25+Vector). It also generates Mermaid diagrams for architecture visualization. The backend is fully async and persists state via ChromaDB. Link: https://github.com/tzzp1224/RepoReaper
Jan 2026 · github.com
- 13VP
2021 · github.com
- 14DA
2013 · hughsk.github.io
- 15NT
I built a CLI tool that turns codebases and PRs into diagrams so you can quickly understand how things fit together. Originally made it because I couldn't follow my own AI-generated repos. Just shipped a big update: - Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
Feb 2026 · github.com
- 16MG
Hello everybody, I built Memory Graph to help students (and myself) build a correct mental model of Python references, mutability, and copying, and to make debugging data structures less “print-driven”. It’s inspired by Python Tutor, but focuses on clearer graphs and on running locally in many different environments and debuggers. The Memory Graph Web Debugger quickly turns your web browser into a Python debugger where the whole program state is visualized in each step, clearly showing aliasing and the structure of the data, giving insight that is hard to get with just printing. Some…
Jan 2026 · memory-graph.com
- 17CL
Hi Hacker News, As a dev extensively using GPT-4 for coding, I've realized its effectiveness significantly increases with richer context (e.g., code samples, execution state - props to DevinAI for famously console.logging itself). This inspired me to push the idea further and create CaptureFlow. This tool equips your coding LLM with a debugger-level view into your Python apps, via a simple one-line decorator. Such detailed tracing improves LLM coding capabilities and opens new use cases, such as auto-bug fix and test case generation. CaptureFlow-py offers an extensible end-to-end pipeline…
2024 · github.com
- 18CS
Hey HN, I'm Long. I started building CodeLayers in November — a 3D code visualization app that started on Apple Vision Pro and is now on iPhone and iPad. Why I built this: AI agents are writing more code than ever, and I realized I had no idea what my codebase actually looked like anymore. I wanted a way to see the architecture at a glance — what depends on what, where changes ripple, where the complexity is hiding. And I wanted it on my phone, not buried in some CI dashboard. But getting the visualization right was harder than I expected. Force-directed graphs were the obvious first…
Feb 2026 · codelayers.ai
- 19CM
I've been vibe-coding tools to automate chunks of my consulting work, fell down a rabbit hole, and started building actual products. Suddenly I'm in a world of unknown-unknowns and known-unknowns. One of the bigger things to solve was understanding code the LLM generated that I didn't fully grasp. What does it touch? What reads and writes where? Is the auth path where I think it is? So I built codeflowmap. Point it at a repo and it maps the dependency and call graph, then surfaces the read / write / auth paths between files and functions. Connect a local model (Ollama) or any…
Jun 2026 · github.com
- 20AC
For the past couple of months, I’ve been building a tool that enables natural language search over large codebases using Tree-Sitter for syntax parsing and Qdrant for vector-based retrieval. https://app.repogram.com ### How It Works - Tree-Sitter is used to parse syntax trees and extract high-quality vector embeddings of code. - These embeddings are stored in Qdrant, enabling fast similarity search across your entire repo. - A combination of re-ranking processes refine search results, producing highly relevant answers to code-related questions. The results have been incredibly…
2025 · app.repogram.com
- 21AP
Hey HN! I've been having a play with the idea of building a Python AST viewer that can be used in the terminal. It's very early days yet, but the basic approach is now available for playing with. Also on PyPi and works fine on GNU/Linux, macOS and Windows.
2022 · github.com
- 22CA
Hey there HN! We're Vivek and Si-Yan from Cartograph (https://cartograph.app). We've built an AI-powered code documentation platform that automatically generates reference documentation and creates a visual interactive map of the codebase that serves as both high level architecture diagram and allows you to zoom in to specific implementations. How it works: We use static analysis to read a codebase and get its symbols and their dependencies, creating a complete map that includes function calls. We use LLMs (Gemini + Claude) to add metadata to this map, as well as augment it in…
2024 · cartograph.app
- 23ME
Hey HN, I'm excited to share a new side project I've been working on. The product is called Matrices. You can check it out here: https://matrices.com/. With Matrices, you can explore, visualize, and share large (100k rows) datasets–all without code. Filter data down to just what you want, visualize it with built-in charts, and share your results with one click. You can use it today (no login or waitlist or anything). Just copy and paste your data from a google sheet or CSV file. It's hard to describe the feeling of "gliding over data" you get with Matrices, so I'd rather…
2023 · matrices.com
- 24AO
Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
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